diff --git a/deep_learning/Q2/q2/train_math_protocol.py b/deep_learning/Q2/q2/train_math_protocol.py index f2471ab..df30b0e 100644 --- a/deep_learning/Q2/q2/train_math_protocol.py +++ b/deep_learning/Q2/q2/train_math_protocol.py @@ -12,6 +12,7 @@ import hashlib import json import math import random +import sys import time from collections import Counter, defaultdict from pathlib import Path @@ -305,7 +306,12 @@ def validation_aurc_bootstrap( return rows -def run(device_name: str = "auto", output_dir: Path = OUTPUT_DIR) -> None: +def run(device_name: str = "auto", output_dir: Path = OUTPUT_DIR, + input_version: str = "aligned_50", batch_size: int = BATCH_SIZE) -> None: + global BATCH_SIZE + if batch_size < 1: + raise ValueError("batch_size must be positive") + BATCH_SIZE = batch_size if output_dir.exists() and any(output_dir.iterdir()): raise FileExistsError(f"refusing to overwrite non-empty result directory: {output_dir}") output_dir.mkdir(parents=True, exist_ok=True) @@ -313,12 +319,37 @@ def run(device_name: str = "auto", output_dir: Path = OUTPUT_DIR) -> None: if device.type == "cuda" and not torch.cuda.is_available(): raise RuntimeError("CUDA was requested but is unavailable") - feature_path = ATTACHMENT2 / "aligned_50.pkl" - raw_splits = load_splits(feature_path) + if input_version not in {"aligned_50", "unaligned_50"}: + raise ValueError(f"unsupported input version: {input_version}") + feature_path = ATTACHMENT2 / f"{input_version}.pkl" + if input_version == "unaligned_50": + repo_root = Path(__file__).resolve().parents[3] + if str(repo_root) not in sys.path: + sys.path.insert(0, str(repo_root)) + from final.adapter import adapt_official_split + from .data import _unpickle, _ids_and_targets + + source = _unpickle(feature_path) + raw_splits = {} + adapter_audit = {} + for name in ("train", "valid", "test"): + arrays, mask, audit = adapt_official_split(source[name]) + ids, y_cls, y_reg = _ids_and_targets(source[name]) + raw_splits[name] = Split(tuple(arrays[m] for m in ("text", "audio", "vision")), + mask, y_cls, y_reg, ids) + adapter_audit[name] = audit + del source + groups = {name: {sid.split("$_$", 1)[0] for sid in split.ids} + for name, split in raw_splits.items()} + if any(groups[a] & groups[b] for a, b in (("train", "valid"), ("train", "test"), ("valid", "test"))): + raise ValueError("official source-video groups overlap") + else: + raw_splits = load_splits(feature_path) + adapter_audit = None train_raw, valid_raw, test_raw = raw_splits["train"], raw_splits["valid"], raw_splits["test"] stats = fit_robust_stats(train_raw) train, valid, test = (apply_robust_stats(s, stats) for s in (train_raw, valid_raw, test_raw)) - stats_path = output_dir / "aligned_robust_stats.npz" + stats_path = output_dir / f"{input_version}_robust_stats.npz" stats.save(stats_path) dims = tuple(int(x.shape[-1]) for x in train.x) valid_scenarios = make_scenarios(valid, SCENARIO_SEED) @@ -432,7 +463,11 @@ def run(device_name: str = "auto", output_dir: Path = OUTPUT_DIR) -> None: "cuda_device": torch.cuda.get_device_name(0) if device.type == "cuda" else None, "feature_file": str(feature_path), "feature_sha256": sha256(feature_path), - "representation": "official aligned_50 ordered positions; not Q1 physical-time bins", + "representation": ("final.adapter relative-progress projection of official unaligned_50; not physical-time alignment" + if input_version == "unaligned_50" else + "official aligned_50 ordered positions; not Q1 physical-time bins"), + "adapter": "final.adapter.adapt_official_split" if input_version == "unaligned_50" else None, + "adapter_audit": adapter_audit, "train_valid_test_counts": {name: split.n for name, split in raw_splits.items()}, "source_video_groups": {name: len({sid.split("$_$", 1)[0] for sid in split.ids}) for name, split in raw_splits.items()}, "official_group_splits_disjoint": True, @@ -509,5 +544,8 @@ if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--device", default="auto", choices=("auto", "cuda", "cpu")) parser.add_argument("--output-dir", type=Path, default=OUTPUT_DIR) + parser.add_argument("--input-version", choices=("aligned_50", "unaligned_50"), default="aligned_50") + parser.add_argument("--batch-size", type=int, default=BATCH_SIZE) arguments = parser.parse_args() - run(device_name=arguments.device, output_dir=arguments.output_dir) + run(device_name=arguments.device, output_dir=arguments.output_dir, + input_version=arguments.input_version, batch_size=arguments.batch_size) diff --git a/final/.gitignore b/final/.gitignore new file mode 100644 index 0000000..bc3e90d --- /dev/null +++ b/final/.gitignore @@ -0,0 +1,9 @@ +data/* +!data/README.md +.venv/ +__pycache__/ +*.py[cod] +q1/cache/ +q2/**/outputs/ +output/q3/model_best.pt +output/q3/preprocessor.npz diff --git a/final/Q1/README.md b/final/Q1/README.md deleted file mode 100644 index ee21f26..0000000 --- a/final/Q1/README.md +++ /dev/null @@ -1,69 +0,0 @@ -# Q1 多模态对齐与训练接口 - -`final/Q1` 收纳了 `math/Q1` 的 Q1 方法、模型比较结果，以及从附件一 100 条样本导出的训练特征。Q1 及后续题目可以复用同一套模态对齐定义。 - -## 对齐与特征 - -- 文本：每词一个 768 维 BERT-base-uncased 末四层均值向量。主版本使用固定转写的 CTC Viterbi 词区间和样本内相对质量权重；另存 B2 前向–后向词占据后验版本。 -- 音频：74 维，包括 40 维 log-Mel、13 维 MFCC、13 维 ΔMFCC、8 维韵律/谱特征。特征提取 hop 为 10 ms。 -- 视觉：35 维，包括 17 个 MediaPipe blendshape 代理、6 维头姿、6 维近似视线、6 维面部几何；采样率为 5 Hz。B1 对姿态采用 SO(3) 均值，对视线采用归一化向量均值。代理量不是 OpenFace AU。 -- 三种模态先依真实时间戳投影到 0.1 秒公共时间网格，再按每条片段的物理时长划为 50 个等宽时间箱。`time_bounds_s` 保存每个箱的片段内秒数边界，掩码表示各维是否观测到。 -- 特征保持原始尺度。训练时只用训练视频组拟合标准化参数；缺失值位置虽填零，但必须使用掩码。 - -## 文件 - -- `features/aligned_50.pkl`：标准训练文件，含 `metadata` 和 `all`。主要数组维度为 `text (100,50,768)`、`audio (100,50,74)`、`vision (100,50,35)`。包括文本后验替代视图、逐维掩码、覆盖率、时间边界、标签、样本/视频 ID 和源视频 SHA-256。 -- `features/native_grid.npz`：保留 0.1 秒网格上的变长 B1 特征。用 `offsets[i]:offsets[i+1]` 读取第 i 条样本；时间边界是片段内秒数。 -- `features/sample_feature_manifest.csv`：300 行样本-模态追溯表，含原始/观测时长、维数、抽取粒度、公共网格长度、50 箱导出粒度和源视频哈希。 -- `features/sample_alignment_summary.csv`、`features/word_alignment_posterior.csv`：逐样本和逐词对齐信息；`feature_manifest.json`、`feature_names.json` 记录版本、模型和字段定义。 -- `results/model_comparison/`：Q1 的 B0–B4 对照结果、报告与典型样本对齐图。 - -标签 `classification_labels` 为 0/1/2（负面/中性/正面），`regression_labels` 保留原始连续情感值。数据集未提供新的训练/验证划分；请按 `video_id` 分组切分，避免同一原视频泄漏到两边。所有时间均相对于片段起点。 - -## Python 调用 - -固定形状数组可直接交给后续训练代码： - -```python -from api import load_aligned50, group_holdout_indices - -payload = load_aligned50(text_variant="hard") # 或 text_variant="posterior" -features = payload["all"] -train_idx, valid_idx = group_holdout_indices(payload, validation_fraction=0.2, seed=42) -text = features["text"][train_idx] # (N_train, 50, 768) -audio = features["audio"][train_idx] # (N_train, 50, 74) -vision = features["vision"][train_idx] # (N_train, 50, 35) -text_mask = features["text_mask"][train_idx] -labels = features["classification_labels"][train_idx] -``` - -PyTorch Dataset 接口返回每条样本的张量、标签、掩码和分组 ID： - -```python -from torch.utils.data import DataLoader, Subset -from api import Q1TorchDataset, group_holdout_indices - -dataset = Q1TorchDataset(text_variant="hard") -train_idx, valid_idx = group_holdout_indices(dataset.payload, seed=42) -train_loader = DataLoader(Subset(dataset, train_idx.tolist()), batch_size=16, shuffle=True) -batch = next(iter(train_loader)) -``` - -## 重新生成 - -在仓库根目录运行： - -```bash -cd final/Q1 -uv sync -uv run python compare_models.py -uv run python export_features.py --cache-dir cache/native --run-manifest results/model_comparison/run_manifest.json -``` - -若复用当前 `math/Q1` 中已生成的缓存，可将导出命令改为： - -```bash -uv run python export_features.py --cache-dir ../../math/Q1/cache/native --run-manifest ../../math/Q1/results/model_comparison/run_manifest.json -``` - -`uv.lock`、虚拟环境、缓存和原始数据不随本目录提交。首次从原始视频重新抽取特征时，需能够访问 BERT、Wav2Vec2 和 MediaPipe 模型权重。 diff --git a/final/Q1/api.py b/final/Q1/api.py deleted file mode 100644 index 3537482..0000000 --- a/final/Q1/api.py +++ /dev/null @@ -1,84 +0,0 @@ -from __future__ import annotations - -import pickle -from pathlib import Path -from typing import Any - -import numpy as np -import torch -from torch.utils.data import Dataset - -DEFAULT_FEATURE_FILE = Path(__file__).resolve().parent / "features" / "aligned_50.pkl" - - -def load_aligned50(path: str | Path | None = None, text_variant: str = "hard") -> dict[str, Any]: - """Load Q1's 50-bin features, selecting the B1 hard or B2 posterior text view.""" - if text_variant not in {"hard", "posterior"}: - raise ValueError("text_variant must be 'hard' or 'posterior'") - source = Path(path) if path is not None else DEFAULT_FEATURE_FILE - with source.open("rb") as stream: - payload = pickle.load(stream) - if payload.get("metadata", {}).get("schema") != "q1-aligned50-v1": - raise ValueError(f"Unsupported aligned feature schema in {source}") - arrays = payload["all"].copy() - if text_variant == "posterior": - arrays["text"] = arrays["text_posterior"] - arrays["text_mask"] = arrays["text_posterior_mask"] - arrays["text_coverage"] = arrays["text_posterior_activity"] - return { - **payload, - "all": arrays, - "metadata": {**payload["metadata"], "selected_text_variant": text_variant}, - } - - -def group_holdout_indices( - payload: dict[str, Any], validation_fraction: float = 0.2, seed: int = 42 -) -> tuple[np.ndarray, np.ndarray]: - """Return train/validation indices while keeping each source video in one split.""" - if not 0.0 < validation_fraction < 1.0: - raise ValueError("validation_fraction must be between 0 and 1") - arrays = payload["all"] if "all" in payload else payload - groups = np.asarray(arrays["video_id"], dtype=str) - unique_groups = np.unique(groups) - if len(unique_groups) < 2: - raise ValueError("At least two distinct video_id groups are required") - shuffled = np.random.default_rng(seed).permutation(unique_groups) - target = max(1, int(np.ceil(len(groups) * validation_fraction))) - validation_groups: list[str] = [] - for group in shuffled: - if validation_groups and np.count_nonzero(np.isin(groups, validation_groups)) >= target: - break - validation_groups.append(str(group)) - if len(validation_groups) == len(unique_groups): - validation_groups.pop() - validation_mask = np.isin(groups, validation_groups) - return np.flatnonzero(~validation_mask), np.flatnonzero(validation_mask) - - -class Q1TorchDataset(Dataset): - """PyTorch Dataset yielding feature tensors, masks, labels, times, and video IDs.""" - - def __init__(self, path: str | Path | None = None, text_variant: str = "hard") -> None: - self.payload = load_aligned50(path, text_variant=text_variant) - self.arrays = self.payload["all"] - self.text_variant = text_variant - - def __len__(self) -> int: - return len(self.arrays["sample_id"]) - - def __getitem__(self, index: int) -> dict[str, Any]: - arrays = self.arrays - sample: dict[str, Any] = {} - for name in ("text", "audio", "vision", "time_bounds_s", "time_s", "progress"): - sample[name] = torch.as_tensor(np.asarray(arrays[name][index], dtype=np.float32)) - for name in ("text_mask", "audio_mask", "vision_mask"): - sample[name] = torch.as_tensor(np.asarray(arrays[name][index], dtype=bool)) - for name in ("text_coverage", "audio_coverage", "vision_coverage"): - sample[name] = torch.as_tensor(np.asarray(arrays[name][index], dtype=np.float32)) - sample["classification_labels"] = torch.tensor(int(arrays["classification_labels"][index]), dtype=torch.long) - sample["regression_labels"] = torch.tensor(float(arrays["regression_labels"][index]), dtype=torch.float32) - sample["valid_length"] = torch.tensor(int(arrays["valid_lengths"][index]), dtype=torch.long) - for name in ("id", "sample_id", "video_id", "clip_id", "raw_text", "annotations", "source_video_sha256"): - sample[name] = str(arrays[name][index]) - return sample diff --git a/final/Q1/export_features.py b/final/Q1/export_features.py deleted file mode 100644 index 3f45936..0000000 --- a/final/Q1/export_features.py +++ /dev/null @@ -1,239 +0,0 @@ -from __future__ import annotations - -import argparse -import csv -import hashlib -import json -import pickle -import shutil -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -import numpy as np - -ROOT = Path(__file__).resolve().parents[2] -Q1_DIR = Path(__file__).resolve().parent -sys.path.insert(0, str(Q1_DIR)) -import compare_models as cm # noqa: E402 - -CACHE_SCHEMA = "q1-b0b4-v3" -EXPORT_BINS = 50 -MODALITIES = ("text", "audio", "vision") - - -def sha256(path: Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as stream: - for chunk in iter(lambda: stream.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def defaults() -> tuple[Path, Path, Path]: - candidates = (ROOT / "math" / "Q1" / "cache" / "native", Q1_DIR / "cache" / "native") - cache = next((path for path in candidates if path.exists()), candidates[0]) - labels = ROOT / "E题数据" / "附件1-数据集原始多模态样本" / "MOSEI数据集部分原始视频-100条" / "label-100.xlsx" - return cache, labels, cache.parent.parent / "results" / "model_comparison" / "run_manifest.json" - - -def pool_to_edges(view: cm.View, modality: str, target_edges: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - values = np.asarray(view.features[modality], dtype=np.float64) - observed = np.asarray(view.observed[modality], dtype=bool) - source_coverage = np.asarray(view.coverage[modality], dtype=np.float64) - dim = values.shape[1] - count = len(target_edges) - 1 - output = np.zeros((count, dim), dtype=np.float32) - mask = np.zeros((count, dim), dtype=bool) - coverage = np.zeros((count, dim), dtype=np.float32) - source_left, source_right = view.edges[:-1], view.edges[1:] - for index, (left, right) in enumerate(zip(target_edges[:-1], target_edges[1:])): - overlap = np.maximum(0.0, np.minimum(source_right, right) - np.maximum(source_left, left)) - weights = overlap[:, None] * source_coverage * observed - denominator = weights.sum(axis=0) - good = denominator > 0 - if good.any(): - output[index, good] = (values * weights).sum(axis=0)[good] / denominator[good] - mask[index, good] = True - coverage[index, good] = np.minimum(1.0, denominator[good] / max(right - left, 1e-8)) - return output, mask, coverage - - -def main() -> None: - cache_default, labels_default, manifest_default = defaults() - parser = argparse.ArgumentParser(description="Export Q1 multimodal features for model training.") - parser.add_argument("--cache-dir", type=Path, default=cache_default) - parser.add_argument("--labels", type=Path, default=labels_default) - parser.add_argument("--run-manifest", type=Path, default=manifest_default) - parser.add_argument("--output-dir", type=Path, default=Q1_DIR / "features") - args = parser.parse_args() - - for path in (args.cache_dir, args.labels, args.run_manifest): - if not path.exists(): - raise FileNotFoundError(f"Required input does not exist: {path}") - run_manifest = json.loads(args.run_manifest.read_text(encoding="utf-8")) - inputs = run_manifest["inputs"] - if run_manifest.get("cache_schema", CACHE_SCHEMA) != CACHE_SCHEMA: - raise ValueError("Run manifest cache_schema does not match the Q1 exporter.") - label_hash = sha256(args.labels) - if inputs.get("label_file_sha256") and inputs["label_file_sha256"] != label_hash: - raise ValueError("Label workbook hash differs from the run manifest.") - records = cm._read_labels(args.labels) - source_hashes = inputs["video_sha256_by_sample"] - if len(records) != 100 or int(inputs.get("sample_count", -1)) != len(records): - raise ValueError("Expected the same 100 samples used by the Q1 comparison run.") - - keys = ( - "text", "text_mask", "text_posterior", "text_posterior_mask", "audio", "audio_mask", "vision", "vision_mask", - "text_coverage", "text_posterior_activity", "audio_coverage", "vision_coverage", "time_bounds_s", "time_s", "progress", - "duration_s", "valid_lengths", "id", "sample_id", "video_id", "clip_id", "raw_text", "classification_labels", - "regression_labels", "annotations", "label_consistent", "source_video_sha256", - ) - rows: dict[str, list[Any]] = {key: [] for key in keys} - native: dict[str, list[np.ndarray]] = {key: [] for key in ( - "text", "text_mask", "audio", "audio_mask", "vision", "vision_mask", "text_coverage", "audio_coverage", "vision_coverage", "time_bounds_s", - )} - offsets = [0] - native_ids: list[np.ndarray] = [] - durations: list[float] = [] - - for sample_index, record in enumerate(records): - sample_id = record["sample_id"] - source_hash = source_hashes[sample_id] - cache_path = args.cache_dir / f"{cm._safe_name(record['video_id'])}__{cm._safe_name(record['clip_id'])}.npz" - sample = cm._load_cache(cache_path, record, source_hash, CACHE_SCHEMA) - _, view_b1, view_b2 = cm._make_views(sample) - target_edges = np.linspace(0.0, sample.duration_s, EXPORT_BINS + 1, dtype=np.float64) - pooled = {name: pool_to_edges(view_b1, name, target_edges) for name in MODALITIES} - posterior = pool_to_edges(view_b2, "text", target_edges) - - step_count = len(view_b1.edges) - 1 - native["time_bounds_s"].append(np.column_stack((view_b1.edges[:-1], view_b1.edges[1:])).astype(np.float32)) - native_ids.append(np.full(step_count, sample_index, dtype=np.int16)) - offsets.append(offsets[-1] + step_count) - durations.append(float(sample.duration_s)) - for name in MODALITIES: - native[name].append(np.asarray(view_b1.features[name], dtype=np.float16)) - native[f"{name}_mask"].append(np.asarray(view_b1.observed[name], dtype=bool)) - native[f"{name}_coverage"].append(view_b1.coverage[name].mean(axis=1).astype(np.float16)) - values, masks, cov = pooled[name] - rows[name].append(values.astype(np.float16)) - rows[f"{name}_mask"].append(masks) - rows[f"{name}_coverage"].append(cov.mean(axis=1).astype(np.float32)) - rows["text_posterior"].append(posterior[0].astype(np.float16)) - rows["text_posterior_mask"].append(posterior[1]) - rows["text_posterior_activity"].append(posterior[2].mean(axis=1).astype(np.float32)) - bounds = np.column_stack((target_edges[:-1], target_edges[1:])).astype(np.float32) - centers = bounds.mean(axis=1) - rows["time_bounds_s"].append(bounds) - rows["time_s"].append(centers) - rows["progress"].append((centers / max(sample.duration_s, 1e-8)).astype(np.float32)) - rows["duration_s"].append(float(sample.duration_s)) - rows["valid_lengths"].append(EXPORT_BINS) - values = { - "id": sample_id, "sample_id": sample_id, "video_id": record["video_id"], "clip_id": record["clip_id"], - "raw_text": record["text"], "classification_labels": int(record["polarity"]), - "regression_labels": float(record["sentiment"]), "annotations": record["annotation"], - "label_consistent": bool(record["label_consistent"]), "source_video_sha256": source_hash, - } - for key, value in values.items(): - rows[key].append(value) - - args.output_dir.mkdir(parents=True, exist_ok=True) - arrays: dict[str, Any] = {} - string_keys = {"id", "sample_id", "video_id", "clip_id", "raw_text", "annotations", "source_video_sha256"} - for key, values in rows.items(): - if key in string_keys: - arrays[key] = np.asarray(values, dtype=str) - elif key in {"duration_s", "regression_labels"}: - arrays[key] = np.asarray(values, dtype=np.float32) - elif key in {"classification_labels", "valid_lengths"}: - arrays[key] = np.asarray(values, dtype=np.int64) - elif key == "label_consistent": - arrays[key] = np.asarray(values, dtype=bool) - else: - arrays[key] = np.stack(values, axis=0) - - dimensions = {"text": 768, "audio": len(cm.AUDIO_NAMES), "vision": len(cm.VISION_NAMES)} - names = { - "text": [f"bert_dim_{index:03d}" for index in range(768)], - "audio": list(cm.AUDIO_NAMES), "vision": list(cm.VISION_NAMES), - } - metadata = { - "schema": "q1-aligned50-v1", "sample_count": len(records), "export_bins": EXPORT_BINS, - "alignment": "Each clip is divided into 50 equal-width physical-time bins; values are duration/coverage-weighted means of Q1 B1 common-grid features.", - "native_grid_step_s": cm.GRID_STEP_S, - "time_semantics": "Seconds relative to clip start; time_bounds_s contains [left, right) for each bin.", - "text": "768-D BERT base uncased last-four-layer mean, word-level features projected by hard CTC Viterbi intervals with within-sample relative quality weighting.", - "text_posterior": "Alternative text projection uses fixed-transcript CTC forward-backward word occupancy. text_posterior_activity is expected occupancy mass, not physical coverage.", - "audio": "74-D: 40 log-Mel, 13 MFCC, 13 delta MFCC, 8 prosodic/spectral features.", - "vision": "35-D: 17 MediaPipe blendshape proxies, 6 head pose, 6 approximate gaze, 6 facial geometry; B1 uses SO(3) pose and normalized gaze pooling.", - "missing_values": "Values are zero where the matching boolean mask is false. Features are unnormalized; fit normalization on training groups only.", - "labels": {"classification_labels": {"0": "negative", "1": "neutral", "2": "positive"}, "regression_labels": "original continuous sentiment label"}, - "split": "No train/validation/test split is supplied. Split by video_id to prevent source-video leakage.", - "dimensions": dimensions, "feature_names_file": "feature_names.json", - "source_run_manifest": str(args.run_manifest.relative_to(ROOT)) if args.run_manifest.is_relative_to(ROOT) else args.run_manifest.name, - } - pkl_path = args.output_dir / "aligned_50.pkl" - with pkl_path.open("wb") as stream: - pickle.dump({"metadata": metadata, "all": arrays}, stream, protocol=pickle.HIGHEST_PROTOCOL) - - native_arrays: dict[str, Any] = { - "offsets": np.asarray(offsets, dtype=np.int32), "sample_indices": np.concatenate(native_ids).astype(np.int16), - "sample_id": np.asarray([record["sample_id"] for record in records], dtype=str), - "time_bounds_s": np.concatenate(native["time_bounds_s"], axis=0), - } - for key in ("text", "text_mask", "audio", "audio_mask", "vision", "vision_mask", "text_coverage", "audio_coverage", "vision_coverage"): - native_arrays[key] = np.concatenate(native[key], axis=0) - native_path = args.output_dir / "native_grid.npz" - np.savez_compressed(native_path, **native_arrays) - names_path = args.output_dir / "feature_names.json" - names_path.write_text(json.dumps(names, ensure_ascii=False, indent=2), encoding="utf-8") - - result_dir = args.run_manifest.parent - for name in ("modality_summary.csv", "sample_alignment_summary.csv", "word_alignment_posterior.csv"): - source = result_dir / name - if source.exists(): - shutil.copy2(source, args.output_dir / name) - summary_src = result_dir / "modality_summary.csv" - summary_path = args.output_dir / "sample_feature_manifest.csv" - modality_rows = [] - with summary_src.open(encoding="utf-8-sig", newline="") as stream: - for row in csv.DictReader(stream): - row.update({ - "source_extract_granularity": { - "text": "word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames)", - "audio": "10 ms feature hop", "vision": "5 Hz sampled frames (about 200 ms)", - }.get(row["modality"], ""), - "common_alignment_grid_s": str(cm.GRID_STEP_S), "training_export_bins": str(EXPORT_BINS), - "traceability_rule": "sample_id + source_video_sha256 + source time bounds", - }) - modality_rows.append(row) - with summary_path.open("w", encoding="utf-8-sig", newline="") as stream: - writer = csv.DictWriter(stream, fieldnames=list(modality_rows[0])) - writer.writeheader() - writer.writerows(modality_rows) - - files = {} - for path in (pkl_path, native_path, names_path, summary_path): - files[path.name] = {"bytes": path.stat().st_size, "sha256": sha256(path)} - feature_manifest = { - **metadata, "created_at_utc": datetime.now(timezone.utc).isoformat(), - "python": run_manifest.get("python"), "packages": run_manifest.get("packages", {}), - "models": run_manifest.get("models", {}), "label_file_sha256": label_hash, - "feature_names": names, "native_grid_step_count_total": int(offsets[-1]), - "native_grid_sample_offsets": offsets, "files": files, - "tables": { - "sample_feature_manifest.csv": "300 rows, one per sample and modality; records source/observed duration, dimension, granularity, grid length and source hash.", - "sample_alignment_summary.csv": "100 sample-level alignment and coverage summaries.", - "word_alignment_posterior.csv": "Word-level hard intervals and CTC posterior interval summaries.", - }, - } - (args.output_dir / "feature_manifest.json").write_text(json.dumps(feature_manifest, ensure_ascii=False, indent=2), encoding="utf-8") - print(f"Exported {len(records)} samples: text={arrays['text'].shape}, audio={arrays['audio'].shape}, vision={arrays['vision'].shape}") - print(f"Native grid: {offsets[-1]} steps across {sum(durations):.3f} seconds") - - -if __name__ == "__main__": - main() diff --git a/final/Q1/features/aligned_50.pkl b/final/Q1/features/aligned_50.pkl deleted file mode 100644 index 321e5bb..0000000 Binary files a/final/Q1/features/aligned_50.pkl and /dev/null differ diff --git a/final/Q1/features/feature_manifest.json b/final/Q1/features/feature_manifest.json deleted file mode 100644 index de03f1b..0000000 --- a/final/Q1/features/feature_manifest.json +++ /dev/null @@ -1,1063 +0,0 @@ -{ - "schema": "q1-aligned50-v1", - "sample_count": 100, - "export_bins": 50, - "alignment": "Each clip is divided into 50 equal-width physical-time bins; values are duration/coverage-weighted means of Q1 B1 common-grid features.", - "native_grid_step_s": 0.1, - "time_semantics": "Seconds relative to clip start; time_bounds_s contains [left, right) for each bin.", - "text": "768-D BERT base uncased last-four-layer mean, word-level features projected by hard CTC Viterbi intervals with within-sample relative quality weighting.", - "text_posterior": "Alternative text projection uses fixed-transcript CTC forward-backward word occupancy. text_posterior_activity is expected occupancy mass, not physical coverage.", - "audio": "74-D: 40 log-Mel, 13 MFCC, 13 delta MFCC, 8 prosodic/spectral features.", - "vision": "35-D: 17 MediaPipe blendshape proxies, 6 head pose, 6 approximate gaze, 6 facial geometry; B1 uses SO(3) pose and normalized gaze pooling.", - "missing_values": "Values are zero where the matching boolean mask is false. Features are unnormalized; fit normalization on training groups only.", - "labels": { - "classification_labels": { - "0": "negative", - "1": "neutral", - "2": "positive" - }, - "regression_labels": "original continuous sentiment label" - }, - "split": "No train/validation/test split is supplied. Split by video_id to prevent source-video leakage.", - "dimensions": { - "text": 768, - "audio": 74, - "vision": 35 - }, - "feature_names_file": "feature_names.json", - "source_run_manifest": "math/Q1/results/model_comparison/run_manifest.json", - "created_at_utc": "2026-09-24T03:53:01.356363+00:00", - "python": "3.14.7 (main, Aug 10 2026, 00:00:00) [GCC 16.1.1 20260515 (Red Hat 16.1.1-2)]", - "packages": { - "torch": "2.14.0+cu130", - "transformers": "5.17.0", - "numpy": "2.5.3", - "scipy": "1.18.1", - "scikit-learn": "1.9.1", - "mediapipe": "1.0.1", - "opencv-python-headless": "5.0.0.93", - "openpyxl": "3.1.5", - "matplotlib": "3.11.2" - }, - "models": { - "text_id": "google-bert/bert-base-uncased", - "text_revision": "86b5e0934494bd15c9632b12f734a8a67f723594", - "speech_id": "facebook/wav2vec2-base-960h", - "speech_revision": "22aad52d435eb6dbaf354bdad9b0da84ce7d6156", - "speech_stride_samples": 320, - "speech_receptive_field_samples": 400 - }, - "label_file_sha256": "827334f782b1f242c84ad944a657bc7f7c63643f71ae9c977811a3eecdd3ab33", - "feature_names": { - "text": [ - "bert_dim_000", - "bert_dim_001", - "bert_dim_002", - "bert_dim_003", - "bert_dim_004", - "bert_dim_005", - "bert_dim_006", - "bert_dim_007", - "bert_dim_008", - "bert_dim_009", - "bert_dim_010", - "bert_dim_011", - "bert_dim_012", - "bert_dim_013", - "bert_dim_014", - "bert_dim_015", - "bert_dim_016", - "bert_dim_017", - "bert_dim_018", - "bert_dim_019", - "bert_dim_020", - "bert_dim_021", - "bert_dim_022", - "bert_dim_023", - "bert_dim_024", - "bert_dim_025", - "bert_dim_026", - "bert_dim_027", - "bert_dim_028", - "bert_dim_029", - "bert_dim_030", - "bert_dim_031", - "bert_dim_032", - "bert_dim_033", - "bert_dim_034", - "bert_dim_035", - "bert_dim_036", - "bert_dim_037", - "bert_dim_038", - "bert_dim_039", - "bert_dim_040", - "bert_dim_041", - "bert_dim_042", - "bert_dim_043", - "bert_dim_044", - "bert_dim_045", - "bert_dim_046", - "bert_dim_047", - "bert_dim_048", - "bert_dim_049", - "bert_dim_050", - "bert_dim_051", - "bert_dim_052", - "bert_dim_053", - "bert_dim_054", - "bert_dim_055", - "bert_dim_056", - "bert_dim_057", - "bert_dim_058", - "bert_dim_059", - "bert_dim_060", - "bert_dim_061", - "bert_dim_062", - "bert_dim_063", - "bert_dim_064", - "bert_dim_065", - "bert_dim_066", - "bert_dim_067", - "bert_dim_068", - "bert_dim_069", - "bert_dim_070", - "bert_dim_071", - "bert_dim_072", - "bert_dim_073", - "bert_dim_074", - "bert_dim_075", - "bert_dim_076", - "bert_dim_077", - "bert_dim_078", - "bert_dim_079", - "bert_dim_080", - "bert_dim_081", - "bert_dim_082", - "bert_dim_083", - "bert_dim_084", - "bert_dim_085", - "bert_dim_086", - "bert_dim_087", - "bert_dim_088", - "bert_dim_089", - "bert_dim_090", - "bert_dim_091", - "bert_dim_092", - "bert_dim_093", - "bert_dim_094", - "bert_dim_095", - "bert_dim_096", - "bert_dim_097", - "bert_dim_098", - "bert_dim_099", - "bert_dim_100", - "bert_dim_101", - "bert_dim_102", - "bert_dim_103", - "bert_dim_104", - "bert_dim_105", - "bert_dim_106", - "bert_dim_107", - "bert_dim_108", - "bert_dim_109", - "bert_dim_110", - "bert_dim_111", - "bert_dim_112", - "bert_dim_113", - "bert_dim_114", - "bert_dim_115", - "bert_dim_116", - "bert_dim_117", - "bert_dim_118", - "bert_dim_119", - "bert_dim_120", - "bert_dim_121", - "bert_dim_122", - "bert_dim_123", - "bert_dim_124", - "bert_dim_125", - "bert_dim_126", - "bert_dim_127", - "bert_dim_128", - "bert_dim_129", - "bert_dim_130", - "bert_dim_131", - "bert_dim_132", - "bert_dim_133", - "bert_dim_134", - "bert_dim_135", - "bert_dim_136", - "bert_dim_137", - "bert_dim_138", - "bert_dim_139", - "bert_dim_140", - "bert_dim_141", - "bert_dim_142", - "bert_dim_143", - "bert_dim_144", - "bert_dim_145", - "bert_dim_146", - "bert_dim_147", - "bert_dim_148", - "bert_dim_149", - "bert_dim_150", - "bert_dim_151", - "bert_dim_152", - "bert_dim_153", - "bert_dim_154", - "bert_dim_155", - "bert_dim_156", - "bert_dim_157", - "bert_dim_158", - "bert_dim_159", - "bert_dim_160", - "bert_dim_161", - "bert_dim_162", - "bert_dim_163", - "bert_dim_164", - "bert_dim_165", - "bert_dim_166", - "bert_dim_167", - "bert_dim_168", - "bert_dim_169", - "bert_dim_170", - "bert_dim_171", - "bert_dim_172", - "bert_dim_173", - "bert_dim_174", - "bert_dim_175", - "bert_dim_176", - "bert_dim_177", - "bert_dim_178", - "bert_dim_179", - "bert_dim_180", - "bert_dim_181", - "bert_dim_182", - "bert_dim_183", - "bert_dim_184", - "bert_dim_185", - "bert_dim_186", - "bert_dim_187", - "bert_dim_188", - "bert_dim_189", - "bert_dim_190", - "bert_dim_191", - "bert_dim_192", - "bert_dim_193", - "bert_dim_194", - "bert_dim_195", - "bert_dim_196", - "bert_dim_197", - "bert_dim_198", - "bert_dim_199", - "bert_dim_200", - "bert_dim_201", - "bert_dim_202", - "bert_dim_203", - "bert_dim_204", - "bert_dim_205", - "bert_dim_206", - "bert_dim_207", - "bert_dim_208", - "bert_dim_209", - "bert_dim_210", - "bert_dim_211", - "bert_dim_212", - "bert_dim_213", - "bert_dim_214", - "bert_dim_215", - "bert_dim_216", - "bert_dim_217", - "bert_dim_218", - "bert_dim_219", - "bert_dim_220", - "bert_dim_221", - "bert_dim_222", - "bert_dim_223", - "bert_dim_224", - "bert_dim_225", - "bert_dim_226", - "bert_dim_227", - "bert_dim_228", - "bert_dim_229", - "bert_dim_230", - "bert_dim_231", - "bert_dim_232", - "bert_dim_233", - "bert_dim_234", - "bert_dim_235", - "bert_dim_236", - "bert_dim_237", - "bert_dim_238", - "bert_dim_239", - "bert_dim_240", - "bert_dim_241", - "bert_dim_242", - "bert_dim_243", - "bert_dim_244", - "bert_dim_245", - "bert_dim_246", - "bert_dim_247", - "bert_dim_248", - "bert_dim_249", - "bert_dim_250", - "bert_dim_251", - "bert_dim_252", - "bert_dim_253", - "bert_dim_254", - "bert_dim_255", - "bert_dim_256", - "bert_dim_257", - "bert_dim_258", - "bert_dim_259", - "bert_dim_260", - "bert_dim_261", - "bert_dim_262", - "bert_dim_263", - "bert_dim_264", - "bert_dim_265", - "bert_dim_266", - "bert_dim_267", - "bert_dim_268", - "bert_dim_269", - "bert_dim_270", - "bert_dim_271", - "bert_dim_272", - "bert_dim_273", - "bert_dim_274", - "bert_dim_275", - "bert_dim_276", - "bert_dim_277", - "bert_dim_278", - "bert_dim_279", - "bert_dim_280", - "bert_dim_281", - "bert_dim_282", - "bert_dim_283", - "bert_dim_284", - "bert_dim_285", - "bert_dim_286", - "bert_dim_287", - "bert_dim_288", - "bert_dim_289", - "bert_dim_290", - "bert_dim_291", - "bert_dim_292", - "bert_dim_293", - "bert_dim_294", - "bert_dim_295", - "bert_dim_296", - "bert_dim_297", - "bert_dim_298", - "bert_dim_299", - "bert_dim_300", - "bert_dim_301", - "bert_dim_302", - "bert_dim_303", - "bert_dim_304", - "bert_dim_305", - "bert_dim_306", - "bert_dim_307", - "bert_dim_308", - "bert_dim_309", - "bert_dim_310", - "bert_dim_311", - "bert_dim_312", - "bert_dim_313", - "bert_dim_314", - "bert_dim_315", - "bert_dim_316", - "bert_dim_317", - "bert_dim_318", - "bert_dim_319", - "bert_dim_320", - "bert_dim_321", - "bert_dim_322", - "bert_dim_323", - "bert_dim_324", - "bert_dim_325", - "bert_dim_326", - "bert_dim_327", - "bert_dim_328", - "bert_dim_329", - "bert_dim_330", - "bert_dim_331", - "bert_dim_332", - "bert_dim_333", - "bert_dim_334", - "bert_dim_335", - "bert_dim_336", - "bert_dim_337", - "bert_dim_338", - "bert_dim_339", - "bert_dim_340", - "bert_dim_341", - "bert_dim_342", - "bert_dim_343", - "bert_dim_344", - "bert_dim_345", - "bert_dim_346", - "bert_dim_347", - "bert_dim_348", - "bert_dim_349", - "bert_dim_350", - "bert_dim_351", - "bert_dim_352", - "bert_dim_353", - "bert_dim_354", - "bert_dim_355", - "bert_dim_356", - "bert_dim_357", - "bert_dim_358", - "bert_dim_359", - "bert_dim_360", - "bert_dim_361", - "bert_dim_362", - "bert_dim_363", - "bert_dim_364", - "bert_dim_365", - "bert_dim_366", - "bert_dim_367", - "bert_dim_368", - "bert_dim_369", - "bert_dim_370", - "bert_dim_371", - "bert_dim_372", - "bert_dim_373", - "bert_dim_374", - "bert_dim_375", - "bert_dim_376", - "bert_dim_377", - "bert_dim_378", - "bert_dim_379", - "bert_dim_380", - "bert_dim_381", - "bert_dim_382", - "bert_dim_383", - "bert_dim_384", - "bert_dim_385", - "bert_dim_386", - "bert_dim_387", - "bert_dim_388", - "bert_dim_389", - "bert_dim_390", - "bert_dim_391", - "bert_dim_392", - "bert_dim_393", - "bert_dim_394", - "bert_dim_395", - "bert_dim_396", - "bert_dim_397", - "bert_dim_398", - "bert_dim_399", - "bert_dim_400", - "bert_dim_401", - "bert_dim_402", - "bert_dim_403", - "bert_dim_404", - "bert_dim_405", - "bert_dim_406", - "bert_dim_407", - "bert_dim_408", - "bert_dim_409", - "bert_dim_410", - "bert_dim_411", - "bert_dim_412", - "bert_dim_413", - "bert_dim_414", - "bert_dim_415", - "bert_dim_416", - "bert_dim_417", - "bert_dim_418", - "bert_dim_419", - "bert_dim_420", - "bert_dim_421", - "bert_dim_422", - "bert_dim_423", - "bert_dim_424", - "bert_dim_425", - "bert_dim_426", - "bert_dim_427", - "bert_dim_428", - "bert_dim_429", - "bert_dim_430", - "bert_dim_431", - "bert_dim_432", - "bert_dim_433", - "bert_dim_434", - "bert_dim_435", - "bert_dim_436", - "bert_dim_437", - "bert_dim_438", - "bert_dim_439", - "bert_dim_440", - "bert_dim_441", - "bert_dim_442", - "bert_dim_443", - "bert_dim_444", - "bert_dim_445", - "bert_dim_446", - "bert_dim_447", - "bert_dim_448", - "bert_dim_449", - "bert_dim_450", - "bert_dim_451", - "bert_dim_452", - "bert_dim_453", - "bert_dim_454", - "bert_dim_455", - "bert_dim_456", - "bert_dim_457", - "bert_dim_458", - "bert_dim_459", - "bert_dim_460", - "bert_dim_461", - "bert_dim_462", - "bert_dim_463", - "bert_dim_464", - "bert_dim_465", - "bert_dim_466", - "bert_dim_467", - "bert_dim_468", - "bert_dim_469", - "bert_dim_470", - "bert_dim_471", - "bert_dim_472", - "bert_dim_473", - "bert_dim_474", - "bert_dim_475", - "bert_dim_476", - "bert_dim_477", - "bert_dim_478", - "bert_dim_479", - "bert_dim_480", - "bert_dim_481", - "bert_dim_482", - "bert_dim_483", - "bert_dim_484", - "bert_dim_485", - "bert_dim_486", - "bert_dim_487", - "bert_dim_488", - "bert_dim_489", - "bert_dim_490", - "bert_dim_491", - "bert_dim_492", - "bert_dim_493", - "bert_dim_494", - "bert_dim_495", - "bert_dim_496", - "bert_dim_497", - "bert_dim_498", - "bert_dim_499", - "bert_dim_500", - "bert_dim_501", - "bert_dim_502", - "bert_dim_503", - "bert_dim_504", - "bert_dim_505", - "bert_dim_506", - "bert_dim_507", - "bert_dim_508", - "bert_dim_509", - "bert_dim_510", - "bert_dim_511", - "bert_dim_512", - "bert_dim_513", - "bert_dim_514", - "bert_dim_515", - "bert_dim_516", - "bert_dim_517", - "bert_dim_518", - "bert_dim_519", - "bert_dim_520", - "bert_dim_521", - "bert_dim_522", - "bert_dim_523", - "bert_dim_524", - "bert_dim_525", - "bert_dim_526", - "bert_dim_527", - "bert_dim_528", - "bert_dim_529", - "bert_dim_530", - "bert_dim_531", - "bert_dim_532", - "bert_dim_533", - "bert_dim_534", - "bert_dim_535", - "bert_dim_536", - "bert_dim_537", - "bert_dim_538", - "bert_dim_539", - "bert_dim_540", - "bert_dim_541", - "bert_dim_542", - "bert_dim_543", - "bert_dim_544", - "bert_dim_545", - "bert_dim_546", - "bert_dim_547", - "bert_dim_548", - "bert_dim_549", - "bert_dim_550", - "bert_dim_551", - "bert_dim_552", - "bert_dim_553", - "bert_dim_554", - "bert_dim_555", - "bert_dim_556", - "bert_dim_557", - "bert_dim_558", - "bert_dim_559", - "bert_dim_560", - "bert_dim_561", - "bert_dim_562", - "bert_dim_563", - "bert_dim_564", - "bert_dim_565", - "bert_dim_566", - "bert_dim_567", - "bert_dim_568", - "bert_dim_569", - "bert_dim_570", - "bert_dim_571", - "bert_dim_572", - "bert_dim_573", - "bert_dim_574", - "bert_dim_575", - "bert_dim_576", - "bert_dim_577", - "bert_dim_578", - "bert_dim_579", - "bert_dim_580", - "bert_dim_581", - "bert_dim_582", - "bert_dim_583", - "bert_dim_584", - "bert_dim_585", - "bert_dim_586", - "bert_dim_587", - "bert_dim_588", - "bert_dim_589", - "bert_dim_590", - "bert_dim_591", - "bert_dim_592", - "bert_dim_593", - "bert_dim_594", - "bert_dim_595", - "bert_dim_596", - "bert_dim_597", - "bert_dim_598", - "bert_dim_599", - "bert_dim_600", - "bert_dim_601", - "bert_dim_602", - "bert_dim_603", - "bert_dim_604", - "bert_dim_605", - "bert_dim_606", - "bert_dim_607", - "bert_dim_608", - "bert_dim_609", - "bert_dim_610", - "bert_dim_611", - "bert_dim_612", - "bert_dim_613", - "bert_dim_614", - "bert_dim_615", - "bert_dim_616", - "bert_dim_617", - "bert_dim_618", - "bert_dim_619", - "bert_dim_620", - "bert_dim_621", - "bert_dim_622", - "bert_dim_623", - "bert_dim_624", - "bert_dim_625", - "bert_dim_626", - "bert_dim_627", - "bert_dim_628", - "bert_dim_629", - "bert_dim_630", - "bert_dim_631", - "bert_dim_632", - "bert_dim_633", - "bert_dim_634", - "bert_dim_635", - "bert_dim_636", - "bert_dim_637", - "bert_dim_638", - "bert_dim_639", - "bert_dim_640", - "bert_dim_641", - "bert_dim_642", - "bert_dim_643", - "bert_dim_644", - "bert_dim_645", - "bert_dim_646", - "bert_dim_647", - "bert_dim_648", - "bert_dim_649", - "bert_dim_650", - "bert_dim_651", - "bert_dim_652", - "bert_dim_653", - "bert_dim_654", - "bert_dim_655", - "bert_dim_656", - "bert_dim_657", - "bert_dim_658", - "bert_dim_659", - "bert_dim_660", - "bert_dim_661", - "bert_dim_662", - "bert_dim_663", - "bert_dim_664", - "bert_dim_665", - "bert_dim_666", - "bert_dim_667", - "bert_dim_668", - "bert_dim_669", - "bert_dim_670", - "bert_dim_671", - "bert_dim_672", - "bert_dim_673", - "bert_dim_674", - "bert_dim_675", - "bert_dim_676", - "bert_dim_677", - "bert_dim_678", - "bert_dim_679", - "bert_dim_680", - "bert_dim_681", - "bert_dim_682", - "bert_dim_683", - "bert_dim_684", - "bert_dim_685", - "bert_dim_686", - "bert_dim_687", - "bert_dim_688", - "bert_dim_689", - "bert_dim_690", - "bert_dim_691", - "bert_dim_692", - "bert_dim_693", - "bert_dim_694", - "bert_dim_695", - "bert_dim_696", - "bert_dim_697", - "bert_dim_698", - "bert_dim_699", - "bert_dim_700", - "bert_dim_701", - "bert_dim_702", - "bert_dim_703", - "bert_dim_704", - "bert_dim_705", - "bert_dim_706", - "bert_dim_707", - "bert_dim_708", - "bert_dim_709", - "bert_dim_710", - "bert_dim_711", - "bert_dim_712", - "bert_dim_713", - "bert_dim_714", - "bert_dim_715", - "bert_dim_716", - "bert_dim_717", - "bert_dim_718", - "bert_dim_719", - "bert_dim_720", - "bert_dim_721", - "bert_dim_722", - "bert_dim_723", - "bert_dim_724", - "bert_dim_725", - "bert_dim_726", - "bert_dim_727", - "bert_dim_728", - "bert_dim_729", - "bert_dim_730", - "bert_dim_731", - "bert_dim_732", - "bert_dim_733", - "bert_dim_734", - "bert_dim_735", - "bert_dim_736", - "bert_dim_737", - "bert_dim_738", - "bert_dim_739", - "bert_dim_740", - "bert_dim_741", - "bert_dim_742", - "bert_dim_743", - "bert_dim_744", - "bert_dim_745", - "bert_dim_746", - "bert_dim_747", - "bert_dim_748", - "bert_dim_749", - "bert_dim_750", - "bert_dim_751", - "bert_dim_752", - "bert_dim_753", - "bert_dim_754", - "bert_dim_755", - "bert_dim_756", - "bert_dim_757", - "bert_dim_758", - "bert_dim_759", - "bert_dim_760", - "bert_dim_761", - "bert_dim_762", - "bert_dim_763", - "bert_dim_764", - "bert_dim_765", - "bert_dim_766", - "bert_dim_767" - ], - "audio": [ - "logmel_00", - "logmel_01", - "logmel_02", - "logmel_03", - "logmel_04", - "logmel_05", - "logmel_06", - "logmel_07", - "logmel_08", - "logmel_09", - "logmel_10", - "logmel_11", - "logmel_12", - "logmel_13", - "logmel_14", - "logmel_15", - "logmel_16", - "logmel_17", - "logmel_18", - "logmel_19", - "logmel_20", - "logmel_21", - "logmel_22", - "logmel_23", - "logmel_24", - "logmel_25", - "logmel_26", - "logmel_27", - "logmel_28", - "logmel_29", - "logmel_30", - "logmel_31", - "logmel_32", - "logmel_33", - "logmel_34", - "logmel_35", - "logmel_36", - "logmel_37", - "logmel_38", - "logmel_39", - "mfcc_00", - "mfcc_01", - "mfcc_02", - "mfcc_03", - "mfcc_04", - "mfcc_05", - "mfcc_06", - "mfcc_07", - "mfcc_08", - "mfcc_09", - "mfcc_10", - "mfcc_11", - "mfcc_12", - "delta_mfcc_00", - "delta_mfcc_01", - "delta_mfcc_02", - "delta_mfcc_03", - "delta_mfcc_04", - "delta_mfcc_05", - "delta_mfcc_06", - "delta_mfcc_07", - "delta_mfcc_08", - "delta_mfcc_09", - "delta_mfcc_10", - "delta_mfcc_11", - "delta_mfcc_12", - "log_energy", - "log_f0_hz", - "voicing_strength", - "spectral_centroid_hz", - "spectral_bandwidth_hz", - "spectral_flux", - "zero_crossing_rate", - "hnr_db" - ], - "vision": [ - "mp_blendshape_browDownLeft", - "mp_blendshape_browDownRight", - "mp_blendshape_browInnerUp", - "mp_blendshape_browOuterUpLeft", - "mp_blendshape_browOuterUpRight", - "mp_blendshape_eyeBlinkLeft", - "mp_blendshape_eyeBlinkRight", - "mp_blendshape_eyeSquintLeft", - "mp_blendshape_eyeSquintRight", - "mp_blendshape_eyeWideLeft", - "mp_blendshape_eyeWideRight", - "mp_blendshape_jawOpen", - "mp_blendshape_mouthFrownLeft", - "mp_blendshape_mouthFrownRight", - "mp_blendshape_mouthPucker", - "mp_blendshape_mouthSmileLeft", - "mp_blendshape_mouthSmileRight", - "pose_rotvec_x", - "pose_rotvec_y", - "pose_rotvec_z", - "pose_translation_x", - "pose_translation_y", - "pose_translation_z", - "gaze_left_x", - "gaze_left_y", - "gaze_left_z", - "gaze_right_x", - "gaze_right_y", - "gaze_right_z", - "eye_aperture_left", - "eye_aperture_right", - "mouth_aperture", - "mouth_width_face_ratio", - "brow_eye_distance_left", - "brow_eye_distance_right" - ] - }, - "native_grid_step_count_total": 7923, - "native_grid_sample_offsets": [ - 0, - 56, - 200, - 294, - 383, - 462, - 502, - 559, - 593, - 671, - 729, - 798, - 947, - 990, - 1071, - 1175, - 1212, - 1254, - 1336, - 1558, - 1645, - 1755, - 1917, - 2122, - 2289, - 2417, - 2478, - 2551, - 2635, - 2691, - 2733, - 2836, - 2899, - 2950, - 3017, - 3074, - 3116, - 3146, - 3181, - 3262, - 3339, - 3487, - 3549, - 3607, - 3630, - 3701, - 3743, - 3808, - 3865, - 4037, - 4105, - 4160, - 4208, - 4327, - 4400, - 4467, - 4559, - 4620, - 4689, - 4723, - 4785, - 4852, - 4922, - 5003, - 5066, - 5117, - 5270, - 5347, - 5439, - 5513, - 5561, - 5635, - 5700, - 5747, - 5808, - 5937, - 6006, - 6035, - 6085, - 6311, - 6394, - 6488, - 6543, - 6649, - 6702, - 6748, - 6804, - 6883, - 6912, - 6973, - 7009, - 7057, - 7126, - 7212, - 7257, - 7327, - 7352, - 7385, - 7678, - 7810, - 7923 - ], - "files": { - "aligned_50.pkl": { - "bytes": 25021343, - "sha256": "434d01fed3e1fc83666c06e18d9cc1135769526602a40d9aab04d32d9664c6db" - }, - "native_grid.npz": { - "bytes": 5007130, - "sha256": "52ec87409747717f67f3af767ef632417c706c3324dfe7b9879c949b310d4162" - }, - "feature_names.json": { - "bytes": 17752, - "sha256": "4ad4367b1ca42a4ab07278a6e12964e99e96ad2a9f1099c1076e70210679dd25" - }, - "sample_feature_manifest.csv": { - "bytes": 83342, - "sha256": "8dc5c5fd9737a34f0677e23ac33cad307279dcca7a3902e33e07ac3adc280db9" - } - }, - "tables": { - "sample_feature_manifest.csv": "300 rows, one per sample and modality; records source/observed duration, dimension, granularity, grid length and source hash.", - "sample_alignment_summary.csv": "100 sample-level alignment and coverage summaries.", - "word_alignment_posterior.csv": "Word-level hard intervals and CTC posterior interval summaries." - } -} \ No newline at end of file diff --git a/final/Q1/features/feature_names.json b/final/Q1/features/feature_names.json deleted file mode 100644 index 38776f2..0000000 --- a/final/Q1/features/feature_names.json +++ /dev/null @@ -1,885 +0,0 @@ -{ - "text": [ - "bert_dim_000", - "bert_dim_001", - "bert_dim_002", - "bert_dim_003", - "bert_dim_004", - "bert_dim_005", - "bert_dim_006", - "bert_dim_007", - "bert_dim_008", - "bert_dim_009", - "bert_dim_010", - "bert_dim_011", - "bert_dim_012", - "bert_dim_013", - "bert_dim_014", - "bert_dim_015", - "bert_dim_016", - "bert_dim_017", - "bert_dim_018", - "bert_dim_019", - "bert_dim_020", - "bert_dim_021", - "bert_dim_022", - "bert_dim_023", - "bert_dim_024", - "bert_dim_025", - "bert_dim_026", - "bert_dim_027", - "bert_dim_028", - "bert_dim_029", - "bert_dim_030", - "bert_dim_031", - "bert_dim_032", - "bert_dim_033", - "bert_dim_034", - "bert_dim_035", - "bert_dim_036", - "bert_dim_037", - "bert_dim_038", - "bert_dim_039", - "bert_dim_040", - "bert_dim_041", - "bert_dim_042", - "bert_dim_043", - "bert_dim_044", - "bert_dim_045", - "bert_dim_046", - "bert_dim_047", - "bert_dim_048", - "bert_dim_049", - "bert_dim_050", - "bert_dim_051", - "bert_dim_052", - "bert_dim_053", - "bert_dim_054", - "bert_dim_055", - "bert_dim_056", - "bert_dim_057", - "bert_dim_058", - "bert_dim_059", - "bert_dim_060", - "bert_dim_061", - "bert_dim_062", - "bert_dim_063", - "bert_dim_064", - "bert_dim_065", - "bert_dim_066", - "bert_dim_067", - "bert_dim_068", - "bert_dim_069", - "bert_dim_070", - "bert_dim_071", - "bert_dim_072", - "bert_dim_073", - "bert_dim_074", - "bert_dim_075", - "bert_dim_076", - "bert_dim_077", - "bert_dim_078", - "bert_dim_079", - "bert_dim_080", - "bert_dim_081", - "bert_dim_082", - "bert_dim_083", - "bert_dim_084", - "bert_dim_085", - "bert_dim_086", - "bert_dim_087", - "bert_dim_088", - "bert_dim_089", - "bert_dim_090", - "bert_dim_091", - "bert_dim_092", - "bert_dim_093", - "bert_dim_094", - "bert_dim_095", - "bert_dim_096", - "bert_dim_097", - "bert_dim_098", - "bert_dim_099", - "bert_dim_100", - "bert_dim_101", - "bert_dim_102", - "bert_dim_103", - "bert_dim_104", - "bert_dim_105", - "bert_dim_106", - "bert_dim_107", - "bert_dim_108", - "bert_dim_109", - "bert_dim_110", - "bert_dim_111", - "bert_dim_112", - "bert_dim_113", - "bert_dim_114", - "bert_dim_115", - "bert_dim_116", - "bert_dim_117", - "bert_dim_118", - "bert_dim_119", - "bert_dim_120", - "bert_dim_121", - "bert_dim_122", - "bert_dim_123", - "bert_dim_124", - "bert_dim_125", - "bert_dim_126", - "bert_dim_127", - "bert_dim_128", - "bert_dim_129", - "bert_dim_130", - "bert_dim_131", - "bert_dim_132", - "bert_dim_133", - "bert_dim_134", - "bert_dim_135", - "bert_dim_136", - "bert_dim_137", - "bert_dim_138", - "bert_dim_139", - "bert_dim_140", - "bert_dim_141", - "bert_dim_142", - "bert_dim_143", - "bert_dim_144", - "bert_dim_145", - "bert_dim_146", - "bert_dim_147", - "bert_dim_148", - "bert_dim_149", - "bert_dim_150", - "bert_dim_151", - "bert_dim_152", - "bert_dim_153", - "bert_dim_154", - "bert_dim_155", - "bert_dim_156", - "bert_dim_157", - "bert_dim_158", - "bert_dim_159", - "bert_dim_160", - "bert_dim_161", - "bert_dim_162", - "bert_dim_163", - "bert_dim_164", - "bert_dim_165", - "bert_dim_166", - "bert_dim_167", - "bert_dim_168", - "bert_dim_169", - "bert_dim_170", - "bert_dim_171", - "bert_dim_172", - "bert_dim_173", - "bert_dim_174", - "bert_dim_175", - "bert_dim_176", - "bert_dim_177", - "bert_dim_178", - "bert_dim_179", - "bert_dim_180", - "bert_dim_181", - "bert_dim_182", - "bert_dim_183", - "bert_dim_184", - "bert_dim_185", - "bert_dim_186", - "bert_dim_187", - "bert_dim_188", - "bert_dim_189", - "bert_dim_190", - "bert_dim_191", - "bert_dim_192", - "bert_dim_193", - "bert_dim_194", - "bert_dim_195", - "bert_dim_196", - "bert_dim_197", - "bert_dim_198", - "bert_dim_199", - "bert_dim_200", - "bert_dim_201", - "bert_dim_202", - "bert_dim_203", - "bert_dim_204", - "bert_dim_205", - "bert_dim_206", - "bert_dim_207", - "bert_dim_208", - "bert_dim_209", - "bert_dim_210", - "bert_dim_211", - "bert_dim_212", - "bert_dim_213", - "bert_dim_214", - "bert_dim_215", - "bert_dim_216", - "bert_dim_217", - "bert_dim_218", - "bert_dim_219", - "bert_dim_220", - "bert_dim_221", - "bert_dim_222", - "bert_dim_223", - "bert_dim_224", - "bert_dim_225", - "bert_dim_226", - "bert_dim_227", - "bert_dim_228", - "bert_dim_229", - "bert_dim_230", - "bert_dim_231", - "bert_dim_232", - "bert_dim_233", - "bert_dim_234", - "bert_dim_235", - "bert_dim_236", - "bert_dim_237", - "bert_dim_238", - "bert_dim_239", - "bert_dim_240", - "bert_dim_241", - "bert_dim_242", - "bert_dim_243", - "bert_dim_244", - "bert_dim_245", - "bert_dim_246", - "bert_dim_247", - "bert_dim_248", - "bert_dim_249", - "bert_dim_250", - "bert_dim_251", - "bert_dim_252", - "bert_dim_253", - "bert_dim_254", - "bert_dim_255", - "bert_dim_256", - "bert_dim_257", - "bert_dim_258", - "bert_dim_259", - "bert_dim_260", - "bert_dim_261", - "bert_dim_262", - "bert_dim_263", - "bert_dim_264", - "bert_dim_265", - "bert_dim_266", - "bert_dim_267", - "bert_dim_268", - "bert_dim_269", - "bert_dim_270", - "bert_dim_271", - "bert_dim_272", - "bert_dim_273", - "bert_dim_274", - "bert_dim_275", - "bert_dim_276", - "bert_dim_277", - "bert_dim_278", - "bert_dim_279", - "bert_dim_280", - "bert_dim_281", - "bert_dim_282", - "bert_dim_283", - "bert_dim_284", - "bert_dim_285", - "bert_dim_286", - "bert_dim_287", - "bert_dim_288", - "bert_dim_289", - "bert_dim_290", - "bert_dim_291", - "bert_dim_292", - "bert_dim_293", - "bert_dim_294", - "bert_dim_295", - "bert_dim_296", - "bert_dim_297", - "bert_dim_298", - "bert_dim_299", - "bert_dim_300", - "bert_dim_301", - "bert_dim_302", - "bert_dim_303", - "bert_dim_304", - "bert_dim_305", - "bert_dim_306", - "bert_dim_307", - "bert_dim_308", - "bert_dim_309", - "bert_dim_310", - "bert_dim_311", - "bert_dim_312", - "bert_dim_313", - "bert_dim_314", - "bert_dim_315", - "bert_dim_316", - "bert_dim_317", - "bert_dim_318", - "bert_dim_319", - "bert_dim_320", - "bert_dim_321", - "bert_dim_322", - "bert_dim_323", - "bert_dim_324", - "bert_dim_325", - "bert_dim_326", - "bert_dim_327", - "bert_dim_328", - "bert_dim_329", - "bert_dim_330", - "bert_dim_331", - "bert_dim_332", - "bert_dim_333", - "bert_dim_334", - "bert_dim_335", - "bert_dim_336", - "bert_dim_337", - "bert_dim_338", - "bert_dim_339", - "bert_dim_340", - "bert_dim_341", - "bert_dim_342", - "bert_dim_343", - "bert_dim_344", - "bert_dim_345", - "bert_dim_346", - "bert_dim_347", - "bert_dim_348", - "bert_dim_349", - "bert_dim_350", - "bert_dim_351", - "bert_dim_352", - "bert_dim_353", - "bert_dim_354", - "bert_dim_355", - "bert_dim_356", - "bert_dim_357", - "bert_dim_358", - "bert_dim_359", - "bert_dim_360", - "bert_dim_361", - "bert_dim_362", - "bert_dim_363", - "bert_dim_364", - "bert_dim_365", - "bert_dim_366", - "bert_dim_367", - "bert_dim_368", - "bert_dim_369", - "bert_dim_370", - "bert_dim_371", - "bert_dim_372", - "bert_dim_373", - "bert_dim_374", - "bert_dim_375", - "bert_dim_376", - "bert_dim_377", - "bert_dim_378", - "bert_dim_379", - "bert_dim_380", - "bert_dim_381", - "bert_dim_382", - "bert_dim_383", - "bert_dim_384", - "bert_dim_385", - "bert_dim_386", - "bert_dim_387", - "bert_dim_388", - "bert_dim_389", - "bert_dim_390", - "bert_dim_391", - "bert_dim_392", - "bert_dim_393", - "bert_dim_394", - "bert_dim_395", - "bert_dim_396", - "bert_dim_397", - "bert_dim_398", - "bert_dim_399", - "bert_dim_400", - "bert_dim_401", - "bert_dim_402", - "bert_dim_403", - "bert_dim_404", - "bert_dim_405", - "bert_dim_406", - "bert_dim_407", - "bert_dim_408", - "bert_dim_409", - "bert_dim_410", - "bert_dim_411", - "bert_dim_412", - "bert_dim_413", - "bert_dim_414", - "bert_dim_415", - "bert_dim_416", - "bert_dim_417", - "bert_dim_418", - "bert_dim_419", - "bert_dim_420", - "bert_dim_421", - "bert_dim_422", - "bert_dim_423", - "bert_dim_424", - "bert_dim_425", - "bert_dim_426", - "bert_dim_427", - "bert_dim_428", - "bert_dim_429", - "bert_dim_430", - "bert_dim_431", - "bert_dim_432", - "bert_dim_433", - "bert_dim_434", - "bert_dim_435", - "bert_dim_436", - "bert_dim_437", - "bert_dim_438", - "bert_dim_439", - "bert_dim_440", - "bert_dim_441", - "bert_dim_442", - "bert_dim_443", - "bert_dim_444", - "bert_dim_445", - "bert_dim_446", - "bert_dim_447", - "bert_dim_448", - "bert_dim_449", - "bert_dim_450", - "bert_dim_451", - "bert_dim_452", - "bert_dim_453", - "bert_dim_454", - "bert_dim_455", - "bert_dim_456", - "bert_dim_457", - "bert_dim_458", - "bert_dim_459", - "bert_dim_460", - "bert_dim_461", - "bert_dim_462", - "bert_dim_463", - "bert_dim_464", - "bert_dim_465", - "bert_dim_466", - "bert_dim_467", - "bert_dim_468", - "bert_dim_469", - "bert_dim_470", - "bert_dim_471", - "bert_dim_472", - "bert_dim_473", - "bert_dim_474", - "bert_dim_475", - "bert_dim_476", - "bert_dim_477", - "bert_dim_478", - "bert_dim_479", - "bert_dim_480", - "bert_dim_481", - "bert_dim_482", - "bert_dim_483", - "bert_dim_484", - "bert_dim_485", - "bert_dim_486", - "bert_dim_487", - "bert_dim_488", - "bert_dim_489", - "bert_dim_490", - "bert_dim_491", - "bert_dim_492", - "bert_dim_493", - "bert_dim_494", - "bert_dim_495", - "bert_dim_496", - "bert_dim_497", - "bert_dim_498", - "bert_dim_499", - "bert_dim_500", - "bert_dim_501", - "bert_dim_502", - "bert_dim_503", - "bert_dim_504", - "bert_dim_505", - "bert_dim_506", - "bert_dim_507", - "bert_dim_508", - "bert_dim_509", - "bert_dim_510", - "bert_dim_511", - "bert_dim_512", - "bert_dim_513", - "bert_dim_514", - "bert_dim_515", - "bert_dim_516", - "bert_dim_517", - "bert_dim_518", - "bert_dim_519", - "bert_dim_520", - "bert_dim_521", - "bert_dim_522", - "bert_dim_523", - "bert_dim_524", - "bert_dim_525", - "bert_dim_526", - "bert_dim_527", - "bert_dim_528", - "bert_dim_529", - "bert_dim_530", - "bert_dim_531", - "bert_dim_532", - "bert_dim_533", - "bert_dim_534", - "bert_dim_535", - "bert_dim_536", - "bert_dim_537", - "bert_dim_538", - "bert_dim_539", - "bert_dim_540", - "bert_dim_541", - "bert_dim_542", - "bert_dim_543", - "bert_dim_544", - "bert_dim_545", - "bert_dim_546", - "bert_dim_547", - "bert_dim_548", - "bert_dim_549", - "bert_dim_550", - "bert_dim_551", - "bert_dim_552", - "bert_dim_553", - "bert_dim_554", - "bert_dim_555", - "bert_dim_556", - "bert_dim_557", - "bert_dim_558", - "bert_dim_559", - "bert_dim_560", - "bert_dim_561", - "bert_dim_562", - "bert_dim_563", - "bert_dim_564", - "bert_dim_565", - "bert_dim_566", - "bert_dim_567", - "bert_dim_568", - "bert_dim_569", - "bert_dim_570", - "bert_dim_571", - "bert_dim_572", - "bert_dim_573", - "bert_dim_574", - "bert_dim_575", - "bert_dim_576", - "bert_dim_577", - "bert_dim_578", - "bert_dim_579", - "bert_dim_580", - "bert_dim_581", - "bert_dim_582", - "bert_dim_583", - "bert_dim_584", - "bert_dim_585", - "bert_dim_586", - "bert_dim_587", - "bert_dim_588", - "bert_dim_589", - "bert_dim_590", - "bert_dim_591", - "bert_dim_592", - "bert_dim_593", - "bert_dim_594", - "bert_dim_595", - "bert_dim_596", - "bert_dim_597", - "bert_dim_598", - "bert_dim_599", - "bert_dim_600", - "bert_dim_601", - "bert_dim_602", - "bert_dim_603", - "bert_dim_604", - "bert_dim_605", - "bert_dim_606", - "bert_dim_607", - "bert_dim_608", - "bert_dim_609", - "bert_dim_610", - "bert_dim_611", - "bert_dim_612", - "bert_dim_613", - "bert_dim_614", - "bert_dim_615", - "bert_dim_616", - "bert_dim_617", - "bert_dim_618", - "bert_dim_619", - "bert_dim_620", - "bert_dim_621", - "bert_dim_622", - "bert_dim_623", - "bert_dim_624", - "bert_dim_625", - "bert_dim_626", - "bert_dim_627", - "bert_dim_628", - "bert_dim_629", - "bert_dim_630", - "bert_dim_631", - "bert_dim_632", - "bert_dim_633", - "bert_dim_634", - "bert_dim_635", - "bert_dim_636", - "bert_dim_637", - "bert_dim_638", - "bert_dim_639", - "bert_dim_640", - "bert_dim_641", - "bert_dim_642", - "bert_dim_643", - "bert_dim_644", - "bert_dim_645", - "bert_dim_646", - "bert_dim_647", - "bert_dim_648", - "bert_dim_649", - "bert_dim_650", - "bert_dim_651", - "bert_dim_652", - "bert_dim_653", - "bert_dim_654", - "bert_dim_655", - "bert_dim_656", - "bert_dim_657", - "bert_dim_658", - "bert_dim_659", - "bert_dim_660", - "bert_dim_661", - "bert_dim_662", - "bert_dim_663", - "bert_dim_664", - "bert_dim_665", - "bert_dim_666", - "bert_dim_667", - "bert_dim_668", - "bert_dim_669", - "bert_dim_670", - "bert_dim_671", - "bert_dim_672", - "bert_dim_673", - "bert_dim_674", - "bert_dim_675", - "bert_dim_676", - "bert_dim_677", - "bert_dim_678", - "bert_dim_679", - "bert_dim_680", - "bert_dim_681", - "bert_dim_682", - "bert_dim_683", - "bert_dim_684", - "bert_dim_685", - "bert_dim_686", - "bert_dim_687", - "bert_dim_688", - "bert_dim_689", - "bert_dim_690", - "bert_dim_691", - "bert_dim_692", - "bert_dim_693", - "bert_dim_694", - "bert_dim_695", - "bert_dim_696", - "bert_dim_697", - "bert_dim_698", - "bert_dim_699", - "bert_dim_700", - "bert_dim_701", - "bert_dim_702", - "bert_dim_703", - "bert_dim_704", - "bert_dim_705", - "bert_dim_706", - "bert_dim_707", - "bert_dim_708", - "bert_dim_709", - "bert_dim_710", - "bert_dim_711", - "bert_dim_712", - "bert_dim_713", - "bert_dim_714", - "bert_dim_715", - "bert_dim_716", - "bert_dim_717", - "bert_dim_718", - "bert_dim_719", - "bert_dim_720", - "bert_dim_721", - "bert_dim_722", - "bert_dim_723", - "bert_dim_724", - "bert_dim_725", - "bert_dim_726", - "bert_dim_727", - "bert_dim_728", - "bert_dim_729", - "bert_dim_730", - "bert_dim_731", - "bert_dim_732", - "bert_dim_733", - "bert_dim_734", - "bert_dim_735", - "bert_dim_736", - "bert_dim_737", - "bert_dim_738", - "bert_dim_739", - "bert_dim_740", - "bert_dim_741", - "bert_dim_742", - "bert_dim_743", - "bert_dim_744", - "bert_dim_745", - "bert_dim_746", - "bert_dim_747", - "bert_dim_748", - "bert_dim_749", - "bert_dim_750", - "bert_dim_751", - "bert_dim_752", - "bert_dim_753", - "bert_dim_754", - "bert_dim_755", - "bert_dim_756", - "bert_dim_757", - "bert_dim_758", - "bert_dim_759", - "bert_dim_760", - "bert_dim_761", - "bert_dim_762", - "bert_dim_763", - "bert_dim_764", - "bert_dim_765", - "bert_dim_766", - "bert_dim_767" - ], - "audio": [ - "logmel_00", - "logmel_01", - "logmel_02", - "logmel_03", - "logmel_04", - "logmel_05", - "logmel_06", - "logmel_07", - "logmel_08", - "logmel_09", - "logmel_10", - "logmel_11", - "logmel_12", - "logmel_13", - "logmel_14", - "logmel_15", - "logmel_16", - "logmel_17", - "logmel_18", - "logmel_19", - "logmel_20", - "logmel_21", - "logmel_22", - "logmel_23", - "logmel_24", - "logmel_25", - "logmel_26", - "logmel_27", - "logmel_28", - "logmel_29", - "logmel_30", - "logmel_31", - "logmel_32", - "logmel_33", - "logmel_34", - "logmel_35", - "logmel_36", - "logmel_37", - "logmel_38", - "logmel_39", - "mfcc_00", - "mfcc_01", - "mfcc_02", - "mfcc_03", - "mfcc_04", - "mfcc_05", - "mfcc_06", - "mfcc_07", - "mfcc_08", - "mfcc_09", - "mfcc_10", - "mfcc_11", - "mfcc_12", - "delta_mfcc_00", - "delta_mfcc_01", - "delta_mfcc_02", - "delta_mfcc_03", - "delta_mfcc_04", - "delta_mfcc_05", - "delta_mfcc_06", - "delta_mfcc_07", - "delta_mfcc_08", - "delta_mfcc_09", - "delta_mfcc_10", - "delta_mfcc_11", - "delta_mfcc_12", - "log_energy", - "log_f0_hz", - "voicing_strength", - "spectral_centroid_hz", - "spectral_bandwidth_hz", - "spectral_flux", - "zero_crossing_rate", - "hnr_db" - ], - "vision": [ - "mp_blendshape_browDownLeft", - "mp_blendshape_browDownRight", - "mp_blendshape_browInnerUp", - "mp_blendshape_browOuterUpLeft", - "mp_blendshape_browOuterUpRight", - "mp_blendshape_eyeBlinkLeft", - "mp_blendshape_eyeBlinkRight", - "mp_blendshape_eyeSquintLeft", - "mp_blendshape_eyeSquintRight", - "mp_blendshape_eyeWideLeft", - "mp_blendshape_eyeWideRight", - "mp_blendshape_jawOpen", - "mp_blendshape_mouthFrownLeft", - "mp_blendshape_mouthFrownRight", - "mp_blendshape_mouthPucker", - "mp_blendshape_mouthSmileLeft", - "mp_blendshape_mouthSmileRight", - "pose_rotvec_x", - "pose_rotvec_y", - "pose_rotvec_z", - "pose_translation_x", - "pose_translation_y", - "pose_translation_z", - "gaze_left_x", - "gaze_left_y", - "gaze_left_z", - "gaze_right_x", - "gaze_right_y", - "gaze_right_z", - "eye_aperture_left", - "eye_aperture_right", - "mouth_aperture", - "mouth_width_face_ratio", - "brow_eye_distance_left", - "brow_eye_distance_right" - ] -} \ No newline at end of file diff --git a/final/Q1/features/modality_summary.csv b/final/Q1/features/modality_summary.csv deleted file mode 100644 index 71fa296..0000000 --- a/final/Q1/features/modality_summary.csv +++ /dev/null @@ -1,301 +0,0 @@ -sample_id,video_id,clip_id,modality,source_duration_s,observed_duration_s_mean_dimension,native_length,native_dimension,main_grid_length,grid_step_s,grid_dimension,mean_grid_coverage,status,source_video_sha256 --3g5yACwYnA/13,-3g5yACwYnA,13,text,5.5139970779418945,2.946523889899254,15,768,56,0.1,768,0.7015532851219177,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f --3g5yACwYnA/13,-3g5yACwYnA,13,audio,5.5139970779418945,5.445240411887298,540,74,56,0.1,74,0.9882608652114868,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f --3g5yACwYnA/13,-3g5yACwYnA,13,vision,5.5139970779418945,5.5139970779418945,43,35,56,0.1,35,1.0,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f --3g5yACwYnA/3,-3g5yACwYnA,3,text,14.388997077941895,7.041063531115653,29,768,144,0.1,768,0.6343300342559814,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d --3g5yACwYnA/3,-3g5yACwYnA,3,audio,14.388997077941895,14.260037878559858,1433,74,144,0.1,74,0.9912122488021851,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d --3g5yACwYnA/3,-3g5yACwYnA,3,vision,14.388997077941895,14.388997077941895,103,35,144,0.1,35,1.0,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d --3g5yACwYnA/2,-3g5yACwYnA,2,text,9.394009590148926,5.1430321040563305,14,768,94,0.1,768,0.7563282251358032,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7 --3g5yACwYnA/2,-3g5yACwYnA,2,audio,9.394009590148926,9.331671435768538,924,74,94,0.1,74,0.9936367273330688,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7 --3g5yACwYnA/2,-3g5yACwYnA,2,vision,9.394009590148926,9.394009590148926,78,35,94,0.1,35,1.0,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7 --3g5yACwYnA/9,-3g5yACwYnA,9,text,8.816991806030273,5.6415157541632714,21,768,89,0.1,768,0.6637077927589417,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016 --3g5yACwYnA/9,-3g5yACwYnA,9,audio,8.816991806030273,8.735654204761659,870,74,89,0.1,74,0.9912108778953552,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016 --3g5yACwYnA/9,-3g5yACwYnA,9,vision,8.816991806030273,8.816991806030273,75,35,89,0.1,35,1.0,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016 --3nNcZdcdvU/5,-3nNcZdcdvU,5,text,7.867969036102295,5.002839090395719,18,768,79,0.1,768,0.7357116341590881,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728 --3nNcZdcdvU/5,-3nNcZdcdvU,5,audio,7.867969036102295,7.843361772234375,779,74,79,0.1,74,0.9971166849136353,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728 --3nNcZdcdvU/5,-3nNcZdcdvU,5,vision,7.867969036102295,7.816666602730405,67,35,79,0.1,35,0.9904456734657288,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,text,3.9820311069488525,2.40418455898877,8,768,40,0.1,768,0.6164926290512085,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,audio,3.9820311069488525,3.9192059241395896,392,74,40,0.1,74,0.9881895780563354,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,vision,3.9820311069488525,0.8653643131256102,27,35,40,0.1,35,0.9814813733100891,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,text,5.6529951095581055,3.0192474961280813,10,768,57,0.1,768,0.7021505832672119,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,audio,5.6529951095581055,5.595076320642555,555,74,57,0.1,74,0.9900854229927063,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,vision,5.6529951095581055,1.199999868869782,44,35,57,0.1,35,0.666666567325592,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,text,3.3580079078674316,1.3907382175326355,7,768,34,0.1,768,0.5562952756881714,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,audio,3.3580079078674316,3.306980685486987,326,74,34,0.1,74,0.9901678562164307,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,vision,3.3580079078674316,1.6413411736488344,21,35,34,0.1,35,0.841666579246521,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,text,7.733983993530273,4.492685443162919,22,768,78,0.1,768,0.6705501675605774,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,audio,7.733983993530273,7.691443904670509,764,74,78,0.1,74,0.9946621060371399,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,vision,7.733983993530273,0.0,65,35,78,0.1,35,0.0,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702 --NFrJFQijFE/1,-NFrJFQijFE,1,text,5.745999813079834,4.074365028738975,16,768,58,0.1,768,0.7835317254066467,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f --NFrJFQijFE/1,-NFrJFQijFE,1,audio,5.745999813079834,5.699824074635634,566,74,58,0.1,74,0.9922494292259216,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f --NFrJFQijFE/1,-NFrJFQijFE,1,vision,5.745999813079834,0.0,44,35,58,0.1,35,0.0,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f --NFrJFQijFE/2,-NFrJFQijFE,2,text,6.855999946594238,3.4186642885208136,18,768,69,0.1,768,0.7431879639625549,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1 --NFrJFQijFE/2,-NFrJFQijFE,2,audio,6.855999946594238,6.798472889210726,670,74,69,0.1,74,0.9926568269729614,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1 --NFrJFQijFE/2,-NFrJFQijFE,2,vision,6.855999946594238,0.0,55,35,69,0.1,35,0.0,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1 --THoVjtIkeU/12,-THoVjtIkeU,12,text,14.896029472351074,7.941008102986966,39,768,149,0.1,768,0.6352806687355042,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4 --THoVjtIkeU/12,-THoVjtIkeU,12,audio,14.896029472351074,14.759312417861578,1481,74,149,0.1,74,0.9911767244338989,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4 --THoVjtIkeU/12,-THoVjtIkeU,12,vision,14.896029472351074,14.896029472351074,106,35,149,0.1,35,1.0,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4 --THoVjtIkeU/2,-THoVjtIkeU,2,text,4.2919921875,2.5793988468125453,10,768,43,0.1,768,0.7164996266365051,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b --THoVjtIkeU/2,-THoVjtIkeU,2,audio,4.2919921875,4.247343715932063,424,74,43,0.1,74,0.9895758628845215,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b --THoVjtIkeU/2,-THoVjtIkeU,2,vision,4.2919921875,4.2919921875,31,35,43,0.1,35,1.0,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b --THoVjtIkeU/6,-THoVjtIkeU,6,text,8.097004890441895,5.15853084437549,30,768,81,0.1,768,0.6787540316581726,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64 --THoVjtIkeU/6,-THoVjtIkeU,6,audio,8.097004890441895,8.027356093840018,799,74,81,0.1,74,0.9927164316177368,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64 --THoVjtIkeU/6,-THoVjtIkeU,6,vision,8.097004890441895,8.097004890441895,68,35,81,0.1,35,1.0,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64 --UuX1xuaiiE/1,-UuX1xuaiiE,1,text,10.350000381469727,5.7645069250837,27,768,104,0.1,768,0.6550575494766235,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c --UuX1xuaiiE/1,-UuX1xuaiiE,1,audio,10.350000381469727,10.268040973753543,1027,74,104,0.1,74,0.9925051927566528,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c --UuX1xuaiiE/1,-UuX1xuaiiE,1,vision,10.350000381469727,0.45000076293945285,83,35,104,0.1,35,0.8333339691162109,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c --UuX1xuaiiE/0,-UuX1xuaiiE,0,text,3.6333329677581787,2.204757870733737,9,768,37,0.1,768,0.6484582424163818,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8 --UuX1xuaiiE/0,-UuX1xuaiiE,0,audio,3.6333329677581787,3.6124320519937045,358,74,37,0.1,74,0.994590699672699,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8 --UuX1xuaiiE/0,-UuX1xuaiiE,0,vision,3.6333329677581787,1.700000047683716,25,35,37,0.1,35,0.9444444179534912,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8 --UuX1xuaiiE/3,-UuX1xuaiiE,3,text,4.1300129890441895,2.0997684210538865,9,768,42,0.1,768,0.7240580320358276,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa --UuX1xuaiiE/3,-UuX1xuaiiE,3,audio,4.1300129890441895,4.093458598368876,404,74,42,0.1,74,0.992123007774353,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa --UuX1xuaiiE/3,-UuX1xuaiiE,3,vision,4.1300129890441895,4.03001308441162,29,35,42,0.1,35,0.976190447807312,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa --UuX1xuaiiE/6,-UuX1xuaiiE,6,text,8.113997459411621,4.803536714613438,22,768,82,0.1,768,0.7064024806022644,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532 --UuX1xuaiiE/6,-UuX1xuaiiE,6,audio,8.113997459411621,7.968213647201255,795,74,82,0.1,74,0.984533965587616,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532 --UuX1xuaiiE/6,-UuX1xuaiiE,6,vision,8.113997459411621,7.9306641817092896,68,35,82,0.1,35,0.9776423573493958,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532 --a55Q6RWvTA/3,-a55Q6RWvTA,3,text,22.15397071838379,13.423765965458015,65,768,222,0.1,768,0.6580277681350708,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946 --a55Q6RWvTA/3,-a55Q6RWvTA,3,audio,22.15397071838379,21.95386351814141,2209,74,222,0.1,74,0.9912921786308289,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946 --a55Q6RWvTA/3,-a55Q6RWvTA,3,vision,22.15397071838379,22.15397071838379,142,35,222,0.1,35,1.0,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946 --aNfi7CP8vM/7,-aNfi7CP8vM,7,text,8.694987297058105,5.3913327027112254,18,768,87,0.1,768,0.7001731395721436,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1 --aNfi7CP8vM/7,-aNfi7CP8vM,7,audio,8.694987297058105,8.639379485394505,854,74,87,0.1,74,0.9945195913314819,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1 --aNfi7CP8vM/7,-aNfi7CP8vM,7,vision,8.694987297058105,8.694987297058105,74,35,87,0.1,35,1.0,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,text,10.966667175292969,5.40069461874664,14,768,110,0.1,768,0.71061772108078,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,audio,10.966667175292969,10.855676183829436,1090,74,110,0.1,74,0.9910455346107483,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,vision,10.966667175292969,10.966667175292969,86,35,110,0.1,35,1.0,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548 --dxfTGcXJoc/1,-dxfTGcXJoc,1,text,16.16100311279297,9.47002421617508,36,768,162,0.1,768,0.6812966465950012,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595 --dxfTGcXJoc/1,-dxfTGcXJoc,1,audio,16.16100311279297,15.984151583668348,1602,74,162,0.1,74,0.9900091290473938,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595 --dxfTGcXJoc/1,-dxfTGcXJoc,1,vision,16.16100311279297,16.16100311279297,112,35,162,0.1,35,1.0,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595 --dxfTGcXJoc/0,-dxfTGcXJoc,0,text,20.5,13.566675072815258,41,768,205,0.1,768,0.7333337664604187,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55 --dxfTGcXJoc/0,-dxfTGcXJoc,0,audio,20.5,20.265135636200775,2042,74,205,0.1,74,0.9891952872276306,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55 --dxfTGcXJoc/0,-dxfTGcXJoc,0,vision,20.5,15.983333587646484,134,35,205,0.1,35,0.99895840883255,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55 --dxfTGcXJoc/2,-dxfTGcXJoc,2,text,16.697982788085938,9.495353608578453,34,768,167,0.1,768,0.6593995094299316,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7 --dxfTGcXJoc/2,-dxfTGcXJoc,2,audio,16.697982788085938,16.517766948940555,1664,74,167,0.1,74,0.9901677966117859,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7 --dxfTGcXJoc/2,-dxfTGcXJoc,2,vision,16.697982788085938,16.697982788085938,115,35,167,0.1,35,1.0,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7 --dxfTGcXJoc/6,-dxfTGcXJoc,6,text,12.735026359558105,8.155348146427425,27,768,128,0.1,768,0.715381383895874,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513 --dxfTGcXJoc/6,-dxfTGcXJoc,6,audio,12.735026359558105,12.576174248392515,1263,74,128,0.1,74,0.9889141917228699,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513 --dxfTGcXJoc/6,-dxfTGcXJoc,6,vision,12.735026359558105,12.5,95,35,128,0.1,35,1.0,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513 --egA8-b7-3M/26,-egA8-b7-3M,26,text,6.030990123748779,3.6452820789068947,15,768,61,0.1,768,0.6877890825271606,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685 --egA8-b7-3M/26,-egA8-b7-3M,26,audio,6.030990123748779,5.994409297769134,593,74,61,0.1,74,0.9945786595344543,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685 --egA8-b7-3M/26,-egA8-b7-3M,26,vision,6.030990123748779,6.030990123748779,47,35,61,0.1,35,1.0,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685 --egA8-b7-3M/17,-egA8-b7-3M,17,text,7.271028995513916,3.4275580286979666,16,768,73,0.1,768,0.7292676568031311,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f --egA8-b7-3M/17,-egA8-b7-3M,17,audio,7.271028995513916,7.202622791158187,710,74,73,0.1,74,0.9916234016418457,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f --egA8-b7-3M/17,-egA8-b7-3M,17,vision,7.271028995513916,7.271028995513916,60,35,73,0.1,35,1.0,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f --egA8-b7-3M/18,-egA8-b7-3M,18,text,8.386002540588379,3.9958887480199343,22,768,84,0.1,768,0.6243576407432556,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90 --egA8-b7-3M/18,-egA8-b7-3M,18,audio,8.386002540588379,8.326583495091747,827,74,84,0.1,74,0.9932008385658264,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90 --egA8-b7-3M/18,-egA8-b7-3M,18,vision,8.386002540588379,8.386002540588379,71,35,84,0.1,35,1.0,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90 --egA8-b7-3M/16,-egA8-b7-3M,16,text,5.538021087646484,3.3190146580338484,11,768,56,0.1,768,0.7375588417053223,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c --egA8-b7-3M/16,-egA8-b7-3M,16,audio,5.538021087646484,5.498074575251824,549,74,56,0.1,74,0.9940068125724792,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c --egA8-b7-3M/16,-egA8-b7-3M,16,vision,5.538021087646484,5.538021087646484,43,35,56,0.1,35,1.0,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c --egA8-b7-3M/13,-egA8-b7-3M,13,text,4.18398380279541,2.166685357689858,11,768,42,0.1,768,0.6372604370117188,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c --egA8-b7-3M/13,-egA8-b7-3M,13,audio,4.18398380279541,4.149822072886132,403,74,42,0.1,74,0.9924017786979675,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c --egA8-b7-3M/13,-egA8-b7-3M,13,vision,4.18398380279541,4.18398380279541,29,35,42,0.1,35,1.0,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c --egA8-b7-3M/1,-egA8-b7-3M,1,text,10.266016006469727,5.581577223725616,20,768,103,0.1,768,0.6490206122398376,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b --egA8-b7-3M/1,-egA8-b7-3M,1,audio,10.266016006469727,10.199569694899225,1016,74,103,0.1,74,0.9937204718589783,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b --egA8-b7-3M/1,-egA8-b7-3M,1,vision,10.266016006469727,10.266016006469727,83,35,103,0.1,35,1.0,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b --egA8-b7-3M/6,-egA8-b7-3M,6,text,6.264974117279053,3.582069296762348,12,768,63,0.1,768,0.7164138555526733,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0 --egA8-b7-3M/6,-egA8-b7-3M,6,audio,6.264974117279053,6.2242991206613745,619,74,63,0.1,74,0.9942464828491211,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0 --egA8-b7-3M/6,-egA8-b7-3M,6,vision,6.264974117279053,6.264974117279053,50,35,63,0.1,35,1.0,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0 --egA8-b7-3M/9,-egA8-b7-3M,9,text,5.0899739265441895,2.328742109239101,10,768,51,0.1,768,0.597113311290741,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c --egA8-b7-3M/9,-egA8-b7-3M,9,audio,5.0899739265441895,5.05666382530251,494,74,51,0.1,74,0.9939422011375427,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c --egA8-b7-3M/9,-egA8-b7-3M,9,vision,5.0899739265441895,5.0899739265441895,38,35,51,0.1,35,1.0,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c --egA8-b7-3M/20,-egA8-b7-3M,20,text,6.644987106323242,3.9119302723556753,20,768,67,0.1,768,0.6985589861869812,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8 --egA8-b7-3M/20,-egA8-b7-3M,20,audio,6.644987106323242,6.588095611333847,648,74,67,0.1,74,0.992087185382843,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8 --egA8-b7-3M/20,-egA8-b7-3M,20,vision,6.644987106323242,6.644987106323242,54,35,67,0.1,35,1.0,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8 --iRBcNs9oI8/3,-iRBcNs9oI8,3,text,5.620999813079834,2.037480006366969,8,768,57,0.1,768,0.6791599988937378,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50 --iRBcNs9oI8/3,-iRBcNs9oI8,3,audio,5.620999813079834,5.578905152469068,554,74,57,0.1,74,0.9954468607902527,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50 --iRBcNs9oI8/3,-iRBcNs9oI8,3,vision,5.620999813079834,0.0,28,35,57,0.1,35,0.0,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50 --iRBcNs9oI8/7,-iRBcNs9oI8,7,text,4.104000091552734,2.2118893228471284,9,768,42,0.1,768,0.7135127186775208,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081 --iRBcNs9oI8/7,-iRBcNs9oI8,7,audio,4.104000091552734,4.083554145129952,401,74,42,0.1,74,0.995795726776123,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081 --iRBcNs9oI8/7,-iRBcNs9oI8,7,vision,4.104000091552734,0.0,21,35,42,0.1,35,0.0,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081 --iRBcNs9oI8/6,-iRBcNs9oI8,6,text,2.9030001163482666,1.5899996414780622,5,768,30,0.1,768,0.7227271199226379,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3 --iRBcNs9oI8/6,-iRBcNs9oI8,6,audio,2.9030001163482666,2.892310954429008,283,74,30,0.1,74,0.9973600506782532,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3 --iRBcNs9oI8/6,-iRBcNs9oI8,6,vision,2.9030001163482666,0.0,15,35,30,0.1,35,0.0,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3 --iRBcNs9oI8/9,-iRBcNs9oI8,9,text,3.4159998893737793,1.094375005364418,5,768,35,0.1,768,0.643750011920929,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74 --iRBcNs9oI8/9,-iRBcNs9oI8,9,audio,3.4159998893737793,3.383608031917263,338,74,35,0.1,74,0.993934154510498,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74 --iRBcNs9oI8/9,-iRBcNs9oI8,9,vision,3.4159998893737793,0.0,17,35,35,0.1,35,0.0,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74 --iRBcNs9oI8/8,-iRBcNs9oI8,8,text,8.093000411987305,3.901277539858493,21,768,81,0.1,768,0.6192578673362732,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412 --iRBcNs9oI8/8,-iRBcNs9oI8,8,audio,8.093000411987305,8.037513972489212,803,74,81,0.1,74,0.9947929382324219,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412 --iRBcNs9oI8/8,-iRBcNs9oI8,8,vision,8.093000411987305,0.0,41,35,81,0.1,35,0.0,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412 --lzEya4AM_4/5,-lzEya4AM_4,5,text,7.675000190734863,4.456424816604702,19,768,77,0.1,768,0.7427374720573425,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043 --lzEya4AM_4/5,-lzEya4AM_4,5,audio,7.675000190734863,7.5937163826178855,757,74,77,0.1,74,0.9901386499404907,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043 --lzEya4AM_4/5,-lzEya4AM_4,5,vision,7.675000190734863,7.675000190734863,64,35,77,0.1,35,1.0,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043 --lzEya4AM_4/6,-lzEya4AM_4,6,text,14.7919921875,9.074837291240696,43,768,148,0.1,768,0.7318416833877563,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18 --lzEya4AM_4/6,-lzEya4AM_4,6,audio,14.7919921875,14.634100490929308,1475,74,148,0.1,74,0.9907644391059875,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18 --lzEya4AM_4/6,-lzEya4AM_4,6,vision,14.7919921875,14.7919921875,105,35,148,0.1,35,1.0,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,text,6.103000164031982,1.9617638647556308,13,768,62,0.1,768,0.8174015879631042,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,audio,6.103000164031982,5.938054213652739,606,74,62,0.1,74,1.0,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,vision,6.103000164031982,0.0,31,35,62,0.1,35,0.0,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,text,5.730999946594238,3.5213801980018595,11,768,58,0.1,768,0.9266790151596069,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,audio,5.730999946594238,5.5761080561457455,571,74,58,0.1,74,1.0,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,vision,5.730999946594238,0.0,29,35,58,0.1,35,0.0,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,text,2.256999969482422,1.1722640581429007,5,768,23,0.1,768,0.6512578129768372,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,audio,2.256999969482422,2.2208648198211596,223,74,23,0.1,74,0.9862568378448486,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,vision,2.256999969482422,2.256999969482422,12,35,23,0.1,35,1.0,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,text,7.0329999923706055,3.813062208145856,22,768,71,0.1,768,0.5777366757392883,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,audio,7.0329999923706055,6.9057026652870945,691,74,71,0.1,74,0.9846946001052856,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,vision,7.0329999923706055,6.832799911499023,35,35,71,0.1,35,0.9856857061386108,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,text,4.191999912261963,2.284389768779285,11,768,42,0.1,768,0.6529467701911926,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,audio,4.191999912261963,4.127891841835266,418,74,42,0.1,74,0.9846959114074707,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,vision,4.191999912261963,4.191999912261963,21,35,42,0.1,35,1.0,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc --mqbVkbCndg/0,-mqbVkbCndg,0,text,6.466667175292969,3.891069056466222,12,768,65,0.1,768,0.7074670791625977,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda --mqbVkbCndg/0,-mqbVkbCndg,0,audio,6.466667175292969,6.400540961445988,639,74,65,0.1,74,0.9905118346214294,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda --mqbVkbCndg/0,-mqbVkbCndg,0,vision,6.466667175292969,6.466667175292969,53,35,65,0.1,35,1.0,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda --t217m2on-s/2,-t217m2on-s,2,text,5.6860032081604,2.3235132679343233,12,768,57,0.1,768,0.6279765367507935,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8 --t217m2on-s/2,-t217m2on-s,2,audio,5.6860032081604,5.64178691047269,564,74,57,0.1,74,0.9959543347358704,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8 --t217m2on-s/2,-t217m2on-s,2,vision,5.6860032081604,5.6860032081604,45,35,57,0.1,35,1.0,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8 --t217m2on-s/7,-t217m2on-s,7,text,17.183008193969727,10.16458021551371,45,768,172,0.1,768,0.6867958903312683,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7 --t217m2on-s/7,-t217m2on-s,7,audio,17.183008193969727,17.025629438258505,1703,74,172,0.1,74,0.9928514361381531,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7 --t217m2on-s/7,-t217m2on-s,7,vision,17.183008193969727,17.183008193969727,117,35,172,0.1,35,1.0,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7 --tANM6ETl_M/3,-tANM6ETl_M,3,text,6.758008003234863,3.281156235933303,22,768,68,0.1,768,0.5859207510948181,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6 --tANM6ETl_M/3,-tANM6ETl_M,3,audio,6.758008003234863,6.702588935075579,661,74,68,0.1,74,0.9936580061912537,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6 --tANM6ETl_M/3,-tANM6ETl_M,3,vision,6.758008003234863,6.5333333015441895,54,35,68,0.1,35,0.9898989200592041,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6 --tPCytz4rww/11,-tPCytz4rww,11,text,5.466991901397705,3.309338267147543,17,768,55,0.1,768,0.6753751635551453,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d --tPCytz4rww/11,-tPCytz4rww,11,audio,5.466991901397705,5.402410984844775,538,74,55,0.1,74,0.9890678524971008,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d --tPCytz4rww/11,-tPCytz4rww,11,vision,5.466991901397705,5.466991901397705,43,35,55,0.1,35,1.0,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d --tPCytz4rww/10,-tPCytz4rww,10,text,4.788997173309326,2.2889054998755456,12,768,48,0.1,768,0.5449775457382202,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8 --tPCytz4rww/10,-tPCytz4rww,10,audio,4.788997173309326,4.71733509463233,468,74,48,0.1,74,0.9894654750823975,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8 --tPCytz4rww/10,-tPCytz4rww,10,vision,4.788997173309326,4.788997173309326,35,35,48,0.1,35,1.0,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8 --tPCytz4rww/12,-tPCytz4rww,12,text,11.863997459411621,6.21423171013594,26,768,119,0.1,768,0.654129683971405,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5 --tPCytz4rww/12,-tPCytz4rww,12,audio,11.863997459411621,11.690646183812941,1174,74,119,0.1,74,0.9875938892364502,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5 --tPCytz4rww/12,-tPCytz4rww,12,vision,11.863997459411621,11.863997459411621,91,35,119,0.1,35,1.0,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5 --tPCytz4rww/16,-tPCytz4rww,16,text,7.205989837646484,2.154985956847668,16,768,73,0.1,768,0.4489554166793823,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93 --tPCytz4rww/16,-tPCytz4rww,16,audio,7.205989837646484,7.1144090478484685,714,74,73,0.1,74,0.988937258720398,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93 --tPCytz4rww/16,-tPCytz4rww,16,vision,7.205989837646484,7.205989837646484,59,35,73,0.1,35,1.0,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93 --tPCytz4rww/18,-tPCytz4rww,18,text,6.644987106323242,3.4940090492367735,16,768,67,0.1,768,0.6719247698783875,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776 --tPCytz4rww/18,-tPCytz4rww,18,audio,6.644987106323242,6.553973502565075,657,74,67,0.1,74,0.9872123599052429,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776 --tPCytz4rww/18,-tPCytz4rww,18,vision,6.644987106323242,6.644987106323242,54,35,67,0.1,35,1.0,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776 --vxjVxOeScU/4,-vxjVxOeScU,4,text,9.127017974853516,5.366417751088739,21,768,92,0.1,768,0.6465563774108887,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719 --vxjVxOeScU/4,-vxjVxOeScU,4,audio,9.127017974853516,9.08216616914079,904,74,92,0.1,74,0.9960808753967285,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719 --vxjVxOeScU/4,-vxjVxOeScU,4,vision,9.127017974853516,9.127017974853516,77,35,92,0.1,35,1.0,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719 --wMB_hJL-3o/7,-wMB_hJL-3o,7,text,6.044010162353516,3.555890290439128,22,768,61,0.1,768,0.658498227596283,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7 --wMB_hJL-3o/7,-wMB_hJL-3o,7,audio,6.044010162353516,5.968699067508853,598,74,61,0.1,74,0.9895980954170227,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7 --wMB_hJL-3o/7,-wMB_hJL-3o,7,vision,6.044010162353516,5.944010257720948,48,35,61,0.1,35,0.9836065769195557,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7 --wny0OAz3g8/1,-wny0OAz3g8,1,text,6.844009876251221,4.170902410242706,23,768,69,0.1,768,0.62252277135849,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8 --wny0OAz3g8/1,-wny0OAz3g8,1,audio,6.844009876251221,6.814914991726747,678,74,69,0.1,74,0.995954155921936,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8 --wny0OAz3g8/1,-wny0OAz3g8,1,vision,6.844009876251221,3.327343463897705,56,35,69,0.1,35,0.9950981140136719,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8 --wny0OAz3g8/0,-wny0OAz3g8,0,text,3.3333330154418945,1.6946923546493056,9,768,34,0.1,768,0.6052471995353699,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d --wny0OAz3g8/0,-wny0OAz3g8,0,audio,3.3333330154418945,3.3145942848276446,328,74,34,0.1,74,0.9955413937568665,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d --wny0OAz3g8/0,-wny0OAz3g8,0,vision,3.3333330154418945,2.6999998092651367,21,35,34,0.1,35,0.9999999403953552,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d --wny0OAz3g8/3,-wny0OAz3g8,3,text,6.146028995513916,4.208628869801758,20,768,62,0.1,768,0.7014381289482117,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce --wny0OAz3g8/3,-wny0OAz3g8,3,audio,6.146028995513916,6.13413711335208,610,74,62,0.1,74,0.9980819821357727,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce --wny0OAz3g8/3,-wny0OAz3g8,3,vision,6.146028995513916,1.5293622016906734,49,35,62,0.1,35,0.9895832538604736,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce --wny0OAz3g8/2,-wny0OAz3g8,2,text,6.686978816986084,4.909538650512694,23,768,67,0.1,768,0.7553136348724365,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a --wny0OAz3g8/2,-wny0OAz3g8,2,audio,6.686978816986084,6.656249635122918,653,74,67,0.1,74,0.9957627058029175,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a --wny0OAz3g8/2,-wny0OAz3g8,2,vision,6.686978816986084,5.4166669845581055,54,35,67,0.1,35,0.9848485589027405,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a --wny0OAz3g8/5,-wny0OAz3g8,5,text,6.9119791984558105,3.941009076312184,20,768,70,0.1,768,0.6063091158866882,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0 --wny0OAz3g8/5,-wny0OAz3g8,5,audio,6.9119791984558105,6.879425679509704,675,74,70,0.1,74,0.9961636066436768,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0 --wny0OAz3g8/5,-wny0OAz3g8,5,vision,6.9119791984558105,1.4953122138977046,56,35,70,0.1,35,0.9895831346511841,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0 --wny0OAz3g8/7,-wny0OAz3g8,7,text,8.06796932220459,5.293590973317621,29,768,81,0.1,768,0.6700748205184937,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd --wny0OAz3g8/7,-wny0OAz3g8,7,audio,8.06796932220459,8.030726903515895,792,74,81,0.1,74,0.9956274628639221,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd --wny0OAz3g8/7,-wny0OAz3g8,7,vision,8.06796932220459,7.06796932220459,68,35,81,0.1,35,1.0,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd --wny0OAz3g8/9,-wny0OAz3g8,9,text,6.2130208015441895,4.012809376046059,20,768,63,0.1,768,0.6688015460968018,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136 --wny0OAz3g8/9,-wny0OAz3g8,9,audio,6.2130208015441895,6.190979680177328,605,74,63,0.1,74,0.9969837069511414,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136 --wny0OAz3g8/9,-wny0OAz3g8,9,vision,6.2130208015441895,5.8130205273628235,49,35,63,0.1,35,0.9672130942344666,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136 --571d8cVauQ/0,-571d8cVauQ,0,text,5.066667079925537,3.21007038205862,12,768,51,0.1,768,0.6978414058685303,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088 --571d8cVauQ/0,-571d8cVauQ,0,audio,5.066667079925537,5.013243622071034,500,74,51,0.1,74,0.9903978705406189,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088 --571d8cVauQ/0,-571d8cVauQ,0,vision,5.066667079925537,4.9666670799255375,39,35,51,0.1,35,1.0,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088 --571d8cVauQ/5,-571d8cVauQ,5,text,15.272981643676758,9.052198391593993,43,768,153,0.1,768,0.7017207741737366,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e --571d8cVauQ/5,-571d8cVauQ,5,audio,15.272981643676758,15.142900936426344,1512,74,153,0.1,74,0.9918006062507629,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e --571d8cVauQ/5,-571d8cVauQ,5,vision,15.272981643676758,15.272981643676758,108,35,153,0.1,35,1.0,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e --I_e4mIh0yE/1,-I_e4mIh0yE,1,text,7.622004985809326,3.535097924340517,18,768,77,0.1,768,0.6546477675437927,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df --I_e4mIh0yE/1,-I_e4mIh0yE,1,audio,7.622004985809326,7.497626475627357,745,74,77,0.1,74,0.985649049282074,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df --I_e4mIh0yE/1,-I_e4mIh0yE,1,vision,7.622004985809326,7.622004985809326,63,35,77,0.1,35,1.0,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df --I_e4mIh0yE/3,-I_e4mIh0yE,3,text,9.163021087646484,4.626299023628236,22,768,92,0.1,768,0.6251755356788635,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e --I_e4mIh0yE/3,-I_e4mIh0yE,3,audio,9.163021087646484,9.017804326318405,900,74,92,0.1,74,0.9852646589279175,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e --I_e4mIh0yE/3,-I_e4mIh0yE,3,vision,9.163021087646484,9.163021087646484,77,35,92,0.1,35,1.0,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e --UacrmKiTn4/10,-UacrmKiTn4,10,text,7.361979007720947,2.732618988305329,18,768,74,0.1,768,0.47940683364868164,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360 --UacrmKiTn4/10,-UacrmKiTn4,10,audio,7.361979007720947,7.319425493478775,725,74,74,0.1,74,0.9955651164054871,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360 --UacrmKiTn4/10,-UacrmKiTn4,10,vision,7.361979007720947,7.361979007720947,60,35,74,0.1,35,1.0,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360 --UacrmKiTn4/4,-UacrmKiTn4,4,text,4.741015911102295,3.0301611527800563,14,768,48,0.1,768,0.7214669585227966,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84 --UacrmKiTn4/4,-UacrmKiTn4,4,audio,4.741015911102295,4.704772258610339,469,74,48,0.1,74,0.9926760792732239,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84 --UacrmKiTn4/4,-UacrmKiTn4,4,vision,4.741015911102295,4.741015911102295,35,35,48,0.1,35,1.0,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84 --hnBHBN8p5A/7,-hnBHBN8p5A,7,text,7.372000217437744,4.009185888338835,13,768,74,0.1,768,0.657243549823761,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb --hnBHBN8p5A/7,-hnBHBN8p5A,7,audio,7.372000217437744,7.281608330236899,731,74,74,0.1,74,0.9905768632888794,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb --hnBHBN8p5A/7,-hnBHBN8p5A,7,vision,7.372000217437744,0.0,37,35,74,0.1,35,0.0,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb --hnBHBN8p5A/6,-hnBHBN8p5A,6,text,6.401000022888184,3.580871792882682,13,768,65,0.1,768,0.7618876099586487,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7 --hnBHBN8p5A/6,-hnBHBN8p5A,6,audio,6.401000022888184,6.346108104731585,634,74,65,0.1,74,0.9932082295417786,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7 --hnBHBN8p5A/6,-hnBHBN8p5A,6,vision,6.401000022888184,0.0,32,35,65,0.1,35,0.0,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7 --qDkUB0GgYY/6,-qDkUB0GgYY,6,text,4.677995204925537,2.927082145679743,16,768,47,0.1,768,0.6504627466201782,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b --qDkUB0GgYY/6,-qDkUB0GgYY,6,audio,4.677995204925537,4.653116947730401,462,74,47,0.1,74,0.9946085214614868,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b --qDkUB0GgYY/6,-qDkUB0GgYY,6,vision,4.677995204925537,4.677995204925537,35,35,47,0.1,35,1.0,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b --uywlfIYOS8/4,-uywlfIYOS8,4,text,6.044987201690674,3.7997015411034236,18,768,61,0.1,768,0.6908547878265381,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9 --uywlfIYOS8/4,-uywlfIYOS8,4,audio,6.044987201690674,6.015325370511492,589,74,61,0.1,74,0.9957761168479919,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9 --uywlfIYOS8/4,-uywlfIYOS8,4,vision,6.044987201690674,6.044987201690674,48,35,61,0.1,35,1.0,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,text,12.805012702941895,6.537867438234387,34,768,129,0.1,768,0.63474440574646,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,audio,12.805012702941895,12.672918005408468,1274,74,129,0.1,74,0.9899758696556091,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,vision,12.805012702941895,12.805012702941895,95,35,129,0.1,35,1.0,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae --9y-fZ3swSY/0,-9y-fZ3swSY,0,text,6.8333330154418945,3.536603624001146,22,768,69,0.1,768,0.5440928339958191,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c --9y-fZ3swSY/0,-9y-fZ3swSY,0,audio,6.8333330154418945,6.794324037513217,676,74,69,0.1,74,0.9948647022247314,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c --9y-fZ3swSY/0,-9y-fZ3swSY,0,vision,6.8333330154418945,6.8333330154418945,57,35,69,0.1,35,1.0,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c --9y-fZ3swSY/4,-9y-fZ3swSY,4,text,2.8210289478302,1.160462707281113,9,768,29,0.1,768,0.5274830460548401,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc --9y-fZ3swSY/4,-9y-fZ3swSY,4,audio,2.8210289478302,2.7937038478819103,267,74,29,0.1,74,0.9926167726516724,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc --9y-fZ3swSY/4,-9y-fZ3swSY,4,vision,2.8210289478302,2.7210289478302006,15,35,29,0.1,35,1.0,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc --9y-fZ3swSY/8,-9y-fZ3swSY,8,text,4.988996982574463,2.1943973237220367,12,768,50,0.1,768,0.562736988067627,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256 --9y-fZ3swSY/8,-9y-fZ3swSY,8,audio,4.988996982574463,4.9497673825665816,492,74,50,0.1,74,0.9931800365447998,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256 --9y-fZ3swSY/8,-9y-fZ3swSY,8,vision,4.988996982574463,3.688997030258178,37,35,50,0.1,35,0.9487179517745972,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,text,22.511003494262695,12.134252551943066,41,768,226,0.1,768,0.6386448740959167,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,audio,22.511003494262695,22.31367928599184,2241,74,226,0.1,74,0.9917553663253784,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,vision,22.511003494262695,22.511003494262695,144,35,226,0.1,35,1.0,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b --HeZS2-Prhc/2,-HeZS2-Prhc,2,text,8.261979103088379,3.552112135011702,16,768,83,0.1,768,0.6830984950065613,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c --HeZS2-Prhc/2,-HeZS2-Prhc,2,audio,8.261979103088379,8.205439110059995,816,74,83,0.1,74,0.9933875799179077,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c --HeZS2-Prhc/2,-HeZS2-Prhc,2,vision,8.261979103088379,8.261979103088379,70,35,83,0.1,35,1.0,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c --MeTTeMJBNc/0,-MeTTeMJBNc,0,text,9.300000190734863,4.416411150246859,21,768,94,0.1,768,0.6400595903396606,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe --MeTTeMJBNc/0,-MeTTeMJBNc,0,audio,9.300000190734863,9.216486695006088,922,74,94,0.1,74,0.9939717650413513,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe --MeTTeMJBNc/0,-MeTTeMJBNc,0,vision,9.300000190734863,9.200000190734862,78,35,94,0.1,35,1.0,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe --MeTTeMJBNc/13,-MeTTeMJBNc,13,text,5.430013179779053,2.997564935684203,14,768,55,0.1,768,0.6661255359649658,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44 --MeTTeMJBNc/13,-MeTTeMJBNc,13,audio,5.430013179779053,5.368323606413764,526,74,55,0.1,74,0.9918515682220459,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44 --MeTTeMJBNc/13,-MeTTeMJBNc,13,vision,5.430013179779053,5.430013179779053,41,35,55,0.1,35,1.0,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44 --MeTTeMJBNc/7,-MeTTeMJBNc,7,text,10.51699161529541,6.728291415888818,31,768,106,0.1,768,0.7313360571861267,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a --MeTTeMJBNc/7,-MeTTeMJBNc,7,audio,10.51699161529541,10.441329748243898,1042,74,106,0.1,74,0.9934103488922119,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a --MeTTeMJBNc/7,-MeTTeMJBNc,7,vision,10.51699161529541,10.51699161529541,84,35,106,0.1,35,1.0,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a --RfYyzHpjk4/11,-RfYyzHpjk4,11,text,5.238996982574463,3.062753976881504,17,768,53,0.1,768,0.6960803866386414,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904 --RfYyzHpjk4/11,-RfYyzHpjk4,11,audio,5.238996982574463,5.202267333462432,508,74,53,0.1,74,0.9958056807518005,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904 --RfYyzHpjk4/11,-RfYyzHpjk4,11,vision,5.238996982574463,5.238996982574463,40,35,53,0.1,35,1.0,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904 --RfYyzHpjk4/8,-RfYyzHpjk4,8,text,4.588996887207031,2.8058770607668286,17,768,46,0.1,768,0.684955358505249,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c --RfYyzHpjk4/8,-RfYyzHpjk4,8,audio,4.588996887207031,4.553010454316745,454,74,46,0.1,74,0.994469404220581,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c --RfYyzHpjk4/8,-RfYyzHpjk4,8,vision,4.588996887207031,4.588996887207031,33,35,46,0.1,35,1.0,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c --RfYyzHpjk4/2,-RfYyzHpjk4,2,text,5.516016006469727,3.372718970850109,25,768,56,0.1,768,0.7175998091697693,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f --RfYyzHpjk4/2,-RfYyzHpjk4,2,audio,5.516016006469727,5.478826378849712,540,74,56,0.1,74,0.9948742389678955,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f --RfYyzHpjk4/2,-RfYyzHpjk4,2,vision,5.516016006469727,5.416016006469727,43,35,56,0.1,35,1.0,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f --UUCSKoHeMA/0,-UUCSKoHeMA,0,text,7.800000190734863,4.133793779369442,18,768,79,0.1,768,0.666740894317627,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898 --UUCSKoHeMA/0,-UUCSKoHeMA,0,audio,7.800000190734863,7.750270468963159,774,74,79,0.1,74,0.9943835139274597,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898 --UUCSKoHeMA/0,-UUCSKoHeMA,0,vision,7.800000190734863,7.700000190734862,66,35,79,0.1,35,1.0,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898 --ri04Z7vwnc/0,-ri04Z7vwnc,0,text,2.806999921798706,2.1131306469440463,8,768,29,0.1,768,0.7826409935951233,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b --ri04Z7vwnc/0,-ri04Z7vwnc,0,audio,2.806999921798706,2.8018783078000356,277,74,29,0.1,74,0.9982975125312805,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b --ri04Z7vwnc/0,-ri04Z7vwnc,0,vision,2.806999921798706,0.0,14,35,29,0.1,35,0.0,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b --ri04Z7vwnc/2,-ri04Z7vwnc,2,text,6.0269999504089355,3.9384737918153405,16,768,61,0.1,768,0.7431082725524902,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148 --ri04Z7vwnc/2,-ri04Z7vwnc,2,audio,6.0269999504089355,6.008905370493193,592,74,61,0.1,74,0.9971521496772766,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148 --ri04Z7vwnc/2,-ri04Z7vwnc,2,vision,6.0269999504089355,2.106416702270508,30,35,61,0.1,35,0.9906440377235413,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148 --ri04Z7vwnc/5,-ri04Z7vwnc,5,text,3.5450000762939453,1.8027290821075441,9,768,36,0.1,768,0.721091628074646,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736 --ri04Z7vwnc/5,-ri04Z7vwnc,5,audio,3.5450000762939453,3.534527108637063,344,74,36,0.1,74,0.9974266886711121,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736 --ri04Z7vwnc/5,-ri04Z7vwnc,5,vision,3.5450000762939453,3.5450000762939453,18,35,36,0.1,35,1.0,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,text,4.7919921875,2.610484106093645,11,768,48,0.1,768,0.7055363059043884,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,audio,4.7919921875,4.72247887628304,465,74,48,0.1,74,0.9860281348228455,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,vision,4.7919921875,4.7919921875,35,35,48,0.1,35,1.0,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,text,6.800000190734863,3.6206328462809334,24,768,69,0.1,768,0.6351987719535828,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,audio,6.800000190734863,6.715675868979983,672,74,69,0.1,74,0.9885490536689758,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,vision,6.800000190734863,6.199999928474425,56,35,69,0.1,35,0.984375,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,text,8.561002731323242,3.4511515218298876,19,768,86,0.1,768,0.5150972604751587,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,audio,8.561002731323242,8.46259727510246,840,74,86,0.1,74,0.9896790981292725,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,vision,8.561002731323242,8.561002731323242,72,35,86,0.1,35,1.0,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,text,4.427018165588379,2.2374289706349377,9,768,45,0.1,768,0.6215080618858337,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,audio,4.427018165588379,4.367301462550421,425,74,45,0.1,74,0.9892621040344238,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,vision,4.427018165588379,3.027018189430237,31,35,45,0.1,35,0.96875,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,text,6.966015815734863,3.9709798723459264,28,768,70,0.1,768,0.661829948425293,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,audio,6.966015815734863,6.848488390969263,684,74,70,0.1,74,0.9849807620048523,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,vision,6.966015815734863,4.0493486523628235,57,35,70,0.1,35,0.9496123194694519,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,text,2.4749999046325684,1.6227161765098577,5,768,25,0.1,768,0.8113580942153931,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,audio,2.4749999046325684,2.4289188214250514,232,74,25,0.1,74,0.9844902753829956,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,vision,2.4749999046325684,1.8999999761581425,14,35,25,0.1,35,1.0,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,text,3.2949869632720947,1.9395141303539274,13,768,33,0.1,768,0.6465047001838684,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,audio,3.2949869632720947,3.253163003921509,319,74,33,0.1,74,0.9880942702293396,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,vision,3.2949869632720947,3.016666531562805,20,35,33,0.1,35,0.9731181859970093,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,text,29.288021087646484,15.053765958920112,49,768,293,0.1,768,0.6873865723609924,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,audio,29.288021087646484,28.759709144524624,2911,74,293,0.1,74,0.9836021065711975,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,vision,29.288021087646484,27.471354246139526,178,35,293,0.1,35,0.999393880367279,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,text,13.169010162353516,8.127011682093151,25,768,132,0.1,768,0.7192046046257019,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,audio,13.169010162353516,12.971401820754682,1305,74,132,0.1,74,0.9867846369743347,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,vision,13.169010162353516,13.169010162353516,97,35,132,0.1,35,1.0,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,text,11.241994857788086,4.740236129239202,19,768,113,0.1,768,0.6869907975196838,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,audio,11.241994857788086,11.070711010679133,1125,74,113,0.1,74,0.9854583740234375,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,vision,11.241994857788086,11.241994857788086,87,35,113,0.1,35,1.0,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989 diff --git a/final/Q1/features/native_grid.npz b/final/Q1/features/native_grid.npz deleted file mode 100644 index d7d01b3..0000000 Binary files a/final/Q1/features/native_grid.npz and /dev/null differ diff --git a/final/Q1/features/sample_alignment_summary.csv b/final/Q1/features/sample_alignment_summary.csv deleted file mode 100644 index 75d637f..0000000 --- a/final/Q1/features/sample_alignment_summary.csv +++ /dev/null @@ -1,101 +0,0 @@ -sample_id,video_id,clip_id,source_video_sha256,duration_s,word_count,hard_aligned_word_count,unlocated_word_count,posterior_usable_word_count,mean_start_interval_width90_s,mean_end_interval_width90_s,text_hard_grid_coverage,text_posterior_grid_occupancy,audio_grid_coverage,vision_grid_coverage,audio_native_rows,vision_native_rows,status --3g5yACwYnA/13,-3g5yACwYnA,13,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,5.5139970779418945,15,15,0,15,0.037333333333333406,0.020000000000000004,0.7015532851219177,0.24074573814868927,0.9882608652114868,1.0,540,43,ok --3g5yACwYnA/3,-3g5yACwYnA,3,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,14.388997077941895,29,29,0,29,0.2793103448275863,0.25931034482758614,0.6343300342559814,0.19729532301425934,0.9912122488021851,1.0,1433,103,ok --3g5yACwYnA/2,-3g5yACwYnA,2,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,9.394009590148926,14,14,0,14,0.2828571428571428,0.19142857142857145,0.7563282251358032,0.15269099175930023,0.9936367273330688,1.0,924,78,ok --3g5yACwYnA/9,-3g5yACwYnA,9,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,8.816991806030273,21,21,0,21,0.22095238095238093,0.1942857142857142,0.6637077927589417,0.26207172870635986,0.9912108778953552,1.0,870,75,ok --3nNcZdcdvU/5,-3nNcZdcdvU,5,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,7.867969036102295,18,18,0,18,0.03555555555555544,0.012222222222222258,0.7357116341590881,0.22051197290420532,0.9971166849136353,0.9904456734657288,779,67,ok --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,3.9820311069488525,8,7,1,7,0.12285714285714287,0.12285714285714275,0.6164926290512085,0.18007618188858032,0.9881895780563354,0.9814813733100891,392,27,ok --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,5.6529951095581055,10,9,1,9,0.037777777777777764,0.040000000000000036,0.7021505832672119,0.22856950759887695,0.9900854229927063,0.666666567325592,555,44,ok --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,3.3580079078674316,7,7,0,7,0.048571428571428564,0.03428571428571422,0.5562952756881714,0.1454559713602066,0.9901678562164307,0.841666579246521,326,21,ok --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,7.733983993530273,22,22,0,22,0.16363636363636375,0.15181818181818188,0.6705501675605774,0.25460928678512573,0.9946621060371399,0.0,764,65,ok --NFrJFQijFE/1,-NFrJFQijFE,1,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,5.745999813079834,16,16,0,16,0.46625000000000005,0.47125000000000006,0.7835317254066467,0.27368244528770447,0.9922494292259216,0.0,566,44,ok --NFrJFQijFE/2,-NFrJFQijFE,2,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,6.855999946594238,18,18,0,18,0.17888888888888899,0.1544444444444445,0.7431879639625549,0.24775999784469604,0.9926568269729614,0.0,670,55,ok --THoVjtIkeU/12,-THoVjtIkeU,12,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,14.896029472351074,39,39,0,39,0.05999999999999991,0.05282051282051286,0.6352806687355042,0.23379945755004883,0.9911767244338989,1.0,1481,106,ok --THoVjtIkeU/2,-THoVjtIkeU,2,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,4.2919921875,10,10,0,10,0.016000000000000004,0.007999999999999985,0.7164996266365051,0.2465074211359024,0.9895758628845215,1.0,424,31,ok --THoVjtIkeU/6,-THoVjtIkeU,6,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,8.097004890441895,30,30,0,30,0.053333333333333295,0.05733333333333333,0.6787540316581726,0.30513063073158264,0.9927164316177368,1.0,799,68,ok --UuX1xuaiiE/1,-UuX1xuaiiE,1,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,10.350000381469727,27,27,0,27,0.09555555555555556,0.060000000000000046,0.6550575494766235,0.22912898659706116,0.9925051927566528,0.8333339691162109,1027,83,ok --UuX1xuaiiE/0,-UuX1xuaiiE,0,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,3.6333329677581787,9,9,0,9,0.03777777777777775,0.04666666666666665,0.6484582424163818,0.2555619180202484,0.994590699672699,0.9444444179534912,358,25,ok --UuX1xuaiiE/3,-UuX1xuaiiE,3,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,4.1300129890441895,9,9,0,9,0.07777777777777779,0.0600000000000001,0.7240580320358276,0.16585363447666168,0.992123007774353,0.976190447807312,404,29,ok --UuX1xuaiiE/6,-UuX1xuaiiE,6,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,8.113997459411621,22,22,0,22,0.06363636363636366,0.0736363636363637,0.7064024806022644,0.25750458240509033,0.984533965587616,0.9776423573493958,795,68,ok --a55Q6RWvTA/3,-a55Q6RWvTA,3,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,22.15397071838379,65,65,0,65,0.2215384615384614,0.18892307692307703,0.6580277681350708,0.23805321753025055,0.9912921786308289,1.0,2209,142,ok --aNfi7CP8vM/7,-aNfi7CP8vM,7,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,8.694987297058105,18,18,0,18,0.2511111111111111,0.24000000000000002,0.7001731395721436,0.23255421221256256,0.9945195913314819,1.0,854,74,ok --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,10.966667175292969,14,13,1,13,0.22461538461538455,0.08615384615384586,0.71061772108078,0.1247716099023819,0.9910455346107483,1.0,1090,86,ok --dxfTGcXJoc/1,-dxfTGcXJoc,1,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,16.16100311279297,36,36,0,36,0.07777777777777778,0.033333333333333375,0.6812966465950012,0.2449914664030075,0.9900091290473938,1.0,1602,112,ok --dxfTGcXJoc/0,-dxfTGcXJoc,0,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,20.5,41,38,3,38,0.09315789473684201,0.06789473684210537,0.7333337664604187,0.209048330783844,0.9891952872276306,0.99895840883255,2042,134,ok --dxfTGcXJoc/2,-dxfTGcXJoc,2,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,16.697982788085938,34,32,2,32,0.048124999999999946,0.03624999999999996,0.6593995094299316,0.2266717106103897,0.9901677966117859,1.0,1664,115,ok --dxfTGcXJoc/6,-dxfTGcXJoc,6,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,12.735026359558105,27,27,0,27,0.05851851851851849,0.05407407407407409,0.715381383895874,0.25197115540504456,0.9889141917228699,1.0,1263,95,ok --egA8-b7-3M/26,-egA8-b7-3M,26,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,6.030990123748779,15,15,0,15,0.08133333333333341,0.02533333333333337,0.6877890825271606,0.22332797944545746,0.9945786595344543,1.0,593,47,ok --egA8-b7-3M/17,-egA8-b7-3M,17,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,7.271028995513916,16,16,0,16,0.017499999999999995,0.019999999999999962,0.7292676568031311,0.24067606031894684,0.9916234016418457,1.0,710,60,ok --egA8-b7-3M/18,-egA8-b7-3M,18,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,8.386002540588379,22,22,0,22,0.052727272727272734,0.021818181818181796,0.6243576407432556,0.26077014207839966,0.9932008385658264,1.0,827,71,ok --egA8-b7-3M/16,-egA8-b7-3M,16,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,5.538021087646484,11,11,0,11,0.010909090909090908,0.0181818181818182,0.7375588417053223,0.23636400699615479,0.9940068125724792,1.0,549,43,ok --egA8-b7-3M/13,-egA8-b7-3M,13,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,4.18398380279541,11,11,0,11,0.23818181818181816,0.23090909090909092,0.6372604370117188,0.2536529004573822,0.9924017786979675,1.0,403,29,ok --egA8-b7-3M/1,-egA8-b7-3M,1,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,10.266016006469727,20,19,1,19,1.0621052631578947,1.0526315789473684,0.6490206122398376,0.19601602852344513,0.9937204718589783,1.0,1016,83,ok --egA8-b7-3M/6,-egA8-b7-3M,6,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,6.264974117279053,12,12,0,12,0.08833333333333332,0.03666666666666665,0.7164138555526733,0.2193591147661209,0.9942464828491211,1.0,619,50,ok --egA8-b7-3M/9,-egA8-b7-3M,9,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,5.0899739265441895,10,10,0,10,0.007999999999999962,0.011999999999999966,0.597113311290741,0.2590937316417694,0.9939422011375427,1.0,494,38,ok --egA8-b7-3M/20,-egA8-b7-3M,20,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,6.644987106323242,20,20,0,20,0.077,0.021000000000000008,0.6985589861869812,0.2369300276041031,0.992087185382843,1.0,648,54,ok --iRBcNs9oI8/3,-iRBcNs9oI8,3,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,5.620999813079834,8,8,0,8,0.10250000000000009,0.07500000000000001,0.6791599988937378,0.1166667565703392,0.9954468607902527,0.0,554,28,ok --iRBcNs9oI8/7,-iRBcNs9oI8,7,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,4.104000091552734,9,9,0,9,0.10222222222222223,0.02666666666666669,0.7135127186775208,0.18537555634975433,0.995795726776123,0.0,401,21,ok --iRBcNs9oI8/6,-iRBcNs9oI8,6,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,2.9030001163482666,5,5,0,5,0.03999999999999999,0.04399999999999998,0.7227271199226379,0.12799954414367676,0.9973600506782532,0.0,283,15,ok --iRBcNs9oI8/9,-iRBcNs9oI8,9,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,3.4159998893737793,5,5,0,5,0.20800000000000002,0.016000000000000014,0.643750011920929,0.14118166267871857,0.993934154510498,0.0,338,17,ok --iRBcNs9oI8/8,-iRBcNs9oI8,8,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,8.093000411987305,21,21,0,21,0.23238095238095247,0.2228571428571429,0.6192578673362732,0.18252728879451752,0.9947929382324219,0.0,803,41,ok --lzEya4AM_4/5,-lzEya4AM_4,5,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,7.675000190734863,19,19,0,19,0.12000000000000004,0.09473684210526319,0.7427374720573425,0.2366195172071457,0.9901386499404907,1.0,757,64,ok --lzEya4AM_4/6,-lzEya4AM_4,6,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,14.7919921875,43,43,0,43,0.05023255813953489,0.024651162790697716,0.7318416833877563,0.2524191737174988,0.9907644391059875,1.0,1475,105,ok --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,6.103000164031982,13,13,0,13,0.733846153846154,0.7399999999999998,0.8174015879631042,0.1573781669139862,1.0,0.0,606,31,ok --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,5.730999946594238,11,11,0,11,0.4636363636363635,0.46181818181818185,0.9266790151596069,0.2666672170162201,1.0,0.0,571,29,ok --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,2.256999969482422,5,5,0,5,0.11199999999999996,0.06400000000000003,0.6512578129768372,0.15656699240207672,0.9862568378448486,1.0,223,12,ok --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,7.0329999923706055,22,22,0,22,0.11999999999999994,0.09454545454545453,0.5777366757392883,0.25714901089668274,0.9846946001052856,0.9856857061386108,691,35,ok --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,4.191999912261963,11,11,0,11,0.14363636363636367,0.06363636363636366,0.6529467701911926,0.26233649253845215,0.9846959114074707,1.0,418,21,ok --mqbVkbCndg/0,-mqbVkbCndg,0,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,6.466667175292969,12,12,0,12,0.06166666666666665,0.03333333333333339,0.7074670791625977,0.20000411570072174,0.9905118346214294,1.0,639,53,ok --t217m2on-s/2,-t217m2on-s,2,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,5.6860032081604,12,12,0,12,0.21666666666666665,0.2333333333333333,0.6279765367507935,0.16491496562957764,0.9959543347358704,1.0,564,45,ok --t217m2on-s/7,-t217m2on-s,7,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,17.183008193969727,45,45,0,45,0.048444444444444436,0.017777777777777892,0.6867958903312683,0.23202678561210632,0.9928514361381531,1.0,1703,117,ok --tANM6ETl_M/3,-tANM6ETl_M,3,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,6.758008003234863,22,22,0,22,0.030909090909090896,0.021818181818181792,0.5859207510948181,0.2757546901702881,0.9936580061912537,0.9898989200592041,661,54,ok --tPCytz4rww/11,-tPCytz4rww,11,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,5.466991901397705,17,17,0,17,0.09764705882352945,0.08588235294117644,0.6753751635551453,0.28518491983413696,0.9890678524971008,1.0,538,43,ok --tPCytz4rww/10,-tPCytz4rww,10,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,4.788997173309326,12,12,0,12,0.04499999999999999,0.00833333333333334,0.5449775457382202,0.225633442401886,0.9894654750823975,1.0,468,35,ok --tPCytz4rww/12,-tPCytz4rww,12,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,11.863997459411621,26,26,0,26,0.14461538461538456,0.08769230769230774,0.654129683971405,0.22372934222221375,0.9875938892364502,1.0,1174,91,ok --tPCytz4rww/16,-tPCytz4rww,16,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,7.205989837646484,16,16,0,16,0.05125000000000004,0.013749999999999984,0.4489554166793823,0.17846456170082092,0.988937258720398,1.0,714,59,ok --tPCytz4rww/18,-tPCytz4rww,18,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,6.644987106323242,16,16,0,16,0.16375,0.1275,0.6719247698783875,0.22222484648227692,0.9872123599052429,1.0,657,54,ok --vxjVxOeScU/4,-vxjVxOeScU,4,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,9.127017974853516,21,21,0,21,0.06380952380952383,0.037142857142857144,0.6465563774108887,0.2219761461019516,0.9960808753967285,1.0,904,77,ok --wMB_hJL-3o/7,-wMB_hJL-3o,7,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,6.044010162353516,22,22,0,22,0.012727272727272738,0.021818181818181816,0.658498227596283,0.31481292843818665,0.9895980954170227,0.9836065769195557,598,48,ok --wny0OAz3g8/1,-wny0OAz3g8,1,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,6.844009876251221,23,22,1,22,0.013636363636363648,0.011818181818181823,0.62252277135849,0.3443901240825653,0.995954155921936,0.9950981140136719,678,56,ok --wny0OAz3g8/0,-wny0OAz3g8,0,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,3.3333330154418945,9,9,0,9,0.32000000000000006,0.2866666666666667,0.6052471995353699,0.2736770212650299,0.9955413937568665,0.9999999403953552,328,21,ok --wny0OAz3g8/3,-wny0OAz3g8,3,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,6.146028995513916,20,20,0,20,0.022999999999999986,0.02399999999999995,0.7014381289482117,0.3213139474391937,0.9980819821357727,0.9895832538604736,610,49,ok --wny0OAz3g8/2,-wny0OAz3g8,2,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,6.686978816986084,23,23,0,23,0.02869565217391302,0.026086956521739167,0.7553136348724365,0.33939871191978455,0.9957627058029175,0.9848485589027405,653,54,ok --wny0OAz3g8/5,-wny0OAz3g8,5,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,6.9119791984558105,20,20,0,20,0.004999999999999982,0.004999999999999982,0.6063091158866882,0.3181923031806946,0.9961636066436768,0.9895831346511841,675,56,ok --wny0OAz3g8/7,-wny0OAz3g8,7,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,8.06796932220459,29,29,0,29,0.0186206896551724,0.017241379310344838,0.6700748205184937,0.3224986493587494,0.9956274628639221,1.0,792,68,ok --wny0OAz3g8/9,-wny0OAz3g8,9,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,6.2130208015441895,20,20,0,20,0.025999999999999975,0.027000000000000024,0.6688015460968018,0.3180274963378906,0.9969837069511414,0.9672130942344666,605,49,ok --571d8cVauQ/0,-571d8cVauQ,0,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,5.066667079925537,12,12,0,12,0.07500000000000001,0.03000000000000001,0.6978414058685303,0.2360049933195114,0.9903978705406189,1.0,500,39,ok --571d8cVauQ/5,-571d8cVauQ,5,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,15.272981643676758,43,43,0,43,0.14604651162790694,0.11534883720930222,0.7017207741737366,0.23815667629241943,0.9918006062507629,1.0,1512,108,ok --I_e4mIh0yE/1,-I_e4mIh0yE,1,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,7.622004985809326,18,18,0,18,0.061111111111111116,0.02333333333333332,0.6546477675437927,0.2152525633573532,0.985649049282074,1.0,745,63,ok --I_e4mIh0yE/3,-I_e4mIh0yE,3,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,9.163021087646484,22,22,0,22,0.13,0.11545454545454543,0.6251755356788635,0.24003465473651886,0.9852646589279175,1.0,900,77,ok --UacrmKiTn4/10,-UacrmKiTn4,10,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,7.361979007720947,18,18,0,18,0.04555555555555555,0.07222222222222224,0.47940683364868164,0.20824021100997925,0.9955651164054871,1.0,725,60,ok --UacrmKiTn4/4,-UacrmKiTn4,4,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,4.741015911102295,14,14,0,14,0.06714285714285716,0.09857142857142863,0.7214669585227966,0.246806800365448,0.9926760792732239,1.0,469,35,ok --hnBHBN8p5A/7,-hnBHBN8p5A,7,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,7.372000217437744,13,13,0,13,0.01846153846153847,0.021538461538461593,0.657243549823761,0.1729772984981537,0.9905768632888794,0.0,731,37,ok --hnBHBN8p5A/6,-hnBHBN8p5A,6,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,6.401000022888184,13,13,0,13,0.04307692307692312,0.012307692307692353,0.7618876099586487,0.22187285125255585,0.9932082295417786,0.0,634,32,ok --qDkUB0GgYY/6,-qDkUB0GgYY,6,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,4.677995204925537,16,16,0,16,0.033749999999999974,0.02250000000000002,0.6504627466201782,0.24682332575321198,0.9946085214614868,1.0,462,35,ok --uywlfIYOS8/4,-uywlfIYOS8,4,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,6.044987201690674,18,18,0,18,0.0433333333333333,0.04333333333333334,0.6908547878265381,0.25089454650878906,0.9957761168479919,1.0,589,48,ok --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,12.805012702941895,34,34,0,34,0.06470588235294118,0.034705882352941086,0.63474440574646,0.22114422917366028,0.9899758696556091,1.0,1274,95,ok --9y-fZ3swSY/0,-9y-fZ3swSY,0,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,6.8333330154418945,22,22,0,22,0.023636363636363688,0.019999999999999997,0.5440928339958191,0.2264895737171173,0.9948647022247314,1.0,676,57,ok --9y-fZ3swSY/4,-9y-fZ3swSY,4,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,2.8210289478302,9,9,0,9,0.1711111111111111,0.15999999999999998,0.5274830460548401,0.1851968914270401,0.9926167726516724,1.0,267,15,ok --9y-fZ3swSY/8,-9y-fZ3swSY,8,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,4.988996982574463,12,12,0,12,0.07333333333333339,0.05333333333333331,0.562736988067627,0.18345493078231812,0.9931800365447998,0.9487179517745972,492,37,ok --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,22.511003494262695,41,41,0,41,0.6434146341463415,0.6102439024390245,0.6386448740959167,0.19374850392341614,0.9917553663253784,1.0,2241,144,ok --HeZS2-Prhc/2,-HeZS2-Prhc,2,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,8.261979103088379,16,16,0,16,0.14125000000000004,0.08125000000000016,0.6830984950065613,0.15250585973262787,0.9933875799179077,1.0,816,70,ok --MeTTeMJBNc/0,-MeTTeMJBNc,0,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,9.300000190734863,21,21,0,21,0.03714285714285711,0.026666666666666637,0.6400595903396606,0.22926528751850128,0.9939717650413513,1.0,922,78,ok --MeTTeMJBNc/13,-MeTTeMJBNc,13,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,5.430013179779053,14,14,0,14,0.042857142857142864,0.00714285714285715,0.6661255359649658,0.2075468748807907,0.9918515682220459,1.0,526,41,ok --MeTTeMJBNc/7,-MeTTeMJBNc,7,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,10.51699161529541,31,31,0,31,0.03225806451612902,0.02387096774193552,0.7313360571861267,0.269477516412735,0.9934103488922119,1.0,1042,84,ok --RfYyzHpjk4/11,-RfYyzHpjk4,11,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,5.238996982574463,17,17,0,17,0.018823529411764742,0.025882352941176467,0.6960803866386414,0.27842891216278076,0.9958056807518005,1.0,508,40,ok --RfYyzHpjk4/8,-RfYyzHpjk4,8,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,4.588996887207031,17,17,0,17,0.04588235294117646,0.005882352941176463,0.684955358505249,0.27527740597724915,0.994469404220581,1.0,454,33,ok --RfYyzHpjk4/2,-RfYyzHpjk4,2,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,5.516016006469727,25,25,0,25,0.12960000000000005,0.12880000000000008,0.7175998091697693,0.3444570004940033,0.9948742389678955,1.0,540,43,ok --UUCSKoHeMA/0,-UUCSKoHeMA,0,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,7.800000190734863,18,18,0,18,0.23000000000000004,0.20999999999999996,0.666740894317627,0.22424933314323425,0.9943835139274597,1.0,774,66,ok --ri04Z7vwnc/0,-ri04Z7vwnc,0,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,2.806999921798706,8,8,0,8,0.35999999999999993,0.3525,0.7826409935951233,0.25714799761772156,0.9982975125312805,0.0,277,14,ok --ri04Z7vwnc/2,-ri04Z7vwnc,2,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,6.0269999504089355,16,16,0,16,0.23374999999999996,0.22875,0.7431082725524902,0.26666611433029175,0.9971521496772766,0.9906440377235413,592,30,ok --ri04Z7vwnc/5,-ri04Z7vwnc,5,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,3.5450000762939453,9,8,1,8,0.21249999999999986,0.23999999999999994,0.721091628074646,0.21714359521865845,0.9974266886711121,1.0,344,18,ok --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,4.7919921875,11,11,0,11,0.01636363636363641,0.021818181818181816,0.7055363059043884,0.16956037282943726,0.9860281348228455,1.0,465,35,ok --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,6.800000190734863,24,24,0,24,0.056666666666666664,0.029999999999999957,0.6351987719535828,0.2500007152557373,0.9885490536689758,0.984375,672,56,ok --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,8.561002731323242,19,19,0,19,0.25578947368421046,0.26210526315789456,0.5150972604751587,0.16428017616271973,0.9896790981292725,1.0,840,72,ok --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,4.427018165588379,9,9,0,9,0.07555555555555557,0.024444444444444453,0.6215080618858337,0.20000608265399933,0.9892621040344238,0.96875,425,31,ok --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,6.966015815734863,28,28,0,28,0.029285714285714304,0.02428571428571429,0.661829948425293,0.29855138063430786,0.9849807620048523,0.9496123194694519,684,57,ok --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,2.4749999046325684,5,5,0,5,0.21599999999999997,0.16400000000000006,0.8113580942153931,0.14165958762168884,0.9844902753829956,1.0,232,14,ok --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,3.2949869632720947,13,12,1,12,0.07166666666666661,0.06166666666666667,0.6465047001838684,0.2687399089336395,0.9880942702293396,0.9731181859970093,319,20,ok --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,29.288021087646484,49,49,0,49,0.2922448979591836,0.2689795918367347,0.6873865723609924,0.18630558252334595,0.9836021065711975,0.999393880367279,2911,178,ok --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,13.169010162353516,25,25,0,25,0.14880000000000007,0.18799999999999997,0.7192046046257019,0.19541765749454498,0.9867846369743347,1.0,1305,97,ok --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,11.241994857788086,19,19,0,19,0.09578947368421038,0.15263157894736842,0.6869907975196838,0.16814078390598297,0.9854583740234375,1.0,1125,87,ok diff --git a/final/Q1/features/sample_feature_manifest.csv b/final/Q1/features/sample_feature_manifest.csv deleted file mode 100644 index 22ce80a..0000000 --- a/final/Q1/features/sample_feature_manifest.csv +++ /dev/null @@ -1,301 +0,0 @@ -sample_id,video_id,clip_id,modality,source_duration_s,observed_duration_s_mean_dimension,native_length,native_dimension,main_grid_length,grid_step_s,grid_dimension,mean_grid_coverage,status,source_video_sha256,source_extract_granularity,common_alignment_grid_s,training_export_bins,traceability_rule --3g5yACwYnA/13,-3g5yACwYnA,13,text,5.5139970779418945,2.946523889899254,15,768,56,0.1,768,0.7015532851219177,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/13,-3g5yACwYnA,13,audio,5.5139970779418945,5.445240411887298,540,74,56,0.1,74,0.9882608652114868,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/13,-3g5yACwYnA,13,vision,5.5139970779418945,5.5139970779418945,43,35,56,0.1,35,1.0,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/3,-3g5yACwYnA,3,text,14.388997077941895,7.041063531115653,29,768,144,0.1,768,0.6343300342559814,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/3,-3g5yACwYnA,3,audio,14.388997077941895,14.260037878559858,1433,74,144,0.1,74,0.9912122488021851,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/3,-3g5yACwYnA,3,vision,14.388997077941895,14.388997077941895,103,35,144,0.1,35,1.0,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/2,-3g5yACwYnA,2,text,9.394009590148926,5.1430321040563305,14,768,94,0.1,768,0.7563282251358032,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/2,-3g5yACwYnA,2,audio,9.394009590148926,9.331671435768538,924,74,94,0.1,74,0.9936367273330688,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/2,-3g5yACwYnA,2,vision,9.394009590148926,9.394009590148926,78,35,94,0.1,35,1.0,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/9,-3g5yACwYnA,9,text,8.816991806030273,5.6415157541632714,21,768,89,0.1,768,0.6637077927589417,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/9,-3g5yACwYnA,9,audio,8.816991806030273,8.735654204761659,870,74,89,0.1,74,0.9912108778953552,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --3g5yACwYnA/9,-3g5yACwYnA,9,vision,8.816991806030273,8.816991806030273,75,35,89,0.1,35,1.0,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --3nNcZdcdvU/5,-3nNcZdcdvU,5,text,7.867969036102295,5.002839090395719,18,768,79,0.1,768,0.7357116341590881,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --3nNcZdcdvU/5,-3nNcZdcdvU,5,audio,7.867969036102295,7.843361772234375,779,74,79,0.1,74,0.9971166849136353,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --3nNcZdcdvU/5,-3nNcZdcdvU,5,vision,7.867969036102295,7.816666602730405,67,35,79,0.1,35,0.9904456734657288,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,text,3.9820311069488525,2.40418455898877,8,768,40,0.1,768,0.6164926290512085,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,audio,3.9820311069488525,3.9192059241395896,392,74,40,0.1,74,0.9881895780563354,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,vision,3.9820311069488525,0.8653643131256102,27,35,40,0.1,35,0.9814813733100891,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,text,5.6529951095581055,3.0192474961280813,10,768,57,0.1,768,0.7021505832672119,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,audio,5.6529951095581055,5.595076320642555,555,74,57,0.1,74,0.9900854229927063,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,vision,5.6529951095581055,1.199999868869782,44,35,57,0.1,35,0.666666567325592,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,text,3.3580079078674316,1.3907382175326355,7,768,34,0.1,768,0.5562952756881714,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,audio,3.3580079078674316,3.306980685486987,326,74,34,0.1,74,0.9901678562164307,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,vision,3.3580079078674316,1.6413411736488344,21,35,34,0.1,35,0.841666579246521,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,text,7.733983993530273,4.492685443162919,22,768,78,0.1,768,0.6705501675605774,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,audio,7.733983993530273,7.691443904670509,764,74,78,0.1,74,0.9946621060371399,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,vision,7.733983993530273,0.0,65,35,78,0.1,35,0.0,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --NFrJFQijFE/1,-NFrJFQijFE,1,text,5.745999813079834,4.074365028738975,16,768,58,0.1,768,0.7835317254066467,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --NFrJFQijFE/1,-NFrJFQijFE,1,audio,5.745999813079834,5.699824074635634,566,74,58,0.1,74,0.9922494292259216,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --NFrJFQijFE/1,-NFrJFQijFE,1,vision,5.745999813079834,0.0,44,35,58,0.1,35,0.0,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --NFrJFQijFE/2,-NFrJFQijFE,2,text,6.855999946594238,3.4186642885208136,18,768,69,0.1,768,0.7431879639625549,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --NFrJFQijFE/2,-NFrJFQijFE,2,audio,6.855999946594238,6.798472889210726,670,74,69,0.1,74,0.9926568269729614,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --NFrJFQijFE/2,-NFrJFQijFE,2,vision,6.855999946594238,0.0,55,35,69,0.1,35,0.0,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/12,-THoVjtIkeU,12,text,14.896029472351074,7.941008102986966,39,768,149,0.1,768,0.6352806687355042,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/12,-THoVjtIkeU,12,audio,14.896029472351074,14.759312417861578,1481,74,149,0.1,74,0.9911767244338989,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/12,-THoVjtIkeU,12,vision,14.896029472351074,14.896029472351074,106,35,149,0.1,35,1.0,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/2,-THoVjtIkeU,2,text,4.2919921875,2.5793988468125453,10,768,43,0.1,768,0.7164996266365051,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/2,-THoVjtIkeU,2,audio,4.2919921875,4.247343715932063,424,74,43,0.1,74,0.9895758628845215,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/2,-THoVjtIkeU,2,vision,4.2919921875,4.2919921875,31,35,43,0.1,35,1.0,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/6,-THoVjtIkeU,6,text,8.097004890441895,5.15853084437549,30,768,81,0.1,768,0.6787540316581726,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/6,-THoVjtIkeU,6,audio,8.097004890441895,8.027356093840018,799,74,81,0.1,74,0.9927164316177368,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --THoVjtIkeU/6,-THoVjtIkeU,6,vision,8.097004890441895,8.097004890441895,68,35,81,0.1,35,1.0,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/1,-UuX1xuaiiE,1,text,10.350000381469727,5.7645069250837,27,768,104,0.1,768,0.6550575494766235,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/1,-UuX1xuaiiE,1,audio,10.350000381469727,10.268040973753543,1027,74,104,0.1,74,0.9925051927566528,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/1,-UuX1xuaiiE,1,vision,10.350000381469727,0.45000076293945285,83,35,104,0.1,35,0.8333339691162109,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/0,-UuX1xuaiiE,0,text,3.6333329677581787,2.204757870733737,9,768,37,0.1,768,0.6484582424163818,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/0,-UuX1xuaiiE,0,audio,3.6333329677581787,3.6124320519937045,358,74,37,0.1,74,0.994590699672699,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/0,-UuX1xuaiiE,0,vision,3.6333329677581787,1.700000047683716,25,35,37,0.1,35,0.9444444179534912,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/3,-UuX1xuaiiE,3,text,4.1300129890441895,2.0997684210538865,9,768,42,0.1,768,0.7240580320358276,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/3,-UuX1xuaiiE,3,audio,4.1300129890441895,4.093458598368876,404,74,42,0.1,74,0.992123007774353,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/3,-UuX1xuaiiE,3,vision,4.1300129890441895,4.03001308441162,29,35,42,0.1,35,0.976190447807312,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/6,-UuX1xuaiiE,6,text,8.113997459411621,4.803536714613438,22,768,82,0.1,768,0.7064024806022644,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/6,-UuX1xuaiiE,6,audio,8.113997459411621,7.968213647201255,795,74,82,0.1,74,0.984533965587616,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --UuX1xuaiiE/6,-UuX1xuaiiE,6,vision,8.113997459411621,7.9306641817092896,68,35,82,0.1,35,0.9776423573493958,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --a55Q6RWvTA/3,-a55Q6RWvTA,3,text,22.15397071838379,13.423765965458015,65,768,222,0.1,768,0.6580277681350708,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --a55Q6RWvTA/3,-a55Q6RWvTA,3,audio,22.15397071838379,21.95386351814141,2209,74,222,0.1,74,0.9912921786308289,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --a55Q6RWvTA/3,-a55Q6RWvTA,3,vision,22.15397071838379,22.15397071838379,142,35,222,0.1,35,1.0,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --aNfi7CP8vM/7,-aNfi7CP8vM,7,text,8.694987297058105,5.3913327027112254,18,768,87,0.1,768,0.7001731395721436,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --aNfi7CP8vM/7,-aNfi7CP8vM,7,audio,8.694987297058105,8.639379485394505,854,74,87,0.1,74,0.9945195913314819,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --aNfi7CP8vM/7,-aNfi7CP8vM,7,vision,8.694987297058105,8.694987297058105,74,35,87,0.1,35,1.0,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,text,10.966667175292969,5.40069461874664,14,768,110,0.1,768,0.71061772108078,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,audio,10.966667175292969,10.855676183829436,1090,74,110,0.1,74,0.9910455346107483,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,vision,10.966667175292969,10.966667175292969,86,35,110,0.1,35,1.0,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/1,-dxfTGcXJoc,1,text,16.16100311279297,9.47002421617508,36,768,162,0.1,768,0.6812966465950012,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/1,-dxfTGcXJoc,1,audio,16.16100311279297,15.984151583668348,1602,74,162,0.1,74,0.9900091290473938,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/1,-dxfTGcXJoc,1,vision,16.16100311279297,16.16100311279297,112,35,162,0.1,35,1.0,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/0,-dxfTGcXJoc,0,text,20.5,13.566675072815258,41,768,205,0.1,768,0.7333337664604187,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/0,-dxfTGcXJoc,0,audio,20.5,20.265135636200775,2042,74,205,0.1,74,0.9891952872276306,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/0,-dxfTGcXJoc,0,vision,20.5,15.983333587646484,134,35,205,0.1,35,0.99895840883255,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/2,-dxfTGcXJoc,2,text,16.697982788085938,9.495353608578453,34,768,167,0.1,768,0.6593995094299316,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/2,-dxfTGcXJoc,2,audio,16.697982788085938,16.517766948940555,1664,74,167,0.1,74,0.9901677966117859,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/2,-dxfTGcXJoc,2,vision,16.697982788085938,16.697982788085938,115,35,167,0.1,35,1.0,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/6,-dxfTGcXJoc,6,text,12.735026359558105,8.155348146427425,27,768,128,0.1,768,0.715381383895874,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/6,-dxfTGcXJoc,6,audio,12.735026359558105,12.576174248392515,1263,74,128,0.1,74,0.9889141917228699,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --dxfTGcXJoc/6,-dxfTGcXJoc,6,vision,12.735026359558105,12.5,95,35,128,0.1,35,1.0,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/26,-egA8-b7-3M,26,text,6.030990123748779,3.6452820789068947,15,768,61,0.1,768,0.6877890825271606,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/26,-egA8-b7-3M,26,audio,6.030990123748779,5.994409297769134,593,74,61,0.1,74,0.9945786595344543,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/26,-egA8-b7-3M,26,vision,6.030990123748779,6.030990123748779,47,35,61,0.1,35,1.0,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/17,-egA8-b7-3M,17,text,7.271028995513916,3.4275580286979666,16,768,73,0.1,768,0.7292676568031311,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/17,-egA8-b7-3M,17,audio,7.271028995513916,7.202622791158187,710,74,73,0.1,74,0.9916234016418457,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/17,-egA8-b7-3M,17,vision,7.271028995513916,7.271028995513916,60,35,73,0.1,35,1.0,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/18,-egA8-b7-3M,18,text,8.386002540588379,3.9958887480199343,22,768,84,0.1,768,0.6243576407432556,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/18,-egA8-b7-3M,18,audio,8.386002540588379,8.326583495091747,827,74,84,0.1,74,0.9932008385658264,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/18,-egA8-b7-3M,18,vision,8.386002540588379,8.386002540588379,71,35,84,0.1,35,1.0,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/16,-egA8-b7-3M,16,text,5.538021087646484,3.3190146580338484,11,768,56,0.1,768,0.7375588417053223,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/16,-egA8-b7-3M,16,audio,5.538021087646484,5.498074575251824,549,74,56,0.1,74,0.9940068125724792,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/16,-egA8-b7-3M,16,vision,5.538021087646484,5.538021087646484,43,35,56,0.1,35,1.0,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/13,-egA8-b7-3M,13,text,4.18398380279541,2.166685357689858,11,768,42,0.1,768,0.6372604370117188,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/13,-egA8-b7-3M,13,audio,4.18398380279541,4.149822072886132,403,74,42,0.1,74,0.9924017786979675,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/13,-egA8-b7-3M,13,vision,4.18398380279541,4.18398380279541,29,35,42,0.1,35,1.0,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/1,-egA8-b7-3M,1,text,10.266016006469727,5.581577223725616,20,768,103,0.1,768,0.6490206122398376,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/1,-egA8-b7-3M,1,audio,10.266016006469727,10.199569694899225,1016,74,103,0.1,74,0.9937204718589783,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/1,-egA8-b7-3M,1,vision,10.266016006469727,10.266016006469727,83,35,103,0.1,35,1.0,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/6,-egA8-b7-3M,6,text,6.264974117279053,3.582069296762348,12,768,63,0.1,768,0.7164138555526733,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/6,-egA8-b7-3M,6,audio,6.264974117279053,6.2242991206613745,619,74,63,0.1,74,0.9942464828491211,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/6,-egA8-b7-3M,6,vision,6.264974117279053,6.264974117279053,50,35,63,0.1,35,1.0,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/9,-egA8-b7-3M,9,text,5.0899739265441895,2.328742109239101,10,768,51,0.1,768,0.597113311290741,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/9,-egA8-b7-3M,9,audio,5.0899739265441895,5.05666382530251,494,74,51,0.1,74,0.9939422011375427,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/9,-egA8-b7-3M,9,vision,5.0899739265441895,5.0899739265441895,38,35,51,0.1,35,1.0,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/20,-egA8-b7-3M,20,text,6.644987106323242,3.9119302723556753,20,768,67,0.1,768,0.6985589861869812,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/20,-egA8-b7-3M,20,audio,6.644987106323242,6.588095611333847,648,74,67,0.1,74,0.992087185382843,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --egA8-b7-3M/20,-egA8-b7-3M,20,vision,6.644987106323242,6.644987106323242,54,35,67,0.1,35,1.0,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/3,-iRBcNs9oI8,3,text,5.620999813079834,2.037480006366969,8,768,57,0.1,768,0.6791599988937378,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/3,-iRBcNs9oI8,3,audio,5.620999813079834,5.578905152469068,554,74,57,0.1,74,0.9954468607902527,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/3,-iRBcNs9oI8,3,vision,5.620999813079834,0.0,28,35,57,0.1,35,0.0,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/7,-iRBcNs9oI8,7,text,4.104000091552734,2.2118893228471284,9,768,42,0.1,768,0.7135127186775208,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/7,-iRBcNs9oI8,7,audio,4.104000091552734,4.083554145129952,401,74,42,0.1,74,0.995795726776123,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/7,-iRBcNs9oI8,7,vision,4.104000091552734,0.0,21,35,42,0.1,35,0.0,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/6,-iRBcNs9oI8,6,text,2.9030001163482666,1.5899996414780622,5,768,30,0.1,768,0.7227271199226379,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/6,-iRBcNs9oI8,6,audio,2.9030001163482666,2.892310954429008,283,74,30,0.1,74,0.9973600506782532,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/6,-iRBcNs9oI8,6,vision,2.9030001163482666,0.0,15,35,30,0.1,35,0.0,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/9,-iRBcNs9oI8,9,text,3.4159998893737793,1.094375005364418,5,768,35,0.1,768,0.643750011920929,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/9,-iRBcNs9oI8,9,audio,3.4159998893737793,3.383608031917263,338,74,35,0.1,74,0.993934154510498,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/9,-iRBcNs9oI8,9,vision,3.4159998893737793,0.0,17,35,35,0.1,35,0.0,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/8,-iRBcNs9oI8,8,text,8.093000411987305,3.901277539858493,21,768,81,0.1,768,0.6192578673362732,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/8,-iRBcNs9oI8,8,audio,8.093000411987305,8.037513972489212,803,74,81,0.1,74,0.9947929382324219,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --iRBcNs9oI8/8,-iRBcNs9oI8,8,vision,8.093000411987305,0.0,41,35,81,0.1,35,0.0,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --lzEya4AM_4/5,-lzEya4AM_4,5,text,7.675000190734863,4.456424816604702,19,768,77,0.1,768,0.7427374720573425,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --lzEya4AM_4/5,-lzEya4AM_4,5,audio,7.675000190734863,7.5937163826178855,757,74,77,0.1,74,0.9901386499404907,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --lzEya4AM_4/5,-lzEya4AM_4,5,vision,7.675000190734863,7.675000190734863,64,35,77,0.1,35,1.0,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --lzEya4AM_4/6,-lzEya4AM_4,6,text,14.7919921875,9.074837291240696,43,768,148,0.1,768,0.7318416833877563,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --lzEya4AM_4/6,-lzEya4AM_4,6,audio,14.7919921875,14.634100490929308,1475,74,148,0.1,74,0.9907644391059875,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --lzEya4AM_4/6,-lzEya4AM_4,6,vision,14.7919921875,14.7919921875,105,35,148,0.1,35,1.0,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,text,6.103000164031982,1.9617638647556308,13,768,62,0.1,768,0.8174015879631042,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,audio,6.103000164031982,5.938054213652739,606,74,62,0.1,74,1.0,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,vision,6.103000164031982,0.0,31,35,62,0.1,35,0.0,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,text,5.730999946594238,3.5213801980018595,11,768,58,0.1,768,0.9266790151596069,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,audio,5.730999946594238,5.5761080561457455,571,74,58,0.1,74,1.0,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,vision,5.730999946594238,0.0,29,35,58,0.1,35,0.0,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,text,2.256999969482422,1.1722640581429007,5,768,23,0.1,768,0.6512578129768372,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,audio,2.256999969482422,2.2208648198211596,223,74,23,0.1,74,0.9862568378448486,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,vision,2.256999969482422,2.256999969482422,12,35,23,0.1,35,1.0,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,text,7.0329999923706055,3.813062208145856,22,768,71,0.1,768,0.5777366757392883,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,audio,7.0329999923706055,6.9057026652870945,691,74,71,0.1,74,0.9846946001052856,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,vision,7.0329999923706055,6.832799911499023,35,35,71,0.1,35,0.9856857061386108,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,text,4.191999912261963,2.284389768779285,11,768,42,0.1,768,0.6529467701911926,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,audio,4.191999912261963,4.127891841835266,418,74,42,0.1,74,0.9846959114074707,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,vision,4.191999912261963,4.191999912261963,21,35,42,0.1,35,1.0,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --mqbVkbCndg/0,-mqbVkbCndg,0,text,6.466667175292969,3.891069056466222,12,768,65,0.1,768,0.7074670791625977,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --mqbVkbCndg/0,-mqbVkbCndg,0,audio,6.466667175292969,6.400540961445988,639,74,65,0.1,74,0.9905118346214294,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --mqbVkbCndg/0,-mqbVkbCndg,0,vision,6.466667175292969,6.466667175292969,53,35,65,0.1,35,1.0,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --t217m2on-s/2,-t217m2on-s,2,text,5.6860032081604,2.3235132679343233,12,768,57,0.1,768,0.6279765367507935,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --t217m2on-s/2,-t217m2on-s,2,audio,5.6860032081604,5.64178691047269,564,74,57,0.1,74,0.9959543347358704,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --t217m2on-s/2,-t217m2on-s,2,vision,5.6860032081604,5.6860032081604,45,35,57,0.1,35,1.0,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --t217m2on-s/7,-t217m2on-s,7,text,17.183008193969727,10.16458021551371,45,768,172,0.1,768,0.6867958903312683,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --t217m2on-s/7,-t217m2on-s,7,audio,17.183008193969727,17.025629438258505,1703,74,172,0.1,74,0.9928514361381531,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --t217m2on-s/7,-t217m2on-s,7,vision,17.183008193969727,17.183008193969727,117,35,172,0.1,35,1.0,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --tANM6ETl_M/3,-tANM6ETl_M,3,text,6.758008003234863,3.281156235933303,22,768,68,0.1,768,0.5859207510948181,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --tANM6ETl_M/3,-tANM6ETl_M,3,audio,6.758008003234863,6.702588935075579,661,74,68,0.1,74,0.9936580061912537,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --tANM6ETl_M/3,-tANM6ETl_M,3,vision,6.758008003234863,6.5333333015441895,54,35,68,0.1,35,0.9898989200592041,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/11,-tPCytz4rww,11,text,5.466991901397705,3.309338267147543,17,768,55,0.1,768,0.6753751635551453,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/11,-tPCytz4rww,11,audio,5.466991901397705,5.402410984844775,538,74,55,0.1,74,0.9890678524971008,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/11,-tPCytz4rww,11,vision,5.466991901397705,5.466991901397705,43,35,55,0.1,35,1.0,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/10,-tPCytz4rww,10,text,4.788997173309326,2.2889054998755456,12,768,48,0.1,768,0.5449775457382202,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/10,-tPCytz4rww,10,audio,4.788997173309326,4.71733509463233,468,74,48,0.1,74,0.9894654750823975,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/10,-tPCytz4rww,10,vision,4.788997173309326,4.788997173309326,35,35,48,0.1,35,1.0,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/12,-tPCytz4rww,12,text,11.863997459411621,6.21423171013594,26,768,119,0.1,768,0.654129683971405,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/12,-tPCytz4rww,12,audio,11.863997459411621,11.690646183812941,1174,74,119,0.1,74,0.9875938892364502,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/12,-tPCytz4rww,12,vision,11.863997459411621,11.863997459411621,91,35,119,0.1,35,1.0,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/16,-tPCytz4rww,16,text,7.205989837646484,2.154985956847668,16,768,73,0.1,768,0.4489554166793823,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/16,-tPCytz4rww,16,audio,7.205989837646484,7.1144090478484685,714,74,73,0.1,74,0.988937258720398,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/16,-tPCytz4rww,16,vision,7.205989837646484,7.205989837646484,59,35,73,0.1,35,1.0,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/18,-tPCytz4rww,18,text,6.644987106323242,3.4940090492367735,16,768,67,0.1,768,0.6719247698783875,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/18,-tPCytz4rww,18,audio,6.644987106323242,6.553973502565075,657,74,67,0.1,74,0.9872123599052429,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --tPCytz4rww/18,-tPCytz4rww,18,vision,6.644987106323242,6.644987106323242,54,35,67,0.1,35,1.0,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --vxjVxOeScU/4,-vxjVxOeScU,4,text,9.127017974853516,5.366417751088739,21,768,92,0.1,768,0.6465563774108887,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --vxjVxOeScU/4,-vxjVxOeScU,4,audio,9.127017974853516,9.08216616914079,904,74,92,0.1,74,0.9960808753967285,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --vxjVxOeScU/4,-vxjVxOeScU,4,vision,9.127017974853516,9.127017974853516,77,35,92,0.1,35,1.0,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --wMB_hJL-3o/7,-wMB_hJL-3o,7,text,6.044010162353516,3.555890290439128,22,768,61,0.1,768,0.658498227596283,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --wMB_hJL-3o/7,-wMB_hJL-3o,7,audio,6.044010162353516,5.968699067508853,598,74,61,0.1,74,0.9895980954170227,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --wMB_hJL-3o/7,-wMB_hJL-3o,7,vision,6.044010162353516,5.944010257720948,48,35,61,0.1,35,0.9836065769195557,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/1,-wny0OAz3g8,1,text,6.844009876251221,4.170902410242706,23,768,69,0.1,768,0.62252277135849,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/1,-wny0OAz3g8,1,audio,6.844009876251221,6.814914991726747,678,74,69,0.1,74,0.995954155921936,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/1,-wny0OAz3g8,1,vision,6.844009876251221,3.327343463897705,56,35,69,0.1,35,0.9950981140136719,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/0,-wny0OAz3g8,0,text,3.3333330154418945,1.6946923546493056,9,768,34,0.1,768,0.6052471995353699,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/0,-wny0OAz3g8,0,audio,3.3333330154418945,3.3145942848276446,328,74,34,0.1,74,0.9955413937568665,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/0,-wny0OAz3g8,0,vision,3.3333330154418945,2.6999998092651367,21,35,34,0.1,35,0.9999999403953552,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/3,-wny0OAz3g8,3,text,6.146028995513916,4.208628869801758,20,768,62,0.1,768,0.7014381289482117,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/3,-wny0OAz3g8,3,audio,6.146028995513916,6.13413711335208,610,74,62,0.1,74,0.9980819821357727,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/3,-wny0OAz3g8,3,vision,6.146028995513916,1.5293622016906734,49,35,62,0.1,35,0.9895832538604736,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/2,-wny0OAz3g8,2,text,6.686978816986084,4.909538650512694,23,768,67,0.1,768,0.7553136348724365,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/2,-wny0OAz3g8,2,audio,6.686978816986084,6.656249635122918,653,74,67,0.1,74,0.9957627058029175,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/2,-wny0OAz3g8,2,vision,6.686978816986084,5.4166669845581055,54,35,67,0.1,35,0.9848485589027405,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/5,-wny0OAz3g8,5,text,6.9119791984558105,3.941009076312184,20,768,70,0.1,768,0.6063091158866882,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/5,-wny0OAz3g8,5,audio,6.9119791984558105,6.879425679509704,675,74,70,0.1,74,0.9961636066436768,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/5,-wny0OAz3g8,5,vision,6.9119791984558105,1.4953122138977046,56,35,70,0.1,35,0.9895831346511841,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/7,-wny0OAz3g8,7,text,8.06796932220459,5.293590973317621,29,768,81,0.1,768,0.6700748205184937,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/7,-wny0OAz3g8,7,audio,8.06796932220459,8.030726903515895,792,74,81,0.1,74,0.9956274628639221,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/7,-wny0OAz3g8,7,vision,8.06796932220459,7.06796932220459,68,35,81,0.1,35,1.0,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/9,-wny0OAz3g8,9,text,6.2130208015441895,4.012809376046059,20,768,63,0.1,768,0.6688015460968018,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/9,-wny0OAz3g8,9,audio,6.2130208015441895,6.190979680177328,605,74,63,0.1,74,0.9969837069511414,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --wny0OAz3g8/9,-wny0OAz3g8,9,vision,6.2130208015441895,5.8130205273628235,49,35,63,0.1,35,0.9672130942344666,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --571d8cVauQ/0,-571d8cVauQ,0,text,5.066667079925537,3.21007038205862,12,768,51,0.1,768,0.6978414058685303,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --571d8cVauQ/0,-571d8cVauQ,0,audio,5.066667079925537,5.013243622071034,500,74,51,0.1,74,0.9903978705406189,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --571d8cVauQ/0,-571d8cVauQ,0,vision,5.066667079925537,4.9666670799255375,39,35,51,0.1,35,1.0,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --571d8cVauQ/5,-571d8cVauQ,5,text,15.272981643676758,9.052198391593993,43,768,153,0.1,768,0.7017207741737366,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --571d8cVauQ/5,-571d8cVauQ,5,audio,15.272981643676758,15.142900936426344,1512,74,153,0.1,74,0.9918006062507629,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --571d8cVauQ/5,-571d8cVauQ,5,vision,15.272981643676758,15.272981643676758,108,35,153,0.1,35,1.0,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --I_e4mIh0yE/1,-I_e4mIh0yE,1,text,7.622004985809326,3.535097924340517,18,768,77,0.1,768,0.6546477675437927,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --I_e4mIh0yE/1,-I_e4mIh0yE,1,audio,7.622004985809326,7.497626475627357,745,74,77,0.1,74,0.985649049282074,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --I_e4mIh0yE/1,-I_e4mIh0yE,1,vision,7.622004985809326,7.622004985809326,63,35,77,0.1,35,1.0,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --I_e4mIh0yE/3,-I_e4mIh0yE,3,text,9.163021087646484,4.626299023628236,22,768,92,0.1,768,0.6251755356788635,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --I_e4mIh0yE/3,-I_e4mIh0yE,3,audio,9.163021087646484,9.017804326318405,900,74,92,0.1,74,0.9852646589279175,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --I_e4mIh0yE/3,-I_e4mIh0yE,3,vision,9.163021087646484,9.163021087646484,77,35,92,0.1,35,1.0,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --UacrmKiTn4/10,-UacrmKiTn4,10,text,7.361979007720947,2.732618988305329,18,768,74,0.1,768,0.47940683364868164,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --UacrmKiTn4/10,-UacrmKiTn4,10,audio,7.361979007720947,7.319425493478775,725,74,74,0.1,74,0.9955651164054871,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --UacrmKiTn4/10,-UacrmKiTn4,10,vision,7.361979007720947,7.361979007720947,60,35,74,0.1,35,1.0,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --UacrmKiTn4/4,-UacrmKiTn4,4,text,4.741015911102295,3.0301611527800563,14,768,48,0.1,768,0.7214669585227966,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --UacrmKiTn4/4,-UacrmKiTn4,4,audio,4.741015911102295,4.704772258610339,469,74,48,0.1,74,0.9926760792732239,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --UacrmKiTn4/4,-UacrmKiTn4,4,vision,4.741015911102295,4.741015911102295,35,35,48,0.1,35,1.0,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --hnBHBN8p5A/7,-hnBHBN8p5A,7,text,7.372000217437744,4.009185888338835,13,768,74,0.1,768,0.657243549823761,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --hnBHBN8p5A/7,-hnBHBN8p5A,7,audio,7.372000217437744,7.281608330236899,731,74,74,0.1,74,0.9905768632888794,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --hnBHBN8p5A/7,-hnBHBN8p5A,7,vision,7.372000217437744,0.0,37,35,74,0.1,35,0.0,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --hnBHBN8p5A/6,-hnBHBN8p5A,6,text,6.401000022888184,3.580871792882682,13,768,65,0.1,768,0.7618876099586487,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --hnBHBN8p5A/6,-hnBHBN8p5A,6,audio,6.401000022888184,6.346108104731585,634,74,65,0.1,74,0.9932082295417786,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --hnBHBN8p5A/6,-hnBHBN8p5A,6,vision,6.401000022888184,0.0,32,35,65,0.1,35,0.0,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --qDkUB0GgYY/6,-qDkUB0GgYY,6,text,4.677995204925537,2.927082145679743,16,768,47,0.1,768,0.6504627466201782,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --qDkUB0GgYY/6,-qDkUB0GgYY,6,audio,4.677995204925537,4.653116947730401,462,74,47,0.1,74,0.9946085214614868,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --qDkUB0GgYY/6,-qDkUB0GgYY,6,vision,4.677995204925537,4.677995204925537,35,35,47,0.1,35,1.0,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --uywlfIYOS8/4,-uywlfIYOS8,4,text,6.044987201690674,3.7997015411034236,18,768,61,0.1,768,0.6908547878265381,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --uywlfIYOS8/4,-uywlfIYOS8,4,audio,6.044987201690674,6.015325370511492,589,74,61,0.1,74,0.9957761168479919,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --uywlfIYOS8/4,-uywlfIYOS8,4,vision,6.044987201690674,6.044987201690674,48,35,61,0.1,35,1.0,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,text,12.805012702941895,6.537867438234387,34,768,129,0.1,768,0.63474440574646,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,audio,12.805012702941895,12.672918005408468,1274,74,129,0.1,74,0.9899758696556091,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,vision,12.805012702941895,12.805012702941895,95,35,129,0.1,35,1.0,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/0,-9y-fZ3swSY,0,text,6.8333330154418945,3.536603624001146,22,768,69,0.1,768,0.5440928339958191,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/0,-9y-fZ3swSY,0,audio,6.8333330154418945,6.794324037513217,676,74,69,0.1,74,0.9948647022247314,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/0,-9y-fZ3swSY,0,vision,6.8333330154418945,6.8333330154418945,57,35,69,0.1,35,1.0,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/4,-9y-fZ3swSY,4,text,2.8210289478302,1.160462707281113,9,768,29,0.1,768,0.5274830460548401,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/4,-9y-fZ3swSY,4,audio,2.8210289478302,2.7937038478819103,267,74,29,0.1,74,0.9926167726516724,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/4,-9y-fZ3swSY,4,vision,2.8210289478302,2.7210289478302006,15,35,29,0.1,35,1.0,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/8,-9y-fZ3swSY,8,text,4.988996982574463,2.1943973237220367,12,768,50,0.1,768,0.562736988067627,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/8,-9y-fZ3swSY,8,audio,4.988996982574463,4.9497673825665816,492,74,50,0.1,74,0.9931800365447998,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --9y-fZ3swSY/8,-9y-fZ3swSY,8,vision,4.988996982574463,3.688997030258178,37,35,50,0.1,35,0.9487179517745972,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,text,22.511003494262695,12.134252551943066,41,768,226,0.1,768,0.6386448740959167,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,audio,22.511003494262695,22.31367928599184,2241,74,226,0.1,74,0.9917553663253784,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,vision,22.511003494262695,22.511003494262695,144,35,226,0.1,35,1.0,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --HeZS2-Prhc/2,-HeZS2-Prhc,2,text,8.261979103088379,3.552112135011702,16,768,83,0.1,768,0.6830984950065613,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --HeZS2-Prhc/2,-HeZS2-Prhc,2,audio,8.261979103088379,8.205439110059995,816,74,83,0.1,74,0.9933875799179077,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --HeZS2-Prhc/2,-HeZS2-Prhc,2,vision,8.261979103088379,8.261979103088379,70,35,83,0.1,35,1.0,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/0,-MeTTeMJBNc,0,text,9.300000190734863,4.416411150246859,21,768,94,0.1,768,0.6400595903396606,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/0,-MeTTeMJBNc,0,audio,9.300000190734863,9.216486695006088,922,74,94,0.1,74,0.9939717650413513,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/0,-MeTTeMJBNc,0,vision,9.300000190734863,9.200000190734862,78,35,94,0.1,35,1.0,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/13,-MeTTeMJBNc,13,text,5.430013179779053,2.997564935684203,14,768,55,0.1,768,0.6661255359649658,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/13,-MeTTeMJBNc,13,audio,5.430013179779053,5.368323606413764,526,74,55,0.1,74,0.9918515682220459,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/13,-MeTTeMJBNc,13,vision,5.430013179779053,5.430013179779053,41,35,55,0.1,35,1.0,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/7,-MeTTeMJBNc,7,text,10.51699161529541,6.728291415888818,31,768,106,0.1,768,0.7313360571861267,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/7,-MeTTeMJBNc,7,audio,10.51699161529541,10.441329748243898,1042,74,106,0.1,74,0.9934103488922119,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --MeTTeMJBNc/7,-MeTTeMJBNc,7,vision,10.51699161529541,10.51699161529541,84,35,106,0.1,35,1.0,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/11,-RfYyzHpjk4,11,text,5.238996982574463,3.062753976881504,17,768,53,0.1,768,0.6960803866386414,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/11,-RfYyzHpjk4,11,audio,5.238996982574463,5.202267333462432,508,74,53,0.1,74,0.9958056807518005,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/11,-RfYyzHpjk4,11,vision,5.238996982574463,5.238996982574463,40,35,53,0.1,35,1.0,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/8,-RfYyzHpjk4,8,text,4.588996887207031,2.8058770607668286,17,768,46,0.1,768,0.684955358505249,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/8,-RfYyzHpjk4,8,audio,4.588996887207031,4.553010454316745,454,74,46,0.1,74,0.994469404220581,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/8,-RfYyzHpjk4,8,vision,4.588996887207031,4.588996887207031,33,35,46,0.1,35,1.0,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/2,-RfYyzHpjk4,2,text,5.516016006469727,3.372718970850109,25,768,56,0.1,768,0.7175998091697693,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/2,-RfYyzHpjk4,2,audio,5.516016006469727,5.478826378849712,540,74,56,0.1,74,0.9948742389678955,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --RfYyzHpjk4/2,-RfYyzHpjk4,2,vision,5.516016006469727,5.416016006469727,43,35,56,0.1,35,1.0,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --UUCSKoHeMA/0,-UUCSKoHeMA,0,text,7.800000190734863,4.133793779369442,18,768,79,0.1,768,0.666740894317627,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --UUCSKoHeMA/0,-UUCSKoHeMA,0,audio,7.800000190734863,7.750270468963159,774,74,79,0.1,74,0.9943835139274597,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --UUCSKoHeMA/0,-UUCSKoHeMA,0,vision,7.800000190734863,7.700000190734862,66,35,79,0.1,35,1.0,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/0,-ri04Z7vwnc,0,text,2.806999921798706,2.1131306469440463,8,768,29,0.1,768,0.7826409935951233,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/0,-ri04Z7vwnc,0,audio,2.806999921798706,2.8018783078000356,277,74,29,0.1,74,0.9982975125312805,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/0,-ri04Z7vwnc,0,vision,2.806999921798706,0.0,14,35,29,0.1,35,0.0,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/2,-ri04Z7vwnc,2,text,6.0269999504089355,3.9384737918153405,16,768,61,0.1,768,0.7431082725524902,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/2,-ri04Z7vwnc,2,audio,6.0269999504089355,6.008905370493193,592,74,61,0.1,74,0.9971521496772766,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/2,-ri04Z7vwnc,2,vision,6.0269999504089355,2.106416702270508,30,35,61,0.1,35,0.9906440377235413,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/5,-ri04Z7vwnc,5,text,3.5450000762939453,1.8027290821075441,9,768,36,0.1,768,0.721091628074646,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/5,-ri04Z7vwnc,5,audio,3.5450000762939453,3.534527108637063,344,74,36,0.1,74,0.9974266886711121,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --ri04Z7vwnc/5,-ri04Z7vwnc,5,vision,3.5450000762939453,3.5450000762939453,18,35,36,0.1,35,1.0,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,text,4.7919921875,2.610484106093645,11,768,48,0.1,768,0.7055363059043884,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,audio,4.7919921875,4.72247887628304,465,74,48,0.1,74,0.9860281348228455,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,vision,4.7919921875,4.7919921875,35,35,48,0.1,35,1.0,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,text,6.800000190734863,3.6206328462809334,24,768,69,0.1,768,0.6351987719535828,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,audio,6.800000190734863,6.715675868979983,672,74,69,0.1,74,0.9885490536689758,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,vision,6.800000190734863,6.199999928474425,56,35,69,0.1,35,0.984375,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,text,8.561002731323242,3.4511515218298876,19,768,86,0.1,768,0.5150972604751587,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,audio,8.561002731323242,8.46259727510246,840,74,86,0.1,74,0.9896790981292725,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,vision,8.561002731323242,8.561002731323242,72,35,86,0.1,35,1.0,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,text,4.427018165588379,2.2374289706349377,9,768,45,0.1,768,0.6215080618858337,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,audio,4.427018165588379,4.367301462550421,425,74,45,0.1,74,0.9892621040344238,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,vision,4.427018165588379,3.027018189430237,31,35,45,0.1,35,0.96875,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,text,6.966015815734863,3.9709798723459264,28,768,70,0.1,768,0.661829948425293,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,audio,6.966015815734863,6.848488390969263,684,74,70,0.1,74,0.9849807620048523,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,vision,6.966015815734863,4.0493486523628235,57,35,70,0.1,35,0.9496123194694519,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,text,2.4749999046325684,1.6227161765098577,5,768,25,0.1,768,0.8113580942153931,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,audio,2.4749999046325684,2.4289188214250514,232,74,25,0.1,74,0.9844902753829956,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,vision,2.4749999046325684,1.8999999761581425,14,35,25,0.1,35,1.0,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,text,3.2949869632720947,1.9395141303539274,13,768,33,0.1,768,0.6465047001838684,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,audio,3.2949869632720947,3.253163003921509,319,74,33,0.1,74,0.9880942702293396,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,vision,3.2949869632720947,3.016666531562805,20,35,33,0.1,35,0.9731181859970093,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,text,29.288021087646484,15.053765958920112,49,768,293,0.1,768,0.6873865723609924,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,audio,29.288021087646484,28.759709144524624,2911,74,293,0.1,74,0.9836021065711975,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,vision,29.288021087646484,27.471354246139526,178,35,293,0.1,35,0.999393880367279,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,text,13.169010162353516,8.127011682093151,25,768,132,0.1,768,0.7192046046257019,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,audio,13.169010162353516,12.971401820754682,1305,74,132,0.1,74,0.9867846369743347,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,vision,13.169010162353516,13.169010162353516,97,35,132,0.1,35,1.0,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,text,11.241994857788086,4.740236129239202,19,768,113,0.1,768,0.6869907975196838,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,word vectors; hard CTC word intervals (posterior alternative uses 20 ms CTC frames),0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,audio,11.241994857788086,11.070711010679133,1125,74,113,0.1,74,0.9854583740234375,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,10 ms feature hop,0.1,50,sample_id + source_video_sha256 + source time bounds --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,vision,11.241994857788086,11.241994857788086,87,35,113,0.1,35,1.0,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,5 Hz sampled frames (about 200 ms),0.1,50,sample_id + source_video_sha256 + source time bounds diff --git a/final/Q1/features/word_alignment_posterior.csv b/final/Q1/features/word_alignment_posterior.csv deleted file mode 100644 index e5714f8..0000000 --- a/final/Q1/features/word_alignment_posterior.csv +++ /dev/null @@ -1,1933 +0,0 @@ -sample_id,video_id,clip_id,word_index,word,hard_start_s,hard_end_s,hard_valid,ctc_quality_score_uncalibrated,relative_quality_weight,start_mean_s,end_mean_s,start_p05_s,start_p95_s,end_p05_s,end_p95_s,start_width90_s,end_width90_s --3g5yACwYnA/13,-3g5yACwYnA,13,0,They've,0.0024999999441206455,0.5625,True,3.1319849491673324e-11,4.0,0.019184879635086593,0.5625060804898443,0.0025000000000000005,0.10250000000000001,0.5625,0.5625,0.1,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,1,been,1.222499966621399,1.3624999523162842,True,5.074469056759456e-12,2.26932692527771,1.2063062407761618,1.38194823633117,1.1425,1.2625,1.3625,1.4224999999999999,0.11999999999999988,0.05999999999999983 --3g5yACwYnA/13,-3g5yACwYnA,13,2,able,1.4424999952316284,1.6024999618530273,True,2.422228541874849e-13,0.25,1.4426709260933925,1.5958374384159129,1.4425,1.4425,1.5825,1.6025,0.0,0.020000000000000018 --3g5yACwYnA/13,-3g5yACwYnA,13,3,to,1.622499942779541,1.7024999856948853,True,1.4663940634679351e-12,0.6557784676551819,1.63353469473686,1.702500930802706,1.6225,1.6425,1.7025,1.7025,0.020000000000000018,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,4,find,1.7625000476837158,2.0225000381469727,True,2.2361119032809906e-12,1.0,1.7625198227574603,2.022487282563605,1.7625,1.7625,2.0225,2.0225,0.0,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,5,solutions,2.1624999046325684,2.6424999237060547,True,7.178173853927827e-12,3.210113763809204,2.162477129647909,2.642508732091731,2.1625,2.1625,2.6425,2.6425,0.0,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,6,or,2.7225000858306885,2.802500009536743,True,1.8666060017102915e-11,4.0,2.7225030011118756,2.8025052458122928,2.7225,2.7225,2.8025,2.8025,0.0,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,7,at,3.0225000381469727,3.122499942779541,True,7.055260780979011e-13,0.3155146539211273,3.0114486420923248,3.1096179351127704,2.9425,3.0225,3.0425,3.1225,0.08000000000000007,0.08000000000000007 --3g5yACwYnA/13,-3g5yACwYnA,13,8,least,3.1624999046325684,3.382499933242798,True,4.716877496230287e-12,2.109410285949707,3.1602214981987653,3.377016650868552,3.1225,3.1625,3.3625,3.3825,0.040000000000000036,0.020000000000000018 --3g5yACwYnA/13,-3g5yACwYnA,13,9,bring,3.4625000953674316,3.7225000858306885,True,5.437177401021454e-13,0.25,3.4542782483017413,3.707435340678973,3.3825,3.4625,3.6825,3.7225,0.08000000000000007,0.040000000000000036 --3g5yACwYnA/13,-3g5yACwYnA,13,10,some,3.762500047683716,3.9625000953674316,True,4.003620351911152e-12,1.7904382944107056,3.7678537500060894,3.962399443995387,3.7625,3.8025,3.9625,3.9625,0.040000000000000036,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,11,answers,4.002500057220459,4.322500228881836,True,3.1072377743718294e-12,1.3895716667175293,4.002374694620699,4.31993317940175,4.0025,4.0025,4.3025,4.322500000000001,0.0,0.020000000000000462 --3g5yACwYnA/13,-3g5yACwYnA,13,12,to,4.362500190734863,4.422500133514404,True,1.1880822931367718e-13,0.25,4.360161068296207,4.4264563847068485,4.3425,4.402500000000001,4.4225,4.4625,0.0600000000000005,0.040000000000000036 --3g5yACwYnA/13,-3g5yACwYnA,13,13,the,4.462500095367432,4.5625,True,2.673985605692892e-15,0.25,4.469530870157028,4.569707820997299,4.4625,4.482500000000001,4.562500000000001,4.5825000000000005,0.020000000000000462,0.019999999999999574 --3g5yACwYnA/13,-3g5yACwYnA,13,14,table.,4.602499961853027,4.882500171661377,True,1.6668160992064363e-13,0.25,4.602502613481098,4.882542159056495,4.602500000000001,4.602500000000001,4.8825,4.8825,0.0,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,0,We're,0.0024999999441206455,0.16249999403953552,True,1.0417753465441493e-10,4.0,0.01892479479475388,0.3908951550217483,0.0025000000000000005,0.0425,0.1625,0.8624999999999999,0.04,0.7 --3g5yACwYnA/3,-3g5yACwYnA,3,1,a,0.20250000059604645,0.2224999964237213,True,2.0192024051055024e-13,0.25,0.5809306189576965,0.6009306189578986,0.2025,1.2425,0.2225,1.2625,1.04,1.04 --3g5yACwYnA/3,-3g5yACwYnA,3,2,huge,0.6025000214576721,0.862500011920929,True,2.080993537212361e-11,4.0,0.8749699312498749,1.1586938794079154,0.6024999999999999,1.3625,0.8624999999999999,1.6625,0.7600000000000001,0.8000000000000002 --3g5yACwYnA/3,-3g5yACwYnA,3,3,user,1.3624999523162842,1.662500023841858,True,1.8008346550080212e-11,4.0,1.6450011455096585,1.9430967049678605,1.3625,2.2425,1.6625,2.5025,0.8800000000000001,0.8399999999999999 --3g5yACwYnA/3,-3g5yACwYnA,3,4,of,2.422499895095825,2.4825000762939453,True,2.5086363108356435e-12,0.6720010638237,2.3888363472402654,2.5262128094487677,2.1625,2.6025,2.4425,2.6625,0.43999999999999995,0.2200000000000002 --3g5yACwYnA/3,-3g5yACwYnA,3,5,adhesives,2.6424999237060547,3.262500047683716,True,1.9735572551193847e-11,4.0,2.6479228501381638,3.2627808520362973,2.5625,2.7225,3.2625,3.2625,0.16000000000000014,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,6,for,3.302500009536743,3.4825000762939453,True,5.313816608953914e-10,4.0,3.305240723840881,3.485738646070515,3.3025,3.3025,3.4825,3.4825,0.0,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,7,our,3.5625,4.34250020980835,True,1.8105811988577969e-12,0.4850095212459564,3.6584500267844233,4.351316019909387,3.5625,4.202500000000001,4.3425,4.442500000000001,0.6400000000000006,0.10000000000000053 --3g5yACwYnA/3,-3g5yACwYnA,3,8,"operation,",4.582499980926514,5.142499923706055,True,2.16136266870115e-12,0.5789751410484314,4.582841088198986,5.142577850811578,4.5825000000000005,4.5825000000000005,5.1425,5.1425,0.0,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,9,called,5.502500057220459,5.78249979019165,True,7.694975732996934e-12,2.0612919330596924,5.469346469231026,5.787966327255339,5.242500000000001,5.5425,5.782500000000001,5.8425,0.2999999999999998,0.05999999999999961 --3g5yACwYnA/3,-3g5yACwYnA,3,10,"flocking,",5.862500190734863,6.242499828338623,True,3.7366615877887366e-12,1.0009584426879883,5.862416384166256,6.255591073381409,5.862500000000001,5.862500000000001,6.242500000000001,6.2625,0.0,0.019999999999999574 --3g5yACwYnA/3,-3g5yACwYnA,3,11,and,6.602499961853027,6.742499828338623,True,3.1151616659494397e-13,0.25,6.595235466538168,6.747981306446609,6.282500000000001,6.6225000000000005,6.702500000000001,6.742500000000001,0.33999999999999986,0.040000000000000036 --3g5yACwYnA/3,-3g5yACwYnA,3,12,we,7.022500038146973,7.082499980926514,True,3.7330837206195344e-12,1.0,7.025503971831001,7.085534043478729,7.022500000000001,7.022500000000001,7.0825000000000005,7.0825000000000005,0.0,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,13,don't,7.162499904632568,7.34250020980835,True,1.3746884186538466e-10,4.0,7.163477889296987,7.344480228729067,7.142500000000001,7.1625000000000005,7.3425,7.3425,0.019999999999999574,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,14,have,7.522500038146973,7.662499904632568,True,1.6831982923887386e-14,0.25,7.518314039598685,7.660886400412066,7.482500000000001,7.522500000000001,7.6225000000000005,7.702500000000001,0.040000000000000036,0.08000000000000007 --3g5yACwYnA/3,-3g5yACwYnA,3,15,the,7.722499847412109,7.882500171661377,True,8.987861453355062e-13,0.25,7.7243982678780885,7.882159094863079,7.7225,7.7625,7.862500000000001,7.8825,0.040000000000000036,0.019999999999999574 --3g5yACwYnA/3,-3g5yACwYnA,3,16,technical,8.022500038146973,8.662500381469727,True,1.3002188301719508e-12,0.348296195268631,8.011902283844488,8.662006267621967,7.942500000000001,8.0425,8.6625,8.6625,0.09999999999999964,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,17,inside,8.72249984741211,9.0024995803833,True,2.5177188350648805e-13,0.25,8.71091233648601,9.001311320857447,8.6825,8.7225,8.9825,9.0025,0.040000000000000924,0.019999999999999574 --3g5yACwYnA/3,-3g5yACwYnA,3,18,compounding,9.40250015258789,10.22249984741211,True,6.742292813465001e-13,0.25,9.359030940393758,10.232189826338123,9.022499999999999,9.4025,10.2225,10.3025,0.3800000000000008,0.08000000000000007 --3g5yACwYnA/3,-3g5yACwYnA,3,19,or,10.342499732971191,10.40250015258789,True,2.5491054037561633e-13,0.25,10.398864243814538,10.46224094293215,10.3425,10.5025,10.4025,10.5625,0.16000000000000014,0.16000000000000014 --3g5yACwYnA/3,-3g5yACwYnA,3,20,the,10.5024995803833,10.682499885559082,True,1.6563738770326852e-13,0.25,10.54237820403615,10.694267109284878,10.4625,10.6625,10.5625,10.782499999999999,0.1999999999999993,0.21999999999999886 --3g5yACwYnA/3,-3g5yACwYnA,3,21,technical,10.802499771118164,11.242500305175781,True,3.798831735291053e-11,4.0,10.778501265601966,11.232468921558434,10.5825,10.8425,10.9625,11.3825,0.2599999999999998,0.41999999999999993 --3g5yACwYnA/3,-3g5yACwYnA,3,22,expertise,11.362500190734863,11.90250015258789,True,2.313237492182485e-12,0.6196585893630981,11.339150095742763,11.878519378812266,10.9825,11.4225,11.4825,11.942499999999999,0.4399999999999995,0.4599999999999991 --3g5yACwYnA/3,-3g5yACwYnA,3,23,to,11.962499618530273,12.022500038146973,True,2.884898700136751e-12,0.772792398929596,11.943536876391573,12.008612822373081,11.6025,12.0025,11.6825,12.0825,0.40000000000000036,0.40000000000000036 --3g5yACwYnA/3,-3g5yACwYnA,3,24,do,12.1225004196167,12.202500343322754,True,8.551721136784707e-11,4.0,12.100686304121547,12.180724080264547,11.8225,12.1225,11.9025,12.2025,0.3000000000000007,0.3000000000000007 --3g5yACwYnA/3,-3g5yACwYnA,3,25,these,12.242500305175781,13.162500381469727,True,2.1788269105593727e-11,4.0,12.236831720783304,13.084602136464406,12.0025,12.2425,12.3225,13.1825,0.2400000000000002,0.8599999999999994 --3g5yACwYnA/3,-3g5yACwYnA,3,26,types,13.202500343322754,13.462499618530273,True,1.7226959442284695e-11,4.0,13.170666582865142,13.46226141424027,12.9225,13.2025,13.3825,13.7225,0.28000000000000114,0.33999999999999986 --3g5yACwYnA/3,-3g5yACwYnA,3,27,of,13.702500343322754,13.802499771118164,True,6.332336998510213e-12,1.6962751150131226,13.783998039741556,13.89545682407746,13.4625,13.9225,13.7225,14.0025,0.4599999999999991,0.27999999999999936 --3g5yACwYnA/3,-3g5yACwYnA,3,28,things.,13.922499656677246,14.262499809265137,True,1.0484706852720294e-11,2.808591365814209,13.996225018186697,14.291213862031034,13.9225,14.0625,14.2625,14.3225,0.14000000000000057,0.0600000000000005 --3g5yACwYnA/2,-3g5yACwYnA,2,0,Key,0.12250000238418579,0.2824999988079071,True,1.2059077694748233e-12,0.46602973341941833,0.12004656147855662,0.2754928971448747,0.0025000000000000005,0.1825,0.2025,0.28250000000000003,0.18,0.08000000000000002 --3g5yACwYnA/2,-3g5yACwYnA,2,1,Polymer,1.4225000143051147,1.9824999570846558,True,1.2241609635699202e-11,4.0,1.2243119624106564,1.9746772787439282,0.2625,1.4224999999999999,1.5225,2.0225,1.16,0.5 --3g5yACwYnA/2,-3g5yACwYnA,2,2,brings,2.0425000190734863,2.302500009536743,True,1.1930802483114955e-12,0.46107247471809387,2.0715362022919037,2.346903905400217,1.8625,2.1025,2.2425,2.5225,0.24,0.2799999999999998 --3g5yACwYnA/2,-3g5yACwYnA,2,3,a,2.502500057220459,2.5225000381469727,True,2.7638483949404824e-12,1.0681045055389404,2.5343152915969336,2.5543152915969847,2.5025,2.6425,2.5225,2.6625,0.14000000000000012,0.14000000000000012 --3g5yACwYnA/2,-3g5yACwYnA,2,4,technical,2.922499895095825,3.682499885559082,True,6.836649302233155e-11,4.0,2.945770103472421,3.662219509655508,2.9225,2.9225,3.5425,3.6825,0.0,0.14000000000000012 --3g5yACwYnA/2,-3g5yACwYnA,2,5,aspect,3.762500047683716,4.102499961853027,True,7.779469071971662e-14,0.25,3.762773828203278,4.127419794909451,3.6625,3.8225000000000002,4.1025,4.1625000000000005,0.16000000000000014,0.0600000000000005 --3g5yACwYnA/2,-3g5yACwYnA,2,6,to,4.382500171661377,4.442500114440918,True,2.0063360695043997e-11,4.0,4.397380835314191,4.461387623157674,4.3825,4.3825,4.442500000000001,4.522500000000001,0.0,0.08000000000000007 --3g5yACwYnA/2,-3g5yACwYnA,2,7,our,4.502500057220459,4.862500190734863,True,9.0564179419661e-12,3.4999029636383057,4.554801215071612,4.891103084451879,4.5025,4.6225000000000005,4.862500000000001,4.862500000000001,0.1200000000000001,0.0 --3g5yACwYnA/2,-3g5yACwYnA,2,8,operation,5.462500095367432,6.142499923706055,True,2.41139139905977e-12,0.9318955540657043,5.434152783262643,6.164972441664202,5.282500000000001,5.5025,6.142500000000001,6.3825,0.21999999999999975,0.23999999999999932 --3g5yACwYnA/2,-3g5yACwYnA,2,9,that,6.222499847412109,6.382500171661377,True,2.55808666350249e-13,0.25,6.2506696287137125,6.425851613401193,6.2225,6.5025,6.3825,6.742500000000001,0.28000000000000025,0.3600000000000003 --3g5yACwYnA/2,-3g5yACwYnA,2,10,we,6.682499885559082,6.742499828338623,True,3.615498471356421e-13,0.25,6.68956208113118,6.750288014655133,6.6825,6.822500000000001,6.742500000000001,6.8825,0.14000000000000057,0.13999999999999968 --3g5yACwYnA/2,-3g5yACwYnA,2,11,don't,6.822500228881836,7.002500057220459,True,1.598295940041794e-10,4.0,6.829173580026902,7.0141967823015605,6.822500000000001,6.9225,6.982500000000001,7.1625000000000005,0.09999999999999964,0.17999999999999972 --3g5yACwYnA/2,-3g5yACwYnA,2,12,have,7.042500019073486,7.202499866485596,True,5.563233250807653e-13,0.25,7.07007014122585,7.245521610851821,7.0425,7.282500000000001,7.202500000000001,7.5425,0.2400000000000002,0.33999999999999986 --3g5yACwYnA/2,-3g5yACwYnA,2,13,internally.,7.28249979019165,9.182499885559082,True,4.959014102134951e-12,1.9164384603500366,7.391144668115456,9.147154329969988,7.282500000000001,8.2625,9.0825,9.2225,0.9799999999999986,0.14000000000000057 --3g5yACwYnA/9,-3g5yACwYnA,9,0,We,0.0024999999441206455,0.0625,True,1.3202740289930404e-10,4.0,0.0037092361625596475,0.06376428622874805,0.0025000000000000005,0.0025000000000000005,0.0625,0.0625,0.0,0.0 --3g5yACwYnA/9,-3g5yACwYnA,9,1,have,0.12250000238418579,0.8824999928474426,True,4.727816987730449e-13,0.4530237317085266,0.24601737150185057,0.9129629357636357,0.1225,0.8025,0.8825,1.0425,0.6799999999999999,0.16000000000000003 --3g5yACwYnA/9,-3g5yACwYnA,9,2,many,0.9225000143051147,1.1024999618530273,True,3.4017011213069437e-13,0.3259541094303131,0.9672695832607601,1.1443447348089129,0.9225,1.1225,1.1025,1.3225,0.20000000000000007,0.21999999999999997 --3g5yACwYnA/9,-3g5yACwYnA,9,3,new,1.1825000047683716,1.3224999904632568,True,3.941063621282215e-12,3.7763631343841553,1.262579923107276,1.4144081895232492,1.1824999999999999,1.6025,1.3225,1.8025,0.42000000000000015,0.48 --3g5yACwYnA/9,-3g5yACwYnA,9,4,opportunities,1.6024999618530273,2.4024999141693115,True,1.088685782149601e-12,1.0431885719299316,1.6586099648067913,2.444467497988983,1.6025,1.8825,2.4025,2.7025,0.28,0.30000000000000027 --3g5yACwYnA/9,-3g5yACwYnA,9,5,through,2.4825000762939453,2.7825000286102295,True,4.15607964167862e-14,0.25,2.5234634994329452,2.8696162143253496,2.4825,2.8025,2.7825,3.4425,0.3200000000000003,0.6599999999999997 --3g5yACwYnA/9,-3g5yACwYnA,9,6,the,2.802500009536743,3.442500114440918,True,1.9428814026362096e-12,1.8616865873336792,2.902911114428035,3.464744278514586,2.8025,3.4825,3.4425,3.6025,0.6799999999999997,0.16000000000000014 --3g5yACwYnA/9,-3g5yACwYnA,9,7,way,3.4825000762939453,3.6024999618530273,True,3.119503787230027e-12,2.9891369342803955,3.5099164031810415,3.63228134889645,3.4825,3.6825,3.6025,3.8225000000000002,0.20000000000000018,0.2200000000000002 --3g5yACwYnA/9,-3g5yACwYnA,9,8,things,3.682499885559082,4.002500057220459,True,6.429745516393914e-13,0.6161040663719177,3.7145892049209013,4.026147698972161,3.6825,3.9225,3.9825,4.2625,0.23999999999999977,0.28000000000000025 --3g5yACwYnA/9,-3g5yACwYnA,9,9,have,4.022500038146973,4.162499904632568,True,1.5793911859775245e-13,0.25,4.060617069421903,4.224488988758646,4.022500000000001,4.3025,4.1625000000000005,4.522500000000001,0.27999999999999936,0.3600000000000003 --3g5yACwYnA/9,-3g5yACwYnA,9,10,changed,4.202499866485596,4.522500038146973,True,2.325777157669018e-12,2.228580951690674,4.267085991580041,4.5723446469859,4.202500000000001,4.5825000000000005,4.522500000000001,4.862500000000001,0.3799999999999999,0.33999999999999986 --3g5yACwYnA/9,-3g5yACwYnA,9,11,through,4.582499980926514,5.582499980926514,True,1.854727959757496e-12,1.7772172689437866,4.6221517393402145,5.554001297056343,4.5825000000000005,4.8825,5.4625,5.6225000000000005,0.2999999999999998,0.16000000000000014 --3g5yACwYnA/9,-3g5yACwYnA,9,12,the,5.622499942779541,5.742499828338623,True,3.626375675441773e-12,3.4748260974884033,5.595031140793692,5.718768123192464,5.522500000000001,5.6625000000000005,5.6225000000000005,5.8025,0.13999999999999968,0.17999999999999972 --3g5yACwYnA/9,-3g5yACwYnA,9,13,"years,",5.78249979019165,5.982500076293945,True,3.45286983954949e-14,0.25,5.757698982112918,5.970677998071189,5.6425,5.8425,5.902500000000001,6.0425,0.20000000000000018,0.13999999999999968 --3g5yACwYnA/9,-3g5yACwYnA,9,14,looking,6.042500019073486,6.442500114440918,True,1.2439189218602098e-12,1.1919344663619995,6.0297255832126435,6.383957853422528,5.9225,6.0825000000000005,6.282500000000001,6.442500000000001,0.16000000000000014,0.16000000000000014 --3g5yACwYnA/9,-3g5yACwYnA,9,15,for,6.462500095367432,6.5625,True,1.4023155466696968e-12,1.343711495399475,6.419190410852593,6.540302343999718,6.3425,6.4625,6.482500000000001,6.562500000000001,0.1200000000000001,0.08000000000000007 --3g5yACwYnA/9,-3g5yACwYnA,9,16,new,6.642499923706055,6.84250020980835,True,1.043613546275468e-12,1.0,6.635691555353821,6.833978589755013,6.642500000000001,6.642500000000001,6.8425,6.8425,0.0,0.0 --3g5yACwYnA/9,-3g5yACwYnA,9,17,niche,6.862500190734863,7.0625,True,5.4846457372493065e-14,0.25,6.854528859600319,7.055299959028502,6.862500000000001,6.862500000000001,7.062500000000001,7.062500000000001,0.0,0.0 --3g5yACwYnA/9,-3g5yACwYnA,9,18,"high-end,",7.082499980926514,7.462500095367432,True,7.439023897749808e-13,0.7128140330314636,7.0769385969042,7.45691194296881,7.0825000000000005,7.0825000000000005,7.4625,7.4625,0.0,0.0 --3g5yACwYnA/9,-3g5yACwYnA,9,19,value-added,7.5625,8.082500457763672,True,5.798322705221487e-14,0.25,7.552404120714275,8.074980337515674,7.522500000000001,7.562500000000001,8.0425,8.0825,0.040000000000000036,0.03999999999999915 --3g5yACwYnA/9,-3g5yACwYnA,9,20,products.,8.102499961853027,8.662500381469727,True,2.1248971752616495e-14,0.25,8.098459094614249,8.634617109598796,8.1025,8.1025,8.5425,8.6825,0.0,0.1399999999999988 --3nNcZdcdvU/5,-3nNcZdcdvU,5,0,Noncompliance,0.5425000190734863,1.5425000190734863,True,3.009791851513177e-13,4.0,0.5421899065110246,1.5434444401076381,0.5225,0.5425,1.5425,1.5425,0.020000000000000018,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,1,can,1.6825000047683716,1.8624999523162842,True,4.914635593020883e-14,0.9133073091506958,1.682499728861315,1.8625016311882543,1.6824999999999999,1.6824999999999999,1.8625,1.8625,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,2,result,1.9824999570846558,2.502500057220459,True,5.090972131707727e-13,4.0,1.9826826486872584,2.502627463381576,1.9825,1.9825,2.5025,2.5025,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,3,in,2.6024999618530273,2.702500104904175,True,1.6868163089196406e-14,0.3134681284427643,2.602500402040593,2.7114411198718202,2.6025,2.6025,2.7025,2.7225,0.0,0.020000000000000018 --3nNcZdcdvU/5,-3nNcZdcdvU,5,4,legal,2.8424999713897705,3.122499942779541,True,1.6298654049659578e-13,3.0288469791412354,2.8319148446350573,3.108045806654291,2.8025,2.8425,3.0625,3.1225,0.03999999999999959,0.06000000000000005 --3nNcZdcdvU/5,-3nNcZdcdvU,5,5,liability,3.1424999237060547,3.802500009536743,True,4.397796688447586e-13,4.0,3.1425001068073284,3.8025137671985,3.1425,3.1425,3.8025,3.8025,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,6,to,3.9825000762939453,4.142499923706055,True,2.3713054631870067e-13,4.0,3.982500462639652,4.142499970675897,3.9825,3.9825,4.1425,4.1425,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,7,you,4.262499809265137,4.522500038146973,True,9.180395498160389e-15,0.25,4.256718628077809,4.512681988741261,4.202500000000001,4.2625,4.4225,4.522500000000001,0.05999999999999961,0.10000000000000053 --3nNcZdcdvU/5,-3nNcZdcdvU,5,8,and,4.5625,5.142499923706055,True,9.738124342279983e-14,1.8096764087677002,4.610781303553796,5.143268143927736,4.562500000000001,5.0425,5.1425,5.1425,0.47999999999999954,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,9,to,5.242499828338623,5.322500228881836,True,1.2475421086802219e-13,2.318359613418579,5.242284573869038,5.322497097245752,5.242500000000001,5.242500000000001,5.322500000000001,5.322500000000001,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,10,the,5.422500133514404,5.522500038146973,True,5.847647127806e-14,1.0866926908493042,5.420521893684469,5.521005673422197,5.402500000000001,5.4225,5.522500000000001,5.522500000000001,0.019999999999999574,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,11,school,5.542500019073486,5.802499771118164,True,5.076406095748951e-15,0.25,5.541309582381759,5.801309274139924,5.5425,5.5425,5.8025,5.8025,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,12,district,5.822500228881836,6.182499885559082,True,2.3873014813431616e-16,0.25,5.822409113735198,6.18241082307903,5.822500000000001,5.822500000000001,6.1825,6.1825,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,13,in,6.202499866485596,6.262499809265137,True,3.187962021713249e-15,0.25,6.202413110773365,6.2624131946284285,6.202500000000001,6.202500000000001,6.2625,6.2625,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,14,which,6.28249979019165,6.462500095367432,True,3.9412292157998865e-16,0.25,6.282413518600741,6.46250329822077,6.282500000000001,6.282500000000001,6.4625,6.4625,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,15,you,6.482500076293945,6.622499942779541,True,4.329610265143072e-14,0.804589569568634,6.482504276688287,6.617728718552279,6.482500000000001,6.482500000000001,6.5825000000000005,6.6225000000000005,0.0,0.040000000000000036 --3nNcZdcdvU/5,-3nNcZdcdvU,5,16,are,6.642499923706055,6.742499828338623,True,4.5697071750531656e-14,0.8492078185081482,6.642361698991119,6.743794056590974,6.642500000000001,6.642500000000001,6.742500000000001,6.742500000000001,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,17,employed.,6.84250020980835,7.28249979019165,True,6.300834149135931e-14,1.170910358428955,6.83813239815432,7.282623043355807,6.822500000000001,6.8425,7.282500000000001,7.282500000000001,0.019999999999999574,0.0 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,0,”,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,1,[President,0.10249999910593033,0.762499988079071,True,8.245064109539332e-14,4.0,0.10250473972383489,0.7625327724371536,0.10250000000000001,0.10250000000000001,0.7625,0.7625,0.0,0.0 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,2,Ronald,0.8224999904632568,1.8025000095367432,True,5.068065997592048e-16,0.25,0.854274069167481,1.7270714047834743,0.8225,1.0225,1.3625,2.0425,0.19999999999999996,0.6799999999999999 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,3,Reagan:],1.8624999523162842,2.802500009536743,True,1.1842246509850916e-14,1.0,1.9694596577449979,2.8033453188270716,1.6625,2.2825,2.8025,2.8225,0.6200000000000001,0.019999999999999574 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,4,"""Look",2.882499933242798,3.0225000381469727,True,2.492327701099535e-14,2.104607105255127,2.881426774537937,3.0216973335913506,2.8825,2.8825,3.0225,3.0225,0.0,0.0 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,5,at,3.1024999618530273,3.1624999046325684,True,2.696593126813346e-15,0.25,3.1034472692660957,3.16550226599371,3.1025,3.1425,3.1625,3.2225,0.040000000000000036,0.06000000000000005 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,6,the,3.242500066757202,3.3424999713897705,True,2.8035099876354622e-14,2.367380142211914,3.2418077886934986,3.342347080125065,3.2425,3.2425,3.3425,3.3425,0.0,0.0 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,7,record,3.422499895095825,3.9024999141693115,True,5.635536779396251e-16,0.25,3.421806202848189,3.881243156703797,3.4225,3.4225,3.8025,3.9025,0.0,0.09999999999999964 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,0,"""",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,1,President,0.8025000095367432,1.2424999475479126,True,1.1130892147120756e-14,0.25,0.7991785910278454,1.249418363734453,0.8025,0.8025,1.2425,1.2625,0.0,0.020000000000000018 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,2,Ronald,1.3224999904632568,1.662500023841858,True,6.521055939158471e-13,3.265170097351074,1.3182879263108291,1.657037437672821,1.2625,1.3225,1.6025,1.6625,0.06000000000000005,0.06000000000000005 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,3,Reagan,1.7024999856948853,2.122499942779541,True,1.9971566447059969e-13,1.0,1.7002046832597368,2.1152442116829513,1.7025,1.7025,2.0625,2.1225,0.0,0.06000000000000005 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,4,blamed,2.202500104904175,2.5625,True,1.681951561534334e-13,0.8421730995178223,2.1942407816196767,2.5565269463501545,2.1225,2.2025,2.5025,2.5625,0.08000000000000007,0.06000000000000005 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,5,the,2.6624999046325684,2.762500047683716,True,1.4306970931858738e-14,0.25,2.643355740531605,2.752176231037962,2.6025,2.6625,2.7225,2.7625,0.06000000000000005,0.040000000000000036 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,6,professional,2.7825000286102295,3.422499895095825,True,1.6846922891011057e-14,0.25,2.7774452139102097,3.4217504683401407,2.7425,2.7825,3.4225,3.4225,0.040000000000000036,0.0 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,7,bureaucracy,3.4625000953674316,4.202499866485596,True,5.344916132951627e-13,2.676262855529785,3.4615893755070632,4.158197134262134,3.4625,3.4625,4.1425,4.202500000000001,0.0,0.0600000000000005 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,8,of,4.302499771118164,4.382500171661377,True,8.464278540218362e-13,4.0,4.265456447231751,4.360272046146851,4.202500000000001,4.3025,4.322500000000001,4.3825,0.09999999999999964,0.05999999999999961 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,9,education,4.462500095367432,5.082499980926514,True,6.528327141028245e-13,3.268810749053955,4.462466272651668,5.082501771556385,4.4625,4.4625,5.0825000000000005,5.0825000000000005,0.0,0.0 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,0,And,0.0024999999441206455,0.6424999833106995,True,9.006679205073899e-16,0.25,0.049993577900290236,0.6401664878744063,0.0025000000000000005,0.2425,0.6224999999999999,0.6425,0.24,0.020000000000000018 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,1,in,0.6825000047683716,0.762499988079071,True,1.0384882278386635e-14,0.25,0.6755059356561959,0.7452473237909909,0.6625,0.6825,0.7224999999999999,0.7625,0.020000000000000018,0.040000000000000036 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,2,"part,",0.8424999713897705,1.1825000047683716,True,4.603873096013615e-14,0.3257012665271759,0.8366663344434105,1.175919532391122,0.7625,0.8424999999999999,1.1025,1.1824999999999999,0.07999999999999996,0.07999999999999985 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,3,Al,1.5425000190734863,1.622499942779541,True,1.3575890628683696e-12,4.0,1.542340600155332,1.622350825487453,1.5425,1.5425,1.6225,1.6225,0.0,0.0 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,4,Shanker,1.722499966621399,2.1624999046325684,True,1.41352627844836e-13,1.0,1.7221858829602623,2.1581962257056984,1.7225,1.7225,2.1025,2.1625,0.0,0.06000000000000005 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,5,did,2.262500047683716,2.4024999141693115,True,8.53964848054195e-13,4.0,2.2624871936417077,2.405549344077747,2.2625,2.2625,2.4025,2.4025,0.0,0.0 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,6,too,2.682499885559082,2.822499990463257,True,9.367995962294984e-12,4.0,2.678787842175452,2.8245599587910464,2.6825,2.6825,2.8025,2.8425,0.0,0.03999999999999959 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,0,It,0.5625,0.6424999833106995,True,9.704259118015809e-15,0.25,0.3968797872067635,0.6168155228213217,0.0625,0.5824999999999999,0.5824999999999999,0.6425,0.5199999999999999,0.06000000000000005 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,1,is,0.6825000047683716,0.7425000071525574,True,1.4060067942810867e-13,0.25,0.6826864336953968,0.7429342737517168,0.6825,0.6825,0.7424999999999999,0.7424999999999999,0.0,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,2,a,0.8424999713897705,0.862500011920929,True,3.836665414622825e-13,0.2704606354236603,0.8431059245975748,0.863105924597575,0.8424999999999999,0.8424999999999999,0.8624999999999999,0.8624999999999999,0.0,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,3,word,0.9225000143051147,1.162500023841858,True,2.349814407723999e-13,0.25,0.9232465709824628,1.1633531719563064,0.9225,0.9225,1.1624999999999999,1.1624999999999999,0.0,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,4,which,1.2625000476837158,1.5625,True,5.4473098801870526e-14,0.25,1.2682948092150728,1.5630203083225693,1.2625,1.3225,1.5625,1.5625,0.06000000000000005,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,5,the,1.722499966621399,1.8224999904632568,True,8.272986245713709e-13,0.5831931829452515,1.6909958021630012,1.7986297285033759,1.6225,1.7225,1.7225,1.8225,0.09999999999999987,0.10000000000000009 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,6,administrator,1.8825000524520874,2.622499942779541,True,7.934183908833714e-13,0.559309720993042,1.8591177881897383,2.7274044722357593,1.8025,1.8825,2.6225,3.1625,0.08000000000000007,0.54 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,7,uses,2.9024999141693115,3.1624999046325684,True,2.1700481309897685e-13,0.25,2.968049678105385,3.2345771687388063,2.8825,3.2425,3.1625,3.5025,0.3600000000000003,0.33999999999999986 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,8,when,3.242500066757202,3.442500114440918,True,4.379272010128499e-13,0.3087109625339508,3.333607512728239,3.547610849410154,3.2425,3.6825,3.4425,3.9025,0.43999999999999995,0.45999999999999996 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,9,he,3.502500057220459,3.5625,True,1.9151127623844022e-13,0.25,3.6091286554749216,3.6789170759396757,3.5025,3.9625,3.5625,4.022500000000001,0.45999999999999996,0.46000000000000085 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,10,turns,3.6424999237060547,3.9024999141693115,True,2.0098356572767484e-12,1.416806936264038,3.750671620423674,3.986732770438924,3.6425,4.062500000000001,3.9025,4.2625,0.4200000000000008,0.3600000000000003 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,11,around,3.9625000953674316,4.262499809265137,True,3.172354393066179e-11,4.0,4.042443095688754,4.331695960114718,3.9625,4.3025,4.2625,4.5425,0.3400000000000003,0.28000000000000025 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,12,anything,4.302499771118164,5.002500057220459,True,4.246567507359966e-12,2.993561267852783,4.397334396251662,5.0187542042154245,4.3025,4.562500000000001,5.0025,5.1225000000000005,0.2600000000000007,0.1200000000000001 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,13,which,5.202499866485596,5.482500076293945,True,1.0097850507151396e-11,4.0,5.1998952268885485,5.481770232068269,5.1825,5.202500000000001,5.482500000000001,5.482500000000001,0.020000000000000462,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,14,he,5.542500019073486,5.602499961853027,True,3.1471665009295824e-13,0.25,5.541826700619963,5.601893321673665,5.5425,5.5425,5.602500000000001,5.602500000000001,0.0,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,15,doesn't,5.662499904632568,6.002500057220459,True,4.0290708269719033e-11,4.0,5.664100729682788,6.0016396736273805,5.6625000000000005,5.702500000000001,6.0025,6.0025,0.040000000000000036,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,16,like,6.042500019073486,6.182499885559082,True,2.3577168863087028e-12,1.662041187286377,6.0421210584664955,6.251301613627984,6.0425,6.0425,6.1825,6.322500000000001,0.0,0.14000000000000057 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,17,that,6.242499828338623,6.402500152587891,True,9.430432129642341e-12,4.0,6.31569748562974,6.490365143363123,6.242500000000001,6.402500000000001,6.402500000000001,6.602500000000001,0.16000000000000014,0.20000000000000018 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,18,the,6.462500095367432,6.602499961853027,True,3.4823832706365465e-10,4.0,6.546923966814362,6.670741888996065,6.4625,6.6625000000000005,6.602500000000001,6.7625,0.20000000000000018,0.15999999999999925 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,19,teacher,6.662499904632568,7.0625,True,3.151389912914304e-11,4.0,6.7149365146030195,7.083825396014788,6.6625000000000005,6.782500000000001,7.062500000000001,7.1225000000000005,0.1200000000000001,0.05999999999999961 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,20,is,7.162499904632568,7.262499809265137,True,2.1730292912303106e-11,4.0,7.167127941420237,7.250665423256899,7.1625000000000005,7.1825,7.2225,7.2625,0.019999999999999574,0.040000000000000036 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,21,doing,7.322500228881836,7.582499980926514,True,2.088004595612869e-11,4.0,7.323569851270532,7.58443002944152,7.322500000000001,7.322500000000001,7.5825000000000005,7.602500000000001,0.0,0.020000000000000462 --NFrJFQijFE/1,-NFrJFQijFE,1,0,Structural,0.0024999999441206455,0.7225000262260437,True,3.124166550461105e-11,0.801537275314331,0.029607444381030804,0.6871020932567873,0.0025000000000000005,0.10250000000000001,0.4825,0.9225,0.1,0.44 --NFrJFQijFE/1,-NFrJFQijFE,1,1,mounts,1.122499942779541,1.7825000286102295,True,3.382181687494601e-11,0.8677337765693665,0.7741501022267345,1.168869413851003,0.5425,1.0425,0.9025,1.4625,0.5,0.5599999999999999 --NFrJFQijFE/1,-NFrJFQijFE,1,2,inside,1.8424999713897705,2.242500066757202,True,3.242017765359151e-11,0.8317732214927673,1.259193099460418,1.646948133757638,0.9824999999999999,1.5625,1.3625,1.9425,0.5800000000000001,0.5799999999999998 --NFrJFQijFE/1,-NFrJFQijFE,1,3,have,2.382499933242798,2.7825000286102295,True,3.251355781830334e-11,0.8341690301895142,1.7308771690731846,1.958774697749872,1.4625,2.0225,1.6824999999999999,2.2625,0.56,0.5800000000000003 --NFrJFQijFE/1,-NFrJFQijFE,1,4,to,3.0625,3.122499942779541,True,3.055768832416206e-11,0.7839891314506531,2.040202773527604,2.1314457321513656,1.7625,2.3625,1.8425,2.4425,0.5999999999999999,0.5999999999999999 --NFrJFQijFE/1,-NFrJFQijFE,1,5,be,3.1624999046325684,3.2225000858306885,True,2.7543876901514608e-11,0.7066667079925537,2.2165689766668426,2.3095375530237123,1.9224999999999999,2.5225,2.0025,2.6225,0.6000000000000001,0.6200000000000001 --NFrJFQijFE/1,-NFrJFQijFE,1,6,properly,3.242500066757202,3.6624999046325684,True,3.094473635667505e-11,0.7939192652702332,2.398161975532348,2.939312401374726,2.0825,2.7025,2.5825,3.2225,0.6200000000000001,0.6400000000000001 --NFrJFQijFE/1,-NFrJFQijFE,1,7,stress,3.7825000286102295,4.042500019073486,True,4.0694180664102575e-11,1.0440514087677002,3.024703822620534,3.3952124304109335,2.6625,3.3025,3.0825,3.6825,0.6400000000000001,0.6000000000000001 --NFrJFQijFE/1,-NFrJFQijFE,1,8,analyzed,4.0625,4.482500076293945,True,5.706426650653462e-11,1.4640429019927979,3.4734230005814695,3.95665797695199,3.1625,3.7625,3.6625,4.202500000000001,0.6000000000000001,0.5400000000000005 --NFrJFQijFE/1,-NFrJFQijFE,1,9,so,4.502500057220459,4.5625,True,3.726018105165707e-11,0.955948531627655,4.019371413642997,4.09926351081791,3.7625,4.2625,3.8425,4.322500000000001,0.5,0.48000000000000087 --NFrJFQijFE/1,-NFrJFQijFE,1,10,we,4.582499980926514,4.682499885559082,True,4.315695370515904e-11,1.107236385345459,4.158343759812449,4.2382602103512115,3.9225,4.3825,4.0025,4.4625,0.4600000000000004,0.45999999999999996 --NFrJFQijFE/1,-NFrJFQijFE,1,11,make,4.702499866485596,4.862500190734863,True,5.602865393861123e-11,1.437473177909851,4.297877403832084,4.498025124451961,4.062500000000001,4.5025,4.2625,4.6825,0.4399999999999995,0.41999999999999993 --NFrJFQijFE/1,-NFrJFQijFE,1,12,sure,4.922500133514404,5.0625,True,5.5529952164290464e-11,1.4246784448623657,4.5595462645139255,4.759348019483343,4.322500000000001,4.742500000000001,4.5425,4.9225,0.41999999999999993,0.3799999999999999 --NFrJFQijFE/1,-NFrJFQijFE,1,13,all,5.082499980926514,5.182499885559082,True,4.774074538471673e-11,1.2248382568359375,4.817843713841208,4.9557359749111045,4.6425,4.9625,4.782500000000001,5.102500000000001,0.3200000000000003,0.3200000000000003 --NFrJFQijFE/1,-NFrJFQijFE,1,14,that,5.202499866485596,5.34250020980835,True,5.2850689102879045e-11,1.3559391498565674,5.012945479106424,5.209031399250358,4.8425,5.1425,5.062500000000001,5.322500000000001,0.2999999999999998,0.2599999999999998 --NFrJFQijFE/1,-NFrJFQijFE,1,15,happens,5.362500190734863,5.642499923706055,True,5.486522347553091e-11,1.4076241254806519,5.265253840657979,5.6252282574922905,5.1425,5.362500000000001,5.5825000000000005,5.6425,0.22000000000000064,0.05999999999999961 --NFrJFQijFE/2,-NFrJFQijFE,2,0,So,1.122499942779541,1.2424999475479126,True,2.0999696732503133e-14,0.3740757703781128,0.7017702038054376,0.8164313921794365,0.6625,0.8225,0.7224999999999999,0.9624999999999999,0.16000000000000003,0.24 --NFrJFQijFE/2,-NFrJFQijFE,2,1,we,1.2825000286102295,1.3825000524520874,True,1.8122491829137936e-14,0.3228229880332947,0.9301739611043676,1.0355676938406366,0.7825,1.1225,0.8825,1.2425,0.3400000000000001,0.36 --NFrJFQijFE/2,-NFrJFQijFE,2,2,coordinate,1.6425000429153442,2.0824999809265137,True,2.99360789792185e-14,0.5332630276679993,1.1385153786376387,1.8848721368518628,0.9624999999999999,1.3425,1.6824999999999999,2.0425,0.3800000000000001,0.3600000000000001 --NFrJFQijFE/2,-NFrJFQijFE,2,3,with,2.1024999618530273,2.242500066757202,True,6.905049177840378e-14,1.2300232648849487,1.955838283594859,2.1544054792982386,1.7625,2.1025,1.9825,2.2625,0.3400000000000001,0.28000000000000025 --NFrJFQijFE/2,-NFrJFQijFE,2,4,all,2.262500047683716,2.362499952316284,True,5.794130230945757e-14,1.0321309566497803,2.2032026778324427,2.3225781228227205,2.0625,2.3025,2.2025,2.4225,0.2400000000000002,0.21999999999999975 --NFrJFQijFE/2,-NFrJFQijFE,2,5,the,2.382499933242798,2.4825000762939453,True,5.4333795763365084e-14,0.9678690433502197,2.3595980088641553,2.4785183487033846,2.2425,2.4625,2.3625,2.5825,0.21999999999999975,0.2200000000000002 --NFrJFQijFE/2,-NFrJFQijFE,2,6,proper,2.5625,2.7825000286102295,True,4.613511653327011e-14,0.8218227624893188,2.5235697929220096,2.7911478799564198,2.3825,2.6425,2.6825,2.9025,0.26000000000000023,0.21999999999999975 --NFrJFQijFE/2,-NFrJFQijFE,2,7,technical,2.802500009536743,3.242500066757202,True,2.8803697572693174e-14,0.5130914449691772,2.826699438084622,3.324501218236374,2.7225,2.9425,3.2025,3.5625,0.21999999999999975,0.3599999999999999 --NFrJFQijFE/2,-NFrJFQijFE,2,8,fields,3.622499942779541,3.9625000953674316,True,3.771829203136345e-12,4.0,3.567640087175455,3.9341314603260984,3.3425,3.6225,3.8825,3.9625,0.28000000000000025,0.08000000000000007 --NFrJFQijFE/2,-NFrJFQijFE,2,9,to,4.102499961853027,4.162499904632568,True,2.0974448035598198e-13,3.736259937286377,4.002873380071735,4.076475277349137,3.9025,4.1025,3.9625,4.1625000000000005,0.20000000000000018,0.20000000000000062 --NFrJFQijFE/2,-NFrJFQijFE,2,10,get,4.182499885559082,4.322500228881836,True,2.997963680149811e-14,0.5340389609336853,4.131802025449857,4.282693539325499,4.1025,4.242500000000001,4.2625,4.3825,0.14000000000000057,0.1200000000000001 --NFrJFQijFE/2,-NFrJFQijFE,2,11,ready,4.34250020980835,4.642499923706055,True,1.2902478564724706e-12,4.0,4.348985787804194,4.638174202884239,4.3425,4.4225,4.6225000000000005,4.6425,0.08000000000000007,0.019999999999999574 --NFrJFQijFE/2,-NFrJFQijFE,2,12,for,4.702499866485596,4.802499771118164,True,3.9598769955347807e-14,0.7053883075714111,4.69689650444899,4.800728795198717,4.6625000000000005,4.702500000000001,4.782500000000001,4.822500000000001,0.040000000000000036,0.040000000000000036 --NFrJFQijFE/2,-NFrJFQijFE,2,13,the,4.822500228881836,4.962500095367432,True,2.13347828986743e-12,4.0,4.832524953971189,4.960742471002819,4.822500000000001,4.8425,4.9625,4.9625,0.019999999999999574,0.0 --NFrJFQijFE/2,-NFrJFQijFE,2,14,instrument,4.982500076293945,5.582499980926514,True,5.864949639226152e-14,1.0447462797164917,4.981819918289069,5.583200022323766,4.982500000000001,4.982500000000001,5.5825000000000005,5.5825000000000005,0.0,0.0 --NFrJFQijFE/2,-NFrJFQijFE,2,15,to,6.0625,6.142499923706055,True,2.7822413291430856e-14,0.4956114590167999,6.008112967011355,6.1294094160023525,5.7625,6.062500000000001,6.0825000000000005,6.142500000000001,0.3000000000000007,0.0600000000000005 --NFrJFQijFE/2,-NFrJFQijFE,2,16,arrive,6.202499866485596,6.502500057220459,True,1.3836344179771198e-12,4.0,6.202900777167125,6.502181568618448,6.202500000000001,6.202500000000001,6.5025,6.5025,0.0,0.0 --NFrJFQijFE/2,-NFrJFQijFE,2,17,here,6.542500019073486,6.682499885559082,True,7.368213569662607e-13,4.0,6.542190924116508,6.682470262173858,6.5425,6.5425,6.6825,6.6825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,0,What,0.0024999999441206455,0.8025000095367432,True,5.184272455771577e-11,4.0,0.0025000003549047693,0.8035597524626871,0.0025000000000000005,0.0025000000000000005,0.8025,0.8025,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,1,ever,1.6425000429153442,1.9225000143051147,True,4.004269246911385e-13,4.0,1.6529561096015108,1.911600849839455,1.6425,1.7225,1.8825,1.9224999999999999,0.07999999999999985,0.039999999999999813 --THoVjtIkeU/12,-THoVjtIkeU,12,2,we,2.002500057220459,2.0625,True,3.836271849234213e-14,0.6021820306777954,2.0016582942541135,2.061671197113673,2.0025,2.0025,2.0625,2.0625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,3,turn,2.1424999237060547,2.302500009536743,True,1.717639139068043e-15,0.25,2.1415002094996507,2.301430511112869,2.1425,2.1425,2.3025,2.3025,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,4,our,2.322499990463257,2.442500114440918,True,6.326280374124166e-14,0.9930402636528015,2.3216422600531423,2.441643863547024,2.3225,2.3225,2.4425,2.4425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,5,attention,2.4825000762939453,2.882499933242798,True,1.9333507962838092e-15,0.25,2.4818262028628437,2.882207397831878,2.4825,2.4825,2.8825,2.8825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,6,to,2.922499895095825,3.0625,True,6.509613540480602e-14,1.0218181610107422,2.9226896623999803,3.0626401735658586,2.9225,2.9225,3.0625,3.0625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,7,is,3.182499885559082,3.2825000286102295,True,3.3738997381495273e-13,4.0,3.1823176206620607,3.2825267449727344,3.1825,3.1825,3.2825,3.2825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,8,what,3.322499990463257,3.502500057220459,True,1.615317071995779e-14,0.25355735421180725,3.3225591942568835,3.5025797752496106,3.3225000000000002,3.3225000000000002,3.5025,3.5025,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,9,grows,3.5225000381469727,3.882499933242798,True,3.4992076239619835e-14,0.5492728352546692,3.522784014159437,3.8827429304851,3.5225,3.5225,3.8825,3.8825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,10,and,4.362500190734863,4.482500076293945,True,9.108250991537309e-14,1.4297280311584473,4.362460354649206,4.482600317036873,4.362500000000001,4.362500000000001,4.482500000000001,4.482500000000001,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,11,so,4.542500019073486,4.642499923706055,True,1.8818568665174285e-12,4.0,4.542616593028849,4.642609848982019,4.5425,4.5425,4.6425,4.6425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,12,if,4.682499885559082,4.78249979019165,True,6.370618144341603e-14,1.0,4.73088135814891,4.8068325905395515,4.6825,4.7625,4.7625,4.822500000000001,0.08000000000000007,0.0600000000000005 --THoVjtIkeU/12,-THoVjtIkeU,12,13,we,4.882500171661377,4.962500095367432,True,6.87291274782155e-14,1.078845500946045,4.882564385938244,4.962677005890677,4.8825,4.8825,4.9625,4.9625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,14,believe,5.022500038146973,5.522500038146973,True,2.0615089225527877e-13,3.235963821411133,5.0245719541936245,5.522680156939918,5.022500000000001,5.022500000000001,5.522500000000001,5.522500000000001,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,15,that,5.582499980926514,5.762499809265137,True,2.316116514818555e-15,0.25,5.582652937970199,5.762661710917487,5.5825000000000005,5.5825000000000005,5.7625,5.7625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,16,anything,5.78249979019165,6.122499942779541,True,1.0723705621683722e-14,0.25,5.7857325380603175,6.128300725814943,5.782500000000001,5.8025,6.1225000000000005,6.1225000000000005,0.019999999999999574,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,17,is,6.182499885559082,6.262499809265137,True,5.072557499909147e-13,4.0,6.186674312374733,6.2654187095178235,6.1825,6.1825,6.242500000000001,6.2625,0.0,0.019999999999999574 --THoVjtIkeU/12,-THoVjtIkeU,12,18,"possible,",6.302499771118164,6.742499828338623,True,4.263187974298463e-15,0.25,6.30960288496603,6.754539570207679,6.3025,6.3025,6.742500000000001,6.742500000000001,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,19,if,6.78249979019165,6.84250020980835,True,1.6299522766650282e-14,0.25585463643074036,6.813844945293333,6.877896626746542,6.782500000000001,6.782500000000001,6.8425,6.8425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,20,we,6.902500152587891,7.042500019073486,True,8.570621968205516e-13,4.0,6.945868750376086,7.086394513895723,6.902500000000001,6.902500000000001,7.0425,7.0425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,21,believe,7.5625,8.142499923706055,True,4.034537461061749e-13,4.0,7.495042199965977,8.16664224609275,7.142500000000001,7.562500000000001,8.0825,8.1425,0.41999999999999993,0.0600000000000005 --THoVjtIkeU/12,-THoVjtIkeU,12,22,that,8.542499542236328,8.802499771118164,True,9.425311009100823e-14,1.4794970750808716,8.552403409813959,8.814869143102763,8.5425,8.5825,8.8025,8.862499999999999,0.03999999999999915,0.05999999999999872 --THoVjtIkeU/12,-THoVjtIkeU,12,23,social,8.90250015258789,9.542499542236328,True,5.2791052779221914e-12,4.0,8.926572150764354,9.554650222126032,8.8425,9.0825,9.282499999999999,9.6825,0.2400000000000002,0.40000000000000036 --THoVjtIkeU/12,-THoVjtIkeU,12,24,justice,9.602499961853027,10.082500457763672,True,3.2318182418417107e-12,4.0,9.622228193294516,10.075158284652437,9.4625,9.782499999999999,9.8825,10.1225,0.3199999999999985,0.2400000000000002 --THoVjtIkeU/12,-THoVjtIkeU,12,25,can,10.182499885559082,10.322500228881836,True,9.062416344695484e-14,1.4225332736968994,10.163757654882376,10.305245554736647,10.022499999999999,10.1825,10.1225,10.3225,0.16000000000000014,0.1999999999999993 --THoVjtIkeU/12,-THoVjtIkeU,12,26,be,10.422499656677246,10.482500076293945,True,1.1201589785944654e-12,4.0,10.406784621799975,10.467071219758102,10.2625,10.4225,10.3225,10.4825,0.16000000000000014,0.16000000000000014 --THoVjtIkeU/12,-THoVjtIkeU,12,27,achieved,10.522500038146973,10.942500114440918,True,2.057594986591433e-14,0.3229820132255554,10.510673160622034,10.934793906175887,10.4225,10.522499999999999,10.942499999999999,10.942499999999999,0.09999999999999964,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,28,and,10.982500076293945,11.082500457763672,True,7.772953694541382e-15,0.25,10.975281306995331,11.075634516296919,10.9825,10.9825,11.0825,11.0825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,29,if,11.102499961853027,11.162500381469727,True,1.2482614429403481e-14,0.25,11.096370572360204,11.156537360099648,11.1025,11.1025,11.1625,11.1625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,30,we,11.182499885559082,11.242500305175781,True,1.2027428749131208e-13,1.8879531621932983,11.177150273118796,11.237494385446567,11.1825,11.1825,11.2425,11.2425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,31,believe,11.322500228881836,11.822500228881836,True,1.71007942507094e-13,2.6843225955963135,11.317170351965828,11.81544774843011,11.3225,11.3225,11.8225,11.8225,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,32,in,11.842499732971191,11.962499618530273,True,1.5597131875974557e-13,2.448291778564453,11.874989445701733,11.986233154258349,11.8425,11.942499999999999,11.9625,12.0425,0.09999999999999964,0.08000000000000007 --THoVjtIkeU/12,-THoVjtIkeU,12,33,"youth,",12.102499961853027,12.40250015258789,True,3.8225936223887716e-14,0.6000349521636963,12.07514111375613,12.372758250652677,12.0625,12.1025,12.362499999999999,12.4025,0.03999999999999915,0.040000000000000924 --THoVjtIkeU/12,-THoVjtIkeU,12,34,they,12.882499694824219,13.042499542236328,True,3.4154286115885973e-14,0.5361220240592957,12.580522630946723,13.026778540456233,12.4025,12.9025,12.9825,13.0425,0.5,0.0600000000000005 --THoVjtIkeU/12,-THoVjtIkeU,12,35,will,13.0625,13.202500343322754,True,1.877775482306797e-15,0.25,13.049451388170713,13.195483131860904,13.022499999999999,13.0625,13.1825,13.2225,0.040000000000000924,0.040000000000000924 --THoVjtIkeU/12,-THoVjtIkeU,12,36,believe,13.242500305175781,13.542499542236328,True,3.5739823372928775e-14,0.5610103011131287,13.23451659139245,13.54107337938477,13.2225,13.2425,13.5425,13.5425,0.019999999999999574,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,37,in,13.602499961853027,13.662500381469727,True,4.70323768402307e-15,0.25,13.59477173018874,13.655624982027442,13.5825,13.6025,13.6425,13.6625,0.019999999999999574,0.019999999999999574 --THoVjtIkeU/12,-THoVjtIkeU,12,38,themselves.,13.682499885559082,14.682499885559082,True,1.3947767975850713e-14,0.25,13.682050784224838,14.477877429780559,13.6825,13.6825,14.1425,14.7225,0.0,0.5800000000000001 --THoVjtIkeU/2,-THoVjtIkeU,2,0,I,0.042500000447034836,0.0625,True,3.5587171095174605e-14,0.5238187909126282,0.042747574508858015,0.06274757450885804,0.0425,0.0425,0.0625,0.0625,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,1,think,0.16249999403953552,0.3824999928474426,True,2.8180750918579967e-13,4.0,0.16252283449036006,0.38258277339076985,0.1625,0.1625,0.3825,0.3825,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,2,social,0.6825000047683716,1.4424999952316284,True,2.6086730974157757e-13,3.8397881984710693,0.6959264616025735,1.4393716808823362,0.6825,0.8025,1.4025,1.4425,0.12,0.039999999999999813 --THoVjtIkeU/2,-THoVjtIkeU,2,3,justice,1.462499976158142,1.7625000476837158,True,5.4383574195609324e-14,0.8004889488220215,1.462500073806679,1.7625047152212947,1.4625,1.4625,1.7625,1.7625,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,4,is,1.8025000095367432,1.8825000524520874,True,4.2815872917413567e-13,4.0,1.8025460129344417,1.8825969079606335,1.8025,1.8025,1.8825,1.8825,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,5,extremely,1.9424999952316284,2.362499952316284,True,7.994530737279529e-15,0.25,1.9425053363639069,2.362500000000592,1.9425,1.9425,2.3625,2.3625,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,6,important,2.382499933242798,2.822499990463257,True,5.668493222011316e-14,0.8343633413314819,2.3825001908451227,2.8223862620997116,2.3825,2.3825,2.8225,2.8225,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,7,for,2.862499952316284,2.9825000762939453,True,4.5019671042305365e-13,4.0,2.8625263658238085,2.98251959707777,2.8625,2.8625,2.9825,2.9825,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,8,young,3.0625,3.242500066757202,True,8.062027404714435e-15,0.25,3.0776871893499806,3.257933224930891,3.0625,3.1025,3.2425,3.2825,0.040000000000000036,0.040000000000000036 --THoVjtIkeU/2,-THoVjtIkeU,2,9,people.,3.322499990463257,3.702500104904175,True,7.919095338249776e-14,1.1656365394592285,3.3220047688676053,3.702550181798884,3.3225000000000002,3.3225000000000002,3.7025,3.7025,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,0,You,0.0024999999441206455,0.10249999910593033,True,7.510342987706448e-15,0.4623764753341675,0.008626349453761577,0.16667783381217036,0.0025000000000000005,0.0625,0.10250000000000001,0.5425,0.06,0.43999999999999995 --THoVjtIkeU/6,-THoVjtIkeU,6,1,know,0.12250000238418579,0.5425000190734863,True,6.107723236777041e-16,0.25,0.1987221878267382,0.5696078762787643,0.1225,0.5824999999999999,0.5425,0.7224999999999999,0.4599999999999999,0.17999999999999994 --THoVjtIkeU/6,-THoVjtIkeU,6,2,that,0.5824999809265137,0.7425000071525574,True,1.469799013943153e-14,0.9048861265182495,0.6074026626126879,0.7695639834093579,0.5824999999999999,0.7424999999999999,0.7424999999999999,0.9225,0.16000000000000003,0.18000000000000005 --THoVjtIkeU/6,-THoVjtIkeU,6,3,I,0.7825000286102295,0.8025000095367432,True,1.0772212982734786e-11,4.0,0.8124906763620903,0.8324906763620635,0.7825,0.9824999999999999,0.8025,1.0025,0.19999999999999996,0.19999999999999996 --THoVjtIkeU/6,-THoVjtIkeU,6,4,think,0.9225000143051147,1.3224999904632568,True,5.037423183120761e-14,3.101304531097412,0.9372821593564936,1.3304404827120164,0.9025,1.0425,1.3225,1.3825,0.14,0.06000000000000005 --THoVjtIkeU/6,-THoVjtIkeU,6,5,listening,1.3825000524520874,1.8825000524520874,True,9.660665720352419e-15,0.5947617292404175,1.3928878982518418,1.8981742756703237,1.3825,1.4825,1.8825,1.9224999999999999,0.09999999999999987,0.039999999999999813 --THoVjtIkeU/6,-THoVjtIkeU,6,6,to,1.9424999952316284,2.002500057220459,True,1.2283348234496012e-14,0.756227970123291,1.942528648184741,2.0025308908655695,1.9425,1.9425,2.0025,2.0025,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,7,young,2.0625,2.262500047683716,True,1.2662656576298303e-15,0.25,2.0621304027330667,2.2621369025721645,2.0625,2.0625,2.2625,2.2625,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,8,"people,",2.302500009536743,2.5625,True,1.7007364483748177e-14,1.0470634698867798,2.3024999785176608,2.5634430580912624,2.3025,2.3025,2.5625,2.5625,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,9,taking,2.622499942779541,3.0225000381469727,True,2.2397052067786236e-14,1.3788810968399048,2.6227230997220414,3.0213912292627607,2.6225,2.6225,3.0225,3.0225,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,10,in,3.0425000190734863,3.1024999618530273,True,1.287970262804335e-14,0.7929427027702332,3.0563697916002726,3.123298588516466,3.0425,3.0825,3.1025,3.1625,0.040000000000000036,0.06000000000000005 --THoVjtIkeU/6,-THoVjtIkeU,6,11,what,3.2225000858306885,3.382499933242798,True,1.399858132353782e-16,0.25,3.2090641191027367,3.368759455696855,3.1625,3.2225,3.3225000000000002,3.3825,0.06000000000000005,0.05999999999999961 --THoVjtIkeU/6,-THoVjtIkeU,6,12,they,3.422499895095825,3.5625,True,1.5478470013954165e-14,0.952936589717865,3.422107918188843,3.5624991299962057,3.4225,3.4225,3.5625,3.5625,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,13,have,3.6024999618530273,3.742500066757202,True,9.16368692624285e-15,0.5641651153564453,3.6024997152443112,3.749687033398412,3.6025,3.6025,3.7425,3.7625,0.0,0.020000000000000018 --THoVjtIkeU/6,-THoVjtIkeU,6,14,to,3.7825000286102295,3.862499952316284,True,1.177859367793065e-14,0.7251526117324829,3.7825058915301977,3.8625039279875057,3.7825,3.7825,3.8625,3.8625,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,15,say,3.942500114440918,4.122499942779541,True,1.6323537844770836e-13,4.0,3.942500199874739,4.122500127971544,3.9425,3.9425,4.1225000000000005,4.1225000000000005,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,16,because,4.162499904632568,4.482500076293945,True,1.3337907980772584e-13,4.0,4.166683924893797,4.48249963454062,4.1625000000000005,4.1825,4.482500000000001,4.482500000000001,0.019999999999999574,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,17,what,4.542500019073486,4.942500114440918,True,7.24388929238982e-17,0.25,4.554594493809835,4.895420128655968,4.522500000000001,4.6425,4.702500000000001,4.942500000000001,0.11999999999999922,0.2400000000000002 --THoVjtIkeU/6,-THoVjtIkeU,6,18,we,5.002500057220459,5.0625,True,2.4732789465553226e-14,1.522681474685669,4.958055151596716,5.01805986357333,4.8825,5.0025,4.942500000000001,5.062500000000001,0.1200000000000001,0.1200000000000001 --THoVjtIkeU/6,-THoVjtIkeU,6,19,have,5.082499980926514,5.222499847412109,True,1.3268368778088669e-14,0.8168710470199585,5.043438433626083,5.2030129096654605,5.0025,5.0825000000000005,5.1825,5.2225,0.08000000000000007,0.040000000000000036 --THoVjtIkeU/6,-THoVjtIkeU,6,20,seen,5.242499828338623,5.422500133514404,True,3.088271961293812e-14,1.901303768157959,5.242158090563371,5.422237301298598,5.242500000000001,5.242500000000001,5.4225,5.4225,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,21,all,5.482500076293945,5.682499885559082,True,1.7910698016940577e-14,1.1026774644851685,5.482469297811507,5.681582380617955,5.482500000000001,5.482500000000001,5.6825,5.6825,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,22,around,5.702499866485596,5.962500095367432,True,4.47852407828124e-14,2.757216691970825,5.702103166805097,5.957460999624755,5.702500000000001,5.702500000000001,5.9225,5.9625,0.0,0.040000000000000036 --THoVjtIkeU/6,-THoVjtIkeU,6,23,the,5.982500076293945,6.082499980926514,True,1.5442178252054344e-15,0.25,5.980183708565806,6.080183708652749,5.942500000000001,5.982500000000001,6.0425,6.0825000000000005,0.040000000000000036,0.040000000000000036 --THoVjtIkeU/6,-THoVjtIkeU,6,24,world,6.102499961853027,6.322500228881836,True,1.8824873148341357e-15,0.25,6.102498364639328,6.324249512896174,6.102500000000001,6.102500000000001,6.322500000000001,6.322500000000001,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,25,is,6.402500152587891,6.522500038146973,True,3.4449125409924397e-13,4.0,6.402505356574926,6.5225017704465404,6.402500000000001,6.402500000000001,6.522500000000001,6.522500000000001,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,26,the,6.582499980926514,6.682499885559082,True,4.761372435105153e-14,2.9313530921936035,6.582500199985874,6.682500449272542,6.5825000000000005,6.5825000000000005,6.6825,6.6825,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,27,power,6.742499828338623,7.042500019073486,True,2.462818089656732e-14,1.516241192817688,6.74240651506444,7.042512615514087,6.742500000000001,6.742500000000001,7.0425,7.0425,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,28,of,7.082499980926514,7.142499923706055,True,2.6027791033556014e-12,4.0,7.083613699835981,7.143614692086718,7.0825000000000005,7.0825000000000005,7.142500000000001,7.142500000000001,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,29,youth.,7.242499828338623,7.522500038146973,True,5.518415246790441e-14,3.3974287509918213,7.2424925780003235,7.522504128690015,7.242500000000001,7.242500000000001,7.522500000000001,7.522500000000001,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,0,^And,0.4625000059604645,0.5625,True,7.444570879352151e-15,0.25,0.43347005061479676,0.5584540097468289,0.1825,0.4625,0.5225,0.5625,0.28,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,1,all,0.5824999809265137,0.7825000286102295,True,4.728579723958792e-13,4.0,0.6187218539498627,0.7942601619890411,0.5824999999999999,0.6625,0.7825,0.8225,0.08000000000000007,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,2,of,0.8424999713897705,0.9024999737739563,True,2.301460346083861e-13,2.2389814853668213,0.8424948712648224,0.9027736800678561,0.8424999999999999,0.8424999999999999,0.9025,0.9025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,3,them,0.9424999952316284,1.122499942779541,True,1.480158904514431e-14,0.25,0.9432060795990252,1.113068420763404,0.9425,0.9425,1.1025,1.1225,0.0,0.020000000000000018 --UuX1xuaiiE/1,-UuX1xuaiiE,1,4,are,1.2024999856948853,1.3224999904632568,True,2.4575976561962143e-13,2.3908801078796387,1.1923363969861287,1.3174479199245037,1.1225,1.2025,1.2825,1.3225,0.07999999999999985,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,5,really,1.3825000524520874,1.662500023841858,True,2.800404426037649e-15,0.25,1.3814252978084791,1.6624772229432363,1.3825,1.3825,1.6625,1.6625,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,6,being,1.6825000047683716,1.9225000143051147,True,1.952734679413603e-14,0.25,1.6824843176111908,1.9214195095315,1.6824999999999999,1.6824999999999999,1.9025,1.9224999999999999,0.0,0.019999999999999796 --UuX1xuaiiE/1,-UuX1xuaiiE,1,7,able,2.5625,2.762500047683716,True,9.968488843626125e-14,0.9697869420051575,2.3964615747960076,2.7372177674963947,2.0225,2.5625,2.7225,2.7625,0.54,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,8,to,2.7825000286102295,2.8424999713897705,True,1.889153675887264e-14,0.25,2.782500876058673,2.842501166223785,2.7825,2.7825,2.8425,2.8425,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,9,explore,2.882499933242798,3.5225000381469727,True,4.779727503546882e-13,4.0,2.9052813661038304,3.531178920856283,2.8825,2.9825,3.5225,3.5825,0.10000000000000009,0.06000000000000005 --UuX1xuaiiE/1,-UuX1xuaiiE,1,10,^these,3.622499942779541,4.242499828338623,True,6.823929984446642e-13,4.0,3.627667145170375,4.240710937512301,3.6225,3.6225,4.242500000000001,4.242500000000001,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,11,different,4.262499809265137,4.802499771118164,True,2.766109756069217e-14,0.26910167932510376,4.382878667595825,4.964671088009417,4.2625,4.522500000000001,4.8025,5.1825,0.2600000000000007,0.3799999999999999 --UuX1xuaiiE/1,-UuX1xuaiiE,1,12,areas,4.902500152587891,5.182499885559082,True,3.5131094675055e-14,0.34177374839782715,5.074588703285389,5.393802822040885,4.902500000000001,5.322500000000001,5.1825,5.6425,0.41999999999999993,0.45999999999999996 --UuX1xuaiiE/1,-UuX1xuaiiE,1,13,in,5.542500019073486,5.862500190734863,True,1.8041015729941545e-12,4.0,5.605517378896713,5.7680139174769876,5.242500000000001,5.8025,5.562500000000001,5.862500000000001,0.5599999999999996,0.2999999999999998 --UuX1xuaiiE/1,-UuX1xuaiiE,1,14,depth,5.962500095367432,6.34250020980835,True,3.677016915774878e-13,3.577195167541504,5.942366997981639,6.342105138567985,5.8425,6.062500000000001,6.3425,6.3425,0.22000000000000064,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,15,^in,6.382500171661377,6.582499980926514,True,7.172436472871468e-13,4.0,6.382663094054936,6.584390946636888,6.3825,6.3825,6.5825000000000005,6.602500000000001,0.0,0.020000000000000462 --UuX1xuaiiE/1,-UuX1xuaiiE,1,16,a,6.682499885559082,6.702499866485596,True,1.4643733274588566e-12,4.0,6.68250055881938,6.702500558819382,6.6825,6.6825,6.702500000000001,6.702500000000001,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,17,way,6.762499809265137,6.922500133514404,True,1.0279050424418304e-13,1.0,6.762495012667477,6.939677273518233,6.7625,6.7625,6.9225,6.9625,0.0,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,18,that,7.022500038146973,7.202499866485596,True,1.8719922801561234e-14,0.25,7.021126903366722,7.195559932352289,7.022500000000001,7.022500000000001,7.1825,7.202500000000001,0.0,0.020000000000000462 --UuX1xuaiiE/1,-UuX1xuaiiE,1,19,really,7.222499847412109,7.522500038146973,True,4.071806385580511e-15,0.25,7.2194584454033,7.522500219334755,7.202500000000001,7.2225,7.522500000000001,7.522500000000001,0.019999999999999574,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,20,wasn't,7.5625,7.802499771118164,True,2.0166210281530317e-12,4.0,7.562499676953594,7.801666052689677,7.562500000000001,7.562500000000001,7.8025,7.8025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,21,available,7.84250020980835,8.322500228881836,True,4.036739475674067e-14,0.3927152156829834,7.842499475900292,8.322500361921357,7.8425,7.8425,8.3225,8.3225,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,22,^before,8.362500190734863,8.702500343322754,True,3.3737571655638454e-13,3.28216814994812,8.362539013059672,8.702503611878011,8.362499999999999,8.362499999999999,8.7025,8.7025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,23,the,8.742500305175781,8.842499732971191,True,2.0222062888683272e-14,0.25,8.74366559409698,8.844477233896045,8.7425,8.7425,8.8425,8.8425,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,24,center,8.862500190734863,9.302499771118164,True,2.695884422816708e-13,2.6226978302001953,8.86645495473147,9.305803311848454,8.862499999999999,8.862499999999999,9.3025,9.3025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,25,was,9.442500114440918,9.602499961853027,True,3.3444106587853656e-14,0.32536181807518005,9.447406086810131,9.607217398306826,9.442499999999999,9.442499999999999,9.6025,9.6025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,26,here,9.922499656677246,10.102499961853027,True,9.340148446335128e-12,4.0,9.927156903525098,10.156147021362736,9.9225,9.942499999999999,10.1025,10.2425,0.019999999999999574,0.14000000000000057 --UuX1xuaiiE/0,-UuX1xuaiiE,0,0,^it's,0.0625,0.18250000476837158,True,5.385001797773847e-13,4.0,0.03084138853451605,0.18886906847383023,0.0025000000000000005,0.0825,0.14250000000000002,0.2425,0.08,0.09999999999999998 --UuX1xuaiiE/0,-UuX1xuaiiE,0,1,amazing,0.30250000953674316,0.8025000095367432,True,2.427161661759658e-13,4.0,0.2978772205761982,0.7982895129975586,0.2425,0.3025,0.7224999999999999,0.8025,0.06,0.08000000000000007 --UuX1xuaiiE/0,-UuX1xuaiiE,0,2,to,0.8824999928474426,0.9624999761581421,True,5.4123535080802254e-14,1.0,0.8823922464530151,0.962499782349916,0.8825,0.8825,0.9624999999999999,0.9624999999999999,0.0,0.0 --UuX1xuaiiE/0,-UuX1xuaiiE,0,3,see,1.0625,1.2424999475479126,True,3.0656412738222505e-13,4.0,1.0624987865229019,1.2425008786156162,1.0625,1.0625,1.2425,1.2425,0.0,0.0 --UuX1xuaiiE/0,-UuX1xuaiiE,0,4,their,1.3224999904632568,1.5225000381469727,True,7.977060365105054e-16,0.25,1.3222094811963274,1.5226780527244042,1.3225,1.3225,1.5225,1.5225,0.0,0.0 --UuX1xuaiiE/0,-UuX1xuaiiE,0,5,enthusiasm,1.5625,2.322499990463257,True,1.264410261605023e-13,2.336156129837036,1.55597757995693,2.3073898823403156,1.5425,1.5625,2.2225,2.3225,0.020000000000000018,0.09999999999999964 --UuX1xuaiiE/0,-UuX1xuaiiE,0,6,and,2.442500114440918,2.5425000190734863,True,3.3708023419493256e-15,0.25,2.4114640999737245,2.5267290614733597,2.3025,2.4425,2.4625,2.5425,0.13999999999999968,0.08000000000000007 --UuX1xuaiiE/0,-UuX1xuaiiE,0,7,their,2.5824999809265137,2.9825000762939453,True,1.4151743673944915e-14,0.261471152305603,2.5825076917084684,2.9839370714614866,2.5825,2.5825,2.9825,2.9825,0.0,0.0 --UuX1xuaiiE/0,-UuX1xuaiiE,0,8,passion,3.122499942779541,3.502500057220459,True,6.910157649427136e-17,0.25,3.1188937037274393,3.5345878746530324,3.0825,3.1225,3.5025,3.5625,0.040000000000000036,0.06000000000000005 --UuX1xuaiiE/3,-UuX1xuaiiE,3,0,My,0.12250000238418579,0.32249999046325684,True,8.870484347056617e-10,4.0,0.12261680417447868,0.32700275790598615,0.1225,0.1225,0.3225,0.3825,0.0,0.06 --UuX1xuaiiE/3,-UuX1xuaiiE,3,1,goal,0.5625,0.8424999713897705,True,2.5603030112675285e-11,1.5297502279281616,0.5562232336384092,0.8381385156691515,0.5625,0.5625,0.8424999999999999,0.8424999999999999,0.0,0.0 --UuX1xuaiiE/3,-UuX1xuaiiE,3,2,is,1.222499966621399,1.2825000286102295,True,1.1648832939914477e-12,0.25,1.146023235323786,1.2660061071273174,1.0425,1.2225,1.2425,1.3425,0.17999999999999994,0.10000000000000009 --UuX1xuaiiE/3,-UuX1xuaiiE,3,3,to,1.462499976158142,1.6425000429153442,True,2.4778476492848256e-10,4.0,1.4593958501682065,1.6262894351115438,1.4625,1.4825,1.5625,1.6625,0.020000000000000018,0.10000000000000009 --UuX1xuaiiE/3,-UuX1xuaiiE,3,4,become,1.722499966621399,2.0425000190734863,True,1.1037502509192443e-11,0.6594774723052979,1.7086218803776163,2.042267428813497,1.6025,1.7225,2.0425,2.0425,0.11999999999999988,0.0 --UuX1xuaiiE/3,-UuX1xuaiiE,3,5,a,2.622499942779541,2.6424999237060547,True,2.320204575342877e-12,0.25,2.6157081677890304,2.6357081677890317,2.6225,2.6225,2.6425,2.6425,0.0,0.0 --UuX1xuaiiE/3,-UuX1xuaiiE,3,6,human,2.702500104904175,2.9024999141693115,True,2.8239372645844085e-12,0.25,2.707741696294927,2.923241916792613,2.7025,2.7625,2.9025,3.0625,0.06000000000000005,0.16000000000000014 --UuX1xuaiiE/3,-UuX1xuaiiE,3,7,rights,2.9825000762939453,3.4825000762939453,True,2.520619477031083e-11,1.5060398578643799,3.020026811992114,3.50541435590898,2.9825,3.2425,3.4825,3.5825,0.26000000000000023,0.10000000000000009 --UuX1xuaiiE/3,-UuX1xuaiiE,3,8,lawyer,3.622499942779541,4.002500057220459,True,1.673673873103798e-11,1.0,3.636799554936652,4.012098400623447,3.6225,3.6825,4.0025,4.022500000000001,0.06000000000000005,0.020000000000000462 --UuX1xuaiiE/6,-UuX1xuaiiE,6,0,I,0.5824999809265137,0.6025000214576721,True,1.787539005704275e-10,4.0,0.270109773646166,0.2901097736461649,0.0225,0.5824999999999999,0.0425,0.6024999999999999,0.5599999999999999,0.5599999999999999 --UuX1xuaiiE/6,-UuX1xuaiiE,6,1,ended,0.6225000023841858,0.8025000095367432,True,2.451882826545043e-13,0.36168426275253296,0.5396114880489867,0.7337382133486879,0.5225,0.6224999999999999,0.7224999999999999,0.8025,0.09999999999999998,0.08000000000000007 --UuX1xuaiiE/6,-UuX1xuaiiE,6,2,up,0.8424999713897705,0.9225000143051147,True,9.941535442092864e-13,1.466504454612732,0.7558957635136302,0.8178524341981249,0.7424999999999999,0.8424999999999999,0.8025,0.9225,0.09999999999999998,0.12 --UuX1xuaiiE/6,-UuX1xuaiiE,6,3,going,0.9424999952316284,1.122499942779541,True,8.119074226679065e-15,0.25,0.8555654063033344,1.1208553058000463,0.8424999999999999,0.9425,1.1225,1.1225,0.10000000000000009,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,4,to,1.162500023841858,1.222499966621399,True,1.4474453062453436e-14,0.25,1.1624981375345769,1.2224993879065165,1.1624999999999999,1.1624999999999999,1.2225,1.2225,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,5,work,1.2625000476837158,1.462499976158142,True,4.01744846970694e-14,0.25,1.2624994413657256,1.4591329219296074,1.2625,1.2625,1.4224999999999999,1.4825,0.0,0.06000000000000005 --UuX1xuaiiE/6,-UuX1xuaiiE,6,6,for,1.5625,1.7424999475479126,True,5.021742028286924e-13,0.7407715916633606,1.5621988027396478,1.7420246341274674,1.5625,1.5625,1.7425,1.7425,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,7,the,1.8224999904632568,1.9824999570846558,True,2.22696115563005e-13,0.32850542664527893,1.8241144657810688,1.9721595686699893,1.8225,1.8225,1.9224999999999999,2.0025,0.0,0.08000000000000007 --UuX1xuaiiE/6,-UuX1xuaiiE,6,8,US,2.1624999046325684,2.242500066757202,True,7.930261482214096e-12,4.0,2.1214027566976656,2.197309546467254,1.9625,2.1625,2.0225,2.2425,0.20000000000000018,0.2200000000000002 --UuX1xuaiiE/6,-UuX1xuaiiE,6,9,government,2.262500047683716,2.7225000858306885,True,1.3203499606856146e-14,0.25,2.2378999774402657,2.7214863527895146,2.1625,2.2625,2.7225,2.7225,0.10000000000000009,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,10,as,2.8424999713897705,2.9024999141693115,True,7.30077456823075e-12,4.0,2.842739371428413,2.902744261495768,2.8425,2.8425,2.9025,2.9025,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,11,an,2.9825000762939453,3.0425000190734863,True,1.1879888349095036e-12,1.7524363994598389,2.972215429222952,3.0322589391232513,2.9425,2.9825,3.0025,3.0425,0.040000000000000036,0.040000000000000036 --UuX1xuaiiE/6,-UuX1xuaiiE,6,12,intelligence,3.0824999809265137,3.7225000858306885,True,5.813590169163885e-13,0.8575793504714966,3.082955856583384,3.7232939342321854,3.0825,3.0825,3.7225,3.7225,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,13,"analyst,",3.762500047683716,4.822500228881836,True,2.2186831365178517e-12,3.2728431224823,3.7644551139300924,4.80955242831551,3.7625,3.7625,4.7625,4.8425,0.0,0.08000000000000007 --UuX1xuaiiE/6,-UuX1xuaiiE,6,14,specializing,4.862500190734863,5.622499942779541,True,1.4901028544747619e-12,2.1980934143066406,4.88489165691581,5.637407196149477,4.862500000000001,4.982500000000001,5.6225000000000005,5.702500000000001,0.1200000000000001,0.08000000000000007 --UuX1xuaiiE/6,-UuX1xuaiiE,6,15,in,5.722499847412109,5.802499771118164,True,6.507028531450854e-13,0.959870457649231,5.719573747226648,5.80167959979051,5.7225,5.7225,5.8025,5.822500000000001,0.0,0.020000000000000462 --UuX1xuaiiE/6,-UuX1xuaiiE,6,16,war,5.882500171661377,6.002500057220459,True,2.1248417522018448e-12,3.1344151496887207,5.878821325527894,5.999303897122543,5.8825,5.8825,6.0025,6.0025,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,17,crimes,6.042500019073486,6.34250020980835,True,1.829281355298501e-13,0.2698425352573395,6.033983176986125,6.344507453138877,6.022500000000001,6.0425,6.3425,6.3425,0.019999999999999574,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,18,and,6.382500171661377,6.482500076293945,True,1.405874928191858e-13,0.25,6.384966204069522,6.485061293202971,6.3825,6.3825,6.482500000000001,6.482500000000001,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,19,human,6.502500057220459,6.702499866485596,True,7.051110455062737e-13,1.040129542350769,6.505737929966874,6.709886966565923,6.5025,6.5025,6.702500000000001,6.7225,0.0,0.019999999999999574 --UuX1xuaiiE/6,-UuX1xuaiiE,6,20,rights,6.722499847412109,6.982500076293945,True,4.419648241132945e-12,4.0,6.7350518605513106,7.002116245068988,6.7225,6.782500000000001,6.982500000000001,7.022500000000001,0.0600000000000005,0.040000000000000036 --UuX1xuaiiE/6,-UuX1xuaiiE,6,21,issues,7.042500019073486,7.34250020980835,True,1.0247198922730405e-11,4.0,7.047294136122448,7.364531168340181,7.0425,7.0425,7.3425,7.562500000000001,0.0,0.22000000000000064 --a55Q6RWvTA/3,-a55Q6RWvTA,3,0,And,0.02250000089406967,0.8224999904632568,True,1.8799320281905063e-14,0.25,0.119519558806842,0.8420489822368266,0.0225,0.7625,0.8225,0.9225,0.74,0.09999999999999998 --a55Q6RWvTA/3,-a55Q6RWvTA,3,1,if,1.0425000190734863,1.122499942779541,True,1.864498572895501e-12,4.0,1.0396889744500983,1.1189426038956152,1.0425,1.0425,1.1025,1.1225,0.0,0.020000000000000018 --a55Q6RWvTA/3,-a55Q6RWvTA,3,2,you,1.1825000047683716,1.3825000524520874,True,8.737685922022287e-12,4.0,1.1823567191989515,1.3813940509165903,1.1824999999999999,1.1824999999999999,1.3825,1.3825,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,3,clip,1.4424999952316284,1.6825000047683716,True,4.2130695091478765e-13,1.162251591682434,1.4420208862780637,1.6970771282427792,1.4425,1.4425,1.6824999999999999,1.7425,0.0,0.06000000000000005 --a55Q6RWvTA/3,-a55Q6RWvTA,3,4,"coupons,",1.7625000476837158,2.5425000190734863,True,1.3545874491538434e-12,3.736875057220459,1.7613384775974255,2.544447333196721,1.7625,1.7625,2.5425,2.5825,0.0,0.040000000000000036 --a55Q6RWvTA/3,-a55Q6RWvTA,3,5,if,2.5824999809265137,2.742500066757202,True,1.0346320154785982e-12,2.854219913482666,2.591940711601199,2.7392036510285633,2.5825,2.7025,2.7225,2.7825,0.1200000000000001,0.06000000000000005 --a55Q6RWvTA/3,-a55Q6RWvTA,3,6,you,2.802500009536743,2.9825000762939453,True,4.206752676240433e-13,1.1605089902877808,2.805593026690063,2.980874186453363,2.8025,2.8425,2.9825,2.9825,0.03999999999999959,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,7,don't,3.0425000190734863,3.242500066757202,True,6.30838103976572e-10,4.0,3.0425001275429757,3.2477120612251564,3.0425,3.0425,3.2425,3.2825,0.0,0.040000000000000036 --a55Q6RWvTA/3,-a55Q6RWvTA,3,8,go,3.3424999713897705,3.442500114440918,True,1.436209983406156e-12,3.962045431137085,3.342501925342056,3.44250103576293,3.3425,3.3425,3.4425,3.4425,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,9,out,3.5625,3.702500104904175,True,1.3597915924767195e-13,0.37512314319610596,3.539193583208049,3.6907864440326263,3.4825,3.5625,3.6625,3.7025,0.08000000000000007,0.040000000000000036 --a55Q6RWvTA/3,-a55Q6RWvTA,3,10,for,3.742500066757202,3.862499952316284,True,5.18228004725476e-14,0.25,3.7424883769630815,3.862544926610272,3.7425,3.7425,3.8625,3.8625,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,11,dinner,3.942500114440918,4.382500171661377,True,1.2317580224026348e-13,0.3398027718067169,3.9419675211510485,4.380234510158347,3.9425,3.9425,4.3825,4.3825,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,12,and,4.462500095367432,4.5625,True,9.629341594930008e-14,0.26564285159111023,4.462225306627924,4.562438974723767,4.4625,4.4625,4.562500000000001,4.562500000000001,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,13,movies,4.662499904632568,5.142499923706055,True,1.1253193689386709e-11,4.0,4.655136187997946,5.1260571327776345,4.5825000000000005,4.6625000000000005,4.9625,5.1425,0.08000000000000007,0.17999999999999972 --a55Q6RWvTA/3,-a55Q6RWvTA,3,14,and,5.182499885559082,5.322500228881836,True,2.892920007279559e-13,0.7980644106864929,5.167784439597989,5.302560216276301,5.022500000000001,5.1825,5.1425,5.322500000000001,0.15999999999999925,0.1800000000000006 --a55Q6RWvTA/3,-a55Q6RWvTA,3,15,things,5.34250020980835,5.602499961853027,True,2.486937793286931e-13,0.6860668659210205,5.327936327257898,5.5895131003357275,5.1825,5.3425,5.4625,5.602500000000001,0.16000000000000014,0.14000000000000057 --a55Q6RWvTA/3,-a55Q6RWvTA,3,16,of,5.622499942779541,5.682499885559082,True,2.622747722128005e-12,4.0,5.610183745847421,5.671949375451719,5.482500000000001,5.6225000000000005,5.562500000000001,5.6825,0.13999999999999968,0.11999999999999922 --a55Q6RWvTA/3,-a55Q6RWvTA,3,17,that,5.702499866485596,5.84250020980835,True,2.375747710538223e-13,0.6553930640220642,5.696998378867096,5.8405395591316625,5.6625000000000005,5.702500000000001,5.8425,5.8425,0.040000000000000036,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,18,nature,5.902500152587891,6.28249979019165,True,2.4568303121086377e-12,4.0,5.901516263387521,6.276519742983215,5.902500000000001,5.902500000000001,6.242500000000001,6.322500000000001,0.0,0.08000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,19,and,6.362500190734863,7.022500038146973,True,7.170564597820672e-13,1.9781302213668823,6.404674142671651,7.0242655162952055,6.362500000000001,6.8825,7.022500000000001,7.022500000000001,0.5199999999999996,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,20,try,7.082499980926514,7.302499771118164,True,9.657237032625887e-13,2.664123773574829,7.0846341886283755,7.303463941016041,7.0825000000000005,7.0825000000000005,7.3025,7.3025,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,21,to,7.402500152587891,7.462500095367432,True,4.870198465838733e-15,0.25,7.39206605640134,7.460182285553983,7.3825,7.402500000000001,7.442500000000001,7.4625,0.020000000000000462,0.019999999999999574 --a55Q6RWvTA/3,-a55Q6RWvTA,3,22,take,7.502500057220459,7.662499904632568,True,2.18452897237471e-14,0.25,7.49860130593234,7.661219461951071,7.4625,7.5025,7.6225000000000005,7.702500000000001,0.040000000000000036,0.08000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,23,up,7.742499828338623,7.802499771118164,True,1.7658331665382915e-13,0.4871370792388916,7.742432282304045,7.802925440257224,7.742500000000001,7.742500000000001,7.8025,7.8025,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,24,things,7.962500095367432,8.302499771118164,True,4.2784580132210204e-13,1.1802902221679688,7.952760213706006,8.299296605177526,7.862500000000001,7.9625,8.2625,8.3025,0.09999999999999964,0.040000000000000924 --a55Q6RWvTA/3,-a55Q6RWvTA,3,25,in,8.362500190734863,8.462499618530273,True,1.1459259105853214e-12,3.1612443923950195,8.362640924084438,8.462201634766528,8.362499999999999,8.362499999999999,8.4625,8.4625,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,26,life,8.542499542236328,8.782500267028809,True,3.432629072479265e-13,0.9469529390335083,8.542499652125546,8.782508521681644,8.5425,8.5425,8.782499999999999,8.782499999999999,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,27,"that,",8.842499732971191,9.102499961853027,True,5.165082771207974e-13,1.4248816967010498,8.8325292619187,9.086651659124355,8.8025,8.862499999999999,9.022499999999999,9.1025,0.05999999999999872,0.08000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,28,"well,",9.22249984741211,9.362500190734863,True,5.009049273453636e-13,1.381837010383606,9.193693502270262,9.36246372511127,9.0825,9.2225,9.362499999999999,9.362499999999999,0.14000000000000057,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,29,they,9.382499694824219,9.582500457763672,True,1.9582944342727907e-14,0.25,9.400462764831364,9.591486656966236,9.3825,9.4625,9.5825,9.6225,0.08000000000000007,0.040000000000000924 --a55Q6RWvTA/3,-a55Q6RWvTA,3,30,always,9.642499923706055,9.922499656677246,True,1.198839747092173e-13,0.3307216763496399,9.642498995341569,9.922498654780092,9.6425,9.6425,9.9225,9.9225,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,31,"say,",9.962499618530273,10.1225004196167,True,4.1787146659588714e-12,4.0,9.962500002041113,10.122500003597148,9.9625,9.9625,10.1225,10.1225,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,32,"""the",10.182499885559082,10.282500267028809,True,1.0157119015221511e-13,0.2802025377750397,10.18358703675862,10.283588063519483,10.1825,10.1825,10.282499999999999,10.282499999999999,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,33,best,10.302499771118164,10.522500038146973,True,4.5164518419670355e-14,0.25,10.323673882731844,10.535599687450915,10.3025,10.3425,10.522499999999999,10.5625,0.03999999999999915,0.040000000000000924 --a55Q6RWvTA/3,-a55Q6RWvTA,3,34,things,10.542499542236328,10.822500228881836,True,5.324179072336947e-14,0.25,10.564405953112635,10.834780913875006,10.5425,10.6025,10.782499999999999,10.862499999999999,0.05999999999999872,0.08000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,35,in,10.842499732971191,10.922499656677246,True,4.024187463835295e-14,0.25,10.922409797559588,10.99278094836726,10.8025,11.0625,10.862499999999999,11.1225,0.2599999999999998,0.26000000000000156 --a55Q6RWvTA/3,-a55Q6RWvTA,3,36,life,10.982500076293945,11.182499885559082,True,2.4966956128393003e-13,0.6887587308883667,11.052018990177505,11.236140451775867,10.942499999999999,11.1825,11.1225,11.3425,0.2400000000000002,0.21999999999999886 --a55Q6RWvTA/3,-a55Q6RWvTA,3,37,are,11.242500305175781,11.342499732971191,True,1.2090145508000805e-12,3.3352856636047363,11.315824128651643,11.482355839813513,11.2425,11.442499999999999,11.3425,11.7225,0.1999999999999993,0.3800000000000008 --a55Q6RWvTA/3,-a55Q6RWvTA,3,38,"free"".",11.442500114440918,11.72249984741211,True,1.108621183677945e-13,0.30583325028419495,11.80240737192858,12.06420304908739,11.442499999999999,12.4225,11.7225,12.6625,0.9800000000000004,0.9399999999999995 --a55Q6RWvTA/3,-a55Q6RWvTA,3,39,Reading,11.782500267028809,12.72249984741211,True,7.512862402504761e-14,0.25,12.147773825953134,12.878409906681043,11.782499999999999,12.7825,12.6825,13.1625,1.0000000000000018,0.4800000000000004 --a55Q6RWvTA/3,-a55Q6RWvTA,3,40,a,12.782500267028809,12.802499771118164,True,9.634311577688681e-12,4.0,12.954937272825967,12.974937272826114,12.7825,13.2025,12.8025,13.2225,0.41999999999999993,0.41999999999999993 --a55Q6RWvTA/3,-a55Q6RWvTA,3,41,"book,",12.90250015258789,13.162500381469727,True,3.839624798662733e-12,4.0,13.294036123397236,13.538265691133404,12.9025,14.0625,13.1625,14.2025,1.1600000000000001,1.040000000000001 --a55Q6RWvTA/3,-a55Q6RWvTA,3,42,going,14.0625,14.542499542236328,True,3.271775645546926e-12,4.0,13.899637684976911,14.479520424724328,13.2025,14.2225,14.1625,14.6025,1.0199999999999996,0.4399999999999995 --a55Q6RWvTA/3,-a55Q6RWvTA,3,43,for,14.642499923706055,14.782500267028809,True,5.69680546767988e-14,0.25,14.57912428176785,14.724128175889916,14.3825,14.6425,14.5425,14.7825,0.2599999999999998,0.2400000000000002 --a55Q6RWvTA/3,-a55Q6RWvTA,3,44,a,14.802499771118164,14.822500228881836,True,1.7280364639216117e-11,4.0,14.777518286815813,14.797518286816297,14.7025,14.8025,14.7225,14.8225,0.09999999999999964,0.09999999999999964 --a55Q6RWvTA/3,-a55Q6RWvTA,3,45,nice,14.962499618530273,15.1225004196167,True,2.8319247988295437e-12,4.0,14.930842492182515,15.106118105713016,14.8825,14.9625,15.0825,15.1225,0.08000000000000007,0.040000000000000924 --a55Q6RWvTA/3,-a55Q6RWvTA,3,46,"walk,",15.182499885559082,15.462499618530273,True,1.6164060107765055e-12,4.0,15.18135379782965,15.461013343972695,15.1825,15.1825,15.4625,15.4625,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,47,"and,",15.5024995803833,15.602499961853027,True,2.406427210006456e-14,0.25,15.501115550027231,15.601660253222741,15.5025,15.5025,15.6025,15.6025,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,48,of,15.6225004196167,15.682499885559082,True,7.75856375673234e-12,4.0,15.621856150264364,15.682008493386707,15.6225,15.6225,15.6825,15.6825,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,49,"course,",15.702500343322754,15.942500114440918,True,5.6591589189325733e-14,0.25,15.702154879797067,15.942473582145436,15.7025,15.7025,15.942499999999999,15.942499999999999,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,50,combining,15.962499618530273,16.542499542236328,True,3.2498599623030644e-13,0.8965327739715576,15.962615579554416,16.54258848862367,15.9625,15.9625,16.5425,16.5425,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,51,exercise,16.5625,17.162500381469727,True,7.296909387485839e-13,2.0129847526550293,16.56451128516722,17.16727364513594,16.5625,16.5625,17.1625,17.2025,0.0,0.03999999999999915 --a55Q6RWvTA/3,-a55Q6RWvTA,3,52,and,17.202499389648438,17.302499771118164,True,1.9747090857576317e-14,0.25,17.212519549668535,17.31286686273573,17.2025,17.242500000000003,17.302500000000002,17.3425,0.0400000000000027,0.03999999999999915 --a55Q6RWvTA/3,-a55Q6RWvTA,3,53,various,17.322500228881836,17.622499465942383,True,9.008352603364495e-14,0.25,17.33331033428284,17.632382986564632,17.3225,17.3625,17.602500000000003,17.6625,0.03999999999999915,0.05999999999999872 --a55Q6RWvTA/3,-a55Q6RWvTA,3,54,things,17.642499923706055,17.88249969482422,True,2.6505485428555443e-14,0.25,17.65600623163198,17.90537406876171,17.642500000000002,17.7025,17.8825,18.0025,0.05999999999999872,0.120000000000001 --a55Q6RWvTA/3,-a55Q6RWvTA,3,55,of,17.90250015258789,17.962499618530273,True,3.624920459285863e-13,1.0,17.93835276685364,18.001385025457154,17.902500000000003,18.0425,17.962500000000002,18.102500000000003,0.13999999999999702,0.14000000000000057 --a55Q6RWvTA/3,-a55Q6RWvTA,3,56,that,18.002500534057617,18.142499923706055,True,1.8815473484812273e-11,4.0,18.04488132385217,18.219963675608064,17.9825,18.1825,18.142500000000002,18.462500000000002,0.1999999999999993,0.3200000000000003 --a55Q6RWvTA/3,-a55Q6RWvTA,3,57,nature,18.1825008392334,18.5625,True,2.4222108693794375e-12,4.0,18.28547153085806,18.701281830499646,18.1825,18.582500000000003,18.5625,19.142500000000002,0.40000000000000213,0.5800000000000018 --a55Q6RWvTA/3,-a55Q6RWvTA,3,58,where,18.582500457763672,19.042499542236328,True,1.1723989698985901e-14,0.25,18.726882158154776,19.134524101321563,18.582500000000003,19.2025,19.0425,19.442500000000003,0.6199999999999974,0.40000000000000213 --a55Q6RWvTA/3,-a55Q6RWvTA,3,59,you're,19.0625,19.262500762939453,True,6.019562018982905e-12,4.0,19.18884854699223,19.43021445240018,19.0625,19.5625,19.262500000000003,19.962500000000002,0.5,0.6999999999999993 --a55Q6RWvTA/3,-a55Q6RWvTA,3,60,not,19.282499313354492,19.40250015258789,True,2.795208725939191e-13,0.7711089849472046,19.521225771376137,19.66507505485541,19.282500000000002,20.282500000000002,19.402500000000003,20.5025,1.0,1.0999999999999979 --a55Q6RWvTA/3,-a55Q6RWvTA,3,61,spending,19.4424991607666,20.082500457763672,True,1.305721616983238e-13,0.3602069616317749,19.728286447221432,20.32897058236136,19.442500000000003,20.642500000000002,20.0225,21.1625,1.1999999999999993,1.1400000000000006 --a55Q6RWvTA/3,-a55Q6RWvTA,3,62,money,20.282499313354492,20.602500915527344,True,2.586418492572795e-13,0.7135103940963745,20.500230104664706,20.79165190936027,20.282500000000002,21.2225,20.602500000000003,21.402500000000003,0.9399999999999977,0.8000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,63,per,20.842500686645508,21.0625,True,4.991007607202391e-13,1.3768599033355713,20.973787732360663,21.205024207243827,20.8425,21.422500000000003,21.0625,21.5425,0.5800000000000018,0.4800000000000004 --a55Q6RWvTA/3,-a55Q6RWvTA,3,64,se.,21.22249984741211,21.582500457763672,True,1.484069464541271e-11,4.0,21.35793839975314,21.641898876505973,21.2225,21.5625,21.582500000000003,21.942500000000003,0.33999999999999986,0.35999999999999943 --aNfi7CP8vM/7,-aNfi7CP8vM,7,0,Maybe,0.5625,0.7425000071525574,True,1.3992611323093707e-12,4.0,0.5699168353417414,0.7684721379873901,0.5625,0.6224999999999999,0.7424999999999999,0.8624999999999999,0.05999999999999994,0.12 --aNfi7CP8vM/7,-aNfi7CP8vM,7,1,they,0.8025000095367432,1.0625,True,7.763144375385819e-14,0.6851189732551575,0.8381540024758877,1.1033643669058124,0.8025,1.0025,1.0625,1.4224999999999999,0.19999999999999996,0.3599999999999999 --aNfi7CP8vM/7,-aNfi7CP8vM,7,2,have,1.0824999809265137,1.2625000476837158,True,4.901391250332343e-13,4.0,1.1800089747111384,1.4216257555081657,1.0825,1.5825,1.2625,1.7425,0.5,0.48 --aNfi7CP8vM/7,-aNfi7CP8vM,7,3,no,1.4225000143051147,1.502500057220459,True,2.19147440537297e-13,1.9340368509292603,1.5502325525781857,1.6303863815472515,1.4025,1.8225,1.4625,1.8825,0.41999999999999993,0.42000000000000015 --aNfi7CP8vM/7,-aNfi7CP8vM,7,4,political,1.5824999809265137,2.0625,True,9.636221535340786e-14,0.8504232168197632,1.7219728765180327,2.217611584075496,1.5825,2.0425,2.0625,2.5825,0.45999999999999996,0.52 --aNfi7CP8vM/7,-aNfi7CP8vM,7,5,party,2.182499885559082,2.4625000953674316,True,4.943981964274091e-13,4.0,2.3189574948447387,2.597655834164009,2.1425,2.7025,2.3825,3.0225,0.56,0.6400000000000001 --aNfi7CP8vM/7,-aNfi7CP8vM,7,6,"affiliation,",2.5625,3.202500104904175,True,3.149363365581581e-13,2.77940034866333,2.6754966075215223,3.384642765575778,2.4425,3.0625,3.1625,3.8825,0.6200000000000001,0.7199999999999998 --aNfi7CP8vM/7,-aNfi7CP8vM,7,7,they're,3.322499990463257,3.882499933242798,True,6.927912236942557e-12,4.0,3.5162592580884375,3.9951804657104297,3.2625,4.2625,3.8825,4.482500000000001,1.0,0.600000000000001 --aNfi7CP8vM/7,-aNfi7CP8vM,7,8,not,4.262499809265137,4.422500133514404,True,9.374659794107731e-14,0.8273397088050842,4.3249445299839655,4.4702296803616,4.2625,4.5025,4.4225,4.6225000000000005,0.2400000000000002,0.20000000000000018 --aNfi7CP8vM/7,-aNfi7CP8vM,7,9,interested,4.502500057220459,5.142499923706055,True,1.493164993775567e-13,1.3177595138549805,4.536229501246246,5.153956404348874,4.5025,4.6625000000000005,5.1425,5.202500000000001,0.16000000000000014,0.0600000000000005 --aNfi7CP8vM/7,-aNfi7CP8vM,7,10,in,5.202499866485596,5.262499809265137,True,3.058207373863968e-14,0.2698952853679657,5.20525492933666,5.265296308868387,5.202500000000001,5.242500000000001,5.2625,5.3025,0.040000000000000036,0.040000000000000036 --aNfi7CP8vM/7,-aNfi7CP8vM,7,11,"politics,",5.34250020980835,5.922500133514404,True,3.0942337179897667e-14,0.2730746865272522,5.342027017595276,5.92221771234432,5.3425,5.3425,5.9225,5.9225,0.0,0.0 --aNfi7CP8vM/7,-aNfi7CP8vM,7,12,they,6.002500057220459,6.142499923706055,True,4.5508526451637586e-15,0.25,5.995617953652946,6.139102854390898,5.942500000000001,6.0025,6.102500000000001,6.142500000000001,0.05999999999999961,0.040000000000000036 --aNfi7CP8vM/7,-aNfi7CP8vM,7,13,could,6.162499904632568,6.502500057220459,True,1.133866492451091e-13,1.0006686449050903,6.165296514612912,6.5001556714207736,6.1625000000000005,6.1625000000000005,6.482500000000001,6.5025,0.0,0.019999999999999574 --aNfi7CP8vM/7,-aNfi7CP8vM,7,14,care,6.5625,6.822500228881836,True,1.1323512521524068e-13,0.9993313550949097,6.562667537244219,6.822789861817852,6.562500000000001,6.562500000000001,6.822500000000001,6.822500000000001,0.0,0.0 --aNfi7CP8vM/7,-aNfi7CP8vM,7,15,less,6.922500133514404,7.122499942779541,True,2.815684697118209e-13,2.484919786453247,6.9223711358609785,7.12232906665508,6.9225,6.9225,7.1225000000000005,7.1225000000000005,0.0,0.0 --aNfi7CP8vM/7,-aNfi7CP8vM,7,16,about,7.142499923706055,7.402500152587891,True,2.719624249110722e-14,0.25,7.14313662311112,7.4259741476470245,7.142500000000001,7.142500000000001,7.402500000000001,7.482500000000001,0.0,0.08000000000000007 --aNfi7CP8vM/7,-aNfi7CP8vM,7,17,politics.,7.462500095367432,8.522500038146973,True,5.850625973274903e-14,0.5163339376449585,7.499265627423223,8.521439814756093,7.4625,7.6625000000000005,8.5025,8.522499999999999,0.20000000000000018,0.019999999999999574 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,0,In,0.36250001192092896,0.48249998688697815,True,2.7085831407619443e-13,0.44671571254730225,0.3661229464627679,0.4824918267992842,0.3625,0.4025,0.4825,0.4825,0.040000000000000036,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,1,"2008,",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,2,there,0.6225000023841858,1.0225000381469727,True,4.3837511203535795e-13,0.7229943871498108,0.644216758746706,1.2320726340392565,0.6024999999999999,0.7424999999999999,1.0225,1.4825,0.14,0.45999999999999996 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,3,were,2.182499885559082,2.922499895095825,True,3.5700947406680505e-12,4.0,2.098391009917989,3.0003518244192926,1.3825,2.6425,2.7625,3.0825,1.26,0.31999999999999984 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,4,very,3.262500047683716,3.502500057220459,True,4.024087920523334e-13,0.6636766195297241,3.2628512483329692,3.5029909181345595,3.2625,3.2625,3.5025,3.5025,0.0,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,5,serious,3.6424999237060547,4.102499961853027,True,1.2576761463864439e-13,0.25,3.604995371759258,4.081283859552203,3.5425,3.6425,4.0425,4.1025,0.10000000000000009,0.05999999999999961 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,6,tribal,4.162499904632568,4.582499980926514,True,6.287949761568656e-12,4.0,4.164219364401541,4.584062047007865,4.1625000000000005,4.1625000000000005,4.5825000000000005,4.5825000000000005,0.0,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,7,warfare,4.722499847412109,5.202499866485596,True,1.5909093432821958e-13,0.26238226890563965,4.706837861170518,5.205690278227914,4.6425,4.7225,5.202500000000001,5.202500000000001,0.08000000000000007,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,8,in,6.142499923706055,6.202499866485596,True,1.019524534240368e-10,4.0,6.1186756160246425,6.21318732554233,6.062500000000001,6.1825,6.202500000000001,6.2625,0.11999999999999922,0.05999999999999961 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,9,Kenya,7.082499980926514,8.0024995803833,True,6.063326381776368e-13,1.0,7.055643839797694,8.001751852174415,6.3425,7.482500000000001,8.0025,8.0025,1.1400000000000006,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,10,following,8.162500381469727,9.282500267028809,True,1.3233601497616987e-12,2.1825644969940186,8.167307698703715,9.274023163387671,8.1625,8.1625,9.2025,9.282499999999999,0.0,0.0799999999999983 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,11,a,9.40250015258789,9.422499656677246,True,3.7868968377763323e-11,4.0,9.40242807987088,9.422428079870883,9.4025,9.4025,9.4225,9.4225,0.0,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,12,disputed,9.462499618530273,9.90250015258789,True,2.7071137757676833e-13,0.4464733600616455,9.47686688744534,9.925606046827598,9.4625,9.5025,9.9025,9.9825,0.03999999999999915,0.08000000000000007 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,13,election.,10.022500038146973,10.802499771118164,True,3.659902676061977e-12,4.0,10.029209911460025,10.806518969512238,10.022499999999999,10.022499999999999,10.8025,10.862499999999999,0.0,0.05999999999999872 --dxfTGcXJoc/1,-dxfTGcXJoc,1,0,For,0.5625,0.7425000071525574,True,2.275428977183136e-12,4.0,0.3649672540175232,0.656470491487688,0.0225,0.5625,0.5824999999999999,0.7424999999999999,0.54,0.16000000000000003 --dxfTGcXJoc/1,-dxfTGcXJoc,1,1,the,0.8025000095367432,0.9424999952316284,True,3.523209827181739e-13,2.672248363494873,0.7190080419087704,0.8384369833953639,0.6425,0.8025,0.7424999999999999,0.9425,0.16000000000000003,0.20000000000000007 --dxfTGcXJoc/1,-dxfTGcXJoc,1,2,10th,1.002500057220459,1.0625,True,7.89121092892287e-16,0.25,0.9201663747546199,0.991584920117422,0.8025,1.0025,0.9025,1.0825,0.19999999999999996,0.18000000000000005 --dxfTGcXJoc/1,-dxfTGcXJoc,1,3,straight,1.0824999809265137,1.462499976158142,True,5.292460907187473e-15,0.25,1.0590410591480313,1.4525213757665048,0.9225,1.1225,1.4025,1.4625,0.20000000000000007,0.05999999999999983 --dxfTGcXJoc/1,-dxfTGcXJoc,1,4,"year,",1.5824999809265137,1.7625000476837158,True,4.7748890480642583e-14,0.36216092109680176,1.5743957871409902,1.7569251246340676,1.4825,1.5825,1.6824999999999999,1.7625,0.10000000000000009,0.08000000000000007 --dxfTGcXJoc/1,-dxfTGcXJoc,1,5,some,2.262500047683716,2.5425000190734863,True,1.943253283981372e-12,4.0,2.2612920543236856,2.5424015098644395,2.2625,2.2625,2.5425,2.5425,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,6,of,2.5824999809265137,2.6624999046325684,True,3.9846300087589825e-13,3.022221565246582,2.5853655723810456,2.6652560546039608,2.5825,2.6025,2.6625,2.6625,0.020000000000000018,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,7,Kentucky’s,2.762500047683716,3.262500047683716,True,1.15754682315089e-12,4.0,2.750634717027689,3.2556658742957265,2.7225,2.7625,3.2425,3.2625,0.040000000000000036,0.020000000000000018 --dxfTGcXJoc/1,-dxfTGcXJoc,1,8,best,3.422499895095825,3.762500047683716,True,8.019150258111243e-14,0.608228325843811,3.4022852380664403,3.72717692242951,3.2825,3.4225,3.7025,3.7625,0.13999999999999968,0.06000000000000005 --dxfTGcXJoc/1,-dxfTGcXJoc,1,9,and,3.922499895095825,4.022500038146973,True,1.6227658797339332e-14,0.25,3.9223628956324568,4.022449556910346,3.9225,3.9225,4.022500000000001,4.022500000000001,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,10,most,4.082499980926514,4.262499809265137,True,9.945570164952497e-14,0.7543414831161499,4.082499603629278,4.262509829162647,4.0825000000000005,4.0825000000000005,4.2625,4.2625,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,11,innovative,4.442500114440918,5.122499942779541,True,1.133854512017085e-12,4.0,4.4410250441935455,5.11803027501793,4.442500000000001,4.442500000000001,5.102500000000001,5.1225000000000005,0.0,0.019999999999999574 --dxfTGcXJoc/1,-dxfTGcXJoc,1,12,biotech,5.162499904632568,5.502500057220459,True,1.5729797564305315e-13,1.193057656288147,5.155285740506763,5.500234169729445,5.1225000000000005,5.1625000000000005,5.4625,5.5025,0.040000000000000036,0.040000000000000036 --dxfTGcXJoc/1,-dxfTGcXJoc,1,13,companies,5.542500019073486,6.022500038146973,True,8.639043798906731e-13,4.0,5.54199486653639,6.021684045459382,5.5425,5.5425,6.022500000000001,6.022500000000001,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,14,and,6.082499980926514,6.182499885559082,True,4.0721878045166596e-14,0.30886316299438477,6.081297587982795,6.181694894313973,6.0825000000000005,6.0825000000000005,6.1825,6.1825,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,15,technologies,6.222499847412109,6.922500133514404,True,2.050334592862066e-13,1.555116891860962,6.222158853837142,6.937339063734151,6.2225,6.2225,6.9225,6.9625,0.0,0.040000000000000036 --dxfTGcXJoc/1,-dxfTGcXJoc,1,16,were,6.942500114440918,7.482500076293945,True,3.483278661169098e-14,0.26419615745544434,7.130313396095074,7.4978267357210475,6.942500000000001,7.322500000000001,7.4625,7.522500000000001,0.3799999999999999,0.0600000000000005 --dxfTGcXJoc/1,-dxfTGcXJoc,1,17,on,7.502500057220459,7.702499866485596,True,1.5179640530660343e-11,4.0,7.540963992421254,7.71163558566263,7.5025,7.6825,7.702500000000001,7.782500000000001,0.17999999999999972,0.08000000000000007 --dxfTGcXJoc/1,-dxfTGcXJoc,1,18,display,7.84250020980835,8.40250015258789,True,7.89802034537046e-13,4.0,7.842515193459164,8.403630710839492,7.8425,7.8425,8.4025,8.4025,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,19,at,8.662500381469727,8.72249984741211,True,2.0993258435071053e-15,0.25,8.662467828886165,8.7226922086402,8.6625,8.6625,8.7225,8.7225,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,20,the,8.822500228881836,8.942500114440918,True,1.2593050246253318e-13,0.9551448225975037,8.822210482336128,8.942231664275779,8.8225,8.8225,8.942499999999999,8.942499999999999,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,21,annual,9.082500457763672,9.382499694824219,True,5.38172479670751e-13,4.0,9.082499874861758,9.382500529056607,9.0825,9.0825,9.3825,9.3825,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,22,BIO,9.662500381469727,9.90250015258789,True,6.07335223778982e-11,4.0,9.578064211583579,9.880240503537104,9.442499999999999,9.6625,9.8225,9.9025,0.22000000000000064,0.08000000000000007 --dxfTGcXJoc/1,-dxfTGcXJoc,1,23,International,9.922499656677246,10.682499885559082,True,7.311739511376911e-14,0.5545733571052551,9.908699669202857,10.700666650198874,9.8825,9.942499999999999,10.6825,10.7425,0.05999999999999872,0.0600000000000005 --dxfTGcXJoc/1,-dxfTGcXJoc,1,24,"Convention,",10.72249984741211,11.262499809265137,True,1.0889742883476994e-13,0.8259541392326355,10.747097081493896,11.267687896899911,10.7225,10.8025,11.2625,11.3025,0.08000000000000007,0.040000000000000924 --dxfTGcXJoc/1,-dxfTGcXJoc,1,25,which,11.322500228881836,12.082500457763672,True,4.037281915573489e-14,0.30621564388275146,11.364414611412695,12.082394236045653,11.3225,11.7425,12.0825,12.0825,0.41999999999999993,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,26,I,12.382499694824219,12.40250015258789,True,1.2588138864622067e-10,4.0,12.382510769537037,12.402510769537036,12.3825,12.3825,12.4025,12.4025,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,27,and,12.482500076293945,12.642499923706055,True,1.533161313562511e-14,0.25,12.48280965262064,12.642572691239474,12.4825,12.4825,12.6425,12.6425,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,28,other,12.702500343322754,12.962499618530273,True,9.549927173927233e-14,0.7243331670761108,12.702509718675014,12.962495484432637,12.7025,12.7025,12.9625,12.9625,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,29,state,12.982500076293945,13.242500305175781,True,6.808946852523978e-14,0.5164380669593811,12.98288355950477,13.242183987541857,12.9825,12.9825,13.2425,13.2425,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,30,officials,13.262499809265137,13.702500343322754,True,2.7725016360957873e-14,0.25,13.26382449943722,13.702492000013828,13.2625,13.2825,13.7025,13.7025,0.02000000000000135,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,31,attended,13.72249984741211,14.162500381469727,True,7.640498070381543e-15,0.25,13.724172197793889,14.162500196491765,13.7225,13.7225,14.1625,14.1625,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,32,this,14.22249984741211,14.362500190734863,True,1.377583214601935e-13,1.0448552370071411,14.222454748006289,14.362454751756776,14.2225,14.2225,14.362499999999999,14.362499999999999,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,33,week,14.422499656677246,14.6225004196167,True,4.912310792512531e-13,3.725839376449585,14.422499999645678,14.622500000213078,14.4225,14.4225,14.6225,14.6225,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,34,in,14.662500381469727,14.882499694824219,True,1.148558245039899e-12,4.0,14.664907397502905,14.88327589515385,14.6625,14.6625,14.8825,14.8825,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,35,Chicago.,14.962499618530273,15.522500038146973,True,7.882481017733312e-13,4.0,14.962501636791323,15.51671348154772,14.9625,14.9625,15.5025,15.522499999999999,0.0,0.019999999999999574 --dxfTGcXJoc/0,-dxfTGcXJoc,0,0,This,0.20250000059604645,0.6625000238418579,True,1.436269511187623e-15,0.25,0.18800400140906554,0.6712783209637588,0.0225,0.3825,0.6625,0.7025,0.36,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,1,is,0.7225000262260437,0.8025000095367432,True,1.321620655796163e-12,4.0,0.7296080131653702,0.8073423085081691,0.7224999999999999,0.7825,0.8025,0.8424999999999999,0.06000000000000005,0.039999999999999925 --dxfTGcXJoc/0,-dxfTGcXJoc,0,2,Governor,0.9024999737739563,1.2424999475479126,True,1.0927341930616619e-12,4.0,0.887471538448594,1.2277552870491857,0.8624999999999999,0.9025,1.2025,1.2425,0.040000000000000036,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,3,Steve,1.2825000286102295,1.5625,True,7.474941753895722e-14,0.2840318977832794,1.281864992925297,1.5529176310112593,1.2825,1.2825,1.5425,1.5625,0.0,0.020000000000000018 --dxfTGcXJoc/0,-dxfTGcXJoc,0,4,Beshear,1.6024999618530273,1.9824999570846558,True,7.465651361004966e-13,2.8367886543273926,1.60314666497837,2.0731438341617148,1.6025,1.6025,1.9825,2.7625,0.0,0.7800000000000002 --dxfTGcXJoc/0,-dxfTGcXJoc,0,5,...,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/0,-dxfTGcXJoc,0,6,About,2.0824999809265137,2.8424999713897705,True,3.1875823053133245e-13,1.21121346950531,2.23390625717333,2.906884357988618,2.0825,2.9425,2.8425,3.3825,0.8599999999999999,0.54 --dxfTGcXJoc/0,-dxfTGcXJoc,0,7,Kentucky,2.942500114440918,4.482500076293945,True,1.9611760132542955e-12,4.0,3.0234051678294898,4.527675130658362,2.9425,3.4225,4.482500000000001,4.862500000000001,0.48,0.3799999999999999 --dxfTGcXJoc/0,-dxfTGcXJoc,0,8,Our,4.78249979019165,5.002500057220459,True,2.0286934025032233e-12,4.0,4.766380309692605,4.975474037119736,4.6225000000000005,4.9225,4.862500000000001,5.0425,0.2999999999999998,0.17999999999999972 --dxfTGcXJoc/0,-dxfTGcXJoc,0,9,state,5.142499923706055,5.482500076293945,True,2.2418547053482119e-13,0.8518571257591248,5.102480756287364,5.457607935480797,4.982500000000001,5.1425,5.3825,5.482500000000001,0.15999999999999925,0.10000000000000053 --dxfTGcXJoc/0,-dxfTGcXJoc,0,10,is,5.542500019073486,5.622499942779541,True,2.1595016898821873e-13,0.8205647468566895,5.542502282433472,5.62249050879839,5.5425,5.5425,5.6225000000000005,5.6225000000000005,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,11,home,5.702499866485596,5.902500152587891,True,7.612381997543563e-14,0.2892543375492096,5.6969075990261855,5.905562707339487,5.6425,5.702500000000001,5.862500000000001,5.902500000000001,0.0600000000000005,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,12,to,5.922500133514404,6.042500019073486,True,7.356884334586491e-14,0.27954596281051636,5.937102992383272,6.056635431575253,5.9225,5.9225,6.0425,6.0425,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,13,more,6.222499847412109,6.622499942779541,True,6.24342970986097e-13,2.372370481491089,6.277575214162152,6.631543457001568,6.2225,6.322500000000001,6.6225000000000005,6.6625000000000005,0.10000000000000053,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,14,than,6.802499771118164,7.102499961853027,True,9.57974747705509e-14,0.3640100359916687,6.803191204364817,7.107020835049525,6.8025,6.8025,7.102500000000001,7.142500000000001,0.0,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,15,"1,500",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/0,-dxfTGcXJoc,0,16,bioscience,7.28249979019165,8.0625,True,5.816785312966199e-13,2.210254669189453,7.267452880689412,8.062370269304813,7.1825,7.282500000000001,8.0625,8.0625,0.10000000000000053,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,17,and,8.102499961853027,8.342499732971191,True,8.574386453673657e-14,0.32580846548080444,8.103254544239489,8.342748976407043,8.1025,8.1025,8.3425,8.3425,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,18,lifescience,8.462499618530273,9.0625,True,9.144152449125365e-13,3.474583387374878,8.440910172928174,9.06317123654407,8.3825,8.4625,9.0625,9.0625,0.08000000000000007,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,19,companies,9.102499961853027,10.102499961853027,True,1.1046472981127153e-12,4.0,9.103525367338998,10.10579297477803,9.1025,9.1025,10.1025,10.1225,0.0,0.02000000000000135 --dxfTGcXJoc/0,-dxfTGcXJoc,0,20,and,10.22249984741211,10.362500190734863,True,6.748803447510776e-14,0.2564401924610138,10.222497809790337,10.356745416243506,10.2225,10.2225,10.3425,10.362499999999999,0.0,0.019999999999999574 --dxfTGcXJoc/0,-dxfTGcXJoc,0,21,more,10.442500114440918,10.602499961853027,True,2.3733483711305126e-13,0.9018219113349915,10.414533955030066,10.579202864997653,10.3825,10.442499999999999,10.5625,10.6025,0.05999999999999872,0.03999999999999915 --dxfTGcXJoc/0,-dxfTGcXJoc,0,22,than,10.72249984741211,10.962499618530273,True,6.653097669138963e-13,2.5280356407165527,10.7225013520628,10.963060296230047,10.7225,10.7225,10.9625,10.9625,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,23,"100,000",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/0,-dxfTGcXJoc,0,24,people,11.102499961853027,11.5024995803833,True,1.0843516568600359e-13,0.41203057765960693,11.102483038622685,11.502500341848476,11.1025,11.1025,11.5025,11.5025,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,25,who,11.5625,11.702500343322754,True,1.174416358433461e-12,4.0,11.576823297591739,11.714007237913536,11.5625,11.6225,11.7025,11.7425,0.0600000000000005,0.03999999999999915 --dxfTGcXJoc/0,-dxfTGcXJoc,0,26,work,11.90250015258789,12.162500381469727,True,7.076656020075026e-14,0.2688978910446167,11.905745763990348,12.163903512245955,11.9025,11.9025,12.1625,12.1625,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,27,for,12.262499809265137,12.40250015258789,True,6.21533965787513e-13,2.36169695854187,12.262508738077944,12.402008549180124,12.2625,12.2625,12.4025,12.4025,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,28,these,12.5024995803833,12.72249984741211,True,5.1970743247115037e-14,0.25,12.50070802564183,12.722592853346624,12.5025,12.5025,12.7225,12.7225,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,29,"firms,",12.822500228881836,13.202500343322754,True,1.987435586383538e-14,0.25,12.810238807852192,13.202497205526639,12.7825,12.8225,13.2025,13.2025,0.03999999999999915,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,30,in,13.22249984741211,13.962499618530273,True,3.6057056862839887e-13,1.370091438293457,13.324914664363726,13.96804775045872,13.2225,13.942499999999999,13.9625,14.0425,0.7199999999999989,0.08000000000000007 --dxfTGcXJoc/0,-dxfTGcXJoc,0,31,areas,14.182499885559082,14.522500038146973,True,1.2572369089813157e-13,0.47772330045700073,14.18246398667518,14.522510488364302,14.1825,14.1825,14.522499999999999,14.522499999999999,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,32,from,14.5625,14.822500228881836,True,8.391539314822356e-15,0.25,14.562750522345475,14.823092307045739,14.5625,14.5625,14.8225,14.8225,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,33,nutrigenomics,15.182499885559082,16.122499465942383,True,1.1121746969558477e-12,4.0,15.177670019934878,16.099262169106762,15.1425,15.1825,16.0225,16.122500000000002,0.03999999999999915,0.10000000000000142 --dxfTGcXJoc/0,-dxfTGcXJoc,0,34,and,16.262500762939453,16.38249969482422,True,4.261740564398195e-14,0.25,16.258652767187964,16.38099827413018,16.262500000000003,16.282500000000002,16.3825,16.3825,0.019999999999999574,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,35,pharmaceuticals,16.422500610351562,17.262500762939453,True,5.213513540151815e-13,1.9810242652893066,16.4224417108849,17.25543344328825,16.422500000000003,16.422500000000003,17.2225,17.262500000000003,0.0,0.0400000000000027 --dxfTGcXJoc/0,-dxfTGcXJoc,0,36,to,17.642499923706055,17.74250030517578,True,4.542984640820008e-13,1.7262375354766846,17.642496491416676,17.742499915475133,17.642500000000002,17.642500000000002,17.742500000000003,17.742500000000003,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,37,nanotechnology,17.782499313354492,18.762500762939453,True,2.8901043342376143e-13,1.0981781482696533,17.782588215090776,18.763284028765124,17.782500000000002,17.782500000000002,18.762500000000003,18.762500000000003,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,38,and,18.822500228881836,18.922500610351562,True,1.1881575943657827e-14,0.25,18.822476509585076,18.922488605782018,18.8225,18.8225,18.922500000000003,18.922500000000003,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,39,medical,18.9424991607666,19.262500762939453,True,1.1681875898474775e-13,0.4438864290714264,18.942492184714514,19.2624987997749,18.942500000000003,18.942500000000003,19.262500000000003,19.262500000000003,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,40,devices.,19.282499313354492,19.802499771118164,True,1.4210033974157432e-12,4.0,19.282502277797803,19.802471720501764,19.282500000000002,19.282500000000002,19.802500000000002,19.802500000000002,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,0,An,0.5625,0.6424999833106995,True,3.528646423450396e-14,2.5437161922454834,0.5517457553728019,0.6400199589743981,0.5625,0.5625,0.6425,0.6425,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,1,estimated,0.762499988079071,1.2625000476837158,True,3.06941976074501e-15,0.25,0.7624537774900568,1.2747010779553254,0.7625,0.7625,1.2625,1.3025,0.0,0.040000000000000036 --dxfTGcXJoc/2,-dxfTGcXJoc,2,2,"20,000",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/2,-dxfTGcXJoc,2,3,biotech,1.502500057220459,2.1424999237060547,True,5.178755102704102e-13,4.0,1.5016645351351747,2.145477612250111,1.5025,1.5025,2.1425,2.1625,0.0,0.020000000000000018 --dxfTGcXJoc/2,-dxfTGcXJoc,2,4,"scientists,",2.362499952316284,3.2825000286102295,True,3.88290907673422e-13,4.0,2.3601136758402634,3.177040551962975,2.3625,2.3625,2.9225,3.2825,0.0,0.3600000000000003 --dxfTGcXJoc/2,-dxfTGcXJoc,2,5,executives,3.3424999713897705,4.502500057220459,True,4.8946347734439566e-14,3.5284242630004883,3.3090474232848885,4.504003094828398,3.1825,3.4425,4.5025,4.5425,0.2599999999999998,0.040000000000000036 --dxfTGcXJoc/2,-dxfTGcXJoc,2,6,and,4.742499828338623,4.862500190734863,True,7.208249534101149e-15,0.5196253657341003,4.737240676908777,4.860440843264866,4.742500000000001,4.742500000000001,4.862500000000001,4.862500000000001,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,7,industry,5.042500019073486,5.442500114440918,True,6.4237835225287344e-15,0.46307510137557983,5.040880535443564,5.443378707143529,5.0425,5.0425,5.442500000000001,5.442500000000001,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,8,experts,5.582499980926514,6.022500038146973,True,3.06460179498938e-14,2.209197759628296,5.5703212329533915,6.014850126754625,5.482500000000001,5.5825000000000005,5.9625,6.022500000000001,0.09999999999999964,0.0600000000000005 --dxfTGcXJoc/2,-dxfTGcXJoc,2,9,from,6.422500133514404,6.582499980926514,True,9.360125936930621e-16,0.25,6.38109006975023,6.616702439780006,6.022500000000001,6.5025,6.5825000000000005,6.702500000000001,0.47999999999999954,0.1200000000000001 --dxfTGcXJoc/2,-dxfTGcXJoc,2,10,more,6.622499942779541,6.882500171661377,True,9.563321136515576e-14,4.0,6.664792178092532,6.8927378471517535,6.6225000000000005,6.7625,6.8825,6.9225,0.13999999999999968,0.040000000000000036 --dxfTGcXJoc/2,-dxfTGcXJoc,2,11,than,7.002500057220459,7.28249979019165,True,5.0399656372152393e-14,3.6331899166107178,7.0024999794329315,7.282500014929406,7.0025,7.0025,7.282500000000001,7.282500000000001,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,12,50,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/2,-dxfTGcXJoc,2,13,nations,7.362500190734863,7.742499828338623,True,3.6337700731715276e-15,0.261949747800827,7.369429892315447,7.749385730355412,7.362500000000001,7.4225,7.742500000000001,7.782500000000001,0.05999999999999961,0.040000000000000036 --dxfTGcXJoc/2,-dxfTGcXJoc,2,14,were,8.0625,8.22249984741211,True,1.6512727735737767e-14,1.190362811088562,8.062279727477186,8.222492308029125,8.0625,8.0625,8.2225,8.2225,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,15,at,8.382499694824219,8.462499618530273,True,2.5949011040230733e-15,0.25,8.382462737272258,8.46255610150032,8.3825,8.3825,8.4625,8.4625,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,16,the,8.522500038146973,8.642499923706055,True,1.0026508991150445e-15,0.25,8.527928748031597,8.648442775581263,8.5025,8.5625,8.6425,8.6825,0.0600000000000005,0.03999999999999915 --dxfTGcXJoc/2,-dxfTGcXJoc,2,17,BIO,8.662500381469727,8.942500114440918,True,7.251367041549717e-12,4.0,8.685797706798342,8.935314419747938,8.6625,8.8225,8.9025,8.9625,0.16000000000000014,0.0600000000000005 --dxfTGcXJoc/2,-dxfTGcXJoc,2,18,conference,9.0024995803833,9.5625,True,1.2436561925687623e-13,4.0,9.002366312686632,9.553113604517561,9.0025,9.0025,9.522499999999999,9.5625,0.0,0.040000000000000924 --dxfTGcXJoc/2,-dxfTGcXJoc,2,19,to,9.602499961853027,9.702500343322754,True,4.1949855590119045e-14,3.024064064025879,9.602500040663637,9.702500020830492,9.6025,9.6025,9.7025,9.7025,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,20,learn,9.802499771118164,10.0625,True,2.779141040325369e-16,0.25,9.80226629806537,10.062706163218133,9.8025,9.8025,10.0625,10.0625,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,21,about,10.202500343322754,10.442500114440918,True,1.123129689158944e-14,0.8096371293067932,10.18387034190013,10.433185442440921,10.1225,10.2025,10.4025,10.442499999999999,0.08000000000000007,0.03999999999999915 --dxfTGcXJoc/2,-dxfTGcXJoc,2,22,the,10.462499618530273,10.5625,True,2.2475309865299534e-15,0.25,10.462392739534936,10.562392741067562,10.4625,10.4625,10.5625,10.5625,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,23,latest,10.582500457763672,10.90250015258789,True,3.027837854573112e-15,0.25,10.582967332999607,10.903517748664571,10.5825,10.5825,10.9025,10.9025,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,24,scientific,10.922499656677246,11.442500114440918,True,9.800098143140554e-16,0.25,10.925212631979305,11.448081060031368,10.9225,10.9625,11.442499999999999,11.522499999999999,0.040000000000000924,0.08000000000000007 --dxfTGcXJoc/2,-dxfTGcXJoc,2,25,"advances,",11.5024995803833,12.042499542236328,True,3.089713950183218e-14,2.2273004055023193,11.505574248933469,12.04758122048326,11.5025,11.5425,12.0425,12.0625,0.040000000000000924,0.019999999999999574 --dxfTGcXJoc/2,-dxfTGcXJoc,2,26,and,12.082500457763672,12.6225004196167,True,2.6814522011229386e-14,1.9329943656921387,12.084524282799858,12.62267486373147,12.0825,12.0825,12.6225,12.6225,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,27,to,12.682499885559082,12.742500305175781,True,4.186913927947634e-15,0.30182453989982605,12.68249796092688,12.742535934060587,12.6825,12.6825,12.7425,12.7425,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,28,exchange,12.862500190734863,13.362500190734863,True,4.882353388631832e-15,0.3519571125507355,12.839402800312625,13.350664314337655,12.7825,12.862499999999999,13.3225,13.362499999999999,0.0799999999999983,0.03999999999999915 --dxfTGcXJoc/2,-dxfTGcXJoc,2,29,ideas,13.482500076293945,13.882499694824219,True,3.945389344004943e-14,2.8441362380981445,13.482486191160033,13.882548812984396,13.4825,13.4825,13.8825,13.8825,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,30,and,13.922499656677246,14.282500267028809,True,2.3429222700387803e-14,1.6889562606811523,13.922678087539742,14.282500215590204,13.9225,13.9225,14.2825,14.2825,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,31,establish,14.322500228881836,14.762499809265137,True,1.651188557322996e-15,0.25,14.322500022955364,14.773487639175407,14.3225,14.3225,14.7625,14.8425,0.0,0.08000000000000007 --dxfTGcXJoc/2,-dxfTGcXJoc,2,32,business,14.822500228881836,15.242500305175781,True,8.650353789394286e-14,4.0,14.832761792509285,15.240279039373075,14.8225,14.862499999999999,15.2025,15.2425,0.03999999999999915,0.03999999999999915 --dxfTGcXJoc/2,-dxfTGcXJoc,2,33,relationships.,15.262499809265137,16.022499084472656,True,2.0376777268147532e-15,0.25,15.26250556174122,16.02250089371535,15.2625,15.2625,16.0225,16.0225,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,0,Kentucky’s,0.42250001430511475,0.9225000143051147,True,5.297549725280515e-12,4.0,0.4224967278309293,0.9225000000223426,0.4225,0.4225,0.9225,0.9225,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,1,participation,0.9424999952316284,1.7825000286102295,True,3.977016199002703e-14,0.8985112905502319,0.9465664649478172,1.7885813004286177,0.9425,0.9824999999999999,1.7825,1.8425,0.039999999999999925,0.06000000000000005 --dxfTGcXJoc/6,-dxfTGcXJoc,6,2,at,1.8424999713897705,1.9225000143051147,True,2.1943811513974038e-15,0.25,1.8533036193237216,1.9368632112892796,1.8425,1.9025,1.9224999999999999,2.0025,0.06000000000000005,0.08000000000000007 --dxfTGcXJoc/6,-dxfTGcXJoc,6,3,the,2.0225000381469727,2.1624999046325684,True,1.865988171812806e-15,0.25,2.020929687527187,2.151507239832155,1.9825,2.0225,2.0825,2.1625,0.040000000000000036,0.08000000000000007 --dxfTGcXJoc/6,-dxfTGcXJoc,6,4,annual,2.3424999713897705,2.682499885559082,True,2.7859146738661023e-13,4.0,2.308788690749572,2.6643921880715054,2.1625,2.3425,2.6225,2.6825,0.17999999999999972,0.06000000000000005 --dxfTGcXJoc/6,-dxfTGcXJoc,6,5,BIO,2.742500066757202,2.942500114440918,True,1.0218228173319854e-11,4.0,2.739909203795047,2.971583070334784,2.7425,2.7425,2.9425,3.0425,0.0,0.10000000000000009 --dxfTGcXJoc/6,-dxfTGcXJoc,6,6,International,2.9825000762939453,3.7225000858306885,True,4.252274124179681e-14,0.9606992602348328,3.0381234983409264,3.7414172328758766,2.9825,3.1225,3.7225,3.7625,0.14000000000000012,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,7,Convention,3.802500009536743,4.882500171661377,True,1.806679534709782e-13,4.0,3.7986267028834164,4.640981911105811,3.7625,3.8025,4.322500000000001,4.8825,0.040000000000000036,0.5599999999999996 --dxfTGcXJoc/6,-dxfTGcXJoc,6,8,is,4.902500152587891,4.962500095367432,True,1.016744997961764e-15,0.25,4.882875855533999,4.9563961056449015,4.862500000000001,4.902500000000001,4.9225,4.9625,0.040000000000000036,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,9,the,5.0625,5.162499904632568,True,1.3455526298201959e-13,3.0399529933929443,5.055186037952798,5.159575162330058,4.9625,5.062500000000001,5.1225000000000005,5.1625000000000005,0.10000000000000053,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,10,primary,5.202499866485596,5.702499866485596,True,1.5114196718723875e-14,0.3414689898490906,5.2024999401486784,5.710515431236941,5.202500000000001,5.202500000000001,5.702500000000001,5.742500000000001,0.0,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,11,means,5.802499771118164,6.142499923706055,True,4.23735550228628e-14,0.957328736782074,5.802486364345739,6.142863221968487,5.8025,5.8025,6.142500000000001,6.142500000000001,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,12,by,6.262499809265137,6.382500171661377,True,7.462917436806826e-12,4.0,6.262499999696703,6.382500000022064,6.2625,6.2625,6.3825,6.3825,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,13,which,6.422500133514404,6.682499885559082,True,4.42622826411776e-14,1.0,6.449507150222789,6.7000271550535855,6.4225,6.482500000000001,6.6825,6.7225,0.0600000000000005,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,14,our,7.002500057220459,7.162499904632568,True,5.836931144583696e-14,1.3187144994735718,6.956397673938863,7.150806240348821,6.7225,7.022500000000001,7.102500000000001,7.1625000000000005,0.3000000000000007,0.05999999999999961 --dxfTGcXJoc/6,-dxfTGcXJoc,6,15,high-tech,7.222499847412109,7.662499904632568,True,1.0861458217201192e-12,4.0,7.2149935824394245,7.665882070120274,7.1625000000000005,7.2225,7.6625000000000005,7.702500000000001,0.05999999999999961,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,16,companies,7.762499809265137,8.202500343322754,True,2.5332166919689314e-13,4.0,7.762376133681743,8.20258015845541,7.7625,7.7625,8.202499999999999,8.202499999999999,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,17,and,8.282500267028809,8.40250015258789,True,5.609055226036587e-14,1.2672313451766968,8.25984785590537,8.380906738814469,8.2225,8.282499999999999,8.362499999999999,8.4025,0.05999999999999872,0.040000000000000924 --dxfTGcXJoc/6,-dxfTGcXJoc,6,18,institutions,8.422499656677246,9.142499923706055,True,5.389259832399695e-15,0.25,8.422248950513739,9.142531347225114,8.4225,8.4225,9.1425,9.1425,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,19,can,9.242500305175781,9.5024995803833,True,2.1174656893472666e-15,0.25,9.282007274928727,9.511500731685478,9.1625,9.362499999999999,9.4825,9.5625,0.1999999999999993,0.08000000000000007 --dxfTGcXJoc/6,-dxfTGcXJoc,6,20,reach,9.5625,9.822500228881836,True,1.6722176960737127e-14,0.3777974247932434,9.569936347326685,9.826579649667691,9.5625,9.6425,9.8225,9.8825,0.08000000000000007,0.0600000000000005 --dxfTGcXJoc/6,-dxfTGcXJoc,6,21,out,9.882499694824219,10.1225004196167,True,3.801270076161413e-14,0.8588057160377502,9.905369645603065,10.128871358968683,9.8825,10.022499999999999,10.1225,10.1625,0.1399999999999988,0.03999999999999915 --dxfTGcXJoc/6,-dxfTGcXJoc,6,22,to,10.262499809265137,10.342499732971191,True,1.2812964393909415e-13,2.8947815895080566,10.262491452605754,10.342524144191376,10.2625,10.2625,10.3425,10.3425,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,23,the,10.422499656677246,10.522500038146973,True,1.0252761232240962e-14,0.25,10.420831128496536,10.52083710974172,10.4225,10.4225,10.522499999999999,10.522499999999999,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,24,global,10.542499542236328,10.90250015258789,True,4.665796625468767e-14,1.0541247129440308,10.54248245260101,10.903017817334318,10.5425,10.5425,10.9025,10.9025,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,25,scientific,10.942500114440918,11.582500457763672,True,1.1520418035607539e-14,0.260276198387146,10.943268176515302,11.582229395041422,10.942499999999999,10.942499999999999,11.5825,11.5825,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,26,community.,11.602499961853027,12.102499961853027,True,2.5937306230745805e-13,4.0,11.612190715637311,12.102458094905488,11.6025,11.6425,12.1025,12.1025,0.040000000000000924,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,0,We're,0.0024999999441206455,0.5824999809265137,True,2.7304946498274418e-11,4.0,0.04607508111870628,0.5898032740548333,0.0025000000000000005,0.5225,0.5824999999999999,0.6625,0.52,0.08000000000000007 --egA8-b7-3M/26,-egA8-b7-3M,26,1,updating,0.6025000214576721,1.1425000429153442,True,1.190343722028142e-12,4.0,0.6201202747881431,1.1412705985929916,0.6024999999999999,0.7025,1.1225,1.1624999999999999,0.10000000000000009,0.039999999999999813 --egA8-b7-3M/26,-egA8-b7-3M,26,2,it,1.2024999856948853,1.2625000476837158,True,6.882085098200777e-15,0.25,1.2068630134194995,1.2669326558135239,1.2025,1.2425,1.2625,1.3025,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/26,-egA8-b7-3M,26,3,all,1.3224999904632568,1.5225000381469727,True,3.317236825397911e-14,1.0,1.3240528697525118,1.5232767925449109,1.3225,1.3225,1.5225,1.5225,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,4,the,1.5824999809265137,1.6825000047683716,True,1.9559788579532342e-15,0.25,1.5769041980110663,1.6835013931160456,1.5625,1.5825,1.6824999999999999,1.6824999999999999,0.020000000000000018,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,5,time,1.7024999856948853,1.9824999570846558,True,4.720274798845024e-16,0.25,1.7062997103515074,1.984997575047607,1.7025,1.7425,1.9825,2.0225,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/26,-egA8-b7-3M,26,6,and,2.0225000381469727,2.5625,True,6.023966183732812e-15,0.25,2.0364141891598666,2.5625021914273565,2.0225,2.1825,2.5625,2.5625,0.16000000000000014,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,7,looking,2.5824999809265137,2.942500114440918,True,5.855222312859884e-14,1.7650902271270752,2.582682866295387,2.9398393089929113,2.5825,2.5825,2.9025,2.9425,0.0,0.040000000000000036 --egA8-b7-3M/26,-egA8-b7-3M,26,8,forward,3.002500057220459,3.3424999713897705,True,3.392760824073946e-15,0.25,2.999731412922151,3.3424460140552243,3.0025,3.0025,3.3425,3.3425,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,9,to,3.382499933242798,3.4625000953674316,True,1.8818388687613652e-14,0.5672910809516907,3.3825160972871147,3.462510902065862,3.3825,3.3825,3.4625,3.4625,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,10,your,3.5225000381469727,3.742500066757202,True,6.742193371796993e-15,0.25,3.522994713898922,3.743594339737526,3.5225,3.5225,3.7425,3.7425,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,11,comments,3.7825000286102295,4.302499771118164,True,6.467650850647266e-14,1.9497103691101074,3.78386669880374,4.305125915713463,3.7825,3.7825,4.3025,4.3025,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,12,on,4.462500095367432,4.5625,True,3.0199590658058773e-12,4.0,4.438194697536424,4.541530148943003,4.3425,4.4625,4.482500000000001,4.562500000000001,0.1200000000000001,0.08000000000000007 --egA8-b7-3M/26,-egA8-b7-3M,26,13,our,4.602499961853027,4.742499828338623,True,1.3194816766981532e-11,4.0,4.597614970972109,4.7378090732319125,4.5425,4.602500000000001,4.6825,4.742500000000001,0.0600000000000005,0.0600000000000005 --egA8-b7-3M/26,-egA8-b7-3M,26,14,videos.,4.882500171661377,5.242499828338623,True,7.06815682371964e-12,4.0,4.839578380641243,5.242501309845208,4.7225,4.8825,5.242500000000001,5.242500000000001,0.16000000000000014,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,0,As,0.6225000023841858,0.6825000047683716,True,6.329900796228638e-13,4.0,0.6225000000896067,0.682500000365255,0.6224999999999999,0.6224999999999999,0.6825,0.6825,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,1,you,0.7825000286102295,0.9424999952316284,True,1.412991631252053e-13,4.0,0.7823893755411854,0.9310866364147513,0.7825,0.7825,0.8825,0.9425,0.0,0.06000000000000005 --egA8-b7-3M/17,-egA8-b7-3M,17,2,have,0.9624999761581421,1.1024999618530273,True,5.60062928109048e-15,0.25,0.9529266344489182,1.098373411090721,0.9225,0.9624999999999999,1.0625,1.1225,0.039999999999999925,0.06000000000000005 --egA8-b7-3M/17,-egA8-b7-3M,17,3,more,1.1825000047683716,1.3424999713897705,True,7.51511771243012e-16,0.25,1.1824028515775171,1.3425010470509353,1.1824999999999999,1.1824999999999999,1.3425,1.3425,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,4,"experience,",1.3825000524520874,2.122499942779541,True,1.3858544574505555e-13,4.0,1.3873374469946451,2.1217125144118096,1.3825,1.4625,2.1225,2.1225,0.07999999999999985,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,5,you,3.2225000858306885,3.3424999713897705,True,3.083890429264255e-14,1.0576536655426025,3.213691251973658,3.354710556297301,3.2225,3.2225,3.3225000000000002,3.3825,0.0,0.05999999999999961 --egA8-b7-3M/17,-egA8-b7-3M,17,6,have,3.362499952316284,3.5425000190734863,True,3.8175747827697004e-14,1.3092786073684692,3.377375456438648,3.5414414297293204,3.3625,3.4025,3.5425,3.5425,0.040000000000000036,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,7,more,3.6624999046325684,3.802500009536743,True,4.685770594101265e-15,0.25,3.6478162339869873,3.7964527218527744,3.5825,3.6625,3.7625,3.8025,0.08000000000000007,0.040000000000000036 --egA8-b7-3M/17,-egA8-b7-3M,17,8,times,3.8424999713897705,4.182499885559082,True,1.295994798836738e-14,0.4444754421710968,3.8424998444900877,4.182500874910728,3.8425,3.8425,4.1825,4.1825,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,9,"exhibiting,",4.222499847412109,4.882500171661377,True,6.736042387940622e-14,2.3101987838745117,4.223205191074807,4.882471799434598,4.2225,4.2225,4.8825,4.8825,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,10,you,5.242499828338623,5.34250020980835,True,1.6752002473117609e-15,0.25,5.242488668737627,5.3432239190882544,5.242500000000001,5.242500000000001,5.3425,5.3425,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,11,get,5.402500152587891,5.522500038146973,True,2.7476793355765e-14,0.9423463940620422,5.402500228003155,5.522500551047494,5.402500000000001,5.402500000000001,5.522500000000001,5.522500000000001,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,12,to,5.582499980926514,5.642499923706055,True,4.209141343033008e-15,0.25,5.582587451647595,5.642588721621551,5.5825000000000005,5.5825000000000005,5.6425,5.6425,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,13,know,5.702499866485596,5.922500133514404,True,6.87243773174473e-14,2.3569769859313965,5.683751382141159,5.890393221205199,5.6625000000000005,5.702500000000001,5.822500000000001,5.9225,0.040000000000000036,0.09999999999999964 --egA8-b7-3M/17,-egA8-b7-3M,17,14,more,5.942500114440918,6.102499961853027,True,3.703195011110858e-15,0.25,5.942366821871721,6.103246214752875,5.942500000000001,5.942500000000001,6.102500000000001,6.102500000000001,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,15,people.,6.182499885559082,6.542500019073486,True,1.1336075036571386e-13,3.8878297805786133,6.182499922873765,6.542499989348836,6.1825,6.1825,6.5425,6.5425,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,0,It,0.5824999809265137,0.6825000047683716,True,5.045397490099392e-14,1.2702128887176514,0.4498708134808255,0.6283878986425523,0.0825,0.5824999999999999,0.6024999999999999,0.6825,0.4999999999999999,0.08000000000000007 --egA8-b7-3M/18,-egA8-b7-3M,18,1,becomes,0.7024999856948853,1.122499942779541,True,1.2237954891819707e-14,0.30809876322746277,0.6740202091094792,1.123904883788223,0.6625,0.7025,1.1225,1.1425,0.040000000000000036,0.020000000000000018 --egA8-b7-3M/18,-egA8-b7-3M,18,2,easier,1.1825000047683716,1.7424999475479126,True,5.1457912231397604e-15,0.25,1.2593575459889583,1.7659632994273018,1.1824999999999999,1.3825,1.7425,1.8225,0.20000000000000018,0.08000000000000007 --egA8-b7-3M/18,-egA8-b7-3M,18,3,at,1.8424999713897705,1.9824999570846558,True,1.3661030043733008e-13,3.439256429672241,1.8446638169367537,1.9840793573308473,1.8425,1.8425,1.9825,1.9825,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,4,each,2.182499885559082,2.362499952316284,True,2.2053683545694182e-14,0.555216372013092,2.175812096254957,2.353765489008425,2.0625,2.1825,2.3225,2.3625,0.1200000000000001,0.040000000000000036 --egA8-b7-3M/18,-egA8-b7-3M,18,5,one,2.4825000762939453,2.6024999618530273,True,8.226489693445582e-14,2.0710742473602295,2.465061924003497,2.5964168548924764,2.3625,2.4825,2.5625,2.6025,0.1200000000000001,0.040000000000000036 --egA8-b7-3M/18,-egA8-b7-3M,18,6,of,2.622499942779541,2.682499885559082,True,2.7406403223782273e-14,0.6899747252464294,2.6223798933822544,2.68237989421122,2.6225,2.6225,2.6825,2.6825,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,7,these,2.702500104904175,2.882499933242798,True,2.363750047882111e-15,0.25,2.702441638482025,2.882768890312429,2.7025,2.7025,2.8825,2.8825,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,8,conferences,2.942500114440918,3.6024999618530273,True,9.357525334024314e-14,2.3558201789855957,2.940330155669175,3.593531353090126,2.9025,2.9425,3.5825,3.6025,0.040000000000000036,0.020000000000000018 --egA8-b7-3M/18,-egA8-b7-3M,18,9,to,4.102499961853027,4.202499866485596,True,2.403003740002879e-13,4.0,4.10248789549808,4.202500336033287,4.1025,4.1025,4.202500000000001,4.202500000000001,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,10,connect,4.222499847412109,4.582499980926514,True,2.2258525589086836e-13,4.0,4.226862793204726,4.588976822861837,4.2225,4.2625,4.5825000000000005,4.6425,0.040000000000000036,0.05999999999999961 --egA8-b7-3M/18,-egA8-b7-3M,18,11,with,4.622499942779541,4.78249979019165,True,3.549643396124941e-15,0.25,4.643047295040439,4.802463991137171,4.6225000000000005,4.6625000000000005,4.782500000000001,4.822500000000001,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/18,-egA8-b7-3M,18,12,people,4.882500171661377,5.162499904632568,True,1.767017623530414e-14,0.4448586106300354,4.882395800547012,5.162499794327169,4.8825,4.8825,5.1625000000000005,5.1625000000000005,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,13,that,5.202499866485596,5.362500190734863,True,5.594892829864539e-17,0.25,5.2024996957554865,5.3532616793556365,5.202500000000001,5.202500000000001,5.3425,5.362500000000001,0.0,0.020000000000000462 --egA8-b7-3M/18,-egA8-b7-3M,18,14,you've,5.382500171661377,5.602499961853027,True,1.3271654608626449e-11,4.0,5.382502593463899,5.602570477903483,5.3825,5.3825,5.602500000000001,5.602500000000001,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,15,met,5.682499885559082,5.882500171661377,True,1.9963176077497646e-13,4.0,5.679981733714091,5.879140283703421,5.6825,5.6825,5.8825,5.8825,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,16,or,6.422500133514404,6.482500076293945,True,7.505055469236507e-14,1.8894484043121338,6.397987073919746,6.459058711645056,6.4225,6.4225,6.482500000000001,6.482500000000001,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,17,that,6.522500038146973,6.662499904632568,True,7.683182416404072e-17,0.25,6.51310953711065,6.660509407784024,6.522500000000001,6.522500000000001,6.6625000000000005,6.6825,0.0,0.019999999999999574 --egA8-b7-3M/18,-egA8-b7-3M,18,18,you're,6.682499885559082,6.862500190734863,True,3.4061416881447926e-12,4.0,6.681564351164622,6.866491034969756,6.6825,6.702500000000001,6.862500000000001,6.8825,0.020000000000000462,0.019999999999999574 --egA8-b7-3M/18,-egA8-b7-3M,18,19,doing,6.902500152587891,7.142499923706055,True,2.8987788325317634e-14,0.7297871112823486,6.902497657128809,7.133894547298574,6.902500000000001,6.902500000000001,7.102500000000001,7.142500000000001,0.0,0.040000000000000036 --egA8-b7-3M/18,-egA8-b7-3M,18,20,business,7.182499885559082,7.5625,True,2.4658436913443244e-14,0.620792806148529,7.175063793795647,7.562567369568958,7.142500000000001,7.1825,7.562500000000001,7.562500000000001,0.03999999999999915,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,21,with.,7.622499942779541,7.882500171661377,True,1.9961424874148648e-11,4.0,7.622509877955194,7.882500128054946,7.6225000000000005,7.6225000000000005,7.8825,7.8825,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,0,"Third,",0.48249998688697815,0.762499988079071,True,1.2783299267217496e-14,0.45957401394844055,0.4823735110546779,0.765816467639421,0.4825,0.4825,0.7625,0.8225,0.0,0.06000000000000005 --egA8-b7-3M/16,-egA8-b7-3M,16,1,another,0.8424999713897705,1.2825000286102295,True,4.566552146731233e-14,1.6417269706726074,0.8410692456432085,1.2830330828196779,0.8225,0.8424999999999999,1.2825,1.3025,0.019999999999999907,0.020000000000000018 --egA8-b7-3M/16,-egA8-b7-3M,16,2,big,1.3224999904632568,1.4824999570846558,True,1.7119000038064505e-14,0.6154473423957825,1.3225007758015552,1.4825008899204788,1.3225,1.3225,1.4825,1.4825,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,3,benefit,1.5625,1.962499976158142,True,4.657146047385048e-14,1.674296498298645,1.5624781565144765,1.962500288652209,1.5625,1.5625,1.9625,1.9625,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,4,of,2.0225000381469727,2.1024999618530273,True,2.248744234171953e-14,0.808448851108551,2.022781738748026,2.097683776003884,2.0225,2.0225,2.0825,2.1025,0.0,0.020000000000000018 --egA8-b7-3M/16,-egA8-b7-3M,16,5,exhibiting,2.122499942779541,2.7225000858306885,True,2.7815540466096834e-14,1.0,2.1197486749478904,2.722499622656068,2.1025,2.1225,2.7225,2.7225,0.020000000000000018,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,6,is,2.822499990463257,2.882499933242798,True,7.896127192852029e-14,2.8387465476989746,2.822455644522566,2.8825844839545818,2.8225,2.8225,2.8825,2.8825,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,7,connecting,2.922499895095825,3.4825000762939453,True,5.372833745970066e-15,0.25,2.9224863158974808,3.4838534184456353,2.9225,2.9225,3.4625,3.5225,0.0,0.06000000000000005 --egA8-b7-3M/16,-egA8-b7-3M,16,8,with,3.502500057220459,3.6624999046325684,True,8.081755225572461e-16,0.25,3.5063061035173675,3.66635899435173,3.5025,3.5425,3.6625,3.7025,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/16,-egA8-b7-3M,16,9,existing,3.742500066757202,4.302499771118164,True,7.100570131868267e-14,2.552734851837158,3.743222429243274,4.303965535822457,3.7425,3.7425,4.3025,4.3025,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,10,clients.,4.362500190734863,4.822500228881836,True,1.5700299133697415e-13,4.0,4.359248951566402,4.822504151992989,4.322500000000001,4.362500000000001,4.822500000000001,4.822500000000001,0.040000000000000036,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,0,What,0.24250000715255737,0.6225000023841858,True,1.5400974069637363e-16,0.25,0.2141840485714987,0.6218597133402548,0.0225,0.4225,0.6224999999999999,0.6224999999999999,0.39999999999999997,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,1,are,0.6424999833106995,0.7425000071525574,True,8.498745056231133e-14,2.449159622192383,0.6421815628124172,0.742500011758617,0.6425,0.6425,0.7424999999999999,0.7424999999999999,0.0,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,2,you,0.762499988079071,0.862500011920929,True,8.022830108047295e-15,0.25,0.7653580882059453,0.8659613972267409,0.7625,0.7825,0.8624999999999999,0.8825,0.020000000000000018,0.020000000000000018 --egA8-b7-3M/13,-egA8-b7-3M,13,3,doing,0.9024999737739563,1.122499942779541,True,2.871552483101579e-14,0.827521026134491,0.9025000213152364,1.1225003642958045,0.9025,0.9025,1.1225,1.1225,0.0,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,4,that's,1.1425000429153442,1.3624999523162842,True,1.606225569217301e-11,4.0,1.1429115027852526,1.3625002174895329,1.1425,1.1425,1.3625,1.3625,0.0,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,5,"different?""",1.3825000524520874,1.7825000286102295,True,1.846142698078406e-14,0.5320194959640503,1.4034251715517392,2.3733903913393863,1.3825,1.4224999999999999,1.7825,2.4425,0.039999999999999813,0.6599999999999999 --egA8-b7-3M/13,-egA8-b7-3M,13,6,They,1.8025000095367432,2.4625000953674316,True,1.3074539687669626e-14,0.3767807185649872,2.441006479437174,2.718606976840083,1.8025,2.5025,2.4825,2.7425,0.7,0.26000000000000023 --egA8-b7-3M/13,-egA8-b7-3M,13,7,always,2.502500057220459,2.7825000286102295,True,5.6516907988432216e-14,1.6286983489990234,2.7741362125960785,3.0562366014397764,2.5025,2.8025,2.7825,3.0825,0.30000000000000027,0.2999999999999998 --egA8-b7-3M/13,-egA8-b7-3M,13,8,want,2.802500009536743,2.9825000762939453,True,3.4700659636314676e-14,1.0,3.1022008047455936,3.3186257301717013,2.8025,3.2025,2.9825,3.4425,0.3999999999999999,0.45999999999999996 --egA8-b7-3M/13,-egA8-b7-3M,13,9,an,3.0625,3.122499942779541,True,3.821534492391525e-14,1.1012858152389526,3.4092592789422267,3.47028094392638,3.0625,3.4625,3.1225,3.5225,0.3999999999999999,0.3999999999999999 --egA8-b7-3M/13,-egA8-b7-3M,13,10,answer.,3.202500104904175,3.5625,True,1.833897183417532e-12,4.0,3.5131397249203156,3.9093662996538545,3.2025,3.5625,3.5625,4.0025,0.3599999999999999,0.4400000000000004 --egA8-b7-3M/1,-egA8-b7-3M,1,0,I,0.0024999999441206455,0.02250000089406967,True,3.349408424224709e-12,4.0,0.00807213853936062,0.02807213853936155,0.0025000000000000005,0.0825,0.0225,0.10250000000000001,0.08,0.08000000000000002 --egA8-b7-3M/1,-egA8-b7-3M,1,1,started,0.042500000447034836,0.30250000953674316,True,2.7094884495759697e-13,0.8343841433525085,0.08285976140362887,0.392640622001406,0.0425,0.6224999999999999,0.3025,1.6225,0.58,1.32 --egA8-b7-3M/1,-egA8-b7-3M,1,2,a,0.6225000023841858,0.6424999833106995,True,1.6140776215589625e-11,4.0,0.7018397959792573,0.7218397959792763,0.6224999999999999,1.7625,0.6425,1.7825,1.1400000000000001,1.1400000000000001 --egA8-b7-3M/1,-egA8-b7-3M,1,3,successful,0.762499988079071,1.622499942779541,True,4.594923345892926e-14,0.25,0.8340952262394151,1.6983804921435448,0.7025,1.8425,1.6225,2.7025,1.1400000000000001,1.08 --egA8-b7-3M/1,-egA8-b7-3M,1,4,Legal,1.8424999713897705,2.382499933242798,True,9.063408118632765e-13,2.791066884994507,1.909492493181274,2.4335311441149527,1.8425,2.7825,2.3825,3.1025,0.9400000000000002,0.7200000000000002 --egA8-b7-3M/1,-egA8-b7-3M,1,5,Nurse,2.4625000953674316,2.702500104904175,True,3.247291487356446e-13,1.0,2.5228978831850655,2.7700245247650632,2.4625,3.2025,2.7025,3.4625,0.7400000000000002,0.7599999999999998 --egA8-b7-3M/1,-egA8-b7-3M,1,6,Consulting,2.7825000286102295,3.4825000762939453,True,9.766556053475428e-13,3.007600784301758,2.8444628086268335,3.550414946085584,2.7825,3.5425,3.4625,4.562500000000001,0.7599999999999998,1.100000000000001 --egA8-b7-3M/1,-egA8-b7-3M,1,7,Business,3.5425000190734863,3.9825000762939453,True,1.635268933068354e-13,0.5035793781280518,3.6237951499738292,4.086650313197705,3.5425,4.702500000000001,3.9825,5.402500000000001,1.1600000000000006,1.4200000000000008 --egA8-b7-3M/1,-egA8-b7-3M,1,8,in,4.002500057220459,4.5625,True,9.467710767934942e-14,0.2915571630001068,4.122795613570893,4.643007217848893,4.0025,5.6225000000000005,4.562500000000001,5.6825,1.62,1.1199999999999992 --egA8-b7-3M/1,-egA8-b7-3M,1,9,1989.,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --egA8-b7-3M/1,-egA8-b7-3M,1,10,I,4.702499866485596,4.722499847412109,True,3.378953340000754e-14,0.25,4.822565464327348,4.842565464327365,4.702500000000001,6.3825,4.7225,6.402500000000001,1.6799999999999997,1.6800000000000006 --egA8-b7-3M/1,-egA8-b7-3M,1,11,mentor,4.802499771118164,5.102499961853027,True,1.5290421923400133e-14,0.25,4.9231409745934025,5.229286423895843,4.8025,6.482500000000001,5.102500000000001,6.8425,1.6800000000000006,1.7399999999999993 --egA8-b7-3M/1,-egA8-b7-3M,1,12,nurses,5.142499923706055,5.402500152587891,True,3.649005231277317e-15,0.25,5.285193221719072,5.554316901085585,5.1425,6.902500000000001,5.402500000000001,7.3025,1.7600000000000007,1.8999999999999995 --egA8-b7-3M/1,-egA8-b7-3M,1,13,who,5.622499942779541,5.722499847412109,True,1.0704910807617096e-13,0.32965660095214844,5.754514660120223,5.858585417759601,5.6225000000000005,7.402500000000001,5.7225,7.522500000000001,1.7800000000000002,1.8000000000000007 --egA8-b7-3M/1,-egA8-b7-3M,1,14,want,6.382500171661377,6.682499885559082,True,5.1592024923052815e-12,4.0,6.4705962452594825,6.762408602745925,6.3825,7.562500000000001,6.6825,7.7625,1.1800000000000006,1.08 --egA8-b7-3M/1,-egA8-b7-3M,1,15,to,6.762499809265137,6.84250020980835,True,2.8021755922591485e-12,4.0,6.843815901061743,6.922242147418595,6.7625,7.8425,6.8425,7.902500000000001,1.08,1.0600000000000005 --egA8-b7-3M/1,-egA8-b7-3M,1,16,become,6.942500114440918,7.462500095367432,True,5.2748408939373714e-12,4.0,7.017877169431938,7.522635350083966,6.942500000000001,7.9225,7.4625,8.2425,0.9799999999999995,0.7799999999999994 --egA8-b7-3M/1,-egA8-b7-3M,1,17,Legal,7.5625,7.902500152587891,True,1.0792557711240824e-13,0.33235567808151245,7.620066755669658,7.950217895020635,7.562500000000001,8.3225,7.902500000000001,8.5425,0.7599999999999989,0.6399999999999997 --egA8-b7-3M/1,-egA8-b7-3M,1,18,Nurse,7.962500095367432,8.242500305175781,True,1.0314321011867245e-12,3.1762843132019043,8.00911242193168,8.283288744907443,7.9625,8.5825,8.2425,8.782499999999999,0.6199999999999992,0.5399999999999991 --egA8-b7-3M/1,-egA8-b7-3M,1,19,Consultants.,8.382499694824219,9.5024995803833,True,3.968895958411656e-12,4.0,8.410315918310687,9.492776884061083,8.3225,8.8225,9.4625,9.5025,0.5,0.03999999999999915 --egA8-b7-3M/6,-egA8-b7-3M,6,0,First,0.5224999785423279,0.8025000095367432,True,2.200081683132425e-15,0.25,0.4853864311485953,0.8069054596275058,0.2025,0.5225,0.8025,0.8624999999999999,0.31999999999999995,0.05999999999999994 --egA8-b7-3M/6,-egA8-b7-3M,6,1,exhibiting,0.8424999713897705,1.5625,True,3.04314916094086e-14,1.037160038948059,0.8581546005495158,1.5735363871558865,0.8424999999999999,1.0425,1.5625,1.6025,0.20000000000000007,0.040000000000000036 --egA8-b7-3M/6,-egA8-b7-3M,6,2,is,1.6425000429153442,1.722499966621399,True,2.2094691798802552e-13,4.0,1.6424968655880368,1.7224987665687466,1.6425,1.6425,1.7225,1.7225,0.0,0.0 --egA8-b7-3M/6,-egA8-b7-3M,6,3,a,1.8025000095367432,1.8224999904632568,True,1.2546567951243759e-11,4.0,1.8023980684775633,1.8223980684775658,1.8025,1.8025,1.8225,1.8225,0.0,0.0 --egA8-b7-3M/6,-egA8-b7-3M,6,4,wonderful,1.9424999952316284,2.4625000953674316,True,2.6952779811077916e-14,0.9185992479324341,1.8748230106996233,2.4290915945601004,1.8425,1.9425,2.3825,2.4625,0.09999999999999987,0.08000000000000007 --egA8-b7-3M/6,-egA8-b7-3M,6,5,opportunity,2.5425000190734863,3.202500104904175,True,1.6584008946381006e-14,0.5652128458023071,2.505562645229575,3.206492567731367,2.4625,2.5425,3.2025,3.2225,0.08000000000000007,0.020000000000000018 --egA8-b7-3M/6,-egA8-b7-3M,6,6,to,3.2825000286102295,3.382499933242798,True,4.802218396700829e-14,1.636682391166687,3.282497056715122,3.3825000470074538,3.2825,3.2825,3.3825,3.3825,0.0,0.0 --egA8-b7-3M/6,-egA8-b7-3M,6,7,network,3.4825000762939453,3.922499895095825,True,2.825085780274513e-14,0.9628400802612305,3.4783378131246514,3.9006160511864714,3.4225,3.4825,3.8625,3.9225,0.06000000000000005,0.06000000000000005 --egA8-b7-3M/6,-egA8-b7-3M,6,8,with,3.9625000953674316,4.122499942779541,True,5.249891572357314e-15,0.25,3.9591645135049784,4.119164923234471,3.9225,3.9625,4.0825000000000005,4.1225000000000005,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/6,-egA8-b7-3M,6,9,other,4.262499809265137,4.502500057220459,True,3.9747078648148457e-14,1.354651927947998,4.241709003588393,4.488159328074618,4.1225000000000005,4.2625,4.442500000000001,4.5025,0.13999999999999968,0.05999999999999961 --egA8-b7-3M/6,-egA8-b7-3M,6,10,legal,4.602499961853027,4.922500133514404,True,2.4844702846676253e-14,0.8467521667480469,4.572833735083782,4.898411272791733,4.482500000000001,4.602500000000001,4.8425,4.9225,0.1200000000000001,0.08000000000000007 --egA8-b7-3M/6,-egA8-b7-3M,6,11,vendors.,4.962500095367432,5.442500114440918,True,5.481644191297416e-14,1.8682429790496826,4.962452113281357,5.442494524553728,4.9625,4.9625,5.442500000000001,5.442500000000001,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,0,"Secondly,",0.5625,1.0824999809265137,True,1.8873178844400207e-13,1.530295729637146,0.5556694643132206,1.0816803932159271,0.5625,0.5625,1.0825,1.0825,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,1,another,1.122499942779541,1.5824999809265137,True,1.2623711483691208e-13,1.0235695838928223,1.1289479746884983,1.585168118194489,1.1225,1.1225,1.5825,1.5825,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,2,goal,1.6825000047683716,1.8825000524520874,True,1.2042343305266462e-13,0.9764304161071777,1.6824995293756553,1.8825667309258272,1.6824999999999999,1.6824999999999999,1.8825,1.8825,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,3,of,1.9824999570846558,2.0425000190734863,True,9.055312055750164e-12,4.0,1.9823969628656442,2.0423972940717987,1.9825,1.9825,2.0425,2.0425,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,4,exhibiting,2.1024999618530273,2.622499942779541,True,2.0145135356413636e-14,0.25,2.102419345902239,2.6224487529212688,2.1025,2.1025,2.6225,2.6225,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,5,is,2.742500066757202,2.8424999713897705,True,2.4083781843819985e-12,4.0,2.742313009892268,2.8279796871804743,2.7425,2.7425,2.8025,2.8425,0.0,0.03999999999999959 --egA8-b7-3M/9,-egA8-b7-3M,9,6,building,2.862499952316284,3.2825000286102295,True,2.3681746938886833e-14,0.25,2.8559528332329007,3.282546562084778,2.8425,2.8625,3.2825,3.2825,0.020000000000000018,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,7,your,3.322499990463257,3.502500057220459,True,8.219796608502968e-15,0.25,3.322499735080081,3.5015646541237775,3.3225000000000002,3.3225000000000002,3.5025,3.5025,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,8,company,3.5225000381469727,3.9024999141693115,True,3.6006809513156046e-13,2.919543504714966,3.5224999329970728,3.9025043103914396,3.5225,3.5225,3.9025,3.9025,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,9,image.,4.042500019073486,4.302499771118164,True,5.5716044266033906e-15,0.25,4.064112624999909,4.345916642191695,4.0425,4.1025,4.3025,4.3825,0.05999999999999961,0.08000000000000007 --egA8-b7-3M/20,-egA8-b7-3M,20,0,Now,0.5824999809265137,0.7425000071525574,True,3.1301773916999134e-14,0.2761642634868622,0.4356619142255531,0.7065679669073096,0.0625,0.5824999999999999,0.6825,0.7424999999999999,0.5199999999999999,0.05999999999999994 --egA8-b7-3M/20,-egA8-b7-3M,20,1,you've,0.762499988079071,0.9624999761581421,True,1.005963715106084e-11,4.0,0.7493537559098281,0.9559724070717779,0.7424999999999999,0.7625,0.9425,0.9624999999999999,0.020000000000000018,0.019999999999999907 --egA8-b7-3M/20,-egA8-b7-3M,20,2,got,0.9825000166893005,1.1825000047683716,True,2.447966891585933e-14,0.25,0.9825000739198075,1.1663853893456297,0.9824999999999999,0.9824999999999999,1.1425,1.1824999999999999,0.0,0.039999999999999813 --egA8-b7-3M/20,-egA8-b7-3M,20,3,a,1.2024999856948853,1.222499966621399,True,5.193426851762828e-12,4.0,1.2024999656061082,1.2224999656061082,1.2025,1.2025,1.2225,1.2225,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,4,face,1.2424999475479126,1.5425000190734863,True,1.842966155119613e-13,1.6259825229644775,1.243189139356799,1.5429338149669567,1.2425,1.2425,1.5425,1.5425,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,5,that,1.6024999618530273,1.7825000286102295,True,4.151051823510308e-15,0.25,1.6025000546147912,1.782485139751137,1.6025,1.6025,1.7825,1.7825,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,6,you,1.8224999904632568,1.9225000143051147,True,2.3299396690012715e-15,0.25,1.8158877928657156,1.9213270670153828,1.8025,1.8225,1.9025,1.9224999999999999,0.020000000000000018,0.019999999999999796 --egA8-b7-3M/20,-egA8-b7-3M,20,7,can,1.962499976158142,2.0625,True,4.888492497086283e-13,4.0,1.9624941818187185,2.062500087680068,1.9625,1.9625,2.0625,2.0625,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,8,connect,2.1024999618530273,2.4625000953674316,True,1.8919876787221873e-13,1.6692323684692383,2.108301743869357,2.4709835100351327,2.1025,2.1425,2.4625,2.5225,0.040000000000000036,0.06000000000000005 --egA8-b7-3M/20,-egA8-b7-3M,20,9,with,2.5225000381469727,2.702500104904175,True,5.3128519548432e-15,0.25,2.5457953478717408,2.7183556023547792,2.5225,2.5825,2.7025,2.7425,0.06000000000000005,0.040000000000000036 --egA8-b7-3M/20,-egA8-b7-3M,20,10,a,2.7825000286102295,2.802500009536743,True,4.2030949033711185e-12,4.0,2.782499639676574,2.8024996396765736,2.7825,2.7825,2.8025,2.8025,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,11,"voice,",2.822499990463257,3.202500104904175,True,2.7150111586021763e-12,4.0,2.83914372742463,3.220260657530159,2.8225,2.8825,3.2025,3.2825,0.06000000000000005,0.08000000000000007 --egA8-b7-3M/20,-egA8-b7-3M,20,12,and,3.2825000286102295,4.022500038146973,True,1.7373433150013468e-13,1.5327953100204468,3.3491538445964393,4.026596308706412,3.2825,3.9425,4.022500000000001,4.062500000000001,0.6599999999999997,0.040000000000000036 --egA8-b7-3M/20,-egA8-b7-3M,20,13,that,4.122499942779541,4.28249979019165,True,4.251538560768285e-14,0.3750979006290436,4.110459146562859,4.271831829133859,4.0425,4.1225000000000005,4.2225,4.282500000000001,0.08000000000000007,0.0600000000000005 --egA8-b7-3M/20,-egA8-b7-3M,20,14,makes,4.302499771118164,4.502500057220459,True,9.645896006851146e-15,0.25,4.295467501888811,4.502881881025231,4.2625,4.3025,4.5025,4.5025,0.040000000000000036,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,15,you,4.542500019073486,4.682499885559082,True,3.1286988109871863e-14,0.27603378891944885,4.5424999966891395,4.682497906328179,4.5425,4.5425,4.6825,4.6825,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,16,closer,4.722499847412109,5.102499961853027,True,1.2777630245108113e-13,1.1273242235183716,4.722500016785279,5.102793700421638,4.7225,4.7225,5.102500000000001,5.102500000000001,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,17,to,5.202499866485596,5.302499771118164,True,9.8913234645049e-14,0.8726757764816284,5.202467187444825,5.302500007946147,5.202500000000001,5.202500000000001,5.3025,5.3025,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,18,that,5.362500190734863,5.5625,True,5.875642854202834e-13,4.0,5.356749432746811,5.56137750147641,5.322500000000001,5.362500000000001,5.562500000000001,5.562500000000001,0.040000000000000036,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,19,client.,5.582499980926514,6.002500057220459,True,8.170173522101254e-14,0.7208248972892761,5.582466544181751,6.002488319140519,5.5825000000000005,5.5825000000000005,6.0025,6.0025,0.0,0.0 --iRBcNs9oI8/3,-iRBcNs9oI8,3,0,"Well,",0.4424999952316284,0.6225000023841858,True,4.203285289272607e-12,1.5519766807556152,0.44251731066523176,0.6402426472765932,0.4425,0.4425,0.6224999999999999,0.7025,0.0,0.08000000000000007 --iRBcNs9oI8/3,-iRBcNs9oI8,3,1,it,0.9024999737739563,0.9825000166893005,True,1.378709565133876e-13,0.25,0.9026934341895801,0.9877443085068495,0.9025,0.9025,0.9824999999999999,1.0025,0.0,0.020000000000000018 --iRBcNs9oI8/3,-iRBcNs9oI8,3,2,is,1.162500023841858,1.2424999475479126,True,2.6363368546599222e-14,0.25,1.1766154327924332,1.258820351938083,1.1624999999999999,1.2225,1.2425,1.3025,0.06000000000000005,0.06000000000000005 --iRBcNs9oI8/3,-iRBcNs9oI8,3,3,the,1.9824999570846558,2.302500009536743,True,7.262065948587804e-12,2.681368589401245,1.9861770102307585,2.318400811512359,1.9825,1.9825,2.3025,2.5425,0.0,0.23999999999999977 --iRBcNs9oI8/3,-iRBcNs9oI8,3,4,secret,3.322499990463257,3.622499942779541,True,1.9209831208372163e-12,0.7092835307121277,3.1189257501802254,3.604293380599604,2.6225,3.3225000000000002,3.5825,3.6225,0.7000000000000002,0.040000000000000036 --iRBcNs9oI8/3,-iRBcNs9oI8,3,5,of,3.7225000858306885,4.102499961853027,True,2.4477631293401414e-12,0.9037861824035645,3.7260529538165423,4.095743694350303,3.7225,3.7225,4.1025,4.1025,0.0,0.0 --iRBcNs9oI8/3,-iRBcNs9oI8,3,6,power,4.402500152587891,4.902500152587891,True,4.5697900324936924e-11,4.0,4.409120587918154,4.902042298636173,4.402500000000001,4.402500000000001,4.902500000000001,4.902500000000001,0.0,0.0 --iRBcNs9oI8/3,-iRBcNs9oI8,3,7,pose,5.042500019073486,5.382500171661377,True,2.968922850621336e-12,1.096213698387146,5.047806403743758,5.364777216140973,5.0425,5.102500000000001,5.2225,5.3825,0.0600000000000005,0.16000000000000014 --iRBcNs9oI8/7,-iRBcNs9oI8,7,0,"Well,",0.10249999910593033,0.36250001192092896,True,2.920177391996931e-13,3.763028383255005,0.10136593814621651,0.3623925371058929,0.10250000000000001,0.10250000000000001,0.3625,0.3625,0.0,0.0 --iRBcNs9oI8/7,-iRBcNs9oI8,7,1,for,1.3224999904632568,1.4424999952316284,True,7.760179082444071e-14,1.0,0.9651913170585473,1.426231262055861,0.4425,1.3225,1.4025,1.4425,0.88,0.039999999999999813 --iRBcNs9oI8/7,-iRBcNs9oI8,7,2,"starters,",1.4824999570846558,1.9424999952316284,True,7.344413299097477e-14,0.9464231729507446,1.482722124857738,1.9316510145653845,1.4825,1.4825,1.8825,1.9425,0.0,0.05999999999999983 --iRBcNs9oI8/7,-iRBcNs9oI8,7,3,let's,1.9824999570846558,2.5625,True,5.298965259636912e-12,4.0,1.9825266210685708,2.562499980618171,1.9825,1.9825,2.5625,2.5625,0.0,0.0 --iRBcNs9oI8/7,-iRBcNs9oI8,7,4,look,2.622499942779541,2.882499933242798,True,8.900332185667048e-14,1.146923542022705,2.621435781036787,2.883659047511814,2.6225,2.6225,2.8825,2.9025,0.0,0.020000000000000018 --iRBcNs9oI8/7,-iRBcNs9oI8,7,5,at,2.9825000762939453,3.0625,True,3.4943981708874736e-14,0.45029863715171814,2.9829116244373175,3.062499663832645,2.9825,2.9825,3.0625,3.0625,0.0,0.0 --iRBcNs9oI8/7,-iRBcNs9oI8,7,6,the,3.1624999046325684,3.322499990463257,True,1.0356679977594624e-13,1.3345929384231567,3.156277465682998,3.3100716739224545,3.1225,3.1625,3.2425,3.3225000000000002,0.040000000000000036,0.08000000000000007 --iRBcNs9oI8/7,-iRBcNs9oI8,7,7,word,3.3424999713897705,3.682499885559082,True,3.161334693983363e-15,0.25,3.3424933417526446,3.682494353615535,3.3425,3.3425,3.6825,3.6825,0.0,0.0 --iRBcNs9oI8/7,-iRBcNs9oI8,7,8,"""power""",3.702500104904175,4.002500057220459,True,2.9010224534024506e-14,0.3738344609737396,3.7025010532407543,3.9910909636389107,3.7025,3.7025,3.9625,4.0025,0.0,0.04000000000000048 --iRBcNs9oI8/6,-iRBcNs9oI8,6,0,What,0.042500000447034836,0.2224999964237213,True,6.827441173182236e-13,1.0,0.05154200673577662,0.2727785157094661,0.0425,0.0825,0.2225,0.3025,0.04,0.07999999999999999 --iRBcNs9oI8/6,-iRBcNs9oI8,6,1,is,0.4625000059604645,0.6025000214576721,True,1.1234021118954399e-13,0.25,0.4747218729776408,0.5963319673877671,0.4625,0.5225,0.5425,0.6024999999999999,0.05999999999999994,0.05999999999999994 --iRBcNs9oI8/6,-iRBcNs9oI8,6,2,a,0.7425000071525574,0.762499988079071,True,2.809957263616783e-14,0.25,0.7333161148797499,0.7533161148798755,0.6825,0.7424999999999999,0.7025,0.7625,0.05999999999999994,0.05999999999999994 --iRBcNs9oI8/6,-iRBcNs9oI8,6,3,power,0.9825000166893005,1.722499966621399,True,3.0594309637782535e-12,4.0,0.9841119228812493,1.722488496427188,0.9824999999999999,0.9824999999999999,1.7225,1.7225,0.0,0.0 --iRBcNs9oI8/6,-iRBcNs9oI8,6,4,pose,2.1024999618530273,2.6024999618530273,True,4.7897163665822085e-12,4.0,2.1029123141162605,2.5993707527881207,2.0625,2.1025,2.5825,2.6025,0.040000000000000036,0.020000000000000018 --iRBcNs9oI8/9,-iRBcNs9oI8,9,0,Power,0.0024999999441206455,0.2224999964237213,True,8.225095646620991e-15,0.25,0.006262492974741897,0.22425165857230403,0.0025000000000000005,0.0225,0.2225,0.2225,0.019999999999999997,0.0 --iRBcNs9oI8/9,-iRBcNs9oI8,9,1,is,0.2824999988079071,0.3425000011920929,True,4.88303009138491e-11,4.0,0.2828602076691416,0.3461376424479182,0.28250000000000003,0.28250000000000003,0.3425,0.3425,0.0,0.0 --iRBcNs9oI8/9,-iRBcNs9oI8,9,2,similar,1.3025000095367432,1.722499966621399,True,1.3116465277459872e-14,0.25,1.1245273514934184,1.7144200210143659,0.4625,1.3025,1.7025,1.7425,0.84,0.040000000000000036 --iRBcNs9oI8/9,-iRBcNs9oI8,9,3,to,1.7424999475479126,1.8025000095367432,True,3.081003117563763e-12,4.0,1.7386692485023425,1.7996332226989926,1.7225,1.7625,1.7825,1.8225,0.040000000000000036,0.040000000000000036 --iRBcNs9oI8/9,-iRBcNs9oI8,9,4,strength,1.9225000143051147,2.5824999809265137,True,4.50202212749079e-13,1.0,1.8981114999728592,2.5831794475236496,1.8025,1.9425,2.5825,2.5825,0.1399999999999999,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,0,I've,0.08250000327825546,0.24250000715255737,True,1.9600367336114477e-12,4.0,0.08247995998981052,0.24164600371421696,0.0825,0.0825,0.2425,0.2425,0.0,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,1,drawn,0.32249999046325684,0.6625000238418579,True,1.3570146525573867e-12,4.0,0.3229530278849659,0.6657272327837613,0.3225,0.3225,0.6625,0.7025,0.0,0.040000000000000036 --iRBcNs9oI8/8,-iRBcNs9oI8,8,2,this,1.0225000381469727,1.3424999713897705,True,7.521722367133041e-14,0.3343006670475006,1.0222614949808086,1.342502502681455,1.0225,1.0225,1.3425,1.3425,0.0,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,3,man,1.5225000381469727,1.6825000047683716,True,7.045138262920872e-14,0.3131190240383148,1.5236198190614774,1.6824829413710227,1.5225,1.5425,1.6824999999999999,1.6824999999999999,0.020000000000000018,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,4,"here,",1.902500033378601,2.0824999809265137,True,2.3500652988829758e-14,0.25,1.894543622342994,2.0867907583195966,1.7025,1.9825,2.0425,2.2025,0.28,0.16000000000000014 --iRBcNs9oI8/8,-iRBcNs9oI8,8,5,and,2.122499942779541,2.262500047683716,True,2.832734047331087e-13,1.2590000629425049,2.1331121793088736,2.2719190723194225,2.1225,2.2825,2.2625,2.3825,0.16000000000000014,0.11999999999999966 --iRBcNs9oI8/8,-iRBcNs9oI8,8,6,if,2.322499990463257,2.382499933242798,True,5.582629085572333e-13,2.48118257522583,2.33868003750859,2.4031823336609714,2.2825,2.5625,2.3825,2.6425,0.2799999999999998,0.26000000000000023 --iRBcNs9oI8/8,-iRBcNs9oI8,8,7,you,2.4825000762939453,2.6424999237060547,True,9.512268766718665e-14,0.4227698743343353,2.5072316046245557,2.6582997433161406,2.4825,2.7425,2.6425,2.8425,0.26000000000000023,0.19999999999999973 --iRBcNs9oI8/8,-iRBcNs9oI8,8,8,can,2.742500066757202,2.8424999713897705,True,4.3768065343882667e-13,1.945258378982544,2.75386187499682,2.937324736774701,2.7425,2.8825,2.8425,4.0425,0.13999999999999968,1.2000000000000006 --iRBcNs9oI8/8,-iRBcNs9oI8,8,9,"tell,",2.882499933242798,4.082499980926514,True,2.2499873058440256e-13,1.0,2.980365531712314,4.098930060097728,2.8825,4.1225000000000005,4.0825000000000005,4.282500000000001,1.2400000000000007,0.20000000000000018 --iRBcNs9oI8/8,-iRBcNs9oI8,8,10,he,4.122499942779541,4.182499885559082,True,4.699343873564542e-14,0.25,4.143350252178951,4.2046936964374835,4.1025,4.402500000000001,4.1825,4.4625,0.3000000000000007,0.28000000000000025 --iRBcNs9oI8/8,-iRBcNs9oI8,8,11,has,4.222499847412109,4.34250020980835,True,1.2339205991609287e-13,0.5484122633934021,4.245734427287916,4.369364316926948,4.2225,4.522500000000001,4.3425,4.6825,0.3000000000000007,0.33999999999999986 --iRBcNs9oI8/8,-iRBcNs9oI8,8,12,a,4.402500152587891,4.422500133514404,True,4.446784954148519e-12,4.0,4.431854037932112,4.451854037932103,4.402500000000001,4.742500000000001,4.4225,4.7625,0.33999999999999986,0.33999999999999986 --iRBcNs9oI8/8,-iRBcNs9oI8,8,13,lot,4.522500038146973,4.682499885559082,True,2.9134436833541666e-14,0.25,4.5482175681919905,4.7054606013575935,4.522500000000001,4.8425,4.6825,4.9625,0.3199999999999994,0.28000000000000025 --iRBcNs9oI8/8,-iRBcNs9oI8,8,14,of,4.742499828338623,4.802499771118164,True,5.46444290031446e-15,0.25,4.767774255389696,4.82777455421578,4.742500000000001,5.062500000000001,4.8025,5.1225000000000005,0.3200000000000003,0.3200000000000003 --iRBcNs9oI8/8,-iRBcNs9oI8,8,15,big,4.84250020980835,4.962500095367432,True,2.147364317611755e-12,4.0,4.875985463534146,5.002288285727535,4.8425,5.202500000000001,4.9625,5.3425,0.3600000000000003,0.3799999999999999 --iRBcNs9oI8/8,-iRBcNs9oI8,8,16,"muscles,",5.0625,5.462500095367432,True,1.9685501397915563e-14,0.25,5.087760129561801,5.490328924230847,5.062500000000001,5.3825,5.4625,5.8025,0.3199999999999994,0.33999999999999986 --iRBcNs9oI8/8,-iRBcNs9oI8,8,17,he,5.682499885559082,5.802499771118164,True,2.0166227628765077e-12,4.0,5.678916934830031,5.797729507728853,5.6225000000000005,5.862500000000001,5.702500000000001,5.9225,0.2400000000000002,0.21999999999999975 --iRBcNs9oI8/8,-iRBcNs9oI8,8,18,looks,6.502500057220459,6.762499809265137,True,4.851144984850675e-13,2.156076669692993,6.49249963470516,6.761737275706995,6.5025,6.5025,6.7625,6.7625,0.0,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,19,very,7.202499866485596,7.402500152587891,True,6.853084722965863e-13,3.045832633972168,7.2021361104523285,7.402531057662287,7.202500000000001,7.202500000000001,7.402500000000001,7.402500000000001,0.0,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,20,strong,7.602499961853027,8.0024995803833,True,4.99631972350912e-14,0.25,7.602283377792524,8.002472983869318,7.602500000000001,7.602500000000001,8.0025,8.0025,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,0,If,0.5224999785423279,0.5824999809265137,True,3.035739655531458e-14,0.25,0.5224777847512705,0.5825087523727438,0.5225,0.5225,0.5824999999999999,0.5824999999999999,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,1,it,0.6625000238418579,0.7425000071525574,True,9.317088658748751e-14,0.5512282848358154,0.6439698113695347,0.7142330631919445,0.6224999999999999,0.6625,0.6825,0.7424999999999999,0.040000000000000036,0.05999999999999994 --lzEya4AM_4/5,-lzEya4AM_4,5,2,falls,0.762499988079071,1.0225000381469727,True,1.690241371345158e-13,1.0,0.7456461893744665,1.0056523214029987,0.7224999999999999,0.7625,0.9824999999999999,1.0225,0.040000000000000036,0.040000000000000036 --lzEya4AM_4/5,-lzEya4AM_4,5,3,into,1.0824999809265137,1.2625000476837158,True,9.029643623526679e-14,0.5342221260070801,1.06263615289812,1.2547635900885998,1.0225,1.0825,1.1624999999999999,1.2625,0.06000000000000005,0.10000000000000009 --lzEya4AM_4/5,-lzEya4AM_4,5,4,oh,1.2825000286102295,1.3424999713897705,True,5.106823641807916e-14,0.30213576555252075,1.3017148062167647,1.3783215908484356,1.1824999999999999,1.3825,1.2625,1.4625,0.20000000000000018,0.19999999999999996 --lzEya4AM_4/5,-lzEya4AM_4,5,5,any,1.3825000524520874,1.4824999570846558,True,2.4951281275484294e-13,1.4761962890625,1.416671421638377,1.5684959034922972,1.2825,1.5025,1.4625,1.7425,0.21999999999999997,0.28 --lzEya4AM_4/5,-lzEya4AM_4,5,6,of,1.6425000429153442,1.7424999475479126,True,2.3230219831082977e-11,4.0,1.6744825900566362,1.7571266553083897,1.6425,1.7825,1.7425,1.8425,0.1399999999999999,0.10000000000000009 --lzEya4AM_4/5,-lzEya4AM_4,5,7,those,1.8025000095367432,2.0425000190734863,True,3.8556565709266244e-13,2.281127691268921,1.813751078471044,2.042598142283778,1.8025,1.8625,2.0425,2.0425,0.06000000000000005,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,8,three,2.0824999809265137,2.2825000286102295,True,9.28581078132526e-14,0.5493777990341187,2.082500668233339,2.282500675241747,2.0825,2.0825,2.2825,2.2825,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,9,"categories,",2.322499990463257,2.9024999141693115,True,7.862823212618705e-14,0.4651893675327301,2.3225014444100087,2.9024997494292215,2.3225,2.3225,2.9025,2.9025,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,10,inducing,3.6424999237060547,4.202499866485596,True,1.9956248025615464e-12,4.0,3.6424999115902814,4.202500006501488,3.6425,3.6425,4.202500000000001,4.202500000000001,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,11,nausea,4.262499809265137,5.382500171661377,True,1.1790844993073146e-12,4.0,4.463467536769676,5.383299142343135,4.2625,5.022500000000001,5.3825,5.3825,0.7600000000000007,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,12,and,5.422500133514404,5.542500019073486,True,8.983205347125323e-13,4.0,5.424328417539918,5.544325310866087,5.4225,5.4225,5.5425,5.5425,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,13,vomiting,5.5625,5.982500076293945,True,5.396987652790675e-13,3.1930277347564697,5.576995124703589,5.9820157407403665,5.562500000000001,5.602500000000001,5.982500000000001,5.982500000000001,0.040000000000000036,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,14,would,6.022500038146973,6.382500171661377,True,4.5605273707840024e-14,0.2698151469230652,6.023217173328293,6.409607625661401,6.022500000000001,6.022500000000001,6.3825,6.4225,0.0,0.040000000000000036 --lzEya4AM_4/5,-lzEya4AM_4,5,15,be,6.402500152587891,6.462500095367432,True,7.951032101427113e-16,0.25,6.483258350281619,6.549422500484275,6.402500000000001,6.522500000000001,6.4625,6.602500000000001,0.1200000000000001,0.14000000000000057 --lzEya4AM_4/5,-lzEya4AM_4,5,16,your,6.502500057220459,6.642499923706055,True,8.889075456611217e-15,0.25,6.592861811750548,6.7463621054641605,6.482500000000001,6.642500000000001,6.6225000000000005,6.8025,0.16000000000000014,0.17999999999999972 --lzEya4AM_4/5,-lzEya4AM_4,5,17,next,6.682499885559082,6.822500228881836,True,8.763239699866032e-13,4.0,6.774462248977959,6.919159147895202,6.642500000000001,6.862500000000001,6.8025,7.0425,0.21999999999999975,0.2400000000000002 --lzEya4AM_4/5,-lzEya4AM_4,5,18,step.,6.862500190734863,7.102499961853027,True,8.070761398602799e-13,4.0,6.9531891126610725,7.268405006346323,6.862500000000001,7.0825000000000005,7.102500000000001,7.522500000000001,0.21999999999999975,0.41999999999999993 --lzEya4AM_4/6,-lzEya4AM_4,6,0,If,0.6225000023841858,0.7024999856948853,True,3.9581296682085487e-13,4.0,0.6218706464646045,0.7023365326741986,0.6224999999999999,0.6224999999999999,0.7025,0.7025,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,1,the,0.7425000071525574,0.8424999713897705,True,7.399572254029266e-15,0.4287492334842682,0.7424768243763704,0.8424768481110348,0.7424999999999999,0.7424999999999999,0.8424999999999999,0.8424999999999999,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,2,product,0.862500011920929,1.2424999475479126,True,1.7258508058543183e-14,1.0,0.8624984023485787,1.2425003773706746,0.8624999999999999,0.8624999999999999,1.2425,1.2425,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,3,or,1.2825000286102295,1.6024999618530273,True,2.486088049834245e-13,4.0,1.282676981052881,1.6025512390288499,1.2825,1.2825,1.6025,1.6025,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,4,poison,1.662500023841858,2.1024999618530273,True,4.366728604144471e-14,2.530189037322998,1.6673157302049542,2.0989654347339073,1.6625,1.7225,2.0625,2.1025,0.05999999999999983,0.040000000000000036 --lzEya4AM_4/6,-lzEya4AM_4,6,5,is,2.202500104904175,2.262500047683716,True,8.725329588346858e-15,0.5055668354034424,2.180319198328119,2.2460606071930846,2.1425,2.2025,2.2225,2.2625,0.06000000000000005,0.040000000000000036 --lzEya4AM_4/6,-lzEya4AM_4,6,6,some,2.2825000286102295,2.442500114440918,True,2.255789346007446e-15,0.25,2.278221478596384,2.4349226931964756,2.2625,2.2825,2.4225,2.4425,0.020000000000000018,0.020000000000000018 --lzEya4AM_4/6,-lzEya4AM_4,6,7,type,2.4625000953674316,2.682499885559082,True,1.0200491167879078e-13,4.0,2.462554132308005,2.682496700492244,2.4625,2.4625,2.6825,2.6825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,8,of,2.702500104904175,2.762500047683716,True,5.058627467109146e-14,2.9310920238494873,2.7039424007353188,2.7639755466185556,2.7025,2.7025,2.7625,2.7625,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,9,petroleum,2.882499933242798,3.3424999713897705,True,4.413881640828038e-13,4.0,2.8775856658907264,3.3593962724351547,2.8225,2.8825,3.3425,3.4225,0.06000000000000005,0.08000000000000007 --lzEya4AM_4/6,-lzEya4AM_4,6,10,product,3.4024999141693115,3.822499990463257,True,5.6658426865127676e-14,3.2829272747039795,3.4179361032192093,3.8208257166363166,3.4025,3.4425,3.8225000000000002,3.8225000000000002,0.040000000000000036,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,11,or,4.222499847412109,4.302499771118164,True,2.7757481439760236e-14,1.608336091041565,4.12428872001621,4.2612516688364535,3.8825,4.2225,4.242500000000001,4.3025,0.3400000000000003,0.05999999999999961 --lzEya4AM_4/6,-lzEya4AM_4,6,12,corrosive,4.362500190734863,4.84250020980835,True,4.3797563774490567e-13,4.0,4.329747394910065,4.842412625596829,4.3025,4.362500000000001,4.8425,4.8425,0.0600000000000005,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,13,"product,",4.862500190734863,5.262499809265137,True,3.187660435632379e-14,1.8470081090927124,4.862499669555491,5.262370885994928,4.862500000000001,4.862500000000001,5.2625,5.2625,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,14,you,5.802499771118164,5.902500152587891,True,1.5641947796290719e-15,0.25,5.53509435686845,5.867162990568692,5.322500000000001,5.8025,5.862500000000001,5.902500000000001,0.47999999999999954,0.040000000000000036 --lzEya4AM_4/6,-lzEya4AM_4,6,15,would,5.922500133514404,6.122499942779541,True,1.0120089614679573e-14,0.5863826274871826,5.922112572905214,6.122047150371503,5.9225,5.9225,6.1225000000000005,6.1225000000000005,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,16,not,6.142499923706055,6.262499809265137,True,6.352139781784071e-15,0.368058443069458,6.142500764751273,6.2625701809161365,6.142500000000001,6.142500000000001,6.2625,6.2625,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,17,attempt,6.302499771118164,6.822500228881836,True,1.9684304540361092e-15,0.25,6.318155035522648,6.821948412854423,6.3025,6.4625,6.822500000000001,6.822500000000001,0.16000000000000014,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,18,to,7.182499885559082,7.28249979019165,True,1.797134727002988e-14,1.0413036346435547,7.182534654775597,7.28252803343239,7.1825,7.1825,7.282500000000001,7.282500000000001,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,19,induce,7.382500171661377,7.922500133514404,True,4.838478183106891e-14,2.803532123565674,7.414923567572577,7.934817600865357,7.3825,7.5025,7.9225,7.9625,0.1200000000000001,0.040000000000000036 --lzEya4AM_4/6,-lzEya4AM_4,6,20,vomiting,7.962500095367432,8.382499694824219,True,8.089727889307174e-13,4.0,7.989677049757497,8.384180167319787,7.9625,8.0025,8.3825,8.3825,0.03999999999999915,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,21,and,8.602499961853027,8.702500343322754,True,7.461027882484355e-15,0.4323101341724396,8.558395721540947,8.70051005570173,8.4225,8.6025,8.6825,8.7025,0.17999999999999972,0.02000000000000135 --lzEya4AM_4/6,-lzEya4AM_4,6,22,"nausea,",8.72249984741211,9.142499923706055,True,1.0365191344119395e-11,4.0,8.722975493432113,9.139562814034488,8.7225,8.7225,9.1225,9.1425,0.0,0.019999999999999574 --lzEya4AM_4/6,-lzEya4AM_4,6,23,as,9.642499923706055,9.702500343322754,True,8.344037537733745e-14,4.0,9.641942999273425,9.701962157826868,9.6425,9.6425,9.7025,9.7025,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,24,as,9.762499809265137,9.822500228881836,True,5.40865399342727e-12,4.0,9.762383360924126,9.822383364858918,9.7625,9.7625,9.8225,9.8225,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,25,the,9.842499732971191,9.942500114440918,True,6.742547008052472e-15,0.39067959785461426,9.842500125440687,9.942500469035092,9.8425,9.8425,9.942499999999999,9.942499999999999,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,26,product,10.0024995803833,10.342499732971191,True,2.207761392052001e-14,1.2792307138442993,9.999605801336743,10.338683442149089,9.9625,10.0025,10.282499999999999,10.3425,0.03999999999999915,0.0600000000000005 --lzEya4AM_4/6,-lzEya4AM_4,6,27,burned,10.362500190734863,10.682499885559082,True,1.995170183038096e-14,1.156050205230713,10.362784650428713,10.68227166700739,10.362499999999999,10.362499999999999,10.6825,10.6825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,28,on,10.702500343322754,10.782500267028809,True,2.3671003850610217e-13,4.0,10.702523528497984,10.782508642676058,10.7025,10.7025,10.782499999999999,10.782499999999999,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,29,the,10.822500228881836,10.922499656677246,True,9.76079348504735e-15,0.5655641555786133,10.823841751296246,10.923854219050918,10.8225,10.8425,10.9225,10.942499999999999,0.019999999999999574,0.019999999999999574 --lzEya4AM_4/6,-lzEya4AM_4,6,30,way,11.0024995803833,11.1225004196167,True,9.980518066729852e-15,0.5782955288887024,10.97704504414781,11.089484271552267,10.942499999999999,11.0025,11.0625,11.1225,0.0600000000000005,0.0600000000000005 --lzEya4AM_4/6,-lzEya4AM_4,6,31,down,11.142499923706055,11.382499694824219,True,1.0334919192959893e-14,0.5988304018974304,11.139335546006153,11.392634807234638,11.1025,11.1425,11.3825,11.4225,0.040000000000000924,0.03999999999999915 --lzEya4AM_4/6,-lzEya4AM_4,6,32,it,11.422499656677246,11.822500228881836,True,4.624125230265992e-15,0.26793307065963745,11.473483985557554,11.813451673400056,11.4225,11.7225,11.7625,11.8225,0.3000000000000007,0.0600000000000005 --lzEya4AM_4/6,-lzEya4AM_4,6,33,could,11.842499732971191,12.022500038146973,True,3.321526835517517e-15,0.25,11.841404836972417,12.022500039924171,11.8225,11.8425,12.022499999999999,12.022499999999999,0.019999999999999574,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,34,cause,12.0625,12.322500228881836,True,5.640726550264078e-15,0.3268374502658844,12.06249996265842,12.322485847197163,12.0625,12.0625,12.3225,12.3225,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,35,damage,12.362500190734863,12.702500343322754,True,1.301325600690283e-14,0.7540197372436523,12.362499995548271,12.702500000019468,12.362499999999999,12.362499999999999,12.7025,12.7025,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,36,and,12.742500305175781,12.842499732971191,True,3.552262157300055e-16,0.25,12.742469306485088,12.842550373760359,12.7425,12.7425,12.8425,12.8425,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,37,burning,12.882499694824219,13.482500076293945,True,1.0955535659435203e-13,4.0,12.882500009396367,13.474533767589687,12.8825,12.8825,13.4825,13.4825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,38,on,13.5625,13.6225004196167,True,6.547006318156912e-14,3.793494939804077,13.558178654761617,13.618923926660116,13.5625,13.5625,13.6225,13.6225,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,39,the,13.642499923706055,13.762499809265137,True,5.965204543566466e-15,0.3456384837627411,13.653632540597108,13.769364286143006,13.6425,13.7025,13.7625,13.8025,0.0600000000000005,0.040000000000000924 --lzEya4AM_4/6,-lzEya4AM_4,6,40,way,13.822500228881836,13.982500076293945,True,1.5995148253318027e-14,0.9267978668212891,13.822149170618747,13.981798894446957,13.8225,13.8225,13.9825,13.9825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,41,back,14.022500038146973,14.182499885559082,True,3.619833416573879e-15,0.25,14.022029813684714,14.182729174722772,14.022499999999999,14.022499999999999,14.1825,14.1825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,42,up.,14.202500343322754,14.542499542236328,True,1.867283073430921e-16,0.25,14.203948102653264,14.521972037563804,14.2025,14.2025,14.2825,14.7025,0.0,0.41999999999999993 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,0,There,0.0024999999441206455,0.48249998688697815,True,8.852508309731277e-11,1.0036022663116455,0.0698258865699199,0.5209542854210507,0.0025000000000000005,0.2225,0.3025,0.8225,0.22,0.52 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,1,are,0.5024999976158142,0.6025000214576721,True,8.811709695244474e-11,0.9989769458770752,0.6773580062375331,0.9126256507986633,0.4025,1.0225,0.6024999999999999,1.2825,0.6199999999999999,0.68 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,2,a,0.6225000023841858,0.6424999833106995,True,8.820733726766505e-11,1.0,1.0689133910196154,1.0889133910215332,0.7224999999999999,1.4825,0.7424999999999999,1.5025,0.76,0.76 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,3,dozen,0.6625000238418579,0.8424999713897705,True,8.838636073038586e-11,1.002029538154602,1.2452262918671384,1.6960141945305303,0.8624999999999999,1.6625,1.2825,2.1625,0.8000000000000002,0.8800000000000001 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,4,or,0.862500011920929,0.9225000143051147,True,8.844412008324198e-11,1.0026843547821045,1.8525124174329262,1.9802406363476905,1.4224999999999999,2.3225,1.5425,2.4625,0.8999999999999999,0.9199999999999999 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,5,more,0.9424999952316284,1.0824999809265137,True,8.837957449214784e-11,1.0019526481628418,2.13674030925521,2.479925987685495,1.6824999999999999,2.6225,2.0025,2.9625,0.9400000000000002,0.96 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,6,hands,1.1024999618530273,1.2825000286102295,True,8.822227670624017e-11,1.0001693964004517,2.636425683706365,3.0873399364897796,2.1625,3.1225,2.6025,3.5825,0.96,0.98 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,7,on,5.002500057220459,5.0625,True,8.802221451720271e-11,0.9979012608528137,3.2438396325030245,3.3715681957004184,2.7425,3.7225,2.8825,3.8625,0.98,0.98 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,8,techniques,5.082499980926514,5.462500095367432,True,8.812522239720622e-11,0.999069094657898,3.5280678917076664,4.517627610187784,3.0425,4.0025,4.062500000000001,4.9225,0.9600000000000004,0.8599999999999994 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,9,that,5.482500076293945,5.622499942779541,True,8.802477496905325e-11,0.9979302883148193,4.674114102900365,5.017234219415555,4.242500000000001,5.062500000000001,4.6225000000000005,5.362500000000001,0.8200000000000003,0.7400000000000002 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,10,we,5.78249979019165,5.84250020980835,True,8.77886582872911e-11,0.9952534437179565,5.173628548001558,5.301276562327708,4.8025,5.482500000000001,4.942500000000001,5.602500000000001,0.6800000000000006,0.6600000000000001 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,11,can,5.862500190734863,5.962500095367432,True,8.818036578706057e-11,0.9996942281723022,5.4575589081038185,5.692827986897282,5.1425,5.702500000000001,5.4225,5.8825,0.5600000000000005,0.45999999999999996 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,12,do,5.982500076293945,6.042500019073486,True,9.066991601969221e-11,1.0279181003570557,5.849047962874614,5.976323928323296,5.6425,5.982500000000001,5.822500000000001,6.0425,0.34000000000000075,0.21999999999999975 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,0,So,0.0024999999441206455,0.0625,True,9.096196018631986e-11,1.0093817710876465,0.032718039902220324,0.12302393990958412,0.0025000000000000005,0.10250000000000001,0.0625,0.2225,0.1,0.16 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,1,physical,0.08250000327825546,0.7425000071525574,True,9.035900500053984e-11,1.0026909112930298,0.20484162269624073,0.7183170211432781,0.10250000000000001,0.3425,0.5425,0.9425,0.24000000000000002,0.4 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,2,therapists,0.762499988079071,1.1425000429153442,True,9.044751059228417e-11,1.0036730766296387,0.7999759850899307,1.454059627827414,0.6024999999999999,1.0225,1.2025,1.7225,0.42000000000000004,0.52 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,3,and,1.162500023841858,1.2625000476837158,True,9.025457464728603e-11,1.0015320777893066,1.5358937446088856,1.6969050105096142,1.2825,1.8225,1.4224999999999999,1.9825,0.54,0.56 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,4,chiropractors,1.3025000095367432,1.8025000095367432,True,9.00861468755565e-11,0.9996631145477295,1.7787420065370874,2.644814585780178,1.5025,2.0625,2.3425,2.9425,0.56,0.6000000000000001 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,5,will,1.8224999904632568,1.962499976158142,True,9.00861468755565e-11,0.9996631145477295,2.72665159286188,2.9581697385244805,2.4225,3.0225,2.6625,3.2625,0.6000000000000001,0.6000000000000001 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,6,do,1.9824999570846558,2.0425000190734863,True,9.00861468755565e-11,0.9996631145477295,3.0400067456061746,3.1305127941614,2.7425,3.3425,2.8225,3.4225,0.5999999999999996,0.6000000000000001 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,7,joint,2.0625,2.242500066757202,True,9.00861468755565e-11,0.9996631145477295,3.2123498012430876,3.51437399545938,2.9225,3.5025,3.2225,3.8025,0.5800000000000001,0.5800000000000001 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,8,manipulations,2.262500047683716,2.762500047683716,True,9.00861468755565e-11,0.9996631145477295,3.5962110025410743,4.462293761119333,3.3025,3.8825,4.202500000000001,4.702500000000001,0.5799999999999996,0.5 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,9,and,4.722499847412109,4.822500228881836,True,9.011650453638609e-11,1.0,4.544132032136779,4.705125324259879,4.3025,4.7625,4.4625,4.9225,0.45999999999999996,0.45999999999999996 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,10,mobilizations,4.84250020980835,5.682499885559082,True,9.054999805524488e-11,1.0048103332519531,4.786928814409058,5.65272602695609,4.562500000000001,4.982500000000001,5.5825000000000005,5.6825,0.41999999999999993,0.09999999999999964 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,0,They,0.02250000089406967,0.3824999928474426,True,5.596440533759718e-14,0.25,0.07685901928112787,0.3990715972593789,0.0225,0.14250000000000002,0.3825,0.4425,0.12000000000000002,0.06 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,1,are,0.4424999952316284,0.7024999856948853,True,6.72760512663434e-13,1.0,0.5160575050547208,0.7227117404819162,0.4425,0.6024999999999999,0.7025,0.7424999999999999,0.15999999999999992,0.039999999999999925 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,2,all,0.7825000286102295,0.9424999952316284,True,2.1975723649668433e-13,0.3266500234603882,0.7935273972149047,0.9526167018557699,0.7424999999999999,0.8424999999999999,0.8624999999999999,0.9425,0.09999999999999998,0.08000000000000007 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,3,very,1.0824999809265137,1.3825000524520874,True,3.322413177908601e-11,4.0,1.1101423300401905,1.4078282823092163,1.0625,1.0825,1.3825,1.3825,0.020000000000000018,0.0 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,4,safe,1.8224999904632568,2.1624999046325684,True,4.374835129578036e-12,4.0,1.859322265183538,2.141602476798535,1.7825,1.9425,2.0225,2.1625,0.15999999999999992,0.14000000000000012 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,0,It's,0.0625,0.26249998807907104,True,1.3943171617292194e-10,4.0,0.047669511258957874,0.2484408818378565,0.0025000000000000005,0.0625,0.1825,0.28250000000000003,0.06,0.10000000000000003 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,1,safe,0.4025000035762787,0.6225000023841858,True,4.5447569319012615e-12,4.0,0.3360747022548775,0.5170328362234636,0.2025,0.4025,0.4225,0.6224999999999999,0.2,0.19999999999999996 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,2,and,0.6424999833106995,0.7425000071525574,True,2.003821819912999e-15,0.25,0.5856019452028779,0.7191063800421992,0.4825,0.6425,0.7025,0.7424999999999999,0.15999999999999998,0.039999999999999925 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,3,the,0.8424999713897705,0.9624999761581421,True,1.243637679816667e-12,2.812455415725708,0.8453878094860923,0.9667462538582235,0.8424999999999999,0.8424999999999999,0.9624999999999999,0.9624999999999999,0.0,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,4,person,1.122499942779541,1.3825000524520874,True,1.8701285366216208e-13,0.4229249060153961,1.0165126982997161,1.308275716440458,0.9824999999999999,1.1225,1.2825,1.3825,0.14000000000000012,0.10000000000000009 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,5,will,1.462499976158142,1.6425000429153442,True,3.865140087329355e-13,0.8740917444229126,1.3867817686280777,1.5999738769213134,1.3625,1.4625,1.5225,1.7225,0.09999999999999987,0.19999999999999996 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,6,feel,1.7024999856948853,1.9225000143051147,True,2.057110043288471e-13,0.46521028876304626,1.6644042882905339,1.8580207414522327,1.5625,1.8425,1.7625,2.0825,0.28,0.32000000000000006 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,7,better,2.0625,2.3424999713897705,True,2.55443778109099e-12,4.0,1.9039668350534087,2.269341184316521,1.8425,2.1225,2.2225,2.3825,0.28,0.1599999999999997 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,8,later,2.382499933242798,2.742500066757202,True,6.280226447392956e-13,1.4202574491500854,2.3391000804104283,2.610403111609425,2.3225,2.4025,2.5225,2.7425,0.08000000000000007,0.2200000000000002 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,9,"on,",2.922499895095825,3.002500057220459,True,4.978645534031712e-13,1.1259082555770874,2.736678888486408,2.8013639750269204,2.6825,2.9225,2.7425,3.0025,0.23999999999999977,0.2599999999999998 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,10,but,3.0225000381469727,3.1424999237060547,True,5.492410657683376e-15,0.25,2.853221959313782,3.0344224455998434,2.8025,3.0225,3.0025,3.1425,0.21999999999999975,0.14000000000000012 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,11,it's,3.182499885559082,3.2825000286102295,True,7.276466755523625e-12,4.0,3.110513745542075,3.269832850162292,3.0625,3.1825,3.2625,3.2825,0.1200000000000001,0.020000000000000018 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,12,sort,3.302500009536743,3.4625000953674316,True,1.0976491254550275e-13,0.25,3.30240099690659,3.461609169642533,3.3025,3.3025,3.4425,3.4825,0.0,0.040000000000000036 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,13,of,3.5425000190734863,3.622499942779541,True,2.677345107768292e-12,4.0,3.5413547723387526,3.6216566418041243,3.5425,3.5425,3.6225,3.6225,0.0,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,14,like,3.6624999046325684,3.8424999713897705,True,8.644191455296421e-14,0.25,3.666813689753435,3.838871048201672,3.6625,3.7025,3.8225000000000002,3.8625,0.040000000000000036,0.03999999999999959 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,15,flossing,3.882499933242798,4.34250020980835,True,1.1762174621874483e-13,0.26599863171577454,3.8838651729144584,4.3424120629860194,3.8825,3.8825,4.3425,4.3425,0.0,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,16,your,4.362500190734863,5.0625,True,3.292849730406923e-14,0.25,4.41819147818272,5.111481728190935,4.362500000000001,4.9625,5.022500000000001,5.1625000000000005,0.5999999999999996,0.13999999999999968 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,17,teeth,5.102499961853027,5.922500133514404,True,1.063016455459323e-13,0.25,5.148201109665343,5.921581298640506,5.102500000000001,5.202500000000001,5.9225,5.9225,0.09999999999999964,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,18,for,5.962500095367432,6.0625,True,6.951333768583784e-14,0.25,5.954329937943031,6.058653654509152,5.942500000000001,5.9625,6.0425,6.062500000000001,0.019999999999999574,0.020000000000000462 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,19,the,6.082499980926514,6.202499866485596,True,4.954940624468662e-11,4.0,6.082503363629208,6.202503801804082,6.0825000000000005,6.0825000000000005,6.202500000000001,6.202500000000001,0.0,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,20,first,6.262499809265137,6.502500057220459,True,5.828269290797383e-12,4.0,6.26250188369506,6.513630284736709,6.2625,6.2625,6.5025,6.562500000000001,0.0,0.0600000000000005 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,21,time,6.662499904632568,6.882500171661377,True,1.4780298296032668e-12,3.342527389526367,6.662037112250419,6.879055062501529,6.6625000000000005,6.6625000000000005,6.862500000000001,6.8825,0.0,0.019999999999999574 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,0,Sometimes,0.7825000286102295,1.162500023841858,True,6.672226790169211e-13,0.2790868580341339,0.4509397771605163,1.1624976149860125,0.0625,0.7825,1.1624999999999999,1.1624999999999999,0.72,0.0 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,1,we,1.222499966621399,1.3624999523162842,True,1.8925133682401452e-10,4.0,1.2224980141426944,1.3624753576252613,1.2225,1.2225,1.3625,1.3625,0.0,0.0 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,2,have,1.4225000143051147,1.6425000429153442,True,1.5286633721009468e-12,0.6394115090370178,1.422078617604423,1.6204572489346851,1.4025,1.4425,1.5825,1.6425,0.039999999999999813,0.06000000000000005 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,3,to,1.7024999856948853,1.8025000095367432,True,2.3907347455887074e-12,1.0,1.7059088038846568,1.799902197420372,1.7025,1.7025,1.7825,1.8025,0.0,0.020000000000000018 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,4,stretch,1.9824999570846558,2.262500047683716,True,5.604740629938654e-12,2.3443591594696045,1.932309459892086,2.2160627365081274,1.8825,1.9825,2.1625,2.2625,0.09999999999999987,0.10000000000000009 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,5,that,2.302500009536743,2.442500114440918,True,2.0492338295013957e-12,0.85715651512146,2.2511005842143135,2.4180146282719335,2.2025,2.3025,2.4025,2.4425,0.10000000000000009,0.040000000000000036 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,6,joint,2.4825000762939453,2.8424999713897705,True,6.914385296957759e-12,2.8921592235565186,2.4558962302559877,2.8143964443535863,2.4225,2.4825,2.7825,2.8425,0.06000000000000005,0.05999999999999961 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,7,that,2.922499895095825,3.182499885559082,True,2.6334511828152163e-12,1.101523756980896,2.889294160998902,3.104634416094476,2.8225,2.9425,3.0625,3.1825,0.1200000000000001,0.1200000000000001 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,8,is,3.2225000858306885,3.2825000286102295,True,1.5851010825088108e-12,0.6630184054374695,3.128230706539492,3.2041105692463807,3.0825,3.2225,3.1825,3.2825,0.14000000000000012,0.10000000000000009 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,9,already,3.302500009536743,3.702500104904175,True,1.9910991258531574e-11,4.0,3.240455573466579,3.5472033751853838,3.2225,3.3025,3.5025,3.7025,0.08000000000000007,0.20000000000000018 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,10,irritated,3.742500066757202,4.142499923706055,True,5.874412826838149e-13,0.25,3.594318712702423,4.142499418606456,3.5225,3.7425,4.1425,4.1425,0.2200000000000002,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,0,"How,",0.16249999403953552,0.36250001192092896,True,7.513402335186659e-13,3.2922470569610596,0.16295956092843808,0.4110743546178127,0.1625,0.1625,0.3625,0.4625,0.0,0.10000000000000003 --mqbVkbCndg/0,-mqbVkbCndg,0,1,at,0.4625000059604645,0.9024999737739563,True,5.430498715638943e-13,2.3795535564422607,0.5327332294690263,0.9094743295388469,0.4625,0.8825,0.9025,0.9624999999999999,0.41999999999999993,0.05999999999999994 --mqbVkbCndg/0,-mqbVkbCndg,0,2,a,0.9825000166893005,1.002500057220459,True,2.976669952192701e-11,4.0,0.9824999384988177,1.0024999384988176,0.9824999999999999,0.9824999999999999,1.0025,1.0025,0.0,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,3,particular,1.0225000381469727,1.5625,True,1.1336772991719923e-13,0.4967584013938904,1.035725275560386,1.5800902946906012,1.0225,1.0625,1.5625,1.6025,0.040000000000000036,0.040000000000000036 --mqbVkbCndg/0,-mqbVkbCndg,0,4,moment,1.6024999618530273,1.9824999570846558,True,1.4489467568476466e-13,0.6349042057991028,1.6349622213695993,2.0086853034404504,1.6025,1.6625,1.9825,2.0425,0.06000000000000005,0.06000000000000005 --mqbVkbCndg/0,-mqbVkbCndg,0,5,in,2.0225000381469727,2.122499942779541,True,1.2200440310805583e-13,0.5346028804779053,2.0643636679285398,2.153146501312387,2.0225,2.1025,2.1025,2.1825,0.08000000000000007,0.08000000000000007 --mqbVkbCndg/0,-mqbVkbCndg,0,6,time—our,2.242500066757202,2.942500114440918,True,1.081351371293171e-12,4.0,2.2427627508097463,2.94254615237048,2.2425,2.2425,2.9425,2.9425,0.0,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,7,moment,3.002500057220459,3.4024999141693115,True,2.059274381875295e-13,0.9023395776748657,3.032264713672813,3.4035362198847228,3.0025,3.0625,3.4025,3.4025,0.06000000000000005,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,8,in,3.442500114440918,3.5425000190734863,True,4.4287579788367115e-13,1.9406076669692993,3.4443813754599724,3.5444835777278967,3.4425,3.4425,3.5425,3.5425,0.0,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,9,time—are,3.682499885559082,4.622499942779541,True,1.3476970461921006e-13,0.5905382633209229,3.679404616920505,4.624780510292684,3.6225,3.6825,4.6225000000000005,4.6625000000000005,0.06000000000000005,0.040000000000000036 --mqbVkbCndg/0,-mqbVkbCndg,0,10,they,4.722499847412109,4.922500133514404,True,7.855013568845021e-14,0.34419354796409607,4.718384341212022,4.920950495392551,4.702500000000001,4.7225,4.9225,4.9225,0.019999999999999574,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,11,limited,5.422500133514404,5.902500152587891,True,2.5050260802315927e-13,1.0976605415344238,5.42248890408293,5.900145834505198,5.4225,5.4225,5.8825,5.902500000000001,0.0,0.020000000000000462 --t217m2on-s/2,-t217m2on-s,2,0,But,0.5224999785423279,0.7024999856948853,True,4.395274508933733e-12,0.7390152812004089,0.5225184924573115,0.725752175317485,0.5225,0.5225,0.7025,0.7424999999999999,0.0,0.039999999999999925 --t217m2on-s/2,-t217m2on-s,2,1,a,0.7225000262260437,0.7425000071525574,True,3.8194742507657864e-11,4.0,0.8000684223499559,0.820068422350041,0.7224999999999999,0.9025,0.7424999999999999,0.9225,0.18000000000000005,0.18000000000000005 --t217m2on-s/2,-t217m2on-s,2,2,good,0.862500011920929,1.002500057220459,True,7.123131945396821e-12,1.1976734399795532,0.9991130601109262,1.1892688242883607,0.7825,1.3625,1.0025,1.5625,0.5800000000000001,0.56 --t217m2on-s/2,-t217m2on-s,2,3,thing,1.0625,1.5625,True,4.771816188714473e-12,0.802326500415802,1.2388210425890023,1.644624686370855,1.0625,1.6025,1.5625,1.8225,0.54,0.26 --t217m2on-s/2,-t217m2on-s,2,4,to,1.6425000429153442,1.7024999856948853,True,1.0024173158207361e-10,4.0,1.7111086033802672,1.7718470918463047,1.6425,1.8625,1.7025,1.9224999999999999,0.21999999999999997,0.21999999999999997 --t217m2on-s/2,-t217m2on-s,2,5,do,1.722499966621399,1.8224999904632568,True,1.4645716280362042e-12,0.25,1.8248417371760293,1.899259041877084,1.7225,2.0025,1.8225,2.0625,0.28,0.24 --t217m2on-s/2,-t217m2on-s,2,6,is,1.8624999523162842,1.9225000143051147,True,2.995276249251333e-11,4.0,1.9473941160787953,2.048927727382598,1.8625,2.1225,1.9224999999999999,2.3225,0.26,0.3999999999999999 --t217m2on-s/2,-t217m2on-s,2,7,"yawning,",2.002500057220459,2.5225000381469727,True,2.0731873249385524e-12,0.34858280420303345,2.126425467276586,2.7815458651990497,2.0025,2.4025,2.5225,3.3825,0.3999999999999999,0.8599999999999999 --t217m2on-s/2,-t217m2on-s,2,8,chewing,3.322499990463257,3.862499952316284,True,8.043577089111853e-12,1.352435827255249,3.3678403420855934,3.876952620924111,3.3225000000000002,3.4625,3.8625,3.9025,0.13999999999999968,0.040000000000000036 --t217m2on-s/2,-t217m2on-s,2,9,gum,4.002500057220459,4.182499885559082,True,4.4149128797243975e-12,0.742317259311676,4.002468735954162,4.1826709100986985,4.0025,4.0025,4.1825,4.1825,0.0,0.0 --t217m2on-s/2,-t217m2on-s,2,10,is,4.302499771118164,4.402500152587891,True,4.180251630958587e-11,4.0,4.302498326491306,4.402498558458009,4.3025,4.3025,4.402500000000001,4.402500000000001,0.0,0.0 --t217m2on-s/2,-t217m2on-s,2,11,effective.,4.422500133514404,5.042500019073486,True,2.2316224389251627e-12,0.3752218782901764,4.423601965630909,5.043327191160557,4.4225,4.4225,5.0425,5.0425,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,0,If,0.5224999785423279,0.6025000214576721,True,2.0082098740962498e-15,0.25,0.3015441331703468,0.6023878008153598,0.0625,0.5425,0.5824999999999999,0.6024999999999999,0.48,0.020000000000000018 --t217m2on-s/7,-t217m2on-s,7,1,you,0.6424999833106995,0.8025000095367432,True,8.301148532669292e-14,0.42261114716529846,0.6434799647001955,0.7975552797224235,0.6425,0.6425,0.7625,0.8025,0.0,0.040000000000000036 --t217m2on-s/7,-t217m2on-s,7,2,find,0.8424999713897705,1.122499942779541,True,2.140681512260989e-12,4.0,0.8412030234650724,1.1153169739376245,0.8424999999999999,0.8424999999999999,1.0625,1.1225,0.0,0.06000000000000005 --t217m2on-s/7,-t217m2on-s,7,3,that,1.1425000429153442,1.3424999713897705,True,1.610306723882004e-15,0.25,1.1383865714841004,1.3366361795938604,1.1025,1.1425,1.2825,1.3425,0.040000000000000036,0.06000000000000005 --t217m2on-s/7,-t217m2on-s,7,4,your,1.402500033378601,1.5425000190734863,True,4.7928254789421365e-14,0.25,1.4035147118997728,1.5474983663610131,1.4025,1.4025,1.5425,1.6025,0.0,0.06000000000000005 --t217m2on-s/7,-t217m2on-s,7,5,ears,1.7024999856948853,2.0225000381469727,True,1.2316479758821275e-13,0.6270315051078796,1.7035309636037015,2.0069588683882187,1.7025,1.7225,1.9425,2.0225,0.020000000000000018,0.08000000000000007 --t217m2on-s/7,-t217m2on-s,7,6,consistently,2.7225000858306885,4.002500057220459,True,5.272141772628969e-13,2.6840453147888184,2.6778890918337224,4.004975277533073,2.0225,2.7225,4.0025,4.0025,0.7000000000000002,0.0 --t217m2on-s/7,-t217m2on-s,7,7,do,4.28249979019165,4.402500152587891,True,2.9934043525164933e-12,4.0,4.279017335996583,4.399301163358553,4.282500000000001,4.282500000000001,4.402500000000001,4.402500000000001,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,8,this,4.522500038146973,4.702499866485596,True,1.2310472770565937e-14,0.25,4.4871016225096705,4.692839750002059,4.442500000000001,4.522500000000001,4.6625000000000005,4.702500000000001,0.08000000000000007,0.040000000000000036 --t217m2on-s/7,-t217m2on-s,7,9,and,4.78249979019165,4.882500171661377,True,2.4961423986807896e-14,0.25,4.771094354005105,4.876832336738395,4.702500000000001,4.782500000000001,4.8425,4.8825,0.08000000000000007,0.040000000000000036 --t217m2on-s/7,-t217m2on-s,7,10,you,4.902500152587891,5.002500057220459,True,2.269763060296537e-15,0.25,4.900825762575524,5.003465900093947,4.902500000000001,4.902500000000001,5.0025,5.022500000000001,0.0,0.020000000000000462 --t217m2on-s/7,-t217m2on-s,7,11,can't,5.042500019073486,5.34250020980835,True,1.9953183549858977e-10,4.0,5.0430587580198285,5.342191859761064,5.0425,5.0425,5.3425,5.3425,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,12,pop,5.5625,5.802499771118164,True,2.0839250464144143e-12,4.0,5.562475953090791,5.802493273965683,5.562500000000001,5.562500000000001,5.8025,5.8025,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,13,them,5.862500190734863,6.0625,True,3.170972870131933e-14,0.25,5.862493429115527,6.062460001350499,5.862500000000001,5.862500000000001,6.062500000000001,6.062500000000001,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,14,"often,",6.182499885559082,6.442500114440918,True,3.143114634123097e-14,0.25,6.167211454501574,6.46103084293285,6.102500000000001,6.1825,6.442500000000001,6.482500000000001,0.07999999999999918,0.040000000000000036 --t217m2on-s/7,-t217m2on-s,7,15,you'll,6.482500076293945,7.082499980926514,True,6.824945990302478e-12,4.0,6.574808668518842,7.082477879268368,6.482500000000001,6.742500000000001,7.0825000000000005,7.0825000000000005,0.2599999999999998,0.0 --t217m2on-s/7,-t217m2on-s,7,16,definitely,7.102499961853027,7.702499866485596,True,7.39595559275108e-13,3.7652781009674072,7.102586666901631,7.699765961681586,7.102500000000001,7.102500000000001,7.6825,7.702500000000001,0.0,0.020000000000000462 --t217m2on-s/7,-t217m2on-s,7,17,need,7.742499828338623,7.902500152587891,True,4.3668524742426773e-13,2.223162889480591,7.742483042947323,7.90343923658525,7.742500000000001,7.742500000000001,7.902500000000001,7.902500000000001,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,18,to,7.982500076293945,8.042499542236328,True,7.402548727805918e-15,0.25,7.967266830139866,8.02793425896618,7.942500000000001,7.982500000000001,8.0025,8.0425,0.040000000000000036,0.040000000000000924 --t217m2on-s/7,-t217m2on-s,7,19,see,8.0625,8.202500343322754,True,6.569796789923643e-13,3.3446807861328125,8.062499998585567,8.202499998830312,8.0625,8.0625,8.202499999999999,8.202499999999999,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,20,a,8.242500305175781,8.262499809265137,True,2.488813842516091e-11,4.0,8.242500408823773,8.262500408823774,8.2425,8.2425,8.2625,8.2625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,21,physician,8.322500228881836,8.882499694824219,True,4.652816760347678e-13,2.3687472343444824,8.318703380787824,8.881929128769334,8.282499999999999,8.3225,8.8825,8.8825,0.040000000000000924,0.0 --t217m2on-s/7,-t217m2on-s,7,22,regarding,8.90250015258789,9.922499656677246,True,1.5294997256568021e-13,0.778667688369751,8.904235783104783,9.922499327985719,8.9025,8.9025,9.9225,9.9225,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,23,those,9.942500114440918,10.162500381469727,True,1.9642521929732343e-13,1.0,9.942499955586987,10.162498402387016,9.942499999999999,9.942499999999999,10.1625,10.1625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,24,"things,",10.202500343322754,10.522500038146973,True,3.872858929171094e-14,0.25,10.202499406780868,10.522499422185247,10.2025,10.2025,10.522499999999999,10.522499999999999,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,25,it,10.90250015258789,10.982500076293945,True,1.0897798505618561e-13,0.5548064708709717,10.90176918863294,10.98161988705827,10.9025,10.9025,10.9825,10.9825,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,26,could,11.022500038146973,11.22249984741211,True,3.0060584013322234e-13,1.5303831100463867,11.022415515548126,11.222436263178336,11.022499999999999,11.022499999999999,11.2225,11.2225,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,27,be,11.242500305175781,11.302499771118164,True,3.1117084820300733e-13,1.5841695070266724,11.242499890968537,11.302500000932035,11.2425,11.2425,11.3025,11.3025,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,28,something,11.362500190734863,11.762499809265137,True,1.3482193775293545e-14,0.25,11.36250003427757,11.76250000280012,11.362499999999999,11.362499999999999,11.7625,11.7625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,29,more,11.782500267028809,11.982500076293945,True,2.739622764758032e-13,1.3947408199310303,11.782500172700903,11.982499985922653,11.782499999999999,11.782499999999999,11.9825,11.9825,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,30,of,12.0024995803833,12.0625,True,1.2251334495505528e-11,4.0,12.00250300781206,12.062503075601885,12.0025,12.0025,12.0625,12.0625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,31,a,12.1225004196167,12.142499923706055,True,1.9523441023572286e-12,4.0,12.122493511797718,12.142493511797717,12.1225,12.1225,12.1425,12.1425,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,32,nasal,12.202500343322754,12.482500076293945,True,1.2489926627667902e-12,4.0,12.202481052988533,12.482563637084958,12.2025,12.2025,12.4825,12.4825,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,33,infection,12.542499542236328,13.082500457763672,True,1.863801268268267e-13,0.9488604664802551,12.548488364093338,13.112190709215227,12.5425,12.5825,13.0825,13.2025,0.03999999999999915,0.120000000000001 --t217m2on-s/7,-t217m2on-s,7,34,or,13.202500343322754,13.362500190734863,True,1.096706384560997e-12,4.0,13.268630794746127,13.380613030877182,13.2025,13.3425,13.362499999999999,13.4025,0.1399999999999988,0.040000000000000924 --t217m2on-s/7,-t217m2on-s,7,35,a,13.482500076293945,13.5024995803833,True,5.468596062097042e-12,4.0,13.480302700842564,13.500302700842568,13.442499999999999,13.4825,13.4625,13.5025,0.040000000000000924,0.03999999999999915 --t217m2on-s/7,-t217m2on-s,7,36,sinus,13.542499542236328,13.802499771118164,True,6.39902573203871e-13,3.2577414512634277,13.55040940879016,13.803847280153091,13.5025,13.5825,13.8025,13.8025,0.08000000000000007,0.0 --t217m2on-s/7,-t217m2on-s,7,37,infection,13.842499732971191,14.5024995803833,True,1.1748278131579193e-12,4.0,13.844256664988658,14.507515066421155,13.8425,13.8425,14.5025,14.522499999999999,0.0,0.019999999999999574 --t217m2on-s/7,-t217m2on-s,7,38,that,14.6225004196167,15.322500228881836,True,1.0841008673450128e-14,0.25,14.62701468477748,15.32177485942577,14.6225,14.6225,15.3225,15.3225,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,39,he,15.382499694824219,15.462499618530273,True,1.5006362038411103e-12,4.0,15.382499899762017,15.462499926162874,15.3825,15.3825,15.4625,15.4625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,40,would,15.522500038146973,15.742500305175781,True,7.458575722102054e-14,0.37971580028533936,15.523687096195976,15.743719806141243,15.522499999999999,15.522499999999999,15.7425,15.7425,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,41,need,15.782500267028809,15.982500076293945,True,6.329921260544644e-14,0.32225602865219116,15.783733230314803,15.975640595301629,15.7825,15.8025,15.9625,15.9825,0.019999999999999574,0.019999999999999574 --t217m2on-s/7,-t217m2on-s,7,42,to,16.022499084472656,16.082500457763672,True,2.435565990424951e-14,0.25,16.01099501177981,16.070999623759587,15.9825,16.0225,16.0425,16.082500000000003,0.040000000000000924,0.0400000000000027 --t217m2on-s/7,-t217m2on-s,7,43,deal,16.102500915527344,16.262500762939453,True,5.6791098482689064e-15,0.25,16.102225899317638,16.262741723317955,16.102500000000003,16.102500000000003,16.262500000000003,16.262500000000003,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,44,with.,16.342500686645508,16.522499084472656,True,4.8448539695074636e-14,0.25,16.342197762523412,16.52250670243474,16.3425,16.3425,16.5225,16.5225,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,0,The,0.5625,0.6625000238418579,True,6.435688570602394e-14,2.004267930984497,0.5356969890918912,0.658616661956421,0.3425,0.5625,0.6224999999999999,0.6625,0.21999999999999997,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,1,first,0.7024999856948853,0.9624999761581421,True,3.526284895593451e-14,1.098191738128662,0.7009661313991286,0.9602388802710622,0.7025,0.7025,0.9225,0.9624999999999999,0.0,0.039999999999999925 --tANM6ETl_M/3,-tANM6ETl_M,3,2,place,1.002500057220459,1.3224999904632568,True,1.331900779580732e-14,0.4147941470146179,0.999946689866764,1.308838714213955,1.0025,1.0025,1.2825,1.3225,0.0,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,3,is,1.3825000524520874,1.4424999952316284,True,4.107557105185273e-15,0.25,1.389821334669526,1.45349568534902,1.3625,1.4224999999999999,1.4425,1.4825,0.05999999999999983,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,4,either,1.5425000190734863,1.7825000286102295,True,2.895699020735547e-14,0.9018082022666931,1.5201676262785533,1.7605858681075623,1.4825,1.5425,1.7225,1.7825,0.06000000000000005,0.06000000000000005 --tANM6ETl_M/3,-tANM6ETl_M,3,5,you,1.8624999523162842,2.0225000381469727,True,3.5744268601835966e-14,1.1131845712661743,1.8553028266965272,2.0036440368509445,1.8225,1.8625,1.9224999999999999,2.0225,0.040000000000000036,0.10000000000000009 --tANM6ETl_M/3,-tANM6ETl_M,3,6,have,2.0625,2.202500104904175,True,7.299865464709121e-15,0.25,2.062385601587532,2.202536012487326,2.0625,2.0625,2.2025,2.2025,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,7,a,2.2825000286102295,2.302500009536743,True,1.1580021663792905e-11,4.0,2.282498533579872,2.302498533579872,2.2825,2.2825,2.3025,2.3025,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,8,"prospect,",2.322499990463257,2.882499933242798,True,4.4684812321421196e-15,0.25,2.3231238527656637,2.882916379552269,2.3225,2.3225,2.8825,2.8825,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,9,or,2.942500114440918,3.002500057220459,True,2.767639904147773e-13,4.0,2.942497276629371,3.0032399939030863,2.9425,2.9425,3.0025,3.0025,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,10,a,3.0625,3.0824999809265137,True,1.837679140803683e-11,4.0,3.0620961698677016,3.0820961698677016,3.0625,3.0625,3.0825,3.0825,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,11,customer,3.1024999618530273,3.4825000762939453,True,1.1064295367489013e-13,3.445755958557129,3.103188292319035,3.4840491250071417,3.1025,3.1025,3.4825,3.4825,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,12,who's,3.5225000381469727,3.682499885559082,True,3.082221630701376e-11,4.0,3.5274568778891453,3.6835674538224654,3.5225,3.5425,3.6825,3.6825,0.020000000000000018,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,13,never,3.882499933242798,4.182499885559082,True,8.976713720498884e-15,0.27956199645996094,3.8070924541804487,4.15445694471168,3.7225,3.8825,4.1225000000000005,4.1825,0.1599999999999997,0.05999999999999961 --tANM6ETl_M/3,-tANM6ETl_M,3,14,done,4.28249979019165,4.462500095367432,True,3.556252158937656e-15,0.25,4.255324390674593,4.449549096474829,4.202500000000001,4.282500000000001,4.4225,4.4625,0.08000000000000007,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,15,it,4.482500076293945,4.542500019073486,True,2.5008395351864936e-14,0.7788370251655579,4.485970581662388,4.545979863534526,4.482500000000001,4.522500000000001,4.5425,4.5825000000000005,0.040000000000000036,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,16,"before,",4.602499961853027,4.982500076293945,True,2.1828892859954152e-14,0.6798176765441895,4.602479292145233,4.978115573980364,4.602500000000001,4.602500000000001,4.982500000000001,4.982500000000001,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,17,or,5.022500038146973,5.082499980926514,True,5.056437717533904e-14,1.5747275352478027,5.0206841105147095,5.0806852891637515,5.022500000000001,5.022500000000001,5.0825000000000005,5.0825000000000005,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,18,they've,5.102499961853027,5.34250020980835,True,3.8970549335291815e-13,4.0,5.101592897531236,5.337826895494357,5.102500000000001,5.102500000000001,5.322500000000001,5.3425,0.0,0.019999999999999574 --tANM6ETl_M/3,-tANM6ETl_M,3,19,never,5.422500133514404,5.662499904632568,True,2.0569947790450085e-14,0.6406103372573853,5.422381810653897,5.662099104183884,5.4225,5.4225,5.6625000000000005,5.6625000000000005,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,20,tried,5.722499847412109,6.002500057220459,True,1.0326153248988758e-14,0.32158762216567993,5.722183877149623,6.001503992763337,5.7225,5.7225,6.0025,6.0025,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,21,it.,6.0625,6.122499942779541,True,2.0461776804175935e-12,4.0,6.063300842860167,6.130318478523356,6.062500000000001,6.062500000000001,6.1225000000000005,6.1225000000000005,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,0,Make,0.0024999999441206455,0.14249999821186066,True,3.090905894946594e-14,0.25,0.02169566240224484,0.17425271469887066,0.0025000000000000005,0.0425,0.14250000000000002,0.1825,0.04,0.03999999999999998 --tPCytz4rww/11,-tPCytz4rww,11,1,sure,0.18250000476837158,0.6225000023841858,True,6.222975260095076e-12,4.0,0.346787166754646,0.6374821536776674,0.1825,0.5025,0.6224999999999999,0.6825,0.31999999999999995,0.06000000000000005 --tPCytz4rww/11,-tPCytz4rww,11,2,that,0.6625000238418579,0.8224999904632568,True,3.477579552449428e-13,1.9737460613250732,0.6725399070879395,0.8375782669395493,0.6625,0.7025,0.8225,0.8825,0.040000000000000036,0.05999999999999994 --tPCytz4rww/11,-tPCytz4rww,11,3,your,0.862500011920929,1.0225000381469727,True,3.126307340515556e-15,0.25,0.8784328640568163,1.0433921603265537,0.8624999999999999,0.9225,1.0225,1.1025,0.06000000000000005,0.08000000000000007 --tPCytz4rww/11,-tPCytz4rww,11,4,using,1.0425000190734863,1.222499966621399,True,1.8500610386110866e-13,1.050026535987854,1.0729127611360898,1.2830561203794688,1.0425,1.1624999999999999,1.2225,1.4625,0.11999999999999988,0.24 --tPCytz4rww/11,-tPCytz4rww,11,5,comps,1.3025000095367432,1.5425000190734863,True,6.064278853384897e-14,0.34418612718582153,1.3432746992012874,1.6062773987725414,1.2425,1.5625,1.5025,1.8825,0.32000000000000006,0.3800000000000001 --tPCytz4rww/11,-tPCytz4rww,11,6,that,1.5625,1.8224999904632568,True,6.134831141355046e-13,3.4819042682647705,1.6472699997478626,1.8896951897898389,1.5625,1.9025,1.8225,2.0625,0.3400000000000001,0.24 --tPCytz4rww/11,-tPCytz4rww,11,7,are,1.9225000143051147,2.0625,True,5.638180792741105e-15,0.25,1.971279454556468,2.097049019461683,1.9224999999999999,2.1025,2.0625,2.2025,0.18000000000000016,0.14000000000000012 --tPCytz4rww/11,-tPCytz4rww,11,8,not,2.1024999618530273,2.302500009536743,True,1.9874132721475757e-12,4.0,2.133150452735309,2.3222142523728997,2.1025,2.2225,2.3025,2.3825,0.1200000000000001,0.07999999999999963 --tPCytz4rww/11,-tPCytz4rww,11,9,"larger,",2.502500057220459,2.8424999713897705,True,2.330519674811904e-13,1.3227171897888184,2.4520214020764155,2.820602708781465,2.4025,2.5025,2.7625,2.8425,0.10000000000000009,0.07999999999999963 --tPCytz4rww/11,-tPCytz4rww,11,10,no,3.2225000858306885,3.322499990463257,True,7.816119171159819e-14,0.4436141550540924,3.2170783747909404,3.32249684334124,3.2225,3.2225,3.3225000000000002,3.3225000000000002,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,11,larger,3.382499933242798,3.682499885559082,True,9.446646752602622e-15,0.25,3.382499571327774,3.6829963136388377,3.3825,3.3825,3.6825,3.6825,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,12,than,3.702500104904175,3.9024999141693115,True,2.853919290018818e-14,0.25,3.703456733094279,3.884414196985789,3.7025,3.7025,3.8425,3.9025,0.0,0.06000000000000005 --tPCytz4rww/11,-tPCytz4rww,11,13,twelve,3.942500114440918,4.242499828338623,True,6.64411762912008e-14,0.37709563970565796,3.9459603741824063,4.243307639989169,3.9425,3.9625,4.242500000000001,4.242500000000001,0.020000000000000018,0.0 --tPCytz4rww/11,-tPCytz4rww,11,14,hundred,4.262499809265137,4.622499942779541,True,1.7619183835439894e-13,1.0,4.263793015967037,4.62249338252602,4.2625,4.2625,4.6225000000000005,4.6225000000000005,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,15,square,4.662499904632568,4.982500076293945,True,3.165683318782919e-13,1.7967252731323242,4.662500502666387,4.982478809793004,4.6625000000000005,4.6625000000000005,4.982500000000001,4.982500000000001,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,16,feet.,5.0625,5.28249979019165,True,3.689433470104725e-13,2.0939865112304688,5.062500417509267,5.282497153233201,5.062500000000001,5.062500000000001,5.282500000000001,5.282500000000001,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,0,Say,0.0024999999441206455,0.12250000238418579,True,3.2502712327131533e-11,4.0,0.002505057883538294,0.1225226855817078,0.0025000000000000005,0.0025000000000000005,0.1225,0.1225,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,1,twenty,0.20250000059604645,0.7225000262260437,True,6.181854507458784e-12,1.105215072631836,0.20256298455497526,0.7225242384992536,0.2025,0.2025,0.7224999999999999,0.7224999999999999,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,2,percent,0.8025000095367432,1.2024999856948853,True,4.0030847560379446e-13,0.25,0.8175673372248227,1.202531136762618,0.8025,0.9025,1.2025,1.2025,0.09999999999999998,0.0 --tPCytz4rww/10,-tPCytz4rww,10,3,so,1.2424999475479126,1.3825000524520874,True,1.546988042224265e-13,0.25,1.2430952066521186,1.3768309486444459,1.2425,1.2425,1.3625,1.3825,0.0,0.020000000000000018 --tPCytz4rww/10,-tPCytz4rww,10,4,if,1.5824999809265137,1.722499966621399,True,2.316052731543561e-12,0.41407257318496704,1.582535696275133,1.722513322878117,1.5825,1.5825,1.7225,1.7225,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,5,you've,1.9424999952316284,2.2825000286102295,True,1.4418195803944656e-10,4.0,1.9519629063531054,2.282409676113536,1.9425,1.9625,2.2825,2.2825,0.020000000000000018,0.0 --tPCytz4rww/10,-tPCytz4rww,10,6,got,2.4825000762939453,2.6424999237060547,True,1.1913363524851395e-11,2.129915714263916,2.4824364558586436,2.642480769217724,2.4825,2.4825,2.6425,2.6425,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,7,a,2.702500104904175,2.7225000858306885,True,8.198944127790764e-12,1.4658379554748535,2.702496664722302,2.7224966647239226,2.7025,2.7025,2.7225,2.7225,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,8,thousand,2.822499990463257,3.2825000286102295,True,6.314457962584841e-13,0.25,2.822534476359815,3.281519597807987,2.8225,2.8225,3.2825,3.2825,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,9,square,3.322499990463257,3.882499933242798,True,5.894128501243712e-13,0.25,3.4688986221058005,3.8957732430891063,3.3225000000000002,3.6825,3.8825,3.9225,0.3599999999999999,0.040000000000000036 --tPCytz4rww/10,-tPCytz4rww,10,10,foot,3.9024999141693115,4.122499942779541,True,2.942341509326596e-11,4.0,3.9231015848774184,4.122296003381612,3.9025,3.9625,4.1225000000000005,4.1225000000000005,0.06000000000000005,0.0 --tPCytz4rww/10,-tPCytz4rww,10,11,condo.,4.422500133514404,4.622499942779541,True,5.004845062689389e-12,0.8947849869728088,4.422276698912244,4.637538051382122,4.4225,4.4225,4.6225000000000005,4.6625000000000005,0.0,0.040000000000000036 --tPCytz4rww/12,-tPCytz4rww,12,0,No,0.08250000327825546,0.16249999403953552,True,1.4483042586402317e-12,2.761003017425537,0.08114481503602712,0.16217620079296532,0.0625,0.0825,0.1625,0.1625,0.020000000000000004,0.0 --tPCytz4rww/12,-tPCytz4rww,12,1,less,0.2224999964237213,0.4625000059604645,True,4.10721673685388e-12,4.0,0.22219314838082402,0.4616554631769186,0.2225,0.2225,0.4625,0.4625,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,2,than,0.5024999976158142,0.6825000047683716,True,5.063582474325312e-13,0.9653059244155884,0.5017505310429379,0.6818042885306567,0.5025,0.5025,0.6825,0.6825,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,3,eight,0.7024999856948853,1.0425000190734863,True,9.050614532997436e-13,1.7253814935684204,0.7020298754293801,1.0413866100393672,0.7025,0.7025,1.0425,1.0425,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,4,hundred,1.3624999523162842,1.7424999475479126,True,1.8136402481824104e-13,0.34574684500694275,1.3600963696569792,1.740637256117597,1.3625,1.3625,1.7425,1.7425,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,5,squire,1.7825000286102295,2.2225000858306885,True,1.852836894328247e-12,3.532191753387451,1.7936826033289333,2.203577332640142,1.7825,1.8225,2.0225,2.2225,0.040000000000000036,0.20000000000000018 --tPCytz4rww/12,-tPCytz4rww,12,6,feet,2.6024999618530273,2.7825000286102295,True,1.4244164658547276e-12,2.7154641151428223,2.562656419923969,2.737386374888778,2.0425,2.6225,2.2225,2.7825,0.5800000000000001,0.56 --tPCytz4rww/12,-tPCytz4rww,12,7,and,2.822499990463257,2.922499895095825,True,7.50539994737176e-12,4.0,2.8040084810806176,2.9083167912758485,2.6625,2.8225,2.8825,2.9225,0.1599999999999997,0.040000000000000036 --tPCytz4rww/12,-tPCytz4rww,12,8,going,2.9825000762939453,3.242500066757202,True,1.8712631270900726e-12,3.567318916320801,2.972907476639749,3.2485900463543764,2.9225,2.9825,3.1825,3.3425,0.06000000000000005,0.1599999999999997 --tPCytz4rww/12,-tPCytz4rww,12,9,to,3.322499990463257,3.4625000953674316,True,1.7221121718317273e-13,0.3282982110977173,3.3804194320253904,3.4819767729461604,3.2425,3.6425,3.3425,3.7225,0.3999999999999999,0.38000000000000034 --tPCytz4rww/12,-tPCytz4rww,12,10,similar,3.6424999237060547,4.022500038146973,True,8.151455313694378e-13,1.5539685487747192,3.6554667644090957,4.022687363378376,3.6025,3.7625,3.9625,4.062500000000001,0.16000000000000014,0.10000000000000098 --tPCytz4rww/12,-tPCytz4rww,12,11,"in,",4.042500019073486,4.122499942779541,True,6.854618001678192e-12,4.0,4.062473519113548,4.135646046857629,4.0425,4.1025,4.1225000000000005,4.1625000000000005,0.05999999999999961,0.040000000000000036 --tPCytz4rww/12,-tPCytz4rww,12,12,similar,4.942500114440918,5.322500228881836,True,3.7164002886398706e-13,0.7084832191467285,4.856473555076607,5.325159731998561,4.202500000000001,4.942500000000001,5.322500000000001,5.322500000000001,0.7400000000000002,0.0 --tPCytz4rww/12,-tPCytz4rww,12,13,with,5.582499980926514,5.78249979019165,True,5.430205913289736e-14,0.25,5.565606085452562,5.774287522003375,5.4625,5.5825000000000005,5.742500000000001,5.782500000000001,0.1200000000000001,0.040000000000000036 --tPCytz4rww/12,-tPCytz4rww,12,14,bedrooms,5.922500133514404,6.322500228881836,True,2.7115378627325104e-13,0.5169193148612976,5.91833513197217,6.321445991446334,5.9225,5.9225,6.322500000000001,6.322500000000001,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,15,and,6.422500133514404,6.582499980926514,True,1.5096515617457618e-12,2.8779537677764893,6.415225660149443,6.570339834181551,6.3825,6.4225,6.5025,6.5825000000000005,0.040000000000000036,0.08000000000000007 --tPCytz4rww/12,-tPCytz4rww,12,16,bathrooms,6.662499904632568,7.102499961853027,True,9.588660296539278e-14,0.25,6.649534180591883,7.103515746095183,6.5825000000000005,6.6625000000000005,7.102500000000001,7.102500000000001,0.08000000000000007,0.0 --tPCytz4rww/12,-tPCytz4rww,12,17,as,7.182499885559082,7.28249979019165,True,2.5051279952358063e-13,0.47756996750831604,7.182713277688271,7.281488350142335,7.1825,7.1825,7.282500000000001,7.282500000000001,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,18,well,7.882500171661377,8.082500457763672,True,1.5330628205714042e-13,0.2922584116458893,7.743203575778251,8.06287017488467,7.3025,7.8825,8.022499999999999,8.0825,0.5800000000000001,0.0600000000000005 --tPCytz4rww/12,-tPCytz4rww,12,19,and,8.242500305175781,8.342499732971191,True,1.736000666135995e-13,0.3309458792209625,8.195382128579398,8.336167495849628,8.1225,8.2425,8.3025,8.362499999999999,0.11999999999999922,0.05999999999999872 --tPCytz4rww/12,-tPCytz4rww,12,20,hopefully,8.362500190734863,8.762499809265137,True,5.427562696155852e-13,1.034693956375122,8.38126225674004,8.800728012660858,8.362499999999999,8.4225,8.7625,8.862499999999999,0.0600000000000005,0.09999999999999964 --tPCytz4rww/12,-tPCytz4rww,12,21,the,8.842499732971191,9.22249984741211,True,1.5777048450234948e-13,0.30076882243156433,8.939716417127721,9.233476473717799,8.8225,9.1425,9.2225,9.3025,0.3200000000000003,0.08000000000000007 --tPCytz4rww/12,-tPCytz4rww,12,22,complexes,9.40250015258789,9.862500190734863,True,4.707200883420637e-13,0.8973664045333862,9.400885044089927,9.865401598848464,9.4025,9.4025,9.862499999999999,9.9025,0.0,0.040000000000000924 --tPCytz4rww/12,-tPCytz4rww,12,23,have,9.942500114440918,10.102499961853027,True,2.9300275313642876e-12,4.0,9.941644931466874,10.10126370487901,9.942499999999999,9.942499999999999,10.1025,10.1025,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,24,similar,10.162500381469727,10.542499542236328,True,1.139832179379921e-13,0.25,10.163551445389935,10.604138254114487,10.1625,10.1625,10.5425,10.7025,0.0,0.16000000000000014 --tPCytz4rww/12,-tPCytz4rww,12,25,amenities.,10.6225004196167,11.602499961853027,True,3.5955082227506763e-12,4.0,10.709443501320298,11.61568048027064,10.6225,10.8425,11.5425,11.7225,0.21999999999999886,0.17999999999999972 --tPCytz4rww/16,-tPCytz4rww,16,0,So,0.5824999809265137,0.7225000262260437,True,1.0900147971726337e-11,2.107679605484009,0.5824988545142109,0.7225003068930176,0.5824999999999999,0.5824999999999999,0.7224999999999999,0.7224999999999999,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,1,make,0.8824999928474426,1.0824999809265137,True,8.273993469531948e-12,1.5998799800872803,0.8873053054868005,1.0895852394390597,0.8624999999999999,0.9624999999999999,1.0825,1.1425,0.09999999999999998,0.06000000000000005 --tPCytz4rww/16,-tPCytz4rww,16,2,sure,1.1825000047683716,1.3624999523162842,True,4.89173209317767e-13,0.25,1.1824972875537523,1.3625090418290506,1.1824999999999999,1.1824999999999999,1.3625,1.3625,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,3,to,1.4824999570846558,1.662500023841858,True,8.923322150633517e-13,0.25,1.4824959809329938,1.6625015961399936,1.4825,1.4825,1.6625,1.6625,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,4,get,1.9225000143051147,2.0425000190734863,True,2.0692742224576177e-12,0.4001200199127197,1.9224425845184057,2.0424873809086117,1.9224999999999999,1.9224999999999999,2.0425,2.0425,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,5,a,2.1024999618530273,2.122499942779541,True,1.6197307384224757e-11,3.1319515705108643,2.1024966862255567,2.122496686225557,2.1025,2.1025,2.1225,2.1225,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,6,through,2.202500104904175,2.5625,True,6.382161509346784e-13,0.25,2.194829264614116,2.557246651427563,2.1425,2.2025,2.5025,2.5625,0.06000000000000005,0.06000000000000005 --tPCytz4rww/16,-tPCytz4rww,16,7,or,3.1424999237060547,3.2225000858306885,True,2.622704137200671e-11,4.0,3.1424300748546017,3.2224545318159237,3.1425,3.1425,3.2225,3.2225,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,8,to,4.262499809265137,4.34250020980835,True,9.882743359562393e-14,0.25,4.210863172668486,4.341936445692636,3.9225,4.2625,4.3425,4.3425,0.3400000000000003,0.0 --tPCytz4rww/16,-tPCytz4rww,16,9,do,4.422500133514404,4.522500038146973,True,1.4996654959520406e-11,2.8997905254364014,4.422508837940596,4.5225076494860605,4.4225,4.4225,4.522500000000001,4.522500000000001,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,10,a,4.662499904632568,4.682499885559082,True,2.231034974820023e-11,4.0,4.6619831203681485,4.681983120368152,4.6625000000000005,4.6625000000000005,4.6825,4.6825,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,11,through,4.722499847412109,5.102499961853027,True,6.987023806748205e-13,0.25,4.722842072869845,5.102508213245117,4.7225,4.7225,5.102500000000001,5.102500000000001,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,12,cleaning,5.122499942779541,5.542500019073486,True,6.977411812388035e-13,0.25,5.123908908733183,5.544329216386563,5.1225000000000005,5.1225000000000005,5.5425,5.5425,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,13,of,5.582499980926514,5.862500190734863,True,1.382491083418147e-11,2.6732192039489746,5.616153867927133,5.8758597493577955,5.5825000000000005,5.8425,5.862500000000001,5.9625,0.2599999999999998,0.09999999999999964 --tPCytz4rww/16,-tPCytz4rww,16,14,the,6.262499809265137,6.502500057220459,True,1.5708401246428139e-10,4.0,6.266147318112281,6.501861147995156,6.2625,6.322500000000001,6.5025,6.5025,0.0600000000000005,0.0 --tPCytz4rww/16,-tPCytz4rww,16,15,property.,6.5625,7.122499942779541,True,1.007599602712017e-12,0.25,6.562252572276781,7.122499604680712,6.562500000000001,6.562500000000001,7.1225000000000005,7.1225000000000005,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,0,Do,0.5425000190734863,0.6424999833106995,True,6.279769673017688e-12,4.0,0.4577015282761664,0.5637259104571192,0.0425,0.5425,0.10250000000000001,0.6425,0.5,0.5399999999999999 --tPCytz4rww/18,-tPCytz4rww,18,1,so,0.762499988079071,0.9225000143051147,True,9.772651438089142e-13,4.0,0.7297637425083854,0.8808360734940381,0.5425,0.7625,0.6425,0.9225,0.21999999999999997,0.28 --tPCytz4rww/18,-tPCytz4rww,18,2,and,1.5225000381469727,1.622499942779541,True,3.8262928524386564e-13,2.566894769668579,1.3848902857859928,1.5188761214324742,0.7025,1.5225,0.9225,1.6225,0.82,0.7000000000000001 --tPCytz4rww/18,-tPCytz4rww,18,3,as,1.7024999856948853,1.7625000476837158,True,1.1480210228634324e-13,0.7701577544212341,1.6801337977048072,1.7422092862293954,1.5625,1.7025,1.6225,1.7625,0.1399999999999999,0.1399999999999999 --tPCytz4rww/18,-tPCytz4rww,18,4,well,1.8224999904632568,2.1024999618530273,True,4.317257136640315e-14,0.2896261513233185,1.8047449158844084,2.0739518481040995,1.7025,1.8225,1.9425,2.1025,0.1200000000000001,0.16000000000000014 --tPCytz4rww/18,-tPCytz4rww,18,5,you'll,2.5625,2.7825000286102295,True,4.398781686121289e-11,4.0,2.5010674825755013,2.7816743887812576,2.1025,2.5625,2.7825,2.7825,0.45999999999999996,0.0 --tPCytz4rww/18,-tPCytz4rww,18,6,also,2.822499990463257,3.0625,True,1.0343581460098283e-13,0.6939062476158142,2.8229435101245044,3.0581425038034475,2.8025,2.8425,3.0225,3.0825,0.03999999999999959,0.06000000000000005 --tPCytz4rww/18,-tPCytz4rww,18,7,want,3.1424999237060547,3.322499990463257,True,7.557796314510475e-15,0.25,3.14970832157837,3.3279337466346512,3.1425,3.2025,3.3225000000000002,3.3625,0.06000000000000005,0.03999999999999959 --tPCytz4rww/18,-tPCytz4rww,18,8,to,3.4024999141693115,3.5425000190734863,True,1.7010866456765306e-13,1.1411856412887573,3.4025001233819,3.5425000693102824,3.4025,3.4025,3.5425,3.5425,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,9,remove,3.622499942779541,3.942500114440918,True,5.3244467347482796e-14,0.3571941554546356,3.626808956541107,3.9453705442557396,3.6225,3.6825,3.9425,3.9825,0.06000000000000005,0.040000000000000036 --tPCytz4rww/18,-tPCytz4rww,18,10,any,3.9625000953674316,4.102499961853027,True,1.28017523881932e-13,0.8588143587112427,3.9746334399483496,4.104266931525665,3.9625,4.0025,4.1025,4.1025,0.04000000000000048,0.0 --tPCytz4rww/18,-tPCytz4rww,18,11,clutter,4.222499847412109,4.662499904632568,True,2.1503506847332307e-14,0.25,4.215421975132926,4.663287259592333,4.1425,4.2225,4.6625000000000005,4.6825,0.08000000000000007,0.019999999999999574 --tPCytz4rww/18,-tPCytz4rww,18,12,from,4.84250020980835,4.982500076293945,True,5.341139314683774e-13,3.5831398963928223,4.837114731944649,4.977657863914085,4.8425,4.8425,4.982500000000001,4.982500000000001,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,13,that,5.002500057220459,5.142499923706055,True,5.396275382785328e-16,0.25,4.999508925414773,5.139697183402384,5.0025,5.0025,5.1425,5.1425,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,14,you've,5.162499904632568,5.34250020980835,True,2.645488515751193e-11,4.0,5.16074853713978,5.342500661315979,5.1625000000000005,5.1625000000000005,5.3425,5.3425,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,15,accumulated.,5.402500152587891,6.522500038146973,True,8.827004340135336e-13,4.0,5.400102213934783,6.512170678859689,5.3825,5.4625,6.4625,6.522500000000001,0.08000000000000007,0.0600000000000005 --vxjVxOeScU/4,-vxjVxOeScU,4,0,You're,0.0024999999441206455,0.32249999046325684,True,1.5470193485619954e-12,3.368727445602417,0.0029563428740617594,0.29346729925949566,0.0025000000000000005,0.0025000000000000005,0.2025,0.4225,0.0,0.21999999999999997 --vxjVxOeScU/4,-vxjVxOeScU,4,1,going,0.5224999785423279,0.7024999856948853,True,9.600089820316349e-13,2.090477228164673,0.5220072174639193,0.7031866953765705,0.5225,0.5225,0.7025,0.7025,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,2,to,0.7225000262260437,0.7825000286102295,True,2.1261818531920912e-13,0.46298885345458984,0.7239908232403981,0.7843177483023872,0.7224999999999999,0.7224999999999999,0.7825,0.7825,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,3,talk,0.8424999713897705,1.122499942779541,True,3.087919934400933e-14,0.25,0.8273659506445371,1.0905327934600164,0.8025,0.8424999999999999,1.0425,1.1225,0.039999999999999925,0.08000000000000007 --vxjVxOeScU/4,-vxjVxOeScU,4,4,about,1.1825000047683716,1.6024999618530273,True,6.140968809853486e-13,1.3372328281402588,1.1852211848482213,1.6047478626253122,1.1824999999999999,1.1824999999999999,1.6025,1.6025,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,5,their,1.6425000429153442,2.0625,True,6.062020409437633e-15,0.25,1.6640391436621444,2.0694865731735606,1.6425,1.8825,2.0625,2.1025,0.24,0.040000000000000036 --vxjVxOeScU/4,-vxjVxOeScU,4,6,"life,",2.1024999618530273,2.5425000190734863,True,1.918054690769333e-12,4.0,2.140782386335001,2.579308133483268,2.1025,2.2025,2.5425,2.6425,0.10000000000000009,0.10000000000000009 --vxjVxOeScU/4,-vxjVxOeScU,4,7,their,2.622499942779541,3.182499885559082,True,1.0584406818343016e-14,0.25,2.7224969696046233,3.1752309276097845,2.6225,2.9625,3.1425,3.1825,0.33999999999999986,0.040000000000000036 --vxjVxOeScU/4,-vxjVxOeScU,4,8,marriage,3.242500066757202,3.822499990463257,True,9.971537823423061e-15,0.25,3.241210169650607,3.825199297988689,3.1825,3.2425,3.8225000000000002,3.8225000000000002,0.06000000000000005,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,9,and,4.462500095367432,4.5625,True,1.410027368302369e-12,3.0704190731048584,4.452967302117755,4.640251380407333,4.3825,4.5025,4.522500000000001,4.782500000000001,0.1200000000000001,0.2599999999999998 --vxjVxOeScU/4,-vxjVxOeScU,4,10,usually,4.762499809265137,5.482500076293945,True,1.6004079572001784e-12,3.4849843978881836,4.851114393665739,5.482486596984726,4.7625,5.0025,5.482500000000001,5.482500000000001,0.2400000000000002,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,11,how,5.522500038146973,5.722499847412109,True,4.592296052978451e-13,1.0,5.525640963404545,5.725972052153117,5.522500000000001,5.522500000000001,5.7225,5.7225,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,12,amazing,5.902500152587891,6.502500057220459,True,6.916904657545964e-13,1.506197452545166,5.9024997564652315,6.502508539881368,5.902500000000001,5.902500000000001,6.5025,6.5025,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,13,it,6.662499904632568,6.722499847412109,True,2.1100000002424735e-13,0.4594651460647583,6.662473520526038,6.722503456624384,6.6625000000000005,6.6625000000000005,6.7225,6.7225,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,14,is,6.84250020980835,6.902500152587891,True,6.980565807329772e-16,0.25,6.834745016706655,6.902340309975699,6.7625,6.8425,6.902500000000001,6.902500000000001,0.08000000000000007,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,15,that,6.982500076293945,7.162499904632568,True,1.3848431678741696e-13,0.30155789852142334,6.982535263961875,7.162875631455382,6.982500000000001,6.982500000000001,7.1625000000000005,7.1625000000000005,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,16,they've,7.182499885559082,7.502500057220459,True,1.4167369534467955e-12,3.0850296020507812,7.18830786025921,7.499636426891534,7.1825,7.202500000000001,7.4625,7.5025,0.020000000000000462,0.040000000000000036 --vxjVxOeScU/4,-vxjVxOeScU,4,17,stayed,7.522500038146973,7.862500190734863,True,2.276652174074134e-13,0.4957546591758728,7.5225191866663135,7.8628040961908825,7.522500000000001,7.522500000000001,7.862500000000001,7.862500000000001,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,18,together,7.922500133514404,8.482500076293945,True,7.508617208898394e-13,1.635046362876892,7.951205794831128,8.482846561288776,7.9225,7.982500000000001,8.4825,8.4825,0.0600000000000005,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,19,this,8.5625,8.782500267028809,True,1.077622973494341e-11,4.0,8.562488552433173,8.782505471531966,8.5625,8.5625,8.782499999999999,8.782499999999999,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,20,long.,8.862500190734863,9.022500038146973,True,1.3307234884873433e-14,0.25,8.84487426972022,9.02198370726463,8.8225,8.862499999999999,9.022499999999999,9.022499999999999,0.03999999999999915,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,0,So,0.6025000214576721,0.8224999904632568,True,4.930794271149064e-13,4.0,0.602499648212456,0.8224999940402619,0.6024999999999999,0.6024999999999999,0.8225,0.8225,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,1,if,0.9424999952316284,1.1425000429153442,True,3.520454937224089e-14,2.2842962741851807,0.9495859338563555,1.1379864720902402,0.9425,0.9425,1.1225,1.1425,0.0,0.020000000000000018 --wMB_hJL-3o/7,-wMB_hJL-3o,7,2,you,1.2024999856948853,1.3224999904632568,True,8.520083340831774e-15,0.5528374910354614,1.1899086153822664,1.3046461227483739,1.1624999999999999,1.2025,1.2625,1.3425,0.040000000000000036,0.08000000000000007 --wMB_hJL-3o/7,-wMB_hJL-3o,7,3,have,1.3825000524520874,1.5225000381469727,True,1.1133222165352064e-13,4.0,1.3555953310480724,1.5160767063261227,1.3225,1.3825,1.5025,1.5225,0.06000000000000005,0.020000000000000018 --wMB_hJL-3o/7,-wMB_hJL-3o,7,4,a,1.5425000190734863,1.5625,True,4.2741949302788074e-13,4.0,1.5406014206323078,1.5606014206323082,1.5225,1.5425,1.5425,1.5625,0.020000000000000018,0.020000000000000018 --wMB_hJL-3o/7,-wMB_hJL-3o,7,5,loved,1.6024999618530273,1.8224999904632568,True,2.6587540762762485e-12,4.0,1.6017163722664503,1.8216123303039624,1.6025,1.6025,1.8225,1.8225,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,6,one,1.902500033378601,2.002500057220459,True,6.939063734013153e-15,0.4502508342266083,1.896415540653591,2.000333519800293,1.8425,1.9025,1.9825,2.0025,0.06000000000000005,0.020000000000000018 --wMB_hJL-3o/7,-wMB_hJL-3o,7,7,who,2.0225000381469727,2.1624999046325684,True,2.149073570807008e-15,0.25,2.0224997179877056,2.149062987763053,2.0225,2.0225,2.1225,2.1625,0.0,0.040000000000000036 --wMB_hJL-3o/7,-wMB_hJL-3o,7,8,is,2.202500104904175,2.262500047683716,True,1.8062620152294213e-14,1.17201828956604,2.1850443997523645,2.2531201231317675,2.1425,2.2025,2.2225,2.2825,0.06000000000000005,0.06000000000000005 --wMB_hJL-3o/7,-wMB_hJL-3o,7,9,depressed,2.302500009536743,2.9024999141693115,True,8.335262128169425e-16,0.25,2.302498985661637,2.8908095885097147,2.3025,2.3025,2.8625,2.9025,0.0,0.040000000000000036 --wMB_hJL-3o/7,-wMB_hJL-3o,7,10,those,2.922499895095825,3.4024999141693115,True,2.3576904588807485e-14,1.5298203229904175,2.922649231893573,3.40250415475127,2.9225,2.9225,3.4025,3.4025,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,11,are,3.442500114440918,3.5625,True,4.111901791281625e-14,2.668064832687378,3.4426174034431227,3.5625004913768072,3.4425,3.4425,3.5625,3.5625,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,12,some,3.6024999618530273,3.762500047683716,True,6.959169839785423e-14,4.0,3.6024968346111734,3.762500000089275,3.6025,3.6025,3.7625,3.7625,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,13,of,3.7825000286102295,3.8424999713897705,True,8.037168681778416e-15,0.5215029120445251,3.782500000350712,3.8425000004085677,3.7825,3.7825,3.8425,3.8425,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,14,the,3.862499952316284,3.9625000953674316,True,9.505426603977334e-15,0.6167728900909424,3.8625000018810254,3.9625000861085344,3.8625,3.8625,3.9625,3.9625,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,15,things,4.022500038146973,4.28249979019165,True,2.8207193254314172e-14,1.8302631378173828,4.02228486995236,4.282719674458678,4.022500000000001,4.022500000000001,4.282500000000001,4.282500000000001,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,16,that,4.302499771118164,4.442500114440918,True,2.5476262894126795e-15,0.25,4.317388672273269,4.4650042095716405,4.3025,4.3425,4.442500000000001,4.5025,0.040000000000000036,0.05999999999999961 --wMB_hJL-3o/7,-wMB_hJL-3o,7,17,you,4.522500038146973,4.622499942779541,True,1.6509219537028477e-15,0.25,4.522330946791593,4.623047680043856,4.522500000000001,4.522500000000001,4.6225000000000005,4.6225000000000005,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,18,should,4.662499904632568,4.882500171661377,True,7.711374716913349e-16,0.25,4.662444498188476,4.892354900163382,4.6625000000000005,4.6625000000000005,4.8825,4.902500000000001,0.0,0.020000000000000462 --wMB_hJL-3o/7,-wMB_hJL-3o,7,19,think,4.922500133514404,5.162499904632568,True,6.412755661775252e-14,4.0,4.922610088256987,5.162437694966212,4.9225,4.9225,5.1625000000000005,5.1625000000000005,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,20,about,5.182499885559082,5.482500076293945,True,1.2760483587750254e-14,0.8279817700386047,5.182502383872996,5.482478605735311,5.1825,5.1825,5.482500000000001,5.482500000000001,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,21,doing.,5.502500057220459,5.942500114440918,True,5.4805903658440016e-17,0.25,5.502500038298437,5.9373994065594875,5.5025,5.5025,5.862500000000001,5.9625,0.0,0.09999999999999964 --wny0OAz3g8/1,-wny0OAz3g8,1,0,And,0.0625,0.16249999403953552,True,4.102848025515013e-14,0.25,0.06047791614036171,0.1625861933013224,0.0625,0.0625,0.1625,0.1625,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,1,because,0.2224999964237213,0.7225000262260437,True,1.2541806785823506e-12,4.0,0.22249961554408024,0.7191211883038924,0.2225,0.2225,0.6825,0.7224999999999999,0.0,0.039999999999999925 --wny0OAz3g8/1,-wny0OAz3g8,1,2,they,0.762499988079071,0.9225000143051147,True,5.174728917936577e-13,1.7670706510543823,0.7614318724074157,0.9217152166737397,0.7625,0.7625,0.9225,0.9225,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,3,couldn’t,0.9624999761581421,1.3224999904632568,True,1.1262601094796931e-12,3.8459622859954834,0.9657010687822808,1.3224324547627764,0.9624999999999999,1.0225,1.3225,1.3225,0.06000000000000005,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,4,join,1.3624999523162842,1.5824999809265137,True,9.85640280330713e-13,3.3657724857330322,1.3624999986916286,1.564069483231479,1.3625,1.3625,1.5225,1.5825,0.0,0.06000000000000005 --wny0OAz3g8/1,-wny0OAz3g8,1,5,"unions,",1.7024999856948853,2.002500057220459,True,9.332146871671859e-14,0.3186749219894409,1.7022222882961973,2.0023825938707542,1.7025,1.7025,2.0025,2.0025,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,6,many,2.1624999046325684,2.322499990463257,True,2.7132393554119005e-12,4.0,2.162479380379709,2.3224794737033037,2.1625,2.1625,2.3225,2.3225,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,7,Black,2.362499952316284,2.5824999809265137,True,1.329381618892303e-14,0.25,2.3624801795036876,2.58248096295503,2.3625,2.3625,2.5825,2.5825,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,8,workers,2.6024999618530273,2.9024999141693115,True,5.2922337329510197e-14,0.25,2.602482919026416,2.9021214065219594,2.6025,2.6025,2.9025,2.9025,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,9,couldn’t,2.922499895095825,3.202500104904175,True,6.70556167016445e-13,2.289820671081543,2.9221269767762283,3.2024805980770332,2.9225,2.9225,3.2025,3.2025,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,10,get,3.242500066757202,3.3424999713897705,True,2.4445779127639684e-14,0.25,3.2424999997519612,3.3425003277495025,3.2425,3.2425,3.3425,3.3425,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,11,manufacturing,3.362499952316284,4.002500057220459,True,1.1555361702220157e-13,0.39459341764450073,3.362711746755879,4.003943023298609,3.3625,3.3625,4.0025,4.022500000000001,0.0,0.020000000000000462 --wny0OAz3g8/1,-wny0OAz3g8,1,12,or,4.042500019073486,4.102499961853027,True,1.3667748573545357e-12,4.0,4.042501825771753,4.102502245324265,4.0425,4.0425,4.1025,4.1025,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,13,trade,4.162499904632568,4.362500190734863,True,2.0616414323870562e-14,0.25,4.157861064149656,4.35788689791687,4.1225000000000005,4.1625000000000005,4.322500000000001,4.362500000000001,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/1,-wny0OAz3g8,1,14,work,4.382500171661377,4.542500019073486,True,5.308752315378489e-15,0.25,4.3896416702266565,4.542659962096714,4.3425,4.402500000000001,4.5425,4.5425,0.0600000000000005,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,15,–,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --wny0OAz3g8/1,-wny0OAz3g8,1,16,which,4.622499942779541,4.822500228881836,True,1.5271720282957705e-15,0.25,4.622532694722632,4.8226636212397045,4.6225000000000005,4.6225000000000005,4.822500000000001,4.822500000000001,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,17,paid,4.862500190734863,5.042500019073486,True,1.2096574826883644e-12,4.0,4.8626972941115385,5.042674934174243,4.862500000000001,4.862500000000001,5.0425,5.0425,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,18,much,5.082499980926514,5.28249979019165,True,3.8547794513690836e-13,1.3163331747055054,5.110245524980024,5.306652598313615,5.0825000000000005,5.1625000000000005,5.282500000000001,5.3425,0.08000000000000007,0.05999999999999961 --wny0OAz3g8/1,-wny0OAz3g8,1,19,better,5.382500171661377,5.702499866485596,True,1.6741794243795537e-14,0.25,5.3826235445812465,5.702633447017242,5.3825,5.3825,5.702500000000001,5.702500000000001,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,20,than,5.84250020980835,6.042500019073486,True,2.0020649634661103e-13,0.6836667656898499,5.8132744013990765,6.02222844530872,5.782500000000001,5.8425,6.0025,6.0425,0.05999999999999961,0.040000000000000036 --wny0OAz3g8/1,-wny0OAz3g8,1,21,service,6.102499961853027,6.482500076293945,True,4.1255077153946884e-13,1.4087817668914795,6.104189832791148,6.482308960960844,6.102500000000001,6.102500000000001,6.482500000000001,6.482500000000001,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,22,work,6.522500038146973,6.722499847412109,True,1.1393435023557275e-12,3.890639543533325,6.522533906902053,6.722653624861335,6.522500000000001,6.522500000000001,6.7225,6.7225,0.0,0.0 --wny0OAz3g8/0,-wny0OAz3g8,0,0,trade,0.4025000035762787,0.6825000047683716,True,6.750676840444614e-12,4.0,0.3219886038225564,0.6163855182678646,0.0425,0.4425,0.5025,0.7224999999999999,0.4,0.21999999999999997 --wny0OAz3g8/0,-wny0OAz3g8,0,1,labor,0.762499988079071,0.9825000166893005,True,2.737547818640329e-12,2.004483222961426,0.6723732232751013,0.8851323232237341,0.5225,0.7825,0.7224999999999999,0.9824999999999999,0.26,0.26 --wny0OAz3g8/0,-wny0OAz3g8,0,2,unions,1.1024999618530273,1.3424999713897705,True,1.97636991407929e-13,0.25,0.9698868422267436,1.2332032969798863,0.7625,1.1025,1.0625,1.3425,0.3400000000000001,0.28 --wny0OAz3g8/0,-wny0OAz3g8,0,3,didn’t,1.3825000524520874,1.6825000047683716,True,1.3657125560659344e-12,1.0,1.2886744936782788,1.5570604996248456,1.1425,1.3825,1.3625,1.6824999999999999,0.24,0.31999999999999984 --wny0OAz3g8/0,-wny0OAz3g8,0,4,allow,1.722499966621399,1.9424999952316284,True,7.948464342223335e-14,0.25,1.597575059821635,1.8485178438401406,1.3825,1.7625,1.6824999999999999,1.9825,0.3799999999999999,0.30000000000000004 --wny0OAz3g8/0,-wny0OAz3g8,0,5,Black,2.002500057220459,2.2825000286102295,True,1.8220999795787174e-12,1.3341753482818604,1.9280555178208747,2.2017594206098794,1.7225,2.0025,1.9425,2.2825,0.28,0.3400000000000003 --wny0OAz3g8/0,-wny0OAz3g8,0,6,workers,2.3424999713897705,2.762500047683716,True,2.0082564354973603e-13,0.25,2.278583747946665,2.7073076409000514,2.0025,2.4025,2.4225,2.8825,0.3999999999999999,0.45999999999999996 --wny0OAz3g8/0,-wny0OAz3g8,0,7,to,2.822499990463257,2.882499933242798,True,1.7081647240343306e-13,0.25,2.795627903779211,2.867191431881903,2.4825,2.9225,2.6225,2.9825,0.43999999999999995,0.3599999999999999 --wny0OAz3g8/0,-wny0OAz3g8,0,8,join,2.9625000953674316,3.202500104904175,True,2.8858948650928307e-12,2.113105535507202,2.9688315656033817,3.2127652271035108,2.9225,3.0625,3.2025,3.2425,0.14000000000000012,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,0,Which,0.0024999999441206455,0.30250000953674316,True,1.063455855494777e-12,4.0,0.002532140686589533,0.2758708790689764,0.0025000000000000005,0.0025000000000000005,0.2225,0.3025,0.0,0.07999999999999999 --wny0OAz3g8/3,-wny0OAz3g8,3,1,then,0.32249999046325684,0.48249998688697815,True,1.6761636837617916e-13,4.0,0.31656018451331996,0.4774565875920375,0.28250000000000003,0.3225,0.4425,0.4825,0.03999999999999998,0.03999999999999998 --wny0OAz3g8/3,-wny0OAz3g8,3,2,allowed,0.5425000190734863,0.8224999904632568,True,3.7471050296899316e-14,1.023621916770935,0.5441020695647343,0.8225031085039468,0.5425,0.5625,0.8225,0.8225,0.020000000000000018,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,3,the,0.9024999737739563,1.0225000381469727,True,2.521534498263695e-15,0.25,0.9020954097118318,1.022499565639348,0.9025,0.9025,1.0225,1.0225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,4,white,1.0625,1.2424999475479126,True,2.5187026130111395e-14,0.6880509853363037,1.0624995479965396,1.242500032360087,1.0625,1.0625,1.2425,1.2425,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,5,workers,1.2825000286102295,1.6825000047683716,True,9.733679113372792e-14,2.6590147018432617,1.2825008640728779,1.6824524891403205,1.2825,1.2825,1.6824999999999999,1.6824999999999999,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,6,and,1.722499966621399,1.8224999904632568,True,3.1632411533893956e-14,0.8641238808631897,1.7225790148273712,1.8226231419127565,1.7225,1.7225,1.8225,1.8225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,7,unions,1.962499976158142,2.2225000858306885,True,5.4908461878297975e-15,0.25,1.9136149388959556,2.1972650430525142,1.8825,1.9625,2.1825,2.2225,0.07999999999999985,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,8,to,2.242500066757202,2.322499990463257,True,2.5397617155523654e-14,0.6938038468360901,2.242499492552246,2.322500654512255,2.2425,2.2425,2.3225,2.3225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,9,justify,2.3424999713897705,2.702500104904175,True,2.7394013164643016e-13,4.0,2.3465313452716563,2.708943472591961,2.3425,2.4025,2.7025,2.8025,0.06000000000000005,0.10000000000000009 --wny0OAz3g8/3,-wny0OAz3g8,3,10,their,2.7825000286102295,3.002500057220459,True,3.7028682257998075e-14,1.0115374326705933,2.7863605350957643,3.0051172336331358,2.7825,2.8425,3.0025,3.0425,0.05999999999999961,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,11,decision,3.0625,3.442500114440918,True,1.0442468660338666e-14,0.28526395559310913,3.061928269798612,3.446658912342991,3.0625,3.0625,3.4425,3.4825,0.0,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,12,to,3.5225000381469727,3.622499942779541,True,1.8596247859745813e-13,4.0,3.522543175857031,3.6225387940545746,3.5225,3.5225,3.6225,3.6225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,13,not,3.762500047683716,3.922499895095825,True,6.781422211971089e-13,4.0,3.7626099906511024,3.922812910626179,3.7625,3.7625,3.9225,3.9225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,14,allow,3.9825000762939453,4.262499809265137,True,3.618399728607356e-14,0.9884626269340515,3.9831660641663293,4.259794101175912,3.9825,3.9825,4.2225,4.2625,0.0,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,15,black,4.302499771118164,4.622499942779541,True,5.304778426425127e-13,4.0,4.302467397441632,4.6238221223566285,4.3025,4.3025,4.6225000000000005,4.6425,0.0,0.019999999999999574 --wny0OAz3g8/3,-wny0OAz3g8,3,16,workers,4.822500228881836,5.182499885559082,True,3.043428004187096e-14,0.8313937187194824,4.7511172240657515,5.1626108171564065,4.6625000000000005,4.822500000000001,5.102500000000001,5.1825,0.16000000000000014,0.07999999999999918 --wny0OAz3g8/3,-wny0OAz3g8,3,17,into,5.202499866485596,5.382500171661377,True,1.884133819828656e-14,0.5147015452384949,5.19585098799925,5.381566976982173,5.1625000000000005,5.202500000000001,5.3825,5.3825,0.040000000000000036,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,18,their,5.422500133514404,5.762499809265137,True,4.205888736515551e-15,0.25,5.421675399709751,5.760589260048857,5.4225,5.4225,5.7625,5.7625,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,19,union,5.78249979019165,6.002500057220459,True,8.382809962674542e-13,4.0,5.782500067776348,6.002500253985618,5.782500000000001,5.782500000000001,6.0025,6.0025,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,0,And,0.0024999999441206455,0.10249999910593033,True,1.7852615273681455e-14,0.39666062593460083,0.009268933724016128,0.11272779056163183,0.0025000000000000005,0.0425,0.10250000000000001,0.1625,0.04,0.06 --wny0OAz3g8/2,-wny0OAz3g8,2,1,because,0.14249999821186066,0.48249998688697815,True,3.0093440760159407e-13,4.0,0.15613830486104457,0.4783389175803099,0.14250000000000002,0.2225,0.4425,0.4825,0.07999999999999999,0.03999999999999998 --wny0OAz3g8/2,-wny0OAz3g8,2,2,they,0.5024999976158142,0.6424999833106995,True,3.2948963314140788e-15,0.25,0.500228014631938,0.6411874151576354,0.4625,0.5025,0.6224999999999999,0.6425,0.039999999999999925,0.020000000000000018 --wny0OAz3g8/2,-wny0OAz3g8,2,3,couldn’t,0.6625000238418579,1.0425000190734863,True,1.0930697536340461e-12,4.0,0.6621778628090622,1.0338893709683967,0.6625,0.6625,0.9824999999999999,1.0425,0.0,0.06000000000000005 --wny0OAz3g8/2,-wny0OAz3g8,2,4,get,1.122499942779541,1.2424999475479126,True,9.124004449103523e-14,2.027228593826294,1.1044792090557718,1.2214760298378058,1.0025,1.1225,1.1025,1.2425,0.1200000000000001,0.1399999999999999 --wny0OAz3g8/2,-wny0OAz3g8,2,5,these,1.2625000476837158,1.4424999952316284,True,4.420967765998837e-15,0.25,1.2414761103051,1.4275101963469414,1.1225,1.2625,1.3425,1.4425,0.1399999999999999,0.09999999999999987 --wny0OAz3g8/2,-wny0OAz3g8,2,6,"jobs,",1.4824999570846558,1.622499942779541,True,9.117111257921076e-17,0.25,1.4671903258153054,1.6149842590168326,1.3825,1.4825,1.5625,1.6225,0.09999999999999987,0.06000000000000005 --wny0OAz3g8/2,-wny0OAz3g8,2,7,Black,1.6425000429153442,2.0225000381469727,True,4.183806079360863e-12,4.0,1.6425267594186002,2.0225037593548576,1.6425,1.6425,2.0225,2.0225,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,8,communities,2.2225000858306885,2.762500047683716,True,8.090853833263301e-13,4.0,2.2210278203653933,2.762521926399968,2.2225,2.2225,2.7625,2.7625,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,9,had,2.822499990463257,2.922499895095825,True,5.415088831576794e-15,0.25,2.8224974932022153,2.922509663136557,2.8225,2.8225,2.9225,2.9225,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,10,more,2.942500114440918,3.122499942779541,True,4.5007278611473855e-14,1.0,2.942523637142635,3.1224999999350835,2.9425,2.9425,3.1225,3.1225,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,11,men,3.1624999046325684,3.262500047683716,True,3.634124725866543e-14,0.8074526786804199,3.162499999504434,3.2625000000072166,3.1625,3.1625,3.2625,3.2625,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,12,out,3.2825000286102295,3.382499933242798,True,1.8249694102019344e-16,0.25,3.282708035980563,3.3827283868097444,3.2825,3.2825,3.3825,3.3825,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,13,of,3.422499895095825,3.4825000762939453,True,3.758835148519324e-13,4.0,3.4222075384617283,3.4822075403563395,3.4225,3.4225,3.4825,3.4825,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,14,"work,",3.5225000381469727,3.6624999046325684,True,3.3016277022459087e-14,0.7335764169692993,3.522500088158193,3.662500670791443,3.5225,3.5225,3.6625,3.6625,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,15,higher,3.7225000858306885,4.202499866485596,True,6.150453190229823e-15,0.25,3.724500860928217,4.153264174821034,3.7225,3.7225,4.1425,4.202500000000001,0.0,0.0600000000000005 --wny0OAz3g8/2,-wny0OAz3g8,2,16,rates,4.222499847412109,4.402500152587891,True,4.178439617420239e-14,0.9283919930458069,4.19143499158515,4.3718706695944265,4.1825,4.2225,4.362500000000001,4.402500000000001,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/2,-wny0OAz3g8,2,17,of,4.422500133514404,4.482500076293945,True,8.391063451712588e-14,1.8643791675567627,4.407059628080299,4.467172046816429,4.402500000000001,4.4225,4.4625,4.482500000000001,0.019999999999999574,0.020000000000000462 --wny0OAz3g8/2,-wny0OAz3g8,2,18,"poverty,",4.502500057220459,4.982500076293945,True,1.2409367424696255e-13,2.7571911811828613,4.502723343495389,4.982492269661619,4.5025,4.5025,4.982500000000001,4.982500000000001,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,19,and,5.082499980926514,5.182499885559082,True,4.206491823973996e-15,0.25,5.082532400809459,5.1832919282128875,5.0825000000000005,5.0825000000000005,5.1825,5.1825,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,20,more,5.28249979019165,5.522500038146973,True,7.057249478813893e-14,1.5680240392684937,5.286690563603926,5.522587796836602,5.282500000000001,5.362500000000001,5.522500000000001,5.522500000000001,0.08000000000000007,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,21,criminal,5.602499961853027,6.002500057220459,True,5.797682348313363e-14,1.2881654500961304,5.6024998811431255,6.004895968302497,5.602500000000001,5.602500000000001,6.0025,6.0025,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,22,behavior,6.042500019073486,6.502500057220459,True,8.128294044784656e-12,4.0,6.044907337430317,6.502260696408047,6.0425,6.0425,6.5025,6.5025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,0,This,0.0024999999441206455,0.26249998807907104,True,1.3640474383694312e-12,2.6988413333892822,0.003992614795843328,0.26402516901857465,0.0025000000000000005,0.0025000000000000005,0.2625,0.2625,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,1,is,0.3425000011920929,0.4025000035762787,True,5.003446875567752e-12,4.0,0.3455404024503229,0.4057867967217855,0.3425,0.3425,0.4025,0.4025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,2,what’s,0.4625000059604645,0.8224999904632568,True,1.3798032161282947e-11,4.0,0.4689256588302098,0.8257665121741922,0.4625,0.4625,0.8225,0.8225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,3,now,0.8824999928474426,1.002500057220459,True,7.477922361193157e-13,1.4795472621917725,0.884681124078631,1.005510603459454,0.8825,0.8825,1.0025,1.0025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,4,known,1.1024999618530273,1.402500033378601,True,6.791148996236271e-14,0.25,1.1045596670118656,1.4033642322333042,1.1025,1.1025,1.4025,1.4025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,5,as,1.5625,1.622499942779541,True,3.495196482499602e-13,0.691543459892273,1.5624999617181765,1.6224999909013247,1.5625,1.5625,1.6225,1.6225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,6,racial,1.7424999475479126,2.0225000381469727,True,2.5938493432124676e-14,0.25,1.7236696275904895,2.0135054081516173,1.7025,1.7425,1.9825,2.0225,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/5,-wny0OAz3g8,5,7,formation,2.0425000190734863,2.502500057220459,True,8.995870048227397e-14,0.25,2.042498883726085,2.502516094158499,2.0425,2.0425,2.5025,2.5025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,8,"theory,",2.5625,2.822499990463257,True,2.28589906335272e-13,0.45227745175361633,2.561166959632408,2.8224983990052683,2.5625,2.5625,2.8225,2.8225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,9,a,3.1024999618530273,3.122499942779541,True,1.5396341970438931e-12,3.0462491512298584,3.1025002400560107,3.122500240056011,3.1025,3.1025,3.1225,3.1225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,10,theory,3.182499885559082,3.4024999141693115,True,8.635281346317664e-14,0.25,3.182499996308189,3.4024999967473804,3.1825,3.1825,3.4025,3.4025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,11,formalized,3.422499895095825,3.922499895095825,True,3.7153637913629745e-13,0.7351047396659851,3.422499999704111,3.9223584723202434,3.4225,3.4225,3.9225,3.9225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,12,by,3.942500114440918,4.002500057220459,True,2.2909525838887834e-12,4.0,3.942500007809117,4.002500007918179,3.9425,3.9425,4.0025,4.0025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,13,modern,4.0625,4.362500190734863,True,4.389206148059169e-14,0.25,4.072012047116495,4.362462041170702,4.062500000000001,4.0825000000000005,4.362500000000001,4.362500000000001,0.019999999999999574,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,14,sociologists,4.382500171661377,5.022500038146973,True,7.646028992239207e-13,1.5128079652786255,4.3826825043211555,5.022640992110379,4.3825,4.3825,5.022500000000001,5.022500000000001,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,15,Michael,5.042500019073486,5.442500114440918,True,2.0837234119153863e-13,0.41227591037750244,5.047794963805033,5.442525017815331,5.0425,5.0825000000000005,5.442500000000001,5.442500000000001,0.040000000000000036,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,16,Omi,5.502500057220459,5.622499942779541,True,2.1487714765063698e-14,0.25,5.502505713472408,5.622502608037449,5.5025,5.5025,5.6225000000000005,5.6225000000000005,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,17,and,5.662499904632568,5.822500228881836,True,4.389145297212238e-12,4.0,5.662574457646359,5.822578829922969,5.6625000000000005,5.6625000000000005,5.822500000000001,5.822500000000001,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,18,Howard,5.922500133514404,6.222499847412109,True,6.393029009822693e-13,1.2648952007293701,5.921724323074964,6.218265066239246,5.9225,5.9225,6.1625000000000005,6.2225,0.0,0.05999999999999961 --wny0OAz3g8/5,-wny0OAz3g8,5,19,Winant,6.302499771118164,6.662499904632568,True,5.3509067836354784e-12,4.0,6.302641526631446,6.662501030109093,6.3025,6.3025,6.6625000000000005,6.6625000000000005,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,0,Omi,0.042500000447034836,0.18250000476837158,True,5.724248657544562e-13,2.633195638656616,0.04244340083416118,0.18338120669739688,0.0425,0.0425,0.1825,0.1825,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,1,and,0.2824999988079071,0.4424999952316284,True,2.6359350899923806e-13,1.212549090385437,0.28245764724904965,0.4467660931471465,0.28250000000000003,0.28250000000000003,0.4425,0.5025,0.0,0.05999999999999994 --wny0OAz3g8/7,-wny0OAz3g8,7,2,Winant,0.48249998688697815,0.8025000095367432,True,8.451231901795975e-12,4.0,0.49016837626293247,0.7853902575957773,0.4825,0.5225,0.7625,0.8025,0.03999999999999998,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,3,argue,0.8224999904632568,1.0625,True,7.892336957582291e-13,3.6305317878723145,0.8224757457922287,1.0676672843497057,0.8225,0.8225,1.0625,1.1025,0.0,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,4,that,1.1425000429153442,1.3224999904632568,True,6.123268938337117e-14,0.2816747725009918,1.1377341883765733,1.3195217703113347,1.0825,1.1425,1.2825,1.3225,0.06000000000000005,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,5,the,1.3424999713897705,1.4424999952316284,True,1.2730490998120927e-14,0.25,1.3440425032977281,1.4448918858978665,1.3425,1.3625,1.4425,1.4625,0.020000000000000018,0.020000000000000018 --wny0OAz3g8/7,-wny0OAz3g8,7,6,concept,1.5225000381469727,1.8624999523162842,True,2.460763797590415e-13,1.1319690942764282,1.5279296219329088,1.8634414995423771,1.5225,1.5425,1.8625,1.8625,0.020000000000000018,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,7,of,1.9225000143051147,1.9824999570846558,True,1.3178159735324768e-12,4.0,1.9226435499615355,1.9829324455007173,1.9224999999999999,1.9224999999999999,1.9825,1.9825,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,8,race,2.0824999809265137,2.242500066757202,True,3.6809178751914806e-14,0.25,2.0824184093373526,2.2424999998142665,2.0825,2.0825,2.2425,2.2425,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,9,came,2.2825000286102295,2.4625000953674316,True,1.1092636412277784e-13,0.510269284248352,2.282500009294918,2.46249946919691,2.2825,2.2825,2.4625,2.4625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,10,about,2.5225000381469727,2.702500104904175,True,1.5981041766614454e-14,0.25,2.50596479396657,2.688239210156162,2.4825,2.5225,2.6625,2.7025,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,11,as,2.742500066757202,2.822499990463257,True,1.3851884862661062e-13,0.6371966600418091,2.7414056060970298,2.820828518642153,2.7425,2.7425,2.8225,2.8225,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,12,a,2.8424999713897705,2.862499952316284,True,1.0622554051653577e-11,4.0,2.8424999992289264,2.862499999228926,2.8425,2.8425,2.8625,2.8625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,13,tool,2.922499895095825,3.122499942779541,True,2.4609784696205672e-12,4.0,2.922499514367321,3.122506056854534,2.9225,2.9225,3.1225,3.1225,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,14,to,3.202500104904175,3.262500047683716,True,2.1649702701149326e-13,0.995901882648468,3.2025010194804278,3.2625016442219503,3.2025,3.2025,3.2625,3.2625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,15,justify,3.322499990463257,3.762500047683716,True,6.311653131564621e-13,2.9034059047698975,3.3224999965992708,3.7625087559140353,3.3225000000000002,3.3225000000000002,3.7625,3.7625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,16,and,3.8424999713897705,3.9825000762939453,True,6.5197286385301235e-15,0.25,3.842662866801597,3.980611906887309,3.8425,3.8425,3.9625,3.9825,0.0,0.020000000000000018 --wny0OAz3g8/7,-wny0OAz3g8,7,17,maintain,4.002500057220459,4.462500095367432,True,2.042441863626585e-15,0.25,4.002481016298704,4.461250026333836,4.0025,4.0025,4.4625,4.4625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,18,the,4.482500076293945,4.582499980926514,True,1.5537561471809513e-12,4.0,4.483034653975056,4.583079213530119,4.482500000000001,4.482500000000001,4.5825000000000005,4.5825000000000005,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,19,economic,4.642499923706055,5.042500019073486,True,2.8485436123597274e-13,1.3103505373001099,4.6367405754513396,5.044914535897253,4.6225000000000005,4.6425,5.0425,5.062500000000001,0.019999999999999574,0.020000000000000462 --wny0OAz3g8/7,-wny0OAz3g8,7,20,and,5.102499961853027,5.202499866485596,True,1.1146189900961348e-13,0.512732744216919,5.10250104206515,5.202626715315435,5.102500000000001,5.102500000000001,5.202500000000001,5.202500000000001,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,21,political,5.242499828338623,5.602499961853027,True,2.099547554381049e-15,0.25,5.244466340076222,5.62200941915277,5.242500000000001,5.242500000000001,5.602500000000001,5.6425,0.0,0.03999999999999915 --wny0OAz3g8/7,-wny0OAz3g8,7,22,power,5.622499942779541,5.982500076293945,True,2.1738790238409744e-13,1.0,5.653380236049285,5.989345211747652,5.6225000000000005,5.6625000000000005,5.942500000000001,5.982500000000001,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,23,held,6.042500019073486,6.222499847412109,True,2.3580451962790586e-14,0.25,6.05669488419283,6.2355620783067325,6.0425,6.0425,6.2225,6.2225,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,24,by,6.28249979019165,6.34250020980835,True,5.590170457070963e-14,0.25715187191963196,6.295711270231749,6.355844879545134,6.282500000000001,6.282500000000001,6.3425,6.3425,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,25,those,6.382500171661377,6.622499942779541,True,2.3303180132078216e-13,1.0719630718231201,6.397350767685814,6.637488449175456,6.3825,6.4225,6.6225000000000005,6.6225000000000005,0.040000000000000036,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,26,of,6.702499866485596,6.762499809265137,True,7.917183923701002e-15,0.25,6.714737727531676,6.779059354262793,6.6625000000000005,6.742500000000001,6.7625,6.8425,0.08000000000000007,0.08000000000000007 --wny0OAz3g8/7,-wny0OAz3g8,7,27,European,6.822500228881836,7.382500171661377,True,3.6068148251064414e-13,1.6591607332229614,6.839878020990127,7.40042919656395,6.822500000000001,7.0025,7.3825,7.442500000000001,0.17999999999999972,0.0600000000000005 --wny0OAz3g8/7,-wny0OAz3g8,7,28,descent,7.5625,7.902500152587891,True,5.926632448693958e-12,4.0,7.563703156515533,7.902497095601888,7.562500000000001,7.562500000000001,7.902500000000001,7.902500000000001,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,0,He,0.02250000089406967,0.10249999910593033,True,3.690228678188134e-12,2.9857406616210938,0.01570359411021808,0.08848826505862535,0.0025000000000000005,0.0225,0.0625,0.10250000000000001,0.019999999999999997,0.04000000000000001 --wny0OAz3g8/9,-wny0OAz3g8,9,1,explores,0.14249999821186066,0.6424999833106995,True,1.33896398742811e-12,1.0833473205566406,0.12249887573356905,0.6389535213723231,0.0825,0.1625,0.5824999999999999,0.6425,0.08,0.06000000000000005 --wny0OAz3g8/9,-wny0OAz3g8,9,2,why,0.6825000047683716,0.7825000286102295,True,9.870467290729046e-12,4.0,0.6788645232657202,0.784400961311651,0.6625,0.6825,0.7825,0.7825,0.020000000000000018,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,3,Black,0.862500011920929,1.1024999618530273,True,3.686124756124842e-12,2.9824202060699463,0.8631983314393331,1.1029731457106366,0.8624999999999999,0.8624999999999999,1.1025,1.1025,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,4,and,1.1825000047683716,1.3424999713897705,True,5.2772343786533504e-12,4.0,1.1825146996856872,1.342497699820921,1.1824999999999999,1.1824999999999999,1.3425,1.3425,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,5,White,1.402500033378601,1.5824999809265137,True,8.070731583043056e-13,0.6529977917671204,1.4024933386901064,1.5832357178381051,1.4025,1.4025,1.5825,1.5825,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,6,Americans,1.6425000429153442,2.0824999809265137,True,1.0394870999120426e-13,0.25,1.6424944033583473,2.0809849259710984,1.6425,1.6425,2.0825,2.0825,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,7,tend,2.1024999618530273,2.262500047683716,True,3.9480879909748953e-14,0.25,2.102222472329328,2.262513185028439,2.1025,2.1025,2.2625,2.2625,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,8,to,2.2825000286102295,2.3424999713897705,True,6.124948638552841e-13,0.4955657422542572,2.28261991037791,2.3426199111197468,2.2825,2.2825,2.3425,2.3425,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,9,have,2.362499952316284,2.502500057220459,True,1.3514602848277435e-12,1.0934580564498901,2.365759465357853,2.5093280317862643,2.3625,2.4025,2.5025,2.5625,0.040000000000000036,0.06000000000000005 --wny0OAz3g8/9,-wny0OAz3g8,9,10,such,2.5425000190734863,2.702500104904175,True,4.4576911606419856e-12,3.606689929962158,2.563265883639007,2.7336550703965714,2.5425,2.5825,2.7025,2.7625,0.040000000000000036,0.06000000000000005 --wny0OAz3g8/9,-wny0OAz3g8,9,11,different,2.802500009536743,3.322499990463257,True,2.1473345020520118e-13,0.25,2.8025875890661442,3.3226400145089827,2.8025,2.8025,3.3225000000000002,3.3225000000000002,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,12,outcomes,3.382499933242798,3.9024999141693115,True,8.532017323550911e-13,0.6903201341629028,3.382868179253032,3.9038235552458773,3.3825,3.3825,3.9025,3.9025,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,13,in,4.002500057220459,4.0625,True,3.568455752243904e-13,0.28872150182724,4.0023310891423645,4.062508621161237,4.0025,4.0025,4.062500000000001,4.062500000000001,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,14,terms,4.162499904632568,4.482500076293945,True,3.854301426631235e-12,3.1184909343719482,4.162879085237475,4.482499860398114,4.1625000000000005,4.1625000000000005,4.482500000000001,4.482500000000001,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,15,of,4.542500019073486,4.662499904632568,True,2.5795234749755556e-14,0.25,4.543504439499194,4.663010117094874,4.522500000000001,4.602500000000001,4.6625000000000005,4.6625000000000005,0.08000000000000007,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,16,"income,",4.722499847412109,5.082499980926514,True,1.1329377106600313e-12,0.9166527390480042,4.722783972994184,5.083036643699158,4.7225,4.7225,5.0825000000000005,5.0825000000000005,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,17,"education,",5.202499866485596,5.722499847412109,True,3.289460934544075e-12,2.661482095718384,5.20112438124031,5.617248567010778,5.202500000000001,5.202500000000001,5.5825000000000005,5.7225,0.0,0.13999999999999968 --wny0OAz3g8/9,-wny0OAz3g8,9,18,and,5.762499809265137,5.862500190734863,True,3.816548178837628e-14,0.25,5.647324687175936,5.747357974508286,5.6225000000000005,5.7625,5.7225,5.862500000000001,0.13999999999999968,0.14000000000000057 --wny0OAz3g8/9,-wny0OAz3g8,9,19,more,5.882500171661377,6.022500038146973,True,2.074348722305719e-12,1.6783424615859985,5.817082481580934,5.993166312381108,5.782500000000001,5.8825,5.982500000000001,6.022500000000001,0.09999999999999964,0.040000000000000036 --571d8cVauQ/0,-571d8cVauQ,0,0,This,0.42250001430511475,1.1425000429153442,True,1.6880530193742792e-15,0.25,0.36545353509037076,1.1497830274676015,0.0425,0.7625,1.1425,1.2025,0.72,0.05999999999999983 --571d8cVauQ/0,-571d8cVauQ,0,1,is,1.2024999856948853,1.2625000476837158,True,1.1562954370034073e-12,4.0,1.2072494672557035,1.2695928728483776,1.2025,1.2425,1.2625,1.3225,0.040000000000000036,0.06000000000000005 --571d8cVauQ/0,-571d8cVauQ,0,2,Rhett,1.3025000095367432,1.5425000190734863,True,1.0972020953467845e-13,0.5990563631057739,1.314310355709599,1.5400492489243816,1.3025,1.3425,1.5025,1.5425,0.040000000000000036,0.040000000000000036 --571d8cVauQ/0,-571d8cVauQ,0,3,"Reiger,",1.5625,1.8825000524520874,True,1.3116477305327723e-13,0.7161405682563782,1.5646701061124126,1.8860143148046669,1.5625,1.5825,1.8825,1.9025,0.020000000000000018,0.020000000000000018 --571d8cVauQ/0,-571d8cVauQ,0,4,White,1.9225000143051147,2.1424999237060547,True,7.403700862020773e-14,0.4042312800884247,1.9225171969481951,2.1425231516004852,1.9224999999999999,1.9224999999999999,2.1425,2.1425,0.0,0.0 --571d8cVauQ/0,-571d8cVauQ,0,5,Caspian,2.1624999046325684,2.5625,True,1.1581609152613859e-12,4.0,2.162561668852808,2.5634643731990616,2.1625,2.1625,2.5625,2.5625,0.0,0.0 --571d8cVauQ/0,-571d8cVauQ,0,6,Studios,2.622499942779541,3.0625,True,1.5272361825711955e-13,0.8338487148284912,2.622576837146804,3.0645311269113757,2.6225,2.6225,3.0625,3.1025,0.0,0.040000000000000036 --571d8cVauQ/0,-571d8cVauQ,0,7,on,3.1424999237060547,3.202500104904175,True,2.5477747082197633e-12,4.0,3.1424877070707216,3.2024918071032786,3.1425,3.1425,3.2025,3.2025,0.0,0.0 --571d8cVauQ/0,-571d8cVauQ,0,8,behalf,3.262500047683716,3.6024999618530273,True,7.087249352926567e-14,0.3869534730911255,3.262499999603474,3.5985239851668043,3.2625,3.2625,3.5825,3.6025,0.0,0.020000000000000018 --571d8cVauQ/0,-571d8cVauQ,0,9,of,3.622499942779541,3.682499885559082,True,2.463900936576502e-13,1.3452539443969727,3.622492865316998,3.6826183503531253,3.6225,3.6225,3.6825,3.6825,0.0,0.0 --571d8cVauQ/0,-571d8cVauQ,0,10,Expert,3.702500104904175,4.082499980926514,True,4.2966633940003107e-13,2.3459155559539795,3.718376212606498,4.069501795831513,3.7025,3.7825,4.0425,4.0825000000000005,0.08000000000000007,0.040000000000000036 --571d8cVauQ/0,-571d8cVauQ,0,11,Village.,4.102499961853027,4.982500076293945,True,2.135865269370374e-13,1.1661512851715088,4.102528781943396,4.942896755710078,4.1025,4.1025,4.902500000000001,4.982500000000001,0.0,0.08000000000000007 --571d8cVauQ/5,-571d8cVauQ,5,0,So,0.0625,0.14249999821186066,True,1.961745210721233e-10,4.0,0.061026267841565805,0.14039837035299824,0.0625,0.0625,0.14250000000000002,0.14250000000000002,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,1,very,0.5425000190734863,0.7225000262260437,True,5.542242012546161e-13,3.911118745803833,0.39099748158334563,0.7070536888457671,0.1825,0.5425,0.6825,0.7224999999999999,0.36,0.039999999999999925 --571d8cVauQ/5,-571d8cVauQ,5,2,important,0.762499988079071,1.3025000095367432,True,1.289873698302746e-13,0.9102542400360107,0.7504742650006566,1.2691000312282303,0.7224999999999999,0.7625,1.2025,1.3425,0.040000000000000036,0.14000000000000012 --571d8cVauQ/5,-571d8cVauQ,5,3,to,1.3424999713897705,1.402500033378601,True,6.078231137740422e-16,0.25,1.3274161608563455,1.3876820258440432,1.2825,1.4224999999999999,1.3425,1.4825,0.1399999999999999,0.1399999999999999 --571d8cVauQ/5,-571d8cVauQ,5,4,go,1.4225000143051147,1.4824999570846558,True,5.364711279376433e-12,4.0,1.4430355265690127,1.512146626666273,1.4224999999999999,1.6025,1.4825,1.7425,0.18000000000000016,0.26 --571d8cVauQ/5,-571d8cVauQ,5,5,out,1.6024999618530273,1.8025000095367432,True,9.859104635120963e-13,4.0,1.637724047396162,1.82970814525818,1.6025,1.9224999999999999,1.8025,2.0425,0.31999999999999984,0.24 --571d8cVauQ/5,-571d8cVauQ,5,6,to,1.9225000143051147,2.002500057220459,True,1.4170477671045928e-13,1.0,1.9843274093437095,2.066520692378383,1.9224999999999999,2.4625,2.0025,2.5625,0.54,0.56 --571d8cVauQ/5,-571d8cVauQ,5,7,the,2.4625000953674316,2.6424999237060547,True,4.532087053733813e-13,3.1982598304748535,2.464986666672805,2.648123686812614,2.4225,2.6225,2.5625,2.7225,0.20000000000000018,0.16000000000000014 --571d8cVauQ/5,-571d8cVauQ,5,8,"locations,",2.742500066757202,3.382499933242798,True,2.9229296392117854e-13,2.0626895427703857,2.7477243046229103,3.379244935643703,2.7425,2.8025,3.3625,3.3825,0.06000000000000005,0.020000000000000018 --571d8cVauQ/5,-571d8cVauQ,5,9,walk,3.4024999141693115,3.742500066757202,True,8.698149042223296e-16,0.25,3.405197916129465,3.7593203164399864,3.4025,3.4025,3.7225,4.022500000000001,0.0,0.3000000000000007 --571d8cVauQ/5,-571d8cVauQ,5,10,"around,",3.762500047683716,4.082499980926514,True,4.603956444055625e-14,0.32489776611328125,3.7894646126851903,4.113669860878586,3.7625,4.1425,4.0825000000000005,4.482500000000001,0.3799999999999999,0.40000000000000036 --571d8cVauQ/5,-571d8cVauQ,5,11,talk,4.142499923706055,4.442500114440918,True,1.35541764986237e-13,0.9565081000328064,4.190362867190777,4.484542398894459,4.1425,4.5025,4.442500000000001,4.9225,0.3600000000000003,0.47999999999999954 --571d8cVauQ/5,-571d8cVauQ,5,12,with,4.502500057220459,4.922500133514404,True,8.38819099358186e-14,0.5919483304023743,4.541720300661576,4.940244070441893,4.5025,4.9625,4.9225,5.102500000000001,0.45999999999999996,0.1800000000000006 --571d8cVauQ/5,-571d8cVauQ,5,13,the,5.002500057220459,5.102499961853027,True,2.0982386767193044e-14,0.25,5.018430928164901,5.1207677393025,5.0025,5.1625000000000005,5.102500000000001,5.2625,0.16000000000000014,0.15999999999999925 --571d8cVauQ/5,-571d8cVauQ,5,14,owners,5.162499904632568,5.482500076293945,True,3.260156359284616e-13,2.3006680011749268,5.183334041083281,5.504277901364218,5.1625000000000005,5.3425,5.482500000000001,5.602500000000001,0.17999999999999972,0.1200000000000001 --571d8cVauQ/5,-571d8cVauQ,5,15,if,5.502500057220459,5.5625,True,1.7678204942973808e-16,0.25,5.535479301056968,5.596502741603346,5.5025,5.6625000000000005,5.562500000000001,5.7225,0.16000000000000014,0.15999999999999925 --571d8cVauQ/5,-571d8cVauQ,5,16,you,5.582499980926514,5.722499847412109,True,4.4787060209583104e-14,0.3160589337348938,5.624735476439397,5.761850848637931,5.5825000000000005,5.742500000000001,5.7225,5.862500000000001,0.16000000000000014,0.14000000000000057 --571d8cVauQ/5,-571d8cVauQ,5,17,"can,",5.742499828338623,5.862500190734863,True,4.924350383299532e-14,0.3475077152252197,5.7829325715282085,5.904529793030309,5.742500000000001,5.8825,5.862500000000001,6.0825000000000005,0.13999999999999968,0.21999999999999975 --571d8cVauQ/5,-571d8cVauQ,5,18,tell,5.882500171661377,6.142499923706055,True,2.1348444930249788e-13,1.506543755531311,5.9288363923069145,6.188435906968508,5.8825,6.1625000000000005,6.142500000000001,6.5025,0.28000000000000025,0.35999999999999943 --571d8cVauQ/5,-571d8cVauQ,5,19,them,6.482500076293945,6.682499885559082,True,1.303422159700668e-13,0.9198152422904968,6.374882165415015,6.68920056196025,6.1625000000000005,6.562500000000001,6.642500000000001,6.742500000000001,0.40000000000000036,0.09999999999999964 --571d8cVauQ/5,-571d8cVauQ,5,20,what,6.722499847412109,6.882500171661377,True,4.340288640102517e-14,0.3062909245491028,6.74280020146315,6.90711834793619,6.6625000000000005,6.782500000000001,6.822500000000001,6.942500000000001,0.1200000000000001,0.1200000000000001 --571d8cVauQ/5,-571d8cVauQ,5,21,you're,6.922500133514404,7.162499904632568,True,6.8854756965497366e-12,4.0,6.949658840062754,7.201229129569963,6.862500000000001,6.982500000000001,7.1625000000000005,7.1625000000000005,0.1200000000000001,0.0 --571d8cVauQ/5,-571d8cVauQ,5,22,"doing,",7.182499885559082,7.422500133514404,True,2.756355798987087e-13,1.9451396465301514,7.2233293105612,7.466068313841497,7.1825,7.1825,7.4225,7.4225,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,23,build,7.682499885559082,7.902500152587891,True,4.2132330203880144e-14,0.29732468724250793,7.713083083513668,7.964945886320176,7.642500000000001,7.6825,7.902500000000001,7.9625,0.03999999999999915,0.05999999999999961 --571d8cVauQ/5,-571d8cVauQ,5,24,a,7.942500114440918,7.962500095367432,True,3.905624535263143e-14,0.27561700344085693,8.004457849233917,8.024457849233954,7.942500000000001,7.982500000000001,7.9625,8.0025,0.040000000000000036,0.03999999999999915 --571d8cVauQ/5,-571d8cVauQ,5,25,little,7.982500076293945,8.262499809265137,True,3.241799393489085e-14,0.25,8.047286338490512,8.319486249797016,7.982500000000001,8.0425,8.2625,8.3025,0.05999999999999961,0.040000000000000924 --571d8cVauQ/5,-571d8cVauQ,5,26,"excitement,",8.282500267028809,8.862500190734863,True,3.179512656306151e-14,0.25,8.340643589235283,8.907759359152092,8.282499999999999,8.3425,8.8225,8.862499999999999,0.0600000000000005,0.03999999999999915 --571d8cVauQ/5,-571d8cVauQ,5,27,put,9.422499656677246,9.522500038146973,True,7.793622653707102e-14,0.5499901175498962,9.448525366526367,9.558693789160388,9.4225,9.4225,9.522499999999999,9.522499999999999,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,28,a,9.5625,9.582500457763672,True,9.874617399804886e-13,4.0,9.599558257830385,9.619558257830356,9.5625,9.5825,9.5825,9.6025,0.019999999999999574,0.019999999999999574 --571d8cVauQ/5,-571d8cVauQ,5,29,little,9.6225004196167,9.882499694824219,True,8.713415053504189e-14,0.6148991584777832,9.657902881779448,9.933980702020806,9.6225,9.6225,9.8825,9.8825,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,30,glitz,9.982500076293945,10.182499885559082,True,2.0400179809310082e-11,4.0,9.989943619156207,10.19563550900826,9.9225,9.9825,10.1225,10.1825,0.0600000000000005,0.05999999999999872 --571d8cVauQ/5,-571d8cVauQ,5,31,on,10.282500267028809,10.382499694824219,True,1.639427634446411e-12,4.0,10.275149544087014,10.392708390362388,10.1625,10.282499999999999,10.3025,10.3825,0.11999999999999922,0.08000000000000007 --571d8cVauQ/5,-571d8cVauQ,5,32,there,10.422499656677246,10.6225004196167,True,4.004103296216359e-14,0.2825665771961212,10.454077564626562,10.673171811912887,10.362499999999999,10.4225,10.6225,10.6225,0.0600000000000005,0.0 --571d8cVauQ/5,-571d8cVauQ,5,33,and,10.642499923706055,11.322500228881836,True,8.731728447924914e-13,4.0,10.912128782316048,11.369185804372504,10.6425,11.2625,11.3225,11.362499999999999,0.6199999999999992,0.03999999999999915 --571d8cVauQ/5,-571d8cVauQ,5,34,let,11.542499542236328,11.662500381469727,True,4.009867524829014e-14,0.28297334909439087,11.569560975686585,11.698227520663279,11.5425,11.5425,11.6625,11.6625,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,35,everybody,11.702500343322754,12.082500457763672,True,9.238042188858264e-13,4.0,11.740536622390566,12.121806518011748,11.7025,11.7025,12.0825,12.0825,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,36,knows,12.102499961853027,12.302499771118164,True,9.206563734032863e-14,0.6497002840042114,12.144266740138423,12.349209984328715,12.1025,12.1025,12.3025,12.3225,0.0,0.019999999999999574 --571d8cVauQ/5,-571d8cVauQ,5,37,this,12.762499809265137,12.942500114440918,True,3.215953165661839e-13,2.2694740295410156,12.73313275369772,12.95957414691892,12.3225,12.7625,12.8825,12.942499999999999,0.4399999999999995,0.05999999999999872 --571d8cVauQ/5,-571d8cVauQ,5,38,is,12.982500076293945,13.042499542236328,True,4.4049882014576824e-13,3.108567237854004,13.019727126882461,13.080592199899773,12.9825,12.9825,13.0425,13.0425,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,39,a,13.162500381469727,13.182499885559082,True,3.718831232540909e-12,4.0,13.198762296608688,13.218762296608658,13.1625,13.1625,13.1825,13.1825,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,40,very,13.322500228881836,13.542499542236328,True,1.494705916113212e-13,1.0548027753829956,13.35522890003284,13.57665212442405,13.3225,13.3225,13.5425,13.5425,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,41,valuable,13.682499885559082,14.442500114440918,True,9.463182190985742e-13,4.0,13.714516252757837,14.474684869756821,13.6825,13.6825,14.442499999999999,14.5025,0.0,0.0600000000000005 --571d8cVauQ/5,-571d8cVauQ,5,42,spot.,14.662500381469727,14.962499618530273,True,1.3657936543884364e-12,4.0,14.673377195931495,14.96784942174552,14.6625,14.6625,14.9025,15.0425,0.0,0.14000000000000057 --I_e4mIh0yE/1,-I_e4mIh0yE,1,0,In,0.16249999403953552,0.24250000715255737,True,8.721677351684887e-13,4.0,0.1142069655201318,0.21499044195057157,0.0625,0.1625,0.1825,0.2425,0.1,0.06 --I_e4mIh0yE/1,-I_e4mIh0yE,1,1,the,0.8224999904632568,0.9624999761581421,True,4.6919015032767866e-14,0.4219171106815338,0.779575729691009,0.9513972402826147,0.28250000000000003,0.8424999999999999,0.8624999999999999,0.9624999999999999,0.5599999999999998,0.09999999999999998 --I_e4mIh0yE/1,-I_e4mIh0yE,1,2,United,1.0225000381469727,1.2825000286102295,True,1.241436966246956e-13,1.116356611251831,1.022454650069054,1.2824630944967872,1.0225,1.0225,1.2825,1.2825,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,3,States,1.3025000095367432,1.5824999809265137,True,2.2734993226268757e-15,0.25,1.3219094885294929,1.6019038732024349,1.3025,1.3425,1.5825,1.6225,0.040000000000000036,0.040000000000000036 --I_e4mIh0yE/1,-I_e4mIh0yE,1,4,we,1.6425000429153442,1.7424999475479126,True,5.195034398323972e-13,4.0,1.6425025929936214,1.7425022697843806,1.6425,1.6425,1.7425,1.7425,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,5,don't,1.8624999523162842,2.0824999809265137,True,3.625834965603758e-09,4.0,1.8625138297841282,2.0820620731977453,1.8625,1.8625,2.0825,2.0825,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,6,punish,2.202500104904175,2.502500057220459,True,9.826499016611992e-14,0.8836435079574585,2.2024907371313374,2.5025001246537304,2.2025,2.2025,2.5025,2.5025,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,7,failure,2.5625,3.0824999809265137,True,3.813583563522238e-14,0.342934787273407,2.57581736001806,3.080994991799556,2.5625,2.6425,3.0825,3.0825,0.08000000000000007,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,8,to,3.5225000381469727,3.622499942779541,True,3.0746702384641666e-13,2.764883279800415,3.52221470372663,3.622506415239828,3.5225,3.5225,3.6225,3.6225,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,9,the,3.682499885559082,3.7825000286102295,True,3.3432115989452324e-14,0.30063679814338684,3.6824927941693253,3.782492797896148,3.6825,3.6825,3.7825,3.7825,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,10,degree,3.802500009536743,4.162499904632568,True,6.262374109946081e-14,0.5631411671638489,3.802499999654366,4.162501191750939,3.8025,3.8025,4.1625000000000005,4.1625000000000005,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,11,to,4.302499771118164,4.362500190734863,True,8.0105626992788e-13,4.0,4.291511506534855,4.352642714953695,4.2625,4.3025,4.322500000000001,4.362500000000001,0.040000000000000036,0.040000000000000036 --I_e4mIh0yE/1,-I_e4mIh0yE,1,12,which,4.422500133514404,4.662499904632568,True,2.4741273347552925e-14,0.25,4.405226899791058,4.654286753863474,4.3425,4.4225,4.6225000000000005,4.6625000000000005,0.08000000000000007,0.040000000000000036 --I_e4mIh0yE/1,-I_e4mIh0yE,1,13,failure,4.742499828338623,5.28249979019165,True,2.8274669931771523e-13,2.5425868034362793,4.739944443800792,5.282521785832095,4.7225,4.742500000000001,5.282500000000001,5.282500000000001,0.020000000000000462,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,14,is,5.34250020980835,5.402500152587891,True,4.104637975539151e-14,0.369107723236084,5.34226851016023,5.4052279359374715,5.3425,5.3425,5.402500000000001,5.442500000000001,0.0,0.040000000000000036 --I_e4mIh0yE/1,-I_e4mIh0yE,1,15,punished,5.502500057220459,5.902500152587891,True,7.313151684706573e-14,0.6576318740844727,5.500985614179578,5.901454375232485,5.5025,5.5025,5.902500000000001,5.902500000000001,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,16,in,6.302499771118164,6.422500133514404,True,2.6584829715230185e-13,2.3906288146972656,6.284817034685479,6.406331516286906,6.1625000000000005,6.322500000000001,6.322500000000001,6.4225,0.16000000000000014,0.09999999999999964 --I_e4mIh0yE/1,-I_e4mIh0yE,1,17,Europe.,6.662499904632568,6.922500133514404,True,5.098422226935961e-13,4.0,6.656895370927335,6.922440434999503,6.642500000000001,6.6625000000000005,6.9225,6.9225,0.019999999999999574,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,0,It's,0.5824999809265137,0.7225000262260437,True,3.472087617417685e-10,4.0,0.5822805233935374,0.7211801408748947,0.5824999999999999,0.5824999999999999,0.7025,0.7224999999999999,0.0,0.019999999999999907 --I_e4mIh0yE/3,-I_e4mIh0yE,3,1,perceived,0.9225000143051147,1.4824999570846558,True,1.4726222086376695e-13,0.25,0.8894760335439114,1.4662801504889216,0.7224999999999999,0.9225,1.4425,1.4825,0.20000000000000007,0.040000000000000036 --I_e4mIh0yE/3,-I_e4mIh0yE,3,2,that,1.5425000190734863,1.7424999475479126,True,2.1773252959714912e-14,0.25,1.5118009805496575,1.7076001797166942,1.4625,1.5425,1.6625,1.7425,0.08000000000000007,0.07999999999999985 --I_e4mIh0yE/3,-I_e4mIh0yE,3,3,you,1.7625000476837158,1.8624999523162842,True,4.304102037343098e-14,0.25,1.7445763600944204,1.8591816436803994,1.7225,1.7625,1.8225,1.8825,0.040000000000000036,0.06000000000000005 --I_e4mIh0yE/3,-I_e4mIh0yE,3,4,have,1.9225000143051147,2.1424999237060547,True,3.9425008396820616e-12,4.0,1.923373173881217,2.165102454180477,1.9224999999999999,1.9224999999999999,2.1425,2.1825,0.0,0.040000000000000036 --I_e4mIh0yE/3,-I_e4mIh0yE,3,5,actually,2.1624999046325684,2.6424999237060547,True,4.010823791145146e-12,4.0,2.1990408804792785,2.68428186359062,2.1625,2.2225,2.6425,2.8025,0.06000000000000005,0.16000000000000014 --I_e4mIh0yE/3,-I_e4mIh0yE,3,6,learned,2.7825000286102295,3.122499942779541,True,4.510655087289206e-13,0.5370558500289917,2.781821592938269,3.1219551838308943,2.6625,2.8425,3.0025,3.1625,0.17999999999999972,0.16000000000000014 --I_e4mIh0yE/3,-I_e4mIh0yE,3,7,"something,",3.2225000858306885,3.702500104904175,True,1.2301406638118295e-12,1.4646525382995605,3.210776660611705,3.685039851830411,3.0625,3.2225,3.4425,3.7025,0.16000000000000014,0.26000000000000023 --I_e4mIh0yE/3,-I_e4mIh0yE,3,8,over,3.762500047683716,3.9625000953674316,True,6.062504827580167e-14,0.25,3.7497129407356837,3.9509308917543975,3.5025,3.8225000000000002,3.7025,4.0425,0.3200000000000003,0.3400000000000003 --I_e4mIh0yE/3,-I_e4mIh0yE,3,9,the,3.9825000762939453,4.082499980926514,True,7.674462736313725e-14,0.25,3.988517118629397,4.105443941306858,3.8225000000000002,4.1225000000000005,4.0425,4.362500000000001,0.30000000000000027,0.3200000000000003 --I_e4mIh0yE/3,-I_e4mIh0yE,3,10,course,4.262499809265137,4.542500019073486,True,1.2368673793505813e-12,1.4726616144180298,4.274087408439977,4.559334574473354,4.2625,4.3825,4.5425,4.6825,0.1200000000000001,0.13999999999999968 --I_e4mIh0yE/3,-I_e4mIh0yE,3,11,of,4.622499942779541,4.682499885559082,True,4.1304034303045467e-13,0.491781622171402,4.680345626946339,4.744915525012013,4.6225000000000005,5.1625000000000005,4.6825,5.2625,0.54,0.5800000000000001 --I_e4mIh0yE/3,-I_e4mIh0yE,3,12,your,5.242499828338623,5.5625,True,1.1803501752394308e-11,4.0,5.248953696227165,5.565865409986523,5.1625000000000005,5.4625,5.562500000000001,5.6225000000000005,0.2999999999999998,0.05999999999999961 --I_e4mIh0yE/3,-I_e4mIh0yE,3,13,"carreer,",5.682499885559082,6.022500038146973,True,5.404035292172482e-12,4.0,5.67524877133456,6.022628998062419,5.6625000000000005,5.6825,6.022500000000001,6.022500000000001,0.019999999999999574,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,14,and,6.042500019073486,6.162499904632568,True,1.5327623068342455e-12,1.824965476989746,6.051594198076872,6.163218343693183,6.0425,6.062500000000001,6.1625000000000005,6.1625000000000005,0.020000000000000462,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,15,that,6.222499847412109,6.362500190734863,True,8.04463049994264e-13,0.9578244686126709,6.223196086715864,6.366824761858561,6.2225,6.2225,6.362500000000001,6.362500000000001,0.0,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,16,you,6.482500076293945,6.742499828338623,True,8.753082893914188e-13,1.042175531387329,6.485121347497934,6.727387463759083,6.482500000000001,6.482500000000001,6.602500000000001,6.782500000000001,0.0,0.17999999999999972 --I_e4mIh0yE/3,-I_e4mIh0yE,3,17,won't,6.902500152587891,7.0625,True,6.68682470505999e-11,4.0,6.850043955382938,7.036261507491488,6.7225,6.902500000000001,7.0025,7.0825000000000005,0.1800000000000006,0.08000000000000007 --I_e4mIh0yE/3,-I_e4mIh0yE,3,18,make,7.082499980926514,7.462500095367432,True,4.19843760451899e-12,4.0,7.142215541761871,7.4625317001592855,7.0825000000000005,7.202500000000001,7.4625,7.4625,0.1200000000000001,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,19,the,7.702499866485596,7.942500114440918,True,2.8825946078898934e-13,0.34321272373199463,7.711799725224728,7.948721670810593,7.702500000000001,7.7225,7.942500000000001,7.9625,0.019999999999999574,0.019999999999999574 --I_e4mIh0yE/3,-I_e4mIh0yE,3,20,mistake,8.0024995803833,8.422499656677246,True,2.3740891522648633e-13,0.2826681435108185,8.001170899583583,8.421542106933641,8.0025,8.0025,8.4225,8.4225,0.0,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,21,twice.,8.662500381469727,8.962499618530273,True,1.2501067889192363e-14,0.25,8.587039530663365,8.962645923111332,8.4825,8.6825,8.9625,8.9625,0.1999999999999993,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,0,"Because,",0.02250000089406967,0.36250001192092896,True,1.475930685448327e-14,0.25,0.022500000004257795,0.3625472694174722,0.0225,0.0225,0.3625,0.3625,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,1,if,0.4025000035762787,0.4625000059604645,True,2.200777673164525e-13,1.5552595853805542,0.4026353313760795,0.46269966085687303,0.4025,0.4025,0.4625,0.4625,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,2,they,0.5224999785423279,0.7825000286102295,True,1.5841776675185049e-13,1.1195167303085327,0.5250717282237793,0.7824865774462136,0.5225,0.5625,0.7825,0.7825,0.040000000000000036,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,3,are,1.0225000381469727,1.162500023841858,True,2.8222001084298072e-14,0.25,0.9993191173234123,1.1572935816657954,0.9425,1.0225,1.1225,1.2425,0.07999999999999996,0.11999999999999988 --UacrmKiTn4/10,-UacrmKiTn4,10,4,not,1.1825000047683716,1.3025000095367432,True,1.917614504687304e-12,4.0,1.212195552950287,1.3411265228208296,1.1824999999999999,1.3825,1.3025,1.6225,0.20000000000000018,0.32000000000000006 --UacrmKiTn4/10,-UacrmKiTn4,10,5,in,1.5225000381469727,1.622499942779541,True,3.806191202059689e-13,2.6897835731506348,1.5472873413699313,1.6685377212106935,1.5225,1.7025,1.6225,2.2225,0.17999999999999994,0.6000000000000001 --UacrmKiTn4/10,-UacrmKiTn4,10,6,any,2.442500114440918,2.5625,True,4.2097759739984086e-13,2.9749913215637207,2.4295782593879096,2.562499437343288,2.2025,2.4425,2.5625,2.5625,0.23999999999999977,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,7,way,2.6424999237060547,2.7825000286102295,True,1.2459322039793524e-13,0.8804833292961121,2.642499960569166,2.782499959619364,2.6425,2.6425,2.7825,2.7825,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,8,held,2.882499933242798,3.0625,True,1.871778990483741e-12,4.0,2.882499663382942,3.06249970189482,2.8825,2.8825,3.0625,3.0625,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,9,accountable,3.1024999618530273,3.8424999713897705,True,2.4792566274706856e-14,0.25,3.1034985154384263,3.843995047957288,3.1025,3.1025,3.8425,3.8625,0.0,0.020000000000000018 --UacrmKiTn4/10,-UacrmKiTn4,10,10,for,3.9625000953674316,4.142499923706055,True,8.828910909655652e-13,4.0,3.9618912743665837,4.1422515787201855,3.9625,3.9625,4.1425,4.1425,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,11,reading,4.202499866485596,4.522500038146973,True,5.040660204231988e-14,0.35621657967567444,4.217612209195304,4.533819065539573,4.202500000000001,4.282500000000001,4.522500000000001,4.5825000000000005,0.08000000000000007,0.05999999999999961 --UacrmKiTn4/10,-UacrmKiTn4,10,12,the,4.602499961853027,4.702499866485596,True,4.6737817744691226e-14,0.3302897810935974,4.602499650444098,4.702499657454214,4.602500000000001,4.602500000000001,4.702500000000001,4.702500000000001,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,13,material,4.722499847412109,5.322500228881836,True,2.7899259508537555e-14,0.25,4.722500025503899,5.3163663313084415,4.7225,4.7225,5.2625,5.322500000000001,0.0,0.0600000000000005 --UacrmKiTn4/10,-UacrmKiTn4,10,14,they,5.34250020980835,6.122499942779541,True,1.6694450184241705e-14,0.25,5.346698826778056,6.105480164175808,5.3425,5.3425,6.062500000000001,6.1225000000000005,0.0,0.05999999999999961 --UacrmKiTn4/10,-UacrmKiTn4,10,15,won't,6.142499923706055,6.302499771118164,True,6.240683664282543e-11,4.0,6.1425008497064475,6.309788862779577,6.142500000000001,6.142500000000001,6.3025,6.3425,0.0,0.040000000000000036 --UacrmKiTn4/10,-UacrmKiTn4,10,16,do,6.402500152587891,6.502500057220459,True,8.948922874431331e-13,4.0,6.4025000340549125,6.502500042623035,6.402500000000001,6.402500000000001,6.5025,6.5025,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,17,it.,6.622499942779541,6.682499885559082,True,2.6122733262547854e-14,0.25,6.623106947941723,6.688042407993376,6.6225000000000005,6.6225000000000005,6.6825,6.702500000000001,0.0,0.020000000000000462 --UacrmKiTn4/4,-UacrmKiTn4,4,0,Just,0.0024999999441206455,0.6424999833106995,True,4.562072507957593e-12,4.0,0.004947655251735573,0.6504811942238563,0.0025000000000000005,0.0225,0.6425,0.7025,0.019999999999999997,0.06000000000000005 --UacrmKiTn4/4,-UacrmKiTn4,4,1,given,0.762499988079071,1.002500057220459,True,6.256041691632408e-14,1.8532153367996216,0.7364139253098347,0.9636047049601082,0.6825,0.7625,0.8825,1.0025,0.07999999999999996,0.12 --UacrmKiTn4/4,-UacrmKiTn4,4,2,to,1.0225000381469727,1.0824999809265137,True,1.5428832189179169e-15,0.25,1.0025937583992537,1.0639978885987913,0.9425,1.0225,1.0025,1.0825,0.07999999999999996,0.08000000000000007 --UacrmKiTn4/4,-UacrmKiTn4,4,3,their,1.1024999618530273,1.3224999904632568,True,2.0475887269816778e-16,0.25,1.0908284753739195,1.3135099105503243,1.0225,1.1025,1.2225,1.3225,0.08000000000000007,0.10000000000000009 --UacrmKiTn4/4,-UacrmKiTn4,4,4,own,1.3825000524520874,1.502500057220459,True,1.73095121474621e-15,0.25,1.37705306367968,1.4988073905995172,1.3225,1.3825,1.4625,1.5025,0.06000000000000005,0.040000000000000036 --UacrmKiTn4/4,-UacrmKiTn4,4,5,devices,1.5425000190734863,2.122499942779541,True,4.961841416797723e-14,1.4698368310928345,1.5408861101749405,2.117455888833504,1.5425,1.5425,2.0825,2.1225,0.0,0.040000000000000036 --UacrmKiTn4/4,-UacrmKiTn4,4,6,they,2.202500104904175,2.3424999713897705,True,6.740486261595097e-14,1.9967213869094849,2.2006234143728935,2.341308883214659,2.2025,2.2025,2.3425,2.3425,0.0,0.0 --UacrmKiTn4/4,-UacrmKiTn4,4,7,are,2.362499952316284,2.4825000762939453,True,3.205953569025005e-13,4.0,2.3615948075228883,2.481585319634036,2.3625,2.3625,2.4825,2.4825,0.0,0.0 --UacrmKiTn4/4,-UacrmKiTn4,4,8,not,2.5225000381469727,2.682499885559082,True,5.5410154410759013e-14,1.6414045095443726,2.52250550991515,2.6825054463052336,2.5225,2.5225,2.6825,2.6825,0.0,0.0 --UacrmKiTn4/4,-UacrmKiTn4,4,9,going,2.762500047683716,3.0425000190734863,True,1.7897125160993774e-14,0.5301631689071655,2.743302852447095,3.0226983210219336,2.7225,2.7625,3.0025,3.0425,0.040000000000000036,0.040000000000000036 --UacrmKiTn4/4,-UacrmKiTn4,4,10,to,3.0824999809265137,3.1424999237060547,True,4.2949559449852355e-15,0.25,3.0824999856606508,3.1425000044551568,3.0825,3.0825,3.1425,3.1425,0.0,0.0 --UacrmKiTn4/4,-UacrmKiTn4,4,11,read,3.2225000858306885,3.422499895095825,True,3.4303006890215463e-15,0.25,3.222702148003313,3.418441830412285,3.2225,3.2425,3.3825,3.4225,0.020000000000000018,0.040000000000000036 --UacrmKiTn4/4,-UacrmKiTn4,4,12,the,3.442500114440918,3.5425000190734863,True,1.6336893860283141e-15,0.25,3.4691372489699823,3.5816860218914264,3.4425,3.6625,3.5425,3.8225000000000002,0.2200000000000002,0.28000000000000025 --UacrmKiTn4/4,-UacrmKiTn4,4,13,textbook.,3.6624999046325684,4.142499923706055,True,1.503377094038208e-13,4.0,3.647318800103063,4.282380916193305,3.5625,3.9025,4.0825000000000005,4.6625000000000005,0.33999999999999986,0.5800000000000001 --hnBHBN8p5A/7,-hnBHBN8p5A,7,0,Picture,0.16249999403953552,0.6424999833106995,True,2.388505246451317e-13,1.0,0.16093215462874963,0.6492774175568524,0.1625,0.1625,0.6425,0.7025,0.0,0.06000000000000005 --hnBHBN8p5A/7,-hnBHBN8p5A,7,1,"this,",0.6825000047683716,1.002500057220459,True,1.7479208185011696e-12,4.0,0.6967693237113916,1.0043499540624952,0.6825,0.7424999999999999,1.0025,1.0025,0.05999999999999994,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,2,before,1.2625000476837158,1.8825000524520874,True,3.712301462326789e-14,0.25,1.2568305382584097,1.8793181771780305,1.1624999999999999,1.2625,1.8225,1.8825,0.10000000000000009,0.06000000000000005 --hnBHBN8p5A/7,-hnBHBN8p5A,7,3,we,1.9824999570846558,2.122499942779541,True,3.7951361346079404e-13,1.5889167785644531,1.9788165575745025,2.1177397110667036,1.9825,1.9825,2.1225,2.1225,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,4,actually,2.2225000858306885,3.682499885559082,True,2.084581151359094e-13,0.8727555274963379,2.217809149643507,3.6792332685589186,2.2225,2.2225,3.6225,3.6825,0.0,0.06000000000000005 --hnBHBN8p5A/7,-hnBHBN8p5A,7,5,start,3.742500066757202,4.082499980926514,True,1.9712341568084435e-12,4.0,3.7424998928545663,4.082502830606967,3.7425,3.7425,4.0825000000000005,4.0825000000000005,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,6,"hacking,",4.222499847412109,4.702499866485596,True,2.2997982980347588e-14,0.25,4.221812245247405,4.727831407783964,4.2225,4.242500000000001,4.702500000000001,4.782500000000001,0.020000000000000462,0.08000000000000007 --hnBHBN8p5A/7,-hnBHBN8p5A,7,7,we’re,4.922500133514404,5.142499923706055,True,1.77046884097809e-11,4.0,4.920433348583369,5.142277923757999,4.9225,4.9225,5.1425,5.1425,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,8,in,5.182499885559082,5.242499828338623,True,3.179557853984216e-13,1.3311915397644043,5.182457696430692,5.242471813790377,5.1825,5.1825,5.242500000000001,5.242500000000001,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,9,a,5.28249979019165,5.302499771118164,True,5.193478754553704e-15,0.25,5.284921857502635,5.3049218575026345,5.282500000000001,5.3025,5.3025,5.322500000000001,0.019999999999999574,0.020000000000000462 --hnBHBN8p5A/7,-hnBHBN8p5A,7,10,room,5.34250020980835,5.502500057220459,True,4.2035475374493404e-15,0.25,5.342924994112858,5.502498287036115,5.3425,5.3425,5.5025,5.5025,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,11,formal,5.522500038146973,5.902500152587891,True,1.3692799877892264e-13,0.5732790231704712,5.522499935669881,5.902497519672031,5.522500000000001,5.522500000000001,5.902500000000001,5.902500000000001,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,12,setting,6.882500171661377,7.242499828338623,True,5.154608294019591e-13,2.1580896377563477,6.876668294203695,7.242903312234083,6.8425,6.8825,7.242500000000001,7.242500000000001,0.040000000000000036,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,0,"Discussion,",0.20250000059604645,0.6025000214576721,True,7.413789807928048e-16,0.25,0.05734253021101946,0.6025331203999742,0.0025000000000000005,0.1825,0.6024999999999999,0.6024999999999999,0.18,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,1,how,0.6424999833106995,0.7825000286102295,True,1.488793626141377e-13,0.9633798003196716,0.6425435974958801,0.7825102245774119,0.6425,0.6425,0.7825,0.7825,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,2,to,0.862500011920929,0.9424999952316284,True,4.985656256329546e-14,0.3226155936717987,0.8625061466012319,0.9433688622350123,0.8624999999999999,0.8624999999999999,0.9425,0.9425,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,3,discuss,1.002500057220459,1.2825000286102295,True,5.747768390797561e-14,0.37193092703819275,1.002478560527885,1.282636894103437,1.0025,1.0025,1.2825,1.2825,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,4,ideas,1.3224999904632568,1.7424999475479126,True,7.848715709475595e-13,4.0,1.3870056407994262,1.7424735743902229,1.3225,1.4825,1.7425,1.7425,0.15999999999999992,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,5,and,1.7625000476837158,1.8624999523162842,True,1.503318303175405e-11,4.0,1.7624947007218714,1.8624953633132175,1.7625,1.7625,1.8625,1.8625,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,6,opinions,1.8825000524520874,2.2225000858306885,True,2.2395399337099553e-12,4.0,1.88249543276377,2.223362908527545,1.8825,1.8825,2.2225,2.2225,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,7,more,3.8424999713897705,3.9825000762939453,True,2.2274653909554187e-12,4.0,3.813089593624601,3.975339622828584,3.7625,3.8625,3.9225,4.0025,0.09999999999999964,0.08000000000000052 --hnBHBN8p5A/6,-hnBHBN8p5A,6,8,effectively,4.002500057220459,4.582499980926514,True,1.0292728990076924e-13,0.6660296320915222,4.001976607641939,4.579303253022034,3.9425,4.0425,4.5425,4.5825000000000005,0.10000000000000053,0.040000000000000036 --hnBHBN8p5A/6,-hnBHBN8p5A,6,9,than,4.602499961853027,4.942500114440918,True,1.545385997989146e-13,1.0,4.602805718995574,4.942454042242472,4.602500000000001,4.602500000000001,4.942500000000001,4.942500000000001,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,10,a,4.982500076293945,5.002500057220459,True,1.1543436345684555e-11,4.0,4.973083161483878,4.993083161483986,4.9625,4.982500000000001,4.982500000000001,5.0025,0.020000000000000462,0.019999999999999574 --hnBHBN8p5A/6,-hnBHBN8p5A,6,11,native,5.022500038146973,5.982500076293945,True,2.7570120123519837e-13,1.7840280532836914,5.023246868555434,5.982429087559479,5.022500000000001,5.022500000000001,5.982500000000001,5.982500000000001,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,12,speaker,6.002500057220459,6.322500228881836,True,1.1699105226248285e-13,0.7570344805717468,6.0024817013264,6.318882058754529,6.0025,6.0025,6.3025,6.322500000000001,0.0,0.020000000000000462 --qDkUB0GgYY/6,-qDkUB0GgYY,6,0,We,0.0024999999441206455,0.0625,True,9.337668442285785e-13,0.4947294592857361,0.004150950202978793,0.06513475794367299,0.0025000000000000005,0.0225,0.0625,0.0825,0.019999999999999997,0.020000000000000004 --qDkUB0GgYY/6,-qDkUB0GgYY,6,1,always,0.10249999910593033,0.4025000035762787,True,2.7101797368811464e-12,1.435910701751709,0.10268331893228248,0.4027179907882608,0.10250000000000001,0.10250000000000001,0.4025,0.4025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,2,love,0.48249998688697815,0.6625000238418579,True,1.558735508288417e-13,0.25,0.4821765173364106,0.6623160427138763,0.4825,0.4825,0.6625,0.6625,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,3,to,0.7225000262260437,0.8025000095367432,True,6.635134065444137e-13,0.35154345631599426,0.7222857214492022,0.8027704858444851,0.7224999999999999,0.7224999999999999,0.8025,0.8025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,4,hear,0.9825000166893005,1.1425000429153442,True,5.576355783382114e-12,2.9544713497161865,0.9824963006452994,1.1424985239327123,0.9824999999999999,0.9824999999999999,1.1425,1.1425,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,5,what,1.222499966621399,1.402500033378601,True,6.127329275473076e-14,0.25,1.2225943761411022,1.4025803637052279,1.2225,1.2225,1.4025,1.4025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,6,you,1.462499976158142,1.5625,True,1.2773379664372201e-14,0.25,1.4508682284819499,1.5565077680365997,1.4224999999999999,1.4625,1.5225,1.5625,0.040000000000000036,0.040000000000000036 --qDkUB0GgYY/6,-qDkUB0GgYY,6,7,"say,",1.5824999809265137,1.7424999475479126,True,2.74772760967823e-12,1.4558043479919434,1.5837011690674971,1.7213764295415506,1.5825,1.5825,1.6824999999999999,1.7625,0.0,0.08000000000000007 --qDkUB0GgYY/6,-qDkUB0GgYY,6,8,and,1.902500033378601,2.122499942779541,True,2.0415532112072476e-11,4.0,1.9027662090732207,2.123002766118228,1.9025,1.9025,2.1225,2.1225,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,9,like,2.202500104904175,2.702500104904175,True,1.0162235636324013e-12,0.5384168028831482,2.2032672035614334,2.67955362682512,2.2025,2.2025,2.6625,2.7025,0.0,0.040000000000000036 --qDkUB0GgYY/6,-qDkUB0GgYY,6,10,and,2.742500066757202,2.882499933242798,True,3.416096572284033e-11,4.0,2.7427614638556794,2.8827771318550117,2.7425,2.7425,2.8825,2.8825,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,11,share,2.942500114440918,3.242500066757202,True,1.1194105538347987e-11,4.0,2.9573490079626183,3.2448006952303574,2.9425,3.0225,3.2425,3.2625,0.08000000000000007,0.020000000000000018 --qDkUB0GgYY/6,-qDkUB0GgYY,6,12,this,3.322499990463257,3.5625,True,1.1903970864590718e-11,4.0,3.3267286352579317,3.5372195818299765,3.3225000000000002,3.3425,3.4825,3.5625,0.019999999999999574,0.08000000000000007 --qDkUB0GgYY/6,-qDkUB0GgYY,6,13,video,3.6024999618530273,3.9024999141693115,True,7.060332787162116e-12,3.7407138347625732,3.601699909740036,3.9012860766225366,3.6025,3.6025,3.9025,3.9025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,14,as,3.922499895095825,4.002500057220459,True,1.0646788355453407e-12,0.5640894174575806,3.9230020734809483,4.002898737678011,3.9225,3.9225,4.0025,4.0025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,15,well.,4.042500019073486,4.522500038146973,True,7.8364842826667e-13,0.4151935279369354,4.1010353264747215,4.535698864584865,4.0425,4.4225,4.522500000000001,4.602500000000001,0.3799999999999999,0.08000000000000007 --uywlfIYOS8/4,-uywlfIYOS8,4,0,And,0.02250000089406967,0.16249999403953552,True,5.988297357983896e-11,4.0,0.022602156621392876,0.1630419189656043,0.0225,0.0225,0.1625,0.1625,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,1,then,0.42250001430511475,0.6625000238418579,True,1.9874759390331453e-12,4.0,0.4046817558066659,0.6583254135084216,0.3825,0.4225,0.6224999999999999,0.6625,0.03999999999999998,0.040000000000000036 --uywlfIYOS8/4,-uywlfIYOS8,4,2,from,0.8224999904632568,0.9825000166893005,True,1.0401202468757198e-12,4.0,0.8134819794270989,0.9735283961462557,0.7625,0.8225,0.9225,0.9824999999999999,0.06000000000000005,0.05999999999999994 --uywlfIYOS8/4,-uywlfIYOS8,4,3,a,1.0824999809265137,1.1024999618530273,True,6.67349883036808e-14,0.5558029413223267,1.0652153145723244,1.0852153145723227,0.9624999999999999,1.0825,0.9824999999999999,1.1025,0.1200000000000001,0.1200000000000001 --uywlfIYOS8/4,-uywlfIYOS8,4,4,brand,1.2024999856948853,1.4424999952316284,True,1.370544624920136e-14,0.25,1.1838669782022566,1.4434563674323948,1.0825,1.2025,1.4425,1.4425,0.11999999999999988,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,5,point,1.5824999809265137,1.9824999570846558,True,1.667973620550836e-13,1.3891733884811401,1.6264036729392484,2.021516023502526,1.5825,1.7625,1.9825,2.2025,0.17999999999999994,0.2200000000000002 --uywlfIYOS8/4,-uywlfIYOS8,4,6,of,2.122499942779541,2.202500104904175,True,3.508121744488779e-12,4.0,2.1405318506933226,2.2203001571442478,2.1225,2.2425,2.2025,2.3025,0.1200000000000001,0.10000000000000009 --uywlfIYOS8/4,-uywlfIYOS8,4,7,"view,",2.382499933242798,2.5425000190734863,True,3.820997066927151e-13,3.1823208332061768,2.382480809329932,2.54428426955582,2.3825,2.3825,2.5425,2.5625,0.0,0.020000000000000018 --uywlfIYOS8/4,-uywlfIYOS8,4,8,we,2.6424999237060547,2.7225000858306885,True,4.428764280761839e-14,0.3688500225543976,2.642474815240312,2.722478205982455,2.6425,2.6425,2.7225,2.7225,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,9,need,2.822499990463257,3.002500057220459,True,7.078356862233112e-14,0.589521586894989,2.8015348641925786,2.9936915393927097,2.7825,2.8225,2.9625,3.0025,0.03999999999999959,0.040000000000000036 --uywlfIYOS8/4,-uywlfIYOS8,4,10,to,3.0824999809265137,3.1424999237060547,True,1.7816755286826497e-14,0.25,3.082466476739016,3.142468401478628,3.0825,3.0825,3.1425,3.1425,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,11,make,3.202500104904175,3.362499952316284,True,7.334166233314773e-14,0.6108267307281494,3.182153732642052,3.342160432953954,3.1625,3.2025,3.3225000000000002,3.3625,0.040000000000000036,0.03999999999999959 --uywlfIYOS8/4,-uywlfIYOS8,4,12,sure,3.442500114440918,3.6024999618530273,True,5.5085622207348e-14,0.45878109335899353,3.43999834296272,3.5975051950710397,3.4025,3.4425,3.5425,3.6025,0.040000000000000036,0.06000000000000005 --uywlfIYOS8/4,-uywlfIYOS8,4,13,we're,3.682499885559082,3.862499952316284,True,3.684579377716268e-11,4.0,3.6833066425391294,3.8624815665334,3.6825,3.6825,3.8625,3.8625,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,14,driving,3.9024999141693115,4.222499847412109,True,4.1946619924260534e-14,0.34935280680656433,3.9024850739862837,4.222491770692106,3.9025,3.9025,4.2225,4.2225,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,15,the,4.242499828338623,4.34250020980835,True,3.531454168263955e-14,0.2941174805164337,4.242503793608108,4.342507015743014,4.242500000000001,4.242500000000001,4.3425,4.3425,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,16,relevant,4.362500190734863,4.722499847412109,True,3.1015102053451316e-13,2.5830955505371094,4.362528100683705,4.7228847343356,4.362500000000001,4.362500000000001,4.7225,4.7225,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,17,insight.,4.882500171661377,5.822500228881836,True,2.73377674664399e-12,4.0,4.881390463456283,5.819079782519511,4.862500000000001,4.8825,5.782500000000001,5.862500000000001,0.019999999999999574,0.08000000000000007 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,0,Many,0.08250000327825546,0.2824999988079071,True,7.850928566109638e-13,3.5132734775543213,0.10585923401689983,0.33275215154873433,0.0825,0.14250000000000002,0.28250000000000003,0.4425,0.06000000000000001,0.15999999999999998 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,1,people,0.4025000035762787,0.7024999856948853,True,9.156772562413096e-13,4.0,0.4426754338118783,0.7227427877341543,0.4025,0.4825,0.7025,0.7424999999999999,0.07999999999999996,0.039999999999999925 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,2,are,0.7825000286102295,0.9825000166893005,True,2.8262345263575794e-13,1.2647337913513184,0.7845461092295155,0.9620080068815149,0.7825,0.7825,0.9225,0.9824999999999999,0.0,0.05999999999999994 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,3,not,1.0225000381469727,1.1425000429153442,True,4.933300133420221e-14,0.25,1.0239882241362008,1.1437603517251953,1.0225,1.0225,1.1425,1.1425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,4,until,1.1825000047683716,1.5425000190734863,True,1.1237945253192438e-13,0.5028955936431885,1.1842781652958174,1.5435631922214206,1.1824999999999999,1.1824999999999999,1.5425,1.5425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,5,they,1.5824999809265137,1.9424999952316284,True,1.3072862562434062e-13,0.5850077271461487,1.5848135452386243,1.940893219335555,1.5825,1.5825,1.9425,1.9425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,6,get,2.0225000381469727,2.1624999046325684,True,1.3913614760384472e-14,0.25,2.0224595608245117,2.162566220706084,2.0225,2.0225,2.1625,2.1625,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,7,to,2.262500047683716,2.3424999713897705,True,2.638859826996614e-14,0.25,2.264419013866157,2.3477816786947727,2.2025,2.3225,2.3425,2.3825,0.11999999999999966,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,8,be,2.4024999141693115,2.502500057220459,True,4.1763540867788274e-13,1.8689093589782715,2.4025004305045847,2.502500872803157,2.4025,2.4025,2.5025,2.5025,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,9,a,2.6624999046325684,2.682499885559082,True,9.004533772262457e-13,4.0,2.6624925234542722,2.6824925234542722,2.6625,2.6625,2.6825,2.6825,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,10,older,3.322499990463257,3.5425000190734863,True,2.0256505620010884e-13,0.906474232673645,3.3198354757754682,3.5423462179745098,3.3225000000000002,3.3225000000000002,3.5425,3.5425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,11,age,3.762500047683716,3.922499895095825,True,3.217204334968887e-13,1.4396919012069702,3.7624739015695634,3.922669449686004,3.7625,3.7625,3.9225,3.9225,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,12,and,4.022500038146973,4.182499885559082,True,1.5305238223713505e-14,0.25,4.034930648788859,4.200137383139412,4.022500000000001,4.1025,4.1825,4.2225,0.07999999999999918,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,13,they,4.322500228881836,4.502500057220459,True,1.6199097185171096e-13,0.7249060869216919,4.321713048262524,4.501433219594749,4.322500000000001,4.322500000000001,4.5025,4.5025,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,14,realize,4.622499942779541,4.982500076293945,True,5.409635413233804e-13,2.420799970626831,4.619013484915951,4.951435893611253,4.6225000000000005,4.6225000000000005,4.9225,4.982500000000001,0.0,0.0600000000000005 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,15,that,5.0625,5.362500190734863,True,1.7663386031596673e-10,4.0,5.061109736838025,5.357915223735426,5.062500000000001,5.062500000000001,5.322500000000001,5.362500000000001,0.0,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,16,gosh,5.402500152587891,5.582499980926514,True,6.985038936146992e-11,4.0,5.412608374385401,5.583867731142024,5.402500000000001,5.442500000000001,5.5825000000000005,5.5825000000000005,0.040000000000000036,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,17,they,5.762499809265137,5.942500114440918,True,9.114082643972565e-14,0.40785321593284607,5.743872692230504,5.933506686984788,5.6225000000000005,5.7625,5.902500000000001,5.942500000000001,0.13999999999999968,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,18,wasted,5.962500095367432,6.28249979019165,True,6.252937400584016e-15,0.25,5.9755017709324125,6.318240025679866,5.9625,6.0025,6.282500000000001,6.3425,0.040000000000000036,0.05999999999999961 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,19,a,6.362500190734863,6.382500171661377,True,9.418957584370058e-13,4.0,6.362501464590465,6.3825014645904625,6.362500000000001,6.362500000000001,6.3825,6.3825,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,20,lot,6.442500114440918,6.5625,True,1.293143381004419e-13,0.5786788463592529,6.442499934859892,6.5625000135512535,6.442500000000001,6.442500000000001,6.562500000000001,6.562500000000001,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,21,of,6.622499942779541,6.702499866485596,True,2.4035458952992306e-12,4.0,6.615005116878734,6.6837688432472016,6.5825000000000005,6.6225000000000005,6.642500000000001,6.702500000000001,0.040000000000000036,0.05999999999999961 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,22,time,6.742499828338623,7.0625,True,3.271341869810242e-14,0.25,6.7425061991759785,7.062501833718776,6.742500000000001,6.742500000000001,7.062500000000001,7.062500000000001,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,23,and,7.142499923706055,7.242499828338623,True,7.342488162614957e-15,0.25,7.114865685343713,7.23577604907428,7.0825000000000005,7.142500000000001,7.202500000000001,7.242500000000001,0.0600000000000005,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,24,money,7.34250020980835,7.5625,True,2.4677303386497207e-13,1.1043039560317993,7.342288899962515,7.563080577866272,7.3425,7.3425,7.562500000000001,7.562500000000001,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,25,in,8.602499961853027,8.982500076293945,True,1.5762575706484983e-13,0.7053718566894531,8.364611154997718,8.85810714822456,7.602500000000001,8.6225,8.6225,8.9825,1.0199999999999996,0.35999999999999943 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,26,placing,9.0625,9.6225004196167,True,1.828831275871648e-13,0.8183979988098145,9.066924678538173,9.62794870027181,8.9625,9.3025,9.6225,9.6625,0.33999999999999986,0.03999999999999915 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,27,bets,9.642499923706055,9.882499694824219,True,4.637042486099752e-12,4.0,9.682127934242377,9.909239237242446,9.6425,9.7025,9.8825,9.9225,0.0600000000000005,0.03999999999999915 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,28,on,9.942500114440918,10.042499542236328,True,3.03088699971088e-11,4.0,9.944177340580937,10.044079828969402,9.942499999999999,9.942499999999999,10.0425,10.0425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,29,stock,10.1225004196167,10.40250015258789,True,1.0609971966757045e-14,0.25,10.13073084545628,10.425039706715383,10.1225,10.1625,10.4025,10.5025,0.03999999999999915,0.09999999999999964 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,30,that's,10.482500076293945,10.72249984741211,True,1.9271074319648918e-11,4.0,10.493521815416006,10.722486771164357,10.4825,10.5425,10.7225,10.7225,0.0600000000000005,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,31,ultimately,10.742500305175781,11.362500190734863,True,2.4436450584384983e-13,1.0935258865356445,10.744089634125213,11.362750520402031,10.7425,10.7425,11.362499999999999,11.362499999999999,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,32,lost,11.462499618530273,11.702500343322754,True,1.1303276904414163e-14,0.25,11.458537715767447,11.702573699169442,11.442499999999999,11.4625,11.7025,11.7025,0.02000000000000135,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,33,value.,11.842499732971191,12.242500305175781,True,1.0531544169731233e-12,4.0,11.844283755647352,12.241341598308948,11.8425,11.8425,12.2425,12.2425,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,0,"is,",0.0024999999441206455,0.0625,True,3.060766640139434e-10,4.0,0.0025112058701024778,0.0626767445741339,0.0025000000000000005,0.0025000000000000005,0.0625,0.0625,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,1,you,0.12250000238418579,0.2224999964237213,True,1.535432336829956e-10,4.0,0.12240190227637165,0.22247758827306965,0.1225,0.1225,0.2225,0.2225,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,2,can,0.26249998807907104,0.3824999928474426,True,2.600926939100301e-11,4.0,0.2624053486672871,0.3825267286438774,0.2625,0.2625,0.3825,0.3825,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,3,"say,",0.4625000059604645,0.6424999833106995,True,3.910752364305603e-12,1.8001569509506226,0.46261070574901725,0.6425522763266137,0.4625,0.4625,0.6425,0.6425,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,4,hey,0.7825000286102295,1.0425000190734863,True,3.1533831535562884e-12,1.4515326023101807,0.7823334202971419,1.04253429725383,0.7825,0.7825,1.0425,1.0425,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,5,I,1.1024999618530273,1.122499942779541,True,2.4848057639248466e-11,4.0,1.106823013333484,1.1268230133336934,1.0825,1.1225,1.1025,1.1425,0.040000000000000036,0.040000000000000036 --9y-fZ3swSY/0,-9y-fZ3swSY,0,6,really,1.462499976158142,1.7625000476837158,True,8.315149783999498e-12,3.8275434970855713,1.4586973624020396,1.760280018786308,1.4625,1.4625,1.7625,1.7625,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,7,like,1.7825000286102295,2.002500057220459,True,2.202190625764499e-14,0.25,1.8349168890968484,2.049505831918696,1.7825,1.9425,2.0025,2.1025,0.15999999999999992,0.10000000000000009 --9y-fZ3swSY/0,-9y-fZ3swSY,0,8,baby,2.0425000190734863,2.302500009536743,True,4.1377170786893736e-12,1.9046310186386108,2.0909214645105583,2.320850643943992,2.0425,2.1425,2.3025,2.3425,0.10000000000000009,0.03999999999999959 --9y-fZ3swSY/0,-9y-fZ3swSY,0,9,"skin,",2.4024999141693115,2.7225000858306885,True,1.806715150751148e-12,0.8316483497619629,2.3998643025533677,2.7200288568107656,2.4025,2.4025,2.7225,2.7225,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,10,they,2.9825000762939453,3.322499990463257,True,5.184020313714344e-12,2.38625431060791,2.982507862329635,3.3109432237495335,2.9825,2.9825,3.2625,3.3225000000000002,0.0,0.06000000000000005 --9y-fZ3swSY/0,-9y-fZ3swSY,0,11,are,3.442500114440918,3.6424999237060547,True,1.3909029093414627e-12,0.6402459740638733,3.442499981962662,3.6424997548724356,3.4425,3.4425,3.6425,3.6425,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,12,so,3.742500066757202,3.822499990463257,True,1.5805114378722451e-13,0.25,3.746053024158016,3.8230914471326036,3.7425,3.7625,3.8225000000000002,3.8225000000000002,0.020000000000000018,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,13,"soft,",3.882499933242798,4.102499961853027,True,2.7519693880477536e-13,0.25,3.8825012869595423,4.102466215181166,3.8825,3.8825,4.1025,4.1025,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,14,they,4.28249979019165,4.602499961853027,True,8.551512904915459e-13,0.3936343491077423,4.282513053129279,4.602513532707415,4.282500000000001,4.282500000000001,4.602500000000001,4.602500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,15,don’t,4.682499885559082,4.902500152587891,True,8.042919108497415e-11,4.0,4.683443564494826,4.903185399030555,4.6825,4.6825,4.902500000000001,4.902500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,16,have,4.962500095367432,5.222499847412109,True,1.4296327793469898e-13,0.25,4.967341030440776,5.226155811124558,4.9625,5.022500000000001,5.2225,5.2625,0.0600000000000005,0.040000000000000036 --9y-fZ3swSY/0,-9y-fZ3swSY,0,17,any,5.322500228881836,5.442500114440918,True,2.458334967883613e-12,1.131595253944397,5.322497337046365,5.442501103881926,5.322500000000001,5.322500000000001,5.442500000000001,5.442500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,18,hair,5.522500038146973,5.78249979019165,True,8.543707733475736e-13,0.39327508211135864,5.522549514283644,5.782686723091677,5.522500000000001,5.522500000000001,5.782500000000001,5.782500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,19,on,5.882500171661377,5.942500114440918,True,1.8865666407547055e-12,0.868404746055603,5.882448764418133,5.9425058227663135,5.8825,5.8825,5.942500000000001,5.942500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,20,their,6.022500038146973,6.34250020980835,True,4.095190170045476e-13,0.25,6.0212367814366665,6.362506654169406,6.022500000000001,6.022500000000001,6.3425,6.402500000000001,0.0,0.0600000000000005 --9y-fZ3swSY/0,-9y-fZ3swSY,0,21,face,6.462500095367432,6.722499847412109,True,3.871533356489959e-13,0.25,6.462437165544646,6.717253186710826,6.3825,6.522500000000001,6.6225000000000005,6.7225,0.14000000000000057,0.09999999999999964 --9y-fZ3swSY/4,-9y-fZ3swSY,4,0,I’ve,0.4025000035762787,0.5024999976158142,True,2.0237548525869897e-08,4.0,0.3604930640300776,0.5025259623963769,0.3025,0.4025,0.5025,0.5025,0.10000000000000003,0.0 --9y-fZ3swSY/4,-9y-fZ3swSY,4,1,seen,0.5625,0.7825000286102295,True,5.362225594107706e-11,1.505155086517334,0.5576079937154002,0.7374851585800298,0.5225,0.5625,0.7025,0.7825,0.040000000000000036,0.07999999999999996 --9y-fZ3swSY/4,-9y-fZ3swSY,4,2,this,0.8224999904632568,1.0425000190734863,True,3.008013326269432e-12,0.25,0.8081106426559449,1.0121021476836018,0.7625,0.8225,0.9225,1.0425,0.06000000000000005,0.12 --9y-fZ3swSY/4,-9y-fZ3swSY,4,3,in,1.122499942779541,1.2024999856948853,True,5.537627301155368e-11,1.5543895959854126,1.1072155754604693,1.192481658849235,1.0225,1.1225,1.1025,1.2225,0.10000000000000009,0.11999999999999988 --9y-fZ3swSY/4,-9y-fZ3swSY,4,4,US,1.2825000286102295,1.4824999570846558,True,5.11657140267463e-11,1.4362009763717651,1.2692331019302276,1.440947430874424,1.1225,1.2825,1.2025,1.4825,0.15999999999999992,0.28 --9y-fZ3swSY/4,-9y-fZ3swSY,4,5,or,1.5625,1.8424999713897705,True,1.1776651702433139e-12,0.25,1.5884781405612562,1.81895963877589,1.2825,1.8225,1.4825,1.9825,0.54,0.5 --9y-fZ3swSY/4,-9y-fZ3swSY,4,6,in,1.902500033378601,1.9824999570846558,True,4.608553664554871e-13,0.25,1.9176942178451357,2.0156957087418754,1.8225,2.0425,1.9825,2.1425,0.21999999999999997,0.16000000000000014 --9y-fZ3swSY/4,-9y-fZ3swSY,4,7,the,2.0425000190734863,2.322499990463257,True,3.562573500093258e-11,1.0,2.0775599959853452,2.2745366471050685,2.0425,2.2025,2.1825,2.3625,0.16000000000000014,0.17999999999999972 --9y-fZ3swSY/4,-9y-fZ3swSY,4,8,US,2.4024999141693115,2.5625,True,2.4587404594961226e-12,0.25,2.3624797829867665,2.563519061540398,2.3025,2.4625,2.5625,2.5625,0.1599999999999997,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,0,That’s,0.08250000327825546,0.42250001430511475,True,9.314120308356877e-11,4.0,0.0806608236315872,0.4421451291187421,0.0625,0.0825,0.4225,0.4825,0.020000000000000004,0.06 --9y-fZ3swSY/8,-9y-fZ3swSY,8,1,a,0.4625000059604645,0.48249998688697815,True,1.3536963433882776e-12,1.1048375368118286,0.5336691584869542,0.5536691584869534,0.4625,0.6024999999999999,0.4825,0.6224999999999999,0.1399999999999999,0.13999999999999996 --9y-fZ3swSY/8,-9y-fZ3swSY,8,2,more,0.9624999761581421,1.162500023841858,True,1.1315475872920519e-13,0.25,0.915860858527888,1.1624825207872158,0.6024999999999999,0.9624999999999999,1.1624999999999999,1.1624999999999999,0.36,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,3,decent,1.3624999523162842,1.8025000095367432,True,3.1133767981229854e-13,0.2541024386882782,1.361983429299652,1.8391916213136885,1.3625,1.3625,1.8025,2.1225,0.0,0.32000000000000006 --9y-fZ3swSY/8,-9y-fZ3swSY,8,4,and,2.1024999618530273,2.322499990463257,True,2.3098985831720986e-12,1.8852548599243164,2.114016786911137,2.3136987008669143,2.1025,2.2025,2.2625,2.3225,0.10000000000000009,0.05999999999999961 --9y-fZ3swSY/8,-9y-fZ3swSY,8,5,more,2.382499933242798,2.7825000286102295,True,1.0729073879334194e-12,0.8756678104400635,2.4241202403087847,2.781629417286628,2.3825,2.4825,2.7825,2.7825,0.10000000000000009,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,6,polite,2.922499895095825,3.2825000286102295,True,1.4056551062013867e-13,0.25,2.9215736524164218,3.2824992595126026,2.9225,2.9225,3.2825,3.2825,0.0,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,7,way,3.4825000762939453,3.6024999618530273,True,3.170587490469723e-12,2.5877177715301514,3.4208157907789016,3.5888738666999616,3.3425,3.4825,3.5425,3.6025,0.14000000000000012,0.06000000000000005 --9y-fZ3swSY/8,-9y-fZ3swSY,8,8,to,3.6624999046325684,4.022500038146973,True,6.365228981917992e-13,0.5195067524909973,3.6700739437613157,4.023422620318217,3.6625,3.6625,4.022500000000001,4.022500000000001,0.0,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,9,call,4.082499980926514,4.34250020980835,True,9.880774150261562e-12,4.0,4.082498071211092,4.342498071249503,4.0825000000000005,4.0825000000000005,4.3425,4.3425,0.0,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,10,a,4.382500171661377,4.402500152587891,True,1.398533979596328e-11,4.0,4.38238720408983,4.402387204089831,4.3825,4.3825,4.402500000000001,4.402500000000001,0.0,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,11,lady,4.722499847412109,4.902500152587891,True,1.096793229155013e-12,0.8951625823974609,4.725094218121915,4.90232210201576,4.7225,4.742500000000001,4.902500000000001,4.902500000000001,0.020000000000000462,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,0,The,0.14249999821186066,0.5824999809265137,True,1.1787321505419158e-14,0.25,0.27252131830482507,0.5876429988753463,0.1225,0.5225,0.5824999999999999,0.6224999999999999,0.39999999999999997,0.040000000000000036 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,1,perfect,0.6025000214576721,1.002500057220459,True,3.2728529088255076e-13,0.25,0.6092378774561676,1.0025942417269524,0.6024999999999999,0.6425,1.0025,1.0025,0.040000000000000036,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,2,soul,1.0425000190734863,1.2625000476837158,True,3.279520058296903e-11,4.0,1.042594908835863,1.262583948424393,1.0425,1.0425,1.2625,1.2625,0.0,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,3,mate,1.3025000095367432,1.5625,True,8.825176700533177e-12,1.7002969980239868,1.302545298794321,1.5265084161584652,1.3025,1.3025,1.4825,1.5625,0.0,0.08000000000000007 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,4,to,1.6024999618530273,1.662500023841858,True,1.4148528269114502e-12,0.2725917100906372,1.6056070545522665,1.6656434794159292,1.6025,1.6025,1.6625,1.6625,0.0,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,5,the,1.722499966621399,1.8224999904632568,True,5.101854268912964e-13,0.25,1.735751104704133,1.8401369240743506,1.7225,1.8625,1.8225,1.9825,0.14000000000000012,0.15999999999999992 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,6,Clarisonic,1.8624999523162842,2.5625,True,3.914031425356068e-12,0.7540943622589111,1.8851971012939985,2.6083585906340376,1.8625,2.0825,2.4825,3.4025,0.21999999999999997,0.9199999999999999 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,7,Spot,3.1024999618530273,3.4024999141693115,True,2.7890302914390652e-12,0.5373467206954956,3.1445458318034225,3.4526482322797505,3.1025,3.5625,3.4025,3.9625,0.45999999999999996,0.56 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,8,Therapy,3.5625,3.9625000953674316,True,1.1581469290533608e-11,2.2313363552093506,3.606693728375404,4.068442576095661,3.5625,4.1025,3.9625,5.0425,0.54,1.0800000000000005 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,9,"Brush,",4.042500019073486,4.322500228881836,True,4.2936924089971573e-13,0.25,4.152623283975696,4.44807120308332,4.0425,5.1825,4.322500000000001,5.562500000000001,1.1399999999999997,1.2400000000000002 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,10,this,4.942500114440918,5.122499942779541,True,2.7933718164084576e-13,0.25,4.98188533431721,5.221250227451901,4.562500000000001,5.742500000000001,5.0825000000000005,6.062500000000001,1.1799999999999997,0.9800000000000004 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,11,"gentle,",5.182499885559082,5.642499923706055,True,1.0272394046495492e-11,1.9791241884231567,5.298987408234679,5.714191899190055,5.1825,6.202500000000001,5.602500000000001,6.562500000000001,1.0200000000000005,0.96 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,12,Ph,5.742499828338623,5.822500228881836,True,4.917092080725105e-11,4.0,5.807498087279645,5.894975688983383,5.6625000000000005,6.6225000000000005,5.7225,6.7625,0.96,1.04 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,13,balanced,5.922500133514404,6.442500114440918,True,2.114638976077887e-12,0.4074155390262604,6.020752430532114,6.566108548962118,5.742500000000001,6.902500000000001,6.3025,7.4625,1.1600000000000001,1.1600000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,14,and,6.502500057220459,6.622499942779541,True,2.047474386215886e-12,0.3944753408432007,6.6474085491968244,6.8075910652999445,6.4225,7.602500000000001,6.562500000000001,7.702500000000001,1.1800000000000006,1.1399999999999997 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,15,hydrating,6.742499828338623,7.462500095367432,True,2.415321089833944e-13,0.25,6.931675287821492,7.679988215120003,6.742500000000001,7.822500000000001,7.4625,8.282499999999999,1.08,0.8199999999999985 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,16,exfoliant,7.682499885559082,9.022500038146973,True,9.834040699818747e-12,1.8946690559387207,7.832606323443743,8.842506718610109,7.5825000000000005,8.6025,8.1025,9.2225,1.0199999999999987,1.120000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,17,containing,9.382499694824219,10.322500228881836,True,7.338613224050494e-12,1.4138891696929932,9.053975217156005,9.999233994578377,8.1625,9.4025,9.1825,10.4825,1.2400000000000002,1.3000000000000007 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,18,jojoba,10.462499618530273,10.962499618530273,True,1.2090047712964846e-11,2.3293211460113525,10.153471942108839,10.632818750234803,9.3825,10.5425,9.862499999999999,10.9825,1.1600000000000001,1.120000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,19,and,11.142499923706055,11.362500190734863,True,6.746394415335644e-11,4.0,10.865587426362282,11.092469474696552,10.0025,11.1425,10.4825,11.362499999999999,1.1400000000000006,0.879999999999999 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,20,acai,11.442500114440918,11.582500457763672,True,3.173180121440744e-11,4.0,11.185082640911101,11.40354090768313,10.5425,11.4825,10.8825,11.6825,0.9399999999999995,0.7999999999999989 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,21,fruit,11.702500343322754,12.5625,True,3.936610586119382e-12,0.7584445476531982,11.542750165286042,12.199442610047605,10.9825,12.3225,11.362499999999999,12.5625,1.3399999999999999,1.200000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,22,oils,12.702500343322754,12.962499618530273,True,1.2418142035508506e-11,2.392533302307129,12.312457137645003,12.53612750534288,11.4225,12.7025,11.5825,12.9625,1.2800000000000011,1.3800000000000008 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,23,and,13.0024995803833,13.182499885559082,True,1.4112935646515279e-11,2.719059705734253,12.838743358082878,13.096751343161257,12.3825,13.1025,12.522499999999999,13.6025,0.7199999999999989,1.08 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,24,bamboo,13.5625,14.0625,True,6.703672773139546e-12,1.2915587425231934,13.33881562914513,13.769541054426064,12.7025,13.7425,13.1225,14.0625,1.0399999999999991,0.9399999999999995 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,25,and,14.142499923706055,14.362500190734863,True,8.946042691360123e-12,1.7235835790634155,13.864704428194672,14.118485920377335,13.2625,14.1425,13.5825,14.362499999999999,0.8800000000000008,0.7799999999999994 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,26,prickly,14.662500381469727,15.102499961853027,True,3.0223447442662144e-13,0.25,14.322593078254965,14.767185447932158,13.6625,14.6625,14.0625,15.1025,1.0,1.0399999999999991 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,27,bear,15.702500343322754,15.862500190734863,True,4.4243590995723947e-13,0.25,15.12995941247925,15.403516804053789,14.1425,15.7025,14.362499999999999,15.8625,1.5600000000000005,1.5000000000000018 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,28,extracts,15.942500114440918,16.322500228881836,True,1.6714140650946757e-13,0.25,15.53453527528529,16.17754300405277,14.6625,15.942499999999999,15.8225,16.4025,1.2799999999999994,0.5800000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,29,deliver,16.38249969482422,16.862499237060547,True,1.0248501672955809e-13,0.25,16.270850501054223,16.686664552019476,15.942499999999999,16.5025,16.282500000000002,16.8625,0.5600000000000023,0.5799999999999983 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,30,intense,16.90250015258789,17.322500228881836,True,7.725160029899147e-13,0.25,16.78021481124627,17.24047823295799,16.5025,16.962500000000002,17.0625,17.3225,0.46000000000000085,0.26000000000000156 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,31,"healing,",17.362499237060547,18.0625,True,4.7344672805685675e-14,0.25,17.313333485444595,18.013450491442516,17.122500000000002,17.622500000000002,17.942500000000003,18.0625,0.5,0.11999999999999744 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,32,while,18.122499465942383,18.38249969482422,True,1.305523180880619e-12,0.2515277862548828,18.121970016579592,18.382910922205134,18.122500000000002,18.122500000000002,18.3825,18.3825,0.0,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,33,smooth,18.462499618530273,18.922500610351562,True,6.875201363082395e-12,1.32460618019104,18.46245288047144,18.91253360874653,18.462500000000002,18.462500000000002,18.902500000000003,18.922500000000003,0.0,0.019999999999999574 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,34,granules,19.0625,19.622499465942383,True,6.236156729899633e-11,4.0,19.060714967031537,19.62165223075318,19.0625,19.0625,19.622500000000002,19.622500000000002,0.0,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,35,buff,19.702499389648438,20.342500686645508,True,5.486440295132677e-12,1.0570415258407593,19.89197269833418,20.35923287241106,19.7025,20.122500000000002,20.3425,20.402500000000003,0.4200000000000017,0.060000000000002274 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,36,away,20.6825008392334,20.962499618530273,True,1.5412348047111335e-12,0.29694101214408875,20.646430815099194,20.960356266677547,20.402500000000003,20.6825,20.962500000000002,20.962500000000002,0.2799999999999976,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,37,"dead,",20.982500076293945,21.142499923706055,True,7.757587107415365e-12,1.4946105480194092,20.986751725007654,21.14226403506907,20.9825,21.0025,21.142500000000002,21.142500000000002,0.019999999999999574,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,38,dry,21.202499389648438,21.38249969482422,True,5.190373738445109e-12,1.0,21.20398053353988,21.388662026269635,21.2025,21.2025,21.3825,21.442500000000003,0.0,0.060000000000002274 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,39,skin,21.482500076293945,21.622499465942383,True,6.363738702663824e-11,4.0,21.480180957830996,21.622991854845147,21.4825,21.4825,21.622500000000002,21.642500000000002,0.0,0.019999999999999574 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,40,cells.,21.642499923706055,21.842500686645508,True,2.2591633425106394e-11,4.0,21.64385152483771,21.84267698383198,21.642500000000002,21.6625,21.8425,21.8425,0.019999999999999574,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,0,The,0.24250000715255737,0.4025000035762787,True,1.1845117768582991e-11,4.0,0.1959311331958721,0.40235082912774206,0.0425,0.2425,0.4025,0.4025,0.19999999999999998,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,1,August,1.402500033378601,2.122499942779541,True,1.4341861032107772e-12,0.8177534341812134,1.4478832208215444,2.1272597779712865,1.4025,1.7625,2.1225,2.1625,0.3599999999999999,0.040000000000000036 --HeZS2-Prhc/2,-HeZS2-Prhc,2,2,jobs,2.322499990463257,2.6024999618530273,True,2.558615125325403e-11,4.0,2.320611337970802,2.6004673835909573,2.3225,2.3225,2.6025,2.6025,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,3,report,2.682499885559082,3.002500057220459,True,2.7453449669839758e-12,1.5653584003448486,2.68170298259558,3.009440710897623,2.6825,2.6825,3.0025,3.0825,0.0,0.08000000000000007 --HeZS2-Prhc/2,-HeZS2-Prhc,2,4,is,3.0625,3.1424999237060547,True,4.514987830221001e-13,0.2574384808540344,3.0812734603350624,3.1623839522789665,3.0625,3.3225000000000002,3.1425,3.4225,0.26000000000000023,0.2799999999999998 --HeZS2-Prhc/2,-HeZS2-Prhc,2,5,out,3.322499990463257,3.4625000953674316,True,2.090342601703682e-13,0.25,3.344295656509894,3.4967551728462754,3.1625,3.8625,3.4225,3.9825,0.6999999999999997,0.56 --HeZS2-Prhc/2,-HeZS2-Prhc,2,6,and,3.862499952316284,3.9825000762939453,True,8.034748539241521e-14,0.25,3.877242383787142,4.00469880646666,3.8625,4.062500000000001,3.9825,4.242500000000001,0.20000000000000107,0.2600000000000007 --HeZS2-Prhc/2,-HeZS2-Prhc,2,7,it,4.302499771118164,4.382500171661377,True,2.6032816310625484e-13,0.25,4.275837833615807,4.386597712761713,4.202500000000001,4.3025,4.3825,4.402500000000001,0.09999999999999964,0.020000000000000462 --HeZS2-Prhc/2,-HeZS2-Prhc,2,8,was,4.502500057220459,4.702499866485596,True,6.574726874042369e-12,3.7488200664520264,4.5026476132951725,4.702857723024613,4.5025,4.5025,4.702500000000001,4.702500000000001,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,9,seen,5.262499809265137,5.642499923706055,True,9.668883749203161e-12,4.0,5.26248580780262,5.643139038744664,5.2625,5.2625,5.6425,5.6625000000000005,0.0,0.020000000000000462 --HeZS2-Prhc/2,-HeZS2-Prhc,2,10,overall,6.202499866485596,6.722499847412109,True,2.0734386430021345e-12,1.1822465658187866,6.135176538397465,6.722530461705236,5.7625,6.202500000000001,6.7225,6.7225,0.4400000000000004,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,11,as,6.802499771118164,6.882500171661377,True,8.576729821144213e-13,0.4890334904193878,6.802730239021207,6.882715843694687,6.8025,6.8025,6.8825,6.8825,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,12,solid,6.942500114440918,7.202499866485596,True,8.672484930714874e-13,0.494493305683136,6.944034109898724,7.203577690574796,6.942500000000001,6.942500000000001,7.202500000000001,7.202500000000001,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,13,but,7.34250020980835,7.602499961853027,True,3.902187947768532e-11,4.0,7.342753371410023,7.602373466405716,7.3425,7.3425,7.602500000000001,7.602500000000001,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,14,not,7.662499904632568,7.802499771118164,True,2.746409328256705e-13,0.25,7.663526959768611,7.802856928638411,7.6625000000000005,7.6625000000000005,7.8025,7.8025,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,15,great.,7.862500190734863,8.082500457763672,True,2.701561847492928e-12,1.5403938293457031,7.8635557979644375,8.088359004012458,7.862500000000001,7.862500000000001,8.0825,8.1225,0.0,0.040000000000000924 --MeTTeMJBNc/0,-MeTTeMJBNc,0,0,"Okay,",0.9424999952316284,1.2424999475479126,True,3.710065674816798e-12,4.0,0.9432326596664109,1.2056759510520048,0.9225,1.0025,1.1025,1.2425,0.07999999999999996,0.1399999999999999 --MeTTeMJBNc/0,-MeTTeMJBNc,0,1,what,1.3025000095367432,1.502500057220459,True,1.2836673354322831e-14,1.0,1.2968934930611797,1.4994382940713709,1.3025,1.3025,1.5025,1.5025,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,2,happens,1.5625,2.122499942779541,True,1.206398787698083e-14,0.9398064017295837,1.558655575902781,2.121205916667169,1.5425,1.5625,2.1225,2.1225,0.020000000000000018,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,3,at,2.202500104904175,2.2825000286102295,True,2.9099730082044395e-15,0.25,2.1952647147514655,2.2751562874460314,2.1425,2.2025,2.2225,2.2825,0.06000000000000005,0.06000000000000005 --MeTTeMJBNc/0,-MeTTeMJBNc,0,4,this,2.322499990463257,2.4825000762939453,True,3.948112470227071e-15,0.3075650930404663,2.3262151924486174,2.488170818444105,2.3225,2.3625,2.4825,2.5225,0.040000000000000036,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,5,point,2.5225000381469727,2.882499933242798,True,3.802389345497915e-15,0.29621297121047974,2.5670758567670444,2.901260034406884,2.5225,2.6225,2.8825,2.9225,0.10000000000000009,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,6,after,3.202500104904175,3.422499895095825,True,4.455512734203183e-15,0.34709247946739197,3.1913281357894654,3.419919056829773,3.1825,3.2025,3.3825,3.4225,0.020000000000000018,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,7,we've,3.4625000953674316,3.682499885559082,True,1.9134102288709265e-11,4.0,3.4638915322588497,3.6825000001063266,3.4625,3.4625,3.6825,3.6825,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,8,taken,3.7225000858306885,4.002500057220459,True,3.697702849480861e-15,0.28805771470069885,3.7225000291431787,4.010663846188186,3.7225,3.7225,4.0025,4.0425,0.0,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,9,this,4.042500019073486,4.222499847412109,True,1.42208513980455e-14,1.1078299283981323,4.0553085178820885,4.231976591647393,4.0425,4.1025,4.2225,4.2625,0.05999999999999961,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,10,brief,4.462500095367432,4.702499866485596,True,1.0028261925834787e-14,0.7812196612358093,4.462443349973335,4.704718386141115,4.4625,4.4625,4.702500000000001,4.702500000000001,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,11,walk,4.78249979019165,5.142499923706055,True,6.560490405050842e-15,0.5110740065574646,4.813190037922622,5.151410892553026,4.7625,4.8825,5.1425,5.1825,0.1200000000000001,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,12,down,5.202499866485596,5.462500095367432,True,7.031535591414326e-14,4.0,5.203195360240823,5.463194049014288,5.202500000000001,5.202500000000001,5.4625,5.4625,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,13,memory,5.582499980926514,5.902500152587891,True,2.5533394842902624e-13,4.0,5.582483141326186,5.9028098326745795,5.5825000000000005,5.5825000000000005,5.902500000000001,5.902500000000001,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,14,"lane,",5.962500095367432,6.302499771118164,True,3.5930408496566424e-13,4.0,5.975316507304133,6.305343427476204,5.9625,6.142500000000001,6.3025,6.3425,0.1800000000000006,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,15,is,6.84250020980835,6.902500152587891,True,7.547921489214904e-12,4.0,6.8424998796201235,6.902499886452121,6.8425,6.8425,6.902500000000001,6.902500000000001,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,16,the,6.982500076293945,7.082499980926514,True,3.779567948558279e-16,0.25,6.9590939849105835,7.061940314452324,6.942500000000001,6.982500000000001,7.0425,7.0825000000000005,0.040000000000000036,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,17,presentation,7.122499942779541,7.862500190734863,True,3.29521523931872e-15,0.2567031979560852,7.122435235689702,7.862573230447805,7.1225000000000005,7.1225000000000005,7.862500000000001,7.862500000000001,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,18,of,7.902500152587891,7.962500095367432,True,4.660046220433811e-13,4.0,7.903398437119373,7.963914730371576,7.902500000000001,7.902500000000001,7.9625,7.9625,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,19,the,8.042499542236328,8.162500381469727,True,4.581407410560179e-14,3.5689990520477295,8.064463282027926,8.1771366928654,8.0425,8.1025,8.1625,8.202499999999999,0.05999999999999872,0.03999999999999915 --MeTTeMJBNc/0,-MeTTeMJBNc,0,20,gift.,8.242500305175781,8.582500457763672,True,2.0623882376197278e-12,4.0,8.242500389132172,8.582544997263367,8.2425,8.2425,8.5825,8.5825,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,0,It,0.3425000011920929,0.4424999952316284,True,1.1344515604694294e-11,4.0,0.34125700409427956,0.4421278439000176,0.3425,0.3425,0.4425,0.4425,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,1,just,0.5024999976158142,1.3224999904632568,True,7.597669889058967e-12,4.0,0.6357895630564467,1.3217086751335387,0.5025,1.0625,1.3225,1.3225,0.56,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,2,needs,1.402500033378601,1.6425000429153442,True,1.3798399814239637e-13,2.5175373554229736,1.4024617994614186,1.6427802038920662,1.4025,1.4025,1.6425,1.6425,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,3,to,1.722499966621399,1.7825000286102295,True,1.4288968093597795e-14,0.2607042193412781,1.722500342761648,1.7825003763940888,1.7225,1.7225,1.7825,1.7825,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,4,be,1.8424999713897705,1.9225000143051147,True,4.578755452046279e-13,4.0,1.8425000061154377,1.922500028437251,1.8425,1.8425,1.9224999999999999,1.9224999999999999,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,5,a,2.0225000381469727,2.0425000190734863,True,4.161020486503908e-11,4.0,2.022500304881204,2.0425003048812034,2.0225,2.0225,2.0425,2.0425,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,6,simple,2.2225000858306885,2.5625,True,1.4861399057989472e-13,2.7114830017089844,2.2205922795367226,2.562499455730098,2.2225,2.2225,2.5625,2.5625,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,7,"statement,",2.5824999809265137,3.202500104904175,True,3.476265990695485e-15,0.25,2.5825113951826295,3.202756423531097,2.5825,2.5825,3.2025,3.2025,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,8,and,3.2225000858306885,3.6424999237060547,True,1.5177185476813494e-14,0.27690985798835754,3.230022374213786,3.639560341276429,3.2225,3.2225,3.6025,3.6425,0.0,0.040000000000000036 --MeTTeMJBNc/13,-MeTTeMJBNc,13,9,then,3.6624999046325684,3.802500009536743,True,8.128732731618369e-16,0.25,3.6595605480135602,3.799753359197992,3.6225,3.6625,3.7625,3.8025,0.040000000000000036,0.040000000000000036 --MeTTeMJBNc/13,-MeTTeMJBNc,13,10,you,3.822499990463257,3.922499895095825,True,3.6388318573610245e-14,0.6639099717140198,3.82250569880218,3.9263450272879767,3.8225000000000002,3.8225000000000002,3.9225,3.9425,0.0,0.020000000000000018 --MeTTeMJBNc/13,-MeTTeMJBNc,13,11,present,3.9825000762939453,4.34250020980835,True,5.4407035314182374e-15,0.25,3.982499819073152,4.342058126051071,3.9825,3.9825,4.3425,4.3425,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,12,the,4.362500190734863,4.482500076293945,True,4.4931062586879914e-14,0.8197734951972961,4.362503466738968,4.482502178652516,4.362500000000001,4.362500000000001,4.482500000000001,4.482500000000001,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,13,gift.,4.522500038146973,4.862500190734863,True,6.468716756908091e-14,1.180226445198059,4.5225058016335105,4.862473226976465,4.522500000000001,4.522500000000001,4.862500000000001,4.862500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,0,Maybe,0.042500000447034836,0.3824999928474426,True,3.770235466600373e-14,0.9178741574287415,0.04382005310267007,0.37623012683122026,0.0425,0.0625,0.3425,0.3825,0.019999999999999997,0.03999999999999998 --MeTTeMJBNc/7,-MeTTeMJBNc,7,1,you,0.6025000214576721,0.7425000071525574,True,2.4669209383598734e-11,4.0,0.6014484098137175,0.7393633434603328,0.5425,0.6224999999999999,0.7025,0.7424999999999999,0.07999999999999996,0.039999999999999925 --MeTTeMJBNc/7,-MeTTeMJBNc,7,2,could,0.8025000095367432,1.1425000429153442,True,6.561903256006862e-14,1.5975133180618286,0.8039196735936374,1.1445578036248207,0.8025,0.8025,1.1425,1.1824999999999999,0.0,0.039999999999999813 --MeTTeMJBNc/7,-MeTTeMJBNc,7,3,find,1.5625,1.8424999713897705,True,4.50442504476567e-12,4.0,1.561707044410993,1.8424368444910948,1.5625,1.5625,1.8425,1.8425,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,4,a,1.9424999952316284,1.962499976158142,True,3.019118375441332e-12,4.0,1.9424658593762911,1.962465859376291,1.9425,1.9425,1.9625,1.9625,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,5,picture,2.002500057220459,2.362499952316284,True,2.292061499094538e-14,0.5580086708068848,2.0026882078534736,2.377115832890999,2.0025,2.0025,2.3625,2.4025,0.0,0.040000000000000036 --MeTTeMJBNc/7,-MeTTeMJBNc,7,6,of,2.422499895095825,2.4825000762939453,True,5.609922791062483e-13,4.0,2.4277161082497907,2.498121486901269,2.4225,2.4625,2.4825,2.5625,0.040000000000000036,0.08000000000000007 --MeTTeMJBNc/7,-MeTTeMJBNc,7,7,the,2.5824999809265137,2.682499885559082,True,8.198723275808228e-15,0.25,2.579265356662479,2.6792832693436104,2.5425,2.5825,2.6425,2.6825,0.040000000000000036,0.040000000000000036 --MeTTeMJBNc/7,-MeTTeMJBNc,7,8,couple,2.702500104904175,3.0425000190734863,True,2.822629892947244e-14,0.6871770024299622,2.699416899972035,3.0720880659627245,2.6625,2.7025,3.0425,3.1025,0.040000000000000036,0.06000000000000005 --MeTTeMJBNc/7,-MeTTeMJBNc,7,9,from,3.0824999809265137,3.322499990463257,True,3.6664202286728655e-15,0.25,3.126486933752154,3.3590129371154913,3.0825,3.1625,3.3225000000000002,3.4225,0.08000000000000007,0.09999999999999964 --MeTTeMJBNc/7,-MeTTeMJBNc,7,10,their,3.4625000953674316,3.922499895095825,True,1.8800955055493264e-14,0.45771440863609314,3.462535896135531,3.9223684291480794,3.4625,3.4625,3.9225,3.9225,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,11,wedding,3.942500114440918,4.442500114440918,True,4.442729279081903e-13,4.0,3.942471261233754,4.44238517073405,3.9425,3.9425,4.442500000000001,4.442500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,12,or,4.642499923706055,4.702499866485596,True,2.026530222657471e-14,0.4933643341064453,4.642065602925446,4.702431704556223,4.6425,4.6425,4.702500000000001,4.702500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,13,sometime,4.902500152587891,5.362500190734863,True,3.989258874409138e-14,0.9711959958076477,4.86246421614156,5.34648215111066,4.8025,4.902500000000001,5.322500000000001,5.362500000000001,0.10000000000000053,0.040000000000000036 --MeTTeMJBNc/7,-MeTTeMJBNc,7,14,in,5.382500171661377,5.442500114440918,True,2.355478347635699e-15,0.25,5.382091465093613,5.442262434223061,5.3825,5.3825,5.442500000000001,5.442500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,15,their,5.462500095367432,5.682499885559082,True,6.219688816305443e-15,0.25,5.462726237673856,5.682500012758481,5.4625,5.4625,5.6825,5.6825,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,16,"life,",5.722499847412109,6.042500019073486,True,9.027134074968135e-12,4.0,5.722500033125576,6.042499810582332,5.7225,5.7225,6.0425,6.0425,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,17,or,6.082499980926514,6.182499885559082,True,3.5410132400055805e-13,4.0,6.0825677398853735,6.184103345429151,6.0825000000000005,6.0825000000000005,6.1825,6.202500000000001,0.0,0.020000000000000462 --MeTTeMJBNc/7,-MeTTeMJBNc,7,18,maybe,6.262499809265137,6.462500095367432,True,1.1368580772955217e-12,4.0,6.2625000261871655,6.462574470158711,6.2625,6.2625,6.4625,6.4625,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,19,even,6.582499980926514,6.762499809265137,True,1.2781007805925948e-12,4.0,6.549329203691951,6.727341946384188,6.5025,6.5825000000000005,6.702500000000001,6.7625,0.08000000000000007,0.05999999999999961 --MeTTeMJBNc/7,-MeTTeMJBNc,7,20,a,6.802499771118164,6.822500228881836,True,7.715516593675975e-12,4.0,6.802500479833359,6.822500479833361,6.8025,6.8025,6.822500000000001,6.822500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,21,current,6.922500133514404,7.322500228881836,True,4.107573452921155e-14,1.0,6.913379676245901,7.315998196321345,6.8425,6.9225,7.2625,7.322500000000001,0.08000000000000007,0.0600000000000005 --MeTTeMJBNc/7,-MeTTeMJBNc,7,22,picture,7.362500190734863,7.822500228881836,True,5.0320147083459527e-14,1.2250577211380005,7.362392721111548,7.822399324363143,7.362500000000001,7.362500000000001,7.822500000000001,7.822500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,23,and,7.84250020980835,8.262499809265137,True,2.99166141620906e-14,0.7283281683921814,7.899502149493908,8.268061882040197,7.8425,8.202499999999999,8.2625,8.3025,0.35999999999999854,0.040000000000000924 --MeTTeMJBNc/7,-MeTTeMJBNc,7,24,have,8.282500267028809,8.422499656677246,True,3.7114418932919155e-14,0.9035606980323792,8.288246235381468,8.42975344190582,8.282499999999999,8.3225,8.4225,8.4625,0.040000000000000924,0.040000000000000924 --MeTTeMJBNc/7,-MeTTeMJBNc,7,25,it,8.482500076293945,8.542499542236328,True,3.107029336504169e-13,4.0,8.482487466508353,8.542487472734381,8.4825,8.4825,8.5425,8.5425,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,26,put,8.582500457763672,8.72249984741211,True,1.557926015841385e-14,0.37928134202957153,8.582500416372213,8.722595724165645,8.5825,8.5825,8.7225,8.7225,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,27,in,8.782500267028809,8.842499732971191,True,1.3454399236162342e-14,0.3275510370731354,8.771773292426657,8.83177402315951,8.7425,8.782499999999999,8.8025,8.8425,0.03999999999999915,0.03999999999999915 --MeTTeMJBNc/7,-MeTTeMJBNc,7,28,a,8.862500190734863,8.882499694824219,True,1.2338593417381832e-13,3.003864288330078,8.862501392958345,8.882501392958348,8.862499999999999,8.862499999999999,8.8825,8.8825,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,29,beautiful,8.922499656677246,9.342499732971191,True,1.4680022876554372e-14,0.3573891818523407,8.922499999807949,9.342591336440261,8.9225,8.9225,9.3425,9.3425,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,30,frame.,9.40250015258789,9.842499732971191,True,2.176588553490233e-12,4.0,9.402788993999224,9.842498636710845,9.4025,9.4025,9.8425,9.8425,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,0,So,0.5625,0.7225000262260437,True,5.925417491739471e-13,1.2584871053695679,0.6104202949033318,0.7450530455063744,0.5625,0.7025,0.7224999999999999,0.8225,0.14,0.10000000000000009 --RfYyzHpjk4/11,-RfYyzHpjk4,11,1,get,1.0425000190734863,1.162500023841858,True,2.0000498653083287e-12,4.0,1.0352633548141328,1.157345829119535,1.0425,1.0425,1.1624999999999999,1.1624999999999999,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,2,your,1.222499966621399,1.462499976158142,True,3.061544344862971e-13,0.6502349972724915,1.2204229605923966,1.4590623617756209,1.2225,1.2225,1.4625,1.4625,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,3,friends,1.5425000190734863,1.8624999523162842,True,4.583915644523588e-14,0.25,1.5348954679823508,1.8560549491335907,1.5225,1.5425,1.8225,1.8625,0.020000000000000018,0.040000000000000036 --RfYyzHpjk4/11,-RfYyzHpjk4,11,4,lined,1.8825000524520874,2.0824999809265137,True,4.106438279694036e-12,4.0,1.8794013903302467,2.108598210328863,1.8825,1.8825,2.0825,2.1425,0.0,0.06000000000000005 --RfYyzHpjk4/11,-RfYyzHpjk4,11,5,"up,",2.1024999618530273,2.1624999046325684,True,2.6779603925011775e-14,0.25,2.132709439233833,2.1930046249082458,2.1025,2.1625,2.1625,2.2225,0.06000000000000005,0.06000000000000005 --RfYyzHpjk4/11,-RfYyzHpjk4,11,6,get,2.242500066757202,2.362499952316284,True,1.191502972675007e-11,4.0,2.236394349302283,2.3507283319084338,2.2025,2.2425,2.3025,2.3625,0.040000000000000036,0.05999999999999961 --RfYyzHpjk4/11,-RfYyzHpjk4,11,7,your,2.4024999141693115,2.5625,True,8.169106125062442e-13,1.7350194454193115,2.3922253609813726,2.5522580570477773,2.3625,2.4025,2.5225,2.5625,0.040000000000000036,0.040000000000000036 --RfYyzHpjk4/11,-RfYyzHpjk4,11,8,business,2.5824999809265137,2.9625000953674316,True,2.262260609961833e-13,0.4804767966270447,2.5807347769870606,2.959944910812453,2.5825,2.5825,2.9625,2.9625,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,9,colleagues,2.9825000762939453,3.502500057220459,True,1.0240140332906655e-11,4.0,2.980355311251425,3.5007947034242104,2.9825,2.9825,3.5025,3.5025,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,10,lined,3.5425000190734863,3.762500047683716,True,1.963730014101911e-13,0.4170725345611572,3.540848027479565,3.7591541239086355,3.5425,3.5425,3.7225,3.7625,0.0,0.040000000000000036 --RfYyzHpjk4/11,-RfYyzHpjk4,11,11,up,3.802500009536743,3.862499952316284,True,2.895594734039081e-13,0.6149893403053284,3.8011925535359468,3.8611960341643234,3.8025,3.8025,3.8625,3.8625,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,12,and,3.9024999141693115,4.042500019073486,True,4.708365858654973e-13,1.0,3.9015861885760605,4.035220914868527,3.9025,3.9025,4.022500000000001,4.0425,0.0,0.019999999999999574 --RfYyzHpjk4/11,-RfYyzHpjk4,11,13,have,4.122499942779541,4.262499809265137,True,1.2910945099489646e-13,0.27421286702156067,4.1214453840766305,4.266966117222006,4.1225000000000005,4.1225000000000005,4.2625,4.282500000000001,0.0,0.020000000000000462 --RfYyzHpjk4/11,-RfYyzHpjk4,11,14,a,4.302499771118164,4.322500228881836,True,1.385675343863529e-14,0.25,4.302061162786527,4.322061162786528,4.3025,4.3025,4.322500000000001,4.322500000000001,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,15,good,4.442500114440918,4.762499809265137,True,2.0156643281560305e-12,4.0,4.440412188277476,4.761898046857764,4.4225,4.442500000000001,4.7625,4.7625,0.020000000000000462,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,16,time.,4.84250020980835,5.042500019073486,True,3.757925221004044e-11,4.0,4.8422608498843465,5.0426273492741265,4.8425,4.8425,5.0425,5.0425,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,0,It's,0.48249998688697815,0.5824999809265137,True,1.4532529624133872e-09,4.0,0.29136950661161193,0.581764070401369,0.0025000000000000005,0.4825,0.5824999999999999,0.5824999999999999,0.48,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,1,"pretty,",0.6025000214576721,0.9624999761581421,True,1.087615783025575e-13,0.44241130352020264,0.602707872197142,0.9658396862173649,0.6024999999999999,0.6024999999999999,0.9624999999999999,1.0025,0.0,0.040000000000000036 --RfYyzHpjk4/8,-RfYyzHpjk4,8,2,pretty,0.9825000166893005,1.3025000095367432,True,1.235450950527392e-13,0.502546489238739,0.9901859507557427,1.3023208099968344,0.9824999999999999,1.0425,1.3025,1.3025,0.06000000000000005,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,3,easy,1.3424999713897705,1.5225000381469727,True,1.3342861429448127e-13,0.54274982213974,1.3425056143864518,1.5225050163964364,1.3425,1.3425,1.5225,1.5225,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,4,to,1.5625,1.6425000429153442,True,1.0378338101836148e-14,0.25,1.5678174351393568,1.645624593702227,1.5625,1.6225,1.6425,1.6824999999999999,0.06000000000000005,0.039999999999999813 --RfYyzHpjk4/8,-RfYyzHpjk4,8,5,do,1.7024999856948853,1.8025000095367432,True,5.799786649204886e-13,2.359189033508301,1.702500208385816,1.8025001764746025,1.7025,1.7025,1.8025,1.8025,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,6,but,1.8825000524520874,2.002500057220459,True,9.461694292910595e-15,0.25,1.8798579047304698,2.0026025026774623,1.8625,1.8825,2.0025,2.0025,0.020000000000000018,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,7,you,2.0425000190734863,2.1624999046325684,True,3.8030705971567325e-13,1.5469814538955688,2.0420726874845885,2.161792162433309,2.0425,2.0425,2.1625,2.1625,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,8,got,2.202500104904175,2.302500009536743,True,2.636872518295766e-14,0.25,2.2024992287053813,2.3025002711087743,2.2025,2.2025,2.3025,2.3025,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,9,to,2.3424999713897705,2.4024999141693115,True,5.37920436546966e-14,0.25,2.342231385558277,2.402500610322223,2.3425,2.3425,2.4025,2.4025,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,10,set,2.442500114440918,2.6024999618530273,True,8.723621326180153e-13,3.5485222339630127,2.4425000521424303,2.6025000862422343,2.4425,2.4425,2.6025,2.6025,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,11,up,2.682499885559082,2.762500047683716,True,1.8668987796649494e-12,4.0,2.6824947822719523,2.762491370232272,2.6825,2.6825,2.7625,2.7625,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,12,those,2.8424999713897705,3.0225000381469727,True,4.680378805875518e-13,1.9038455486297607,2.8410879425174627,3.021367438715059,2.8425,2.8425,3.0225,3.0225,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,13,numbers,3.0425000190734863,3.382499933242798,True,3.799781940917041e-13,1.54564368724823,3.0424927295428903,3.382497615488898,3.0425,3.0425,3.3825,3.3825,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,14,ahead,3.422499895095825,3.622499942779541,True,2.4583815343669213e-13,1.0,3.4224973293396848,3.623183535894426,3.4225,3.4225,3.6225,3.6225,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,15,of,3.862499952316284,3.942500114440918,True,4.153858246952469e-12,4.0,3.8543357537960174,3.9395657009074068,3.7025,3.8625,3.9225,3.9425,0.1599999999999997,0.020000000000000018 --RfYyzHpjk4/8,-RfYyzHpjk4,8,16,time.,3.9825000762939453,4.522500038146973,True,2.1563972124865466e-13,0.8771613240242004,3.987601471873267,4.522151300490132,3.9825,3.9825,4.522500000000001,4.522500000000001,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,0,You,0.5024999976158142,0.6025000214576721,True,3.148869782542557e-13,0.5932096838951111,0.4641556042763561,0.6001832615386288,0.2025,0.5025,0.5824999999999999,0.6024999999999999,0.29999999999999993,0.020000000000000018 --RfYyzHpjk4/2,-RfYyzHpjk4,2,1,can,0.6225000023841858,0.7225000262260437,True,5.3242779109174965e-12,4.0,0.6224966097439738,0.722793687589131,0.6224999999999999,0.6224999999999999,0.7224999999999999,0.7224999999999999,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,2,make,0.762499988079071,0.9024999737739563,True,9.325262784187771e-12,4.0,0.7624999520131719,0.902500457618032,0.7625,0.7625,0.9025,0.9025,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,3,a,0.9225000143051147,0.9424999952316284,True,5.164016322846063e-14,0.25,0.9225004608316872,0.9425004608316874,0.9225,0.9225,0.9425,0.9425,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,4,conference,0.9624999761581421,1.3624999523162842,True,2.2286452197595175e-12,4.0,0.9625004885635444,1.36250528925742,0.9624999999999999,0.9624999999999999,1.3625,1.3625,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,5,call,1.3825000524520874,1.5225000381469727,True,1.5288667684285051e-12,2.8802037239074707,1.3825053283407196,1.5225057774228026,1.3825,1.3825,1.5225,1.5225,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,6,but,1.5625,1.662500023841858,True,3.5439503436908437e-13,0.6676381826400757,1.5625044965401071,1.6625261687119126,1.5625,1.5625,1.6625,1.6625,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,7,it,1.6825000047683716,1.7424999475479126,True,1.332392182695763e-11,4.0,1.682547174299906,1.7425472119934313,1.6824999999999999,1.6824999999999999,1.7425,1.7425,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,8,takes,1.7625000476837158,1.9424999952316284,True,1.057303116586139e-11,4.0,1.762547757390463,1.9664725912063723,1.7625,1.7625,1.9425,2.0025,0.0,0.06000000000000005 --RfYyzHpjk4/2,-RfYyzHpjk4,2,9,quite,1.9824999570846558,2.182499885559082,True,8.99263163933739e-11,4.0,1.9998589771697997,2.2030919285306947,1.9825,2.0225,2.1825,2.2425,0.040000000000000036,0.06000000000000005 --RfYyzHpjk4/2,-RfYyzHpjk4,2,10,a,2.202500104904175,2.2225000858306885,True,6.754598508122711e-14,0.25,2.2377339721451066,2.257733972145132,2.2025,2.2825,2.2225,2.3025,0.08000000000000007,0.08000000000000007 --RfYyzHpjk4/2,-RfYyzHpjk4,2,11,bit,2.242500066757202,2.3424999713897705,True,5.308189868560853e-13,1.0,2.2777869381841898,2.3777903079199927,2.2425,2.3225,2.3425,2.4225,0.07999999999999963,0.08000000000000007 --RfYyzHpjk4/2,-RfYyzHpjk4,2,12,of,2.362499952316284,2.422499895095825,True,2.1314319666870807e-13,0.4015364944934845,2.402322583593929,2.4623288813321236,2.3625,2.4825,2.4225,2.5425,0.1200000000000001,0.1200000000000001 --RfYyzHpjk4/2,-RfYyzHpjk4,2,13,"planning,",2.442500114440918,2.802500009536743,True,8.171992948871956e-14,0.25,2.491071073576032,2.876893066167285,2.4425,2.5825,2.8025,2.9825,0.14000000000000012,0.17999999999999972 --RfYyzHpjk4/2,-RfYyzHpjk4,2,14,well,2.8424999713897705,2.9825000762939453,True,1.5282908402344808e-13,0.28791186213493347,2.9062972987384375,3.0472606332625687,2.8225,3.0025,2.9825,3.1425,0.18000000000000016,0.16000000000000014 --RfYyzHpjk4/2,-RfYyzHpjk4,2,15,not,3.002500057220459,3.1024999618530273,True,1.5203597658174778e-13,0.28641775250434875,3.0757937390130943,3.190636335135603,3.0025,3.1825,3.1025,3.3225000000000002,0.18000000000000016,0.2200000000000002 --RfYyzHpjk4/2,-RfYyzHpjk4,2,16,a,3.122499942779541,3.1424999237060547,True,5.646774280804179e-12,4.0,3.2204657819657214,3.2404657819657814,3.1225,3.3625,3.1425,3.3825,0.23999999999999977,0.23999999999999977 --RfYyzHpjk4/2,-RfYyzHpjk4,2,17,lot,3.182499885559082,3.322499990463257,True,2.893943290842478e-14,0.25,3.284991537547337,3.429295019753589,3.1825,3.4625,3.3025,3.6225,0.2799999999999998,0.31999999999999984 --RfYyzHpjk4/2,-RfYyzHpjk4,2,18,of,3.362499952316284,3.442500114440918,True,7.363686049810525e-12,4.0,3.5028824001740775,3.5721282708109663,3.3625,3.7225,3.4225,3.7825,0.3600000000000003,0.3600000000000003 --RfYyzHpjk4/2,-RfYyzHpjk4,2,19,planning,3.4625000953674316,3.882499933242798,True,4.804686379658585e-13,0.9051459431648254,3.6038900191011245,4.016084286438504,3.4625,3.8225000000000002,3.8825,4.242500000000001,0.3600000000000003,0.36000000000000076 --RfYyzHpjk4/2,-RfYyzHpjk4,2,20,it's,3.9825000762939453,4.162499904632568,True,4.394659983142368e-12,4.0,4.101904154902036,4.272254213499532,3.9825,4.3025,4.1625000000000005,4.442500000000001,0.3200000000000003,0.28000000000000025 --RfYyzHpjk4/2,-RfYyzHpjk4,2,21,pretty,4.28249979019165,4.582499980926514,True,3.0015099021681035e-13,0.5654488801956177,4.3755106093167,4.666803222682432,4.282500000000001,4.522500000000001,4.5825000000000005,4.822500000000001,0.2400000000000002,0.2400000000000002 --RfYyzHpjk4/2,-RfYyzHpjk4,2,22,easy,4.602499961853027,4.802499771118164,True,1.904853228276715e-13,0.35885176062583923,4.695244949059977,4.955538123762717,4.602500000000001,4.862500000000001,4.8025,5.202500000000001,0.2599999999999998,0.40000000000000036 --RfYyzHpjk4/2,-RfYyzHpjk4,2,23,to,5.182499885559082,5.262499809265137,True,6.027319702367473e-12,4.0,5.204784669311505,5.2778043991771115,5.1825,5.242500000000001,5.2625,5.3025,0.0600000000000005,0.040000000000000036 --RfYyzHpjk4/2,-RfYyzHpjk4,2,24,do.,5.322500228881836,5.382500171661377,True,6.078794152070133e-12,4.0,5.321774080990339,5.382084433603389,5.322500000000001,5.322500000000001,5.3825,5.3825,0.0,0.0 --UUCSKoHeMA/0,-UUCSKoHeMA,0,0,I,1.2625000476837158,1.2825000286102295,True,2.3367625542891624e-11,4.0,1.2625116119279252,1.282511611927958,1.2625,1.2625,1.2825,1.2825,0.0,0.0 --UUCSKoHeMA/0,-UUCSKoHeMA,0,1,mentioned,1.3424999713897705,1.9824999570846558,True,4.0802847212084714e-12,4.0,1.3425128709237222,1.9809538948415186,1.3425,1.3425,1.9025,2.0025,0.0,0.09999999999999987 --UUCSKoHeMA/0,-UUCSKoHeMA,0,2,a,2.0625,2.0824999809265137,True,1.506490071578881e-11,4.0,2.0622694400311663,2.082269440031173,2.0625,2.0625,2.0825,2.0825,0.0,0.0 --UUCSKoHeMA/0,-UUCSKoHeMA,0,3,word,2.122499942779541,2.442500114440918,True,2.1203724269013707e-13,0.25,2.122499953395551,2.4426974718634,2.1225,2.1225,2.4425,2.4425,0.0,0.0 --UUCSKoHeMA/0,-UUCSKoHeMA,0,4,in,2.5225000381469727,2.702500104904175,True,7.434052830788962e-13,0.8034750819206238,2.584625951615478,2.729305029738961,2.5225,2.6825,2.7025,2.8225,0.16000000000000014,0.11999999999999966 --UUCSKoHeMA/0,-UUCSKoHeMA,0,5,the,2.802500009536743,2.9024999141693115,True,3.7196781028578374e-13,0.40202412009239197,2.8131936626394123,2.9242575602781646,2.8025,2.9225,2.9025,3.1425,0.11999999999999966,0.2400000000000002 --UUCSKoHeMA/0,-UUCSKoHeMA,0,6,last,2.922499895095825,3.202500104904175,True,1.1070697310266997e-12,1.1965248584747314,2.9627315540783767,3.240944821647639,2.9225,3.3625,3.2025,3.6225,0.43999999999999995,0.41999999999999993 --UUCSKoHeMA/0,-UUCSKoHeMA,0,7,clip,3.362499952316284,3.622499942779541,True,4.579349486416584e-12,4.0,3.3990179872718147,3.655597836538976,3.3625,3.7625,3.6225,3.9825,0.40000000000000036,0.3599999999999999 --UUCSKoHeMA/0,-UUCSKoHeMA,0,8,called,3.682499885559082,4.042500019073486,True,3.3319351509623896e-13,0.36011672019958496,3.721438804024425,4.085925612332038,3.6825,4.1025,4.0425,4.522500000000001,0.41999999999999993,0.4800000000000004 --UUCSKoHeMA/0,-UUCSKoHeMA,0,9,"'monotone',",4.102499961853027,4.962500095367432,True,2.0431005498533494e-10,4.0,4.159192072906399,5.018715781241488,4.1025,4.562500000000001,4.9625,5.5025,0.46000000000000085,0.54 --UUCSKoHeMA/0,-UUCSKoHeMA,0,10,and,5.042500019073486,5.542500019073486,True,1.1826091050447934e-13,0.25,5.092525670092953,5.5518883043060585,5.0425,5.5825000000000005,5.522500000000001,5.702500000000001,0.54,0.17999999999999972 --UUCSKoHeMA/0,-UUCSKoHeMA,0,11,I,5.582499980926514,5.602499961853027,True,7.732506995816735e-11,4.0,5.606475013180348,5.626475013180339,5.5825000000000005,5.8425,5.602500000000001,5.862500000000001,0.2599999999999998,0.2599999999999998 --UUCSKoHeMA/0,-UUCSKoHeMA,0,12,want,5.622499942779541,5.862500190734863,True,5.911523983000155e-13,0.6389195919036865,5.658054633318278,5.887746639201687,5.6225000000000005,5.8825,5.862500000000001,6.102500000000001,0.2599999999999998,0.2400000000000002 --UUCSKoHeMA/0,-UUCSKoHeMA,0,13,to,6.022500038146973,6.102499961853027,True,3.9711322338126243e-13,0.4292013645172119,6.043176971260156,6.1243497078647895,6.022500000000001,6.2625,6.102500000000001,6.3425,0.23999999999999932,0.23999999999999932 --UUCSKoHeMA/0,-UUCSKoHeMA,0,14,talk,6.202499866485596,6.422500133514404,True,3.8754988259358247e-13,0.41886529326438904,6.231623776195076,6.445624482990393,6.202500000000001,6.522500000000001,6.4225,6.6825,0.3200000000000003,0.2599999999999998 --UUCSKoHeMA/0,-UUCSKoHeMA,0,15,about,6.462500095367432,6.682499885559082,True,1.5894668122917038e-13,0.25,6.486890400692468,6.713296690480922,6.442500000000001,6.742500000000001,6.6825,6.982500000000001,0.2999999999999998,0.3000000000000007 --UUCSKoHeMA/0,-UUCSKoHeMA,0,16,that,6.922500133514404,7.182499885559082,True,2.632421711168398e-11,4.0,6.914653107260793,7.186191805860277,6.8825,7.0425,7.1825,7.2225,0.16000000000000014,0.040000000000000036 --UUCSKoHeMA/0,-UUCSKoHeMA,0,17,briefly.,7.28249979019165,7.682499885559082,True,2.8430669277157428e-12,3.0727968215942383,7.266711655048036,7.682512766971292,7.2225,7.282500000000001,7.6825,7.6825,0.0600000000000005,0.0 --ri04Z7vwnc/0,-ri04Z7vwnc,0,0,going,0.0024999999441206455,0.18250000476837158,True,5.097316457813861e-10,1.124098539352417,0.024398001194100988,0.2924496876375644,0.0025000000000000005,0.0825,0.2025,0.4225,0.08,0.21999999999999997 --ri04Z7vwnc/0,-ri04Z7vwnc,0,1,to,0.20250000059604645,0.26249998807907104,True,4.4608275406865516e-10,0.983735203742981,0.3627568244570231,0.44950644808341267,0.2425,0.5025,0.3225,0.6024999999999999,0.25999999999999995,0.2799999999999999 --ri04Z7vwnc/0,-ri04Z7vwnc,0,2,get,0.32249999046325684,0.48249998688697815,True,4.529311370404798e-10,0.9988377690315247,0.5278888053411263,0.7010825325354665,0.3625,0.7025,0.5025,0.9824999999999999,0.34,0.48 --ri04Z7vwnc/0,-ri04Z7vwnc,0,3,married,0.6625000238418579,1.402500033378601,True,4.135821685125052e-10,0.9120624661445618,0.8307791916512809,1.3855115536618379,0.5625,1.1824999999999999,1.0425,1.5825,0.6199999999999999,0.54 --ri04Z7vwnc/0,-ri04Z7vwnc,0,4,or,1.4225000143051147,1.5225000381469727,True,4.414137388941697e-10,0.9734387397766113,1.4655582811151127,1.5522323688932067,1.2025,1.6824999999999999,1.3025,1.7625,0.48,0.45999999999999996 --ri04Z7vwnc/0,-ri04Z7vwnc,0,5,are,1.6825000047683716,1.8025000095367432,True,6.311645650569631e-10,1.3918914794921875,1.633247732983863,1.7776768200035915,1.3825,1.8225,1.5425,1.9625,0.43999999999999995,0.41999999999999993 --ri04Z7vwnc/0,-ri04Z7vwnc,0,6,already,1.8224999904632568,2.0824999809265137,True,4.5398515502448333e-10,1.0011621713638306,1.84121973750598,2.23857055338298,1.6425,2.0025,2.0425,2.3825,0.3599999999999999,0.33999999999999986 --ri04Z7vwnc/0,-ri04Z7vwnc,0,7,married,2.182499885559082,2.7225000858306885,True,4.677451204138094e-10,1.0315066576004028,2.3092338374060906,2.7215355637832084,2.1425,2.4425,2.6625,2.7425,0.2999999999999998,0.08000000000000007 --ri04Z7vwnc/2,-ri04Z7vwnc,2,0,most,0.042500000447034836,0.2824999988079071,True,3.2669008557389967e-12,0.9973236918449402,0.03533530632279461,0.2915191734222421,0.0025000000000000005,0.0825,0.1625,0.3425,0.08,0.18000000000000002 --ri04Z7vwnc/2,-ri04Z7vwnc,2,1,people,0.3824999928474426,0.8424999713897705,True,3.1715836554258026e-12,0.9682251214981079,0.37951785526938786,0.8040705105858642,0.3425,0.4225,0.6224999999999999,0.8825,0.07999999999999996,0.26 --ri04Z7vwnc/2,-ri04Z7vwnc,2,2,think,0.8824999928474426,1.3025000095367432,True,1.2376642679473582e-12,0.377835750579834,0.880992595479909,1.2249863793838607,0.6825,0.9225,0.8825,1.3425,0.24,0.4600000000000001 --ri04Z7vwnc/2,-ri04Z7vwnc,2,3,it'll,1.402500033378601,1.7825000286102295,True,2.4132093545681244e-10,4.0,1.3095751724347637,1.530903636824568,0.9225,1.4025,1.3025,1.7825,0.4800000000000001,0.48 --ri04Z7vwnc/2,-ri04Z7vwnc,2,4,sort,1.8624999523162842,2.302500009536743,True,4.662647004605169e-12,1.4234188795089722,1.606748818471774,1.8158763538890752,1.3825,1.8625,1.6824999999999999,2.0825,0.48,0.40000000000000013 --ri04Z7vwnc/2,-ri04Z7vwnc,2,5,itself,2.322499990463257,2.5425000190734863,True,2.164716756541951e-12,0.660847544670105,1.8871830849169948,2.3170325866349173,1.8425,2.1625,2.1625,2.4625,0.32000000000000006,0.2999999999999998 --ri04Z7vwnc/2,-ri04Z7vwnc,2,6,"out,",2.5625,2.702500104904175,True,1.7914156486345534e-12,0.5468856692314148,2.372672664661856,2.4993875621536525,2.2225,2.4825,2.3625,2.6025,0.2599999999999998,0.2400000000000002 --ri04Z7vwnc/2,-ri04Z7vwnc,2,7,"""we'll",2.7225000858306885,2.862499952316284,True,7.49354953089032e-11,4.0,2.5485761578230206,2.735453096562189,2.4025,2.6225,2.5825,2.8025,0.2200000000000002,0.2200000000000002 --ri04Z7vwnc/2,-ri04Z7vwnc,2,8,discuss,2.882499933242798,3.1424999237060547,True,1.244877790261556e-12,0.38003790378570557,2.769634619145668,3.0783780752313397,2.6025,2.8425,2.9625,3.1425,0.23999999999999977,0.18000000000000016 --ri04Z7vwnc/2,-ri04Z7vwnc,2,9,it,3.1624999046325684,3.2225000858306885,True,3.2844339227511288e-12,1.002676248550415,3.1168374269808057,3.182508050326935,3.0225,3.1625,3.1025,3.2425,0.14000000000000012,0.14000000000000012 --ri04Z7vwnc/2,-ri04Z7vwnc,2,10,"later,""",3.242500066757202,3.422499895095825,True,6.055528040810332e-12,1.8486393690109253,3.213576245227149,3.4151321092630402,3.1425,3.2825,3.3425,3.5025,0.14000000000000012,0.16000000000000014 --ri04Z7vwnc/2,-ri04Z7vwnc,2,11,it's,3.442500114440918,3.5425000190734863,True,1.0349616302862685e-10,4.0,3.451680714649028,3.576908987243269,3.3625,3.5425,3.4825,3.7225,0.18000000000000016,0.2400000000000002 --ri04Z7vwnc/2,-ri04Z7vwnc,2,12,not,3.5625,3.702500104904175,True,1.5995723625858438e-12,0.4883195161819458,3.6143920521257327,3.7361958558643753,3.5225,3.7825,3.6425,3.9025,0.26000000000000023,0.2599999999999998 --ri04Z7vwnc/2,-ri04Z7vwnc,2,13,something,3.742500066757202,4.422500133514404,True,2.2604188486263777e-12,0.6900635957717896,3.777755699176747,4.436873888478261,3.7025,3.9625,4.402500000000001,4.4625,0.2599999999999998,0.05999999999999961 --ri04Z7vwnc/2,-ri04Z7vwnc,2,14,too,4.84250020980835,5.022500038146973,True,2.015573125069281e-10,4.0,4.78410811013368,4.995321876065659,4.482500000000001,4.8425,4.942500000000001,5.022500000000001,0.35999999999999943,0.08000000000000007 --ri04Z7vwnc/2,-ri04Z7vwnc,2,15,important,5.042500019073486,5.542500019073486,True,5.276962460748491e-12,1.6109578609466553,5.042487498132745,5.543272605933794,5.0425,5.0425,5.5425,5.5425,0.0,0.0 --ri04Z7vwnc/5,-ri04Z7vwnc,5,0,"""",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --ri04Z7vwnc/5,-ri04Z7vwnc,5,1,So,0.02250000089406967,0.08250000327825546,True,5.531704955208383e-11,4.0,0.06342992056271926,0.14143729395540497,0.0225,0.2225,0.0825,0.28250000000000003,0.2,0.2 --ri04Z7vwnc/5,-ri04Z7vwnc,5,2,I,0.2224999964237213,0.24250000715255737,True,1.6104359599339313e-12,0.4176913797855377,0.303244394241129,0.3232443942411471,0.2225,0.5225,0.2425,0.5425,0.29999999999999993,0.3 --ri04Z7vwnc/5,-ri04Z7vwnc,5,3,think,0.5224999785423279,0.8025000095367432,True,8.501941013185077e-13,0.25,0.5468122658585075,0.7930804306754985,0.5225,0.6425,0.7424999999999999,0.8424999999999999,0.12,0.09999999999999998 --ri04Z7vwnc/5,-ri04Z7vwnc,5,4,it's,0.8824999928474426,1.0824999809265137,True,3.3593516857166605e-11,4.0,0.8827280561963028,1.0827702576220137,0.8825,0.8825,1.0825,1.0825,0.0,0.0 --ri04Z7vwnc/5,-ri04Z7vwnc,5,5,important,1.162500023841858,1.662500023841858,True,6.419234935273188e-12,1.6649274826049805,1.1539397855459228,1.6509180681704647,1.1225,1.2225,1.5425,1.7225,0.09999999999999987,0.17999999999999994 --ri04Z7vwnc/5,-ri04Z7vwnc,5,6,to,2.322499990463257,2.4625000953674316,True,3.383222955261056e-12,0.8774909973144531,2.098755755525236,2.258181340358846,1.6625,2.3225,1.7225,2.4625,0.6599999999999997,0.74 --ri04Z7vwnc/5,-ri04Z7vwnc,5,7,discuss,2.5225000381469727,2.862499952316284,True,2.7650663876610526e-12,0.717162549495697,2.4394224473580834,2.792790803676593,2.3025,2.5225,2.6625,2.8625,0.21999999999999975,0.19999999999999973 --ri04Z7vwnc/5,-ri04Z7vwnc,5,8,finances,2.9024999141693115,3.322499990463257,True,4.327905221701567e-12,1.1225088834762573,2.866859719198108,3.2956421389284056,2.8025,2.9025,3.2225,3.4225,0.09999999999999964,0.19999999999999973 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,0,But,0.5625,0.7024999856948853,True,2.1161275043455469e-13,1.0,0.5496646247778796,0.702164602009161,0.5425,0.5625,0.7025,0.7025,0.020000000000000018,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,1,the,0.8424999713897705,1.402500033378601,True,6.26251817330975e-13,2.959423780441284,0.8420559143387191,1.406478256178167,0.8225,0.9225,1.4025,1.4224999999999999,0.09999999999999998,0.019999999999999796 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,2,hardest,1.4824999570846558,1.8424999713897705,True,1.8739304270647128e-13,0.8855470418930054,1.4825189926134468,1.8518094365299989,1.4825,1.4825,1.8425,1.8825,0.0,0.040000000000000036 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,3,thing,2.0225000381469727,2.262500047683716,True,8.017903425612885e-14,0.378895103931427,2.020780201902659,2.2611176783830214,2.0225,2.0225,2.2625,2.2625,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,4,in,2.322499990463257,2.422499895095825,True,6.493226502269842e-14,0.3068447709083557,2.320865793192878,2.422328440790031,2.3225,2.3225,2.4225,2.4825,0.0,0.06000000000000005 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,5,life,2.6024999618530273,3.202500104904175,True,2.1193372848771902e-13,1.0015168190002441,2.600471167683731,3.1896348019912866,2.6025,2.6025,3.2025,3.2025,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,6,to,3.262500047683716,3.3424999713897705,True,6.471526873413902e-14,0.3058193325996399,3.2608217425607964,3.3421634910399116,3.2625,3.2625,3.3425,3.3425,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,7,"learn,",3.4024999141693115,3.6624999046325684,True,3.52563437428996e-13,1.6660784482955933,3.4027175839840345,3.66329101504063,3.4025,3.4025,3.6625,3.6625,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,8,is,3.802500009536743,3.882499933242798,True,1.9704459689341008e-13,0.9311565160751343,3.80444740581124,3.884674702639147,3.8025,3.8025,3.8825,3.8825,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,9,to,3.9825000762939453,4.042500019073486,True,4.005100016826052e-13,1.892655372619629,3.973553794154936,4.04829152917298,3.9425,4.0025,4.0025,4.1225000000000005,0.0600000000000005,0.1200000000000001 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,10,lose,4.222499847412109,4.502500057220459,True,8.517983193789824e-12,4.0,4.222786002066446,4.50249136136073,4.2225,4.2225,4.5025,4.5025,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,0,they,0.042500000447034836,0.24250000715255737,True,1.2452759092992927e-12,1.144152045249939,0.035296410608304025,0.24507960324400196,0.0225,0.0625,0.2225,0.3025,0.04,0.07999999999999999 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,1,have,0.2824999988079071,0.5425000190734863,True,9.242065663120358e-13,0.8491554260253906,0.30454331855208594,0.5638608046103386,0.28250000000000003,0.3425,0.5425,0.6024999999999999,0.06,0.05999999999999994 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,2,"doubt,",0.7024999856948853,0.9225000143051147,True,2.921006535608339e-13,0.2683803141117096,0.7016751768490946,0.9389234569792527,0.7025,0.7025,0.9225,0.9824999999999999,0.0,0.05999999999999994 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,3,they,1.0824999809265137,1.3025000095367432,True,2.414696914643244e-12,2.218608856201172,1.0824749234219044,1.3025575291805016,1.0825,1.0825,1.3025,1.3025,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,4,have,1.402500033378601,1.8424999713897705,True,1.4843991921059674e-12,1.3638570308685303,1.480454771743787,1.8441928116392656,1.4025,1.5825,1.8425,1.8625,0.17999999999999994,0.020000000000000018 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,5,"fear,",1.8624999523162842,2.0824999809265137,True,6.6919907115714494e-12,4.0,1.8672921296696843,2.08198887608499,1.8625,1.9025,2.0825,2.0825,0.040000000000000036,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,6,they,2.122499942779541,2.2825000286102295,True,7.773068213395851e-13,0.7141848206520081,2.1272049813809994,2.285785138186709,2.1225,2.1625,2.2625,2.3225,0.040000000000000036,0.05999999999999961 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,7,lose,2.302500009536743,2.4825000762939453,True,2.6457091725773374e-12,2.430861711502075,2.3155930936973776,2.470759112376862,2.3025,2.3425,2.4425,2.5025,0.03999999999999959,0.06000000000000005 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,8,in,2.502500057220459,2.5824999809265137,True,1.923453930852137e-11,4.0,2.5130796331063547,2.582583582398577,2.4825,2.5225,2.5825,2.5825,0.040000000000000036,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,9,a,2.622499942779541,2.6424999237060547,True,2.0894479722810555e-12,1.9197721481323242,2.622205727070115,2.6422057270701145,2.6225,2.6225,2.6425,2.6425,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,10,Ferrari,2.6624999046325684,3.0824999809265137,True,4.712646288831945e-13,0.43299511075019836,2.680606250238554,3.083650895889502,2.6625,2.7025,3.0825,3.0825,0.040000000000000036,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,11,"race,",3.1024999618530273,3.242500066757202,True,9.314907786078797e-13,0.8558481335639954,3.103672235105714,3.2438796035307136,3.1025,3.1025,3.2425,3.2425,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,12,or,3.262500047683716,3.3424999713897705,True,4.8605338504037476e-11,4.0,3.264086176296221,3.344207480546319,3.2625,3.2625,3.3425,3.3425,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,13,they,3.422499895095825,3.682499885559082,True,2.5745997347947913e-11,4.0,3.414911960971616,3.675032305819791,3.3625,3.4225,3.6425,3.6825,0.06000000000000005,0.040000000000000036 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,14,lose,3.7825000286102295,3.942500114440918,True,7.575693002602468e-13,0.6960501074790955,3.7616974887882315,3.920602352332232,3.7225,3.7825,3.8825,3.9425,0.06000000000000005,0.06000000000000005 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,15,in,4.102499961853027,4.162499904632568,True,3.397961165912955e-12,3.122026205062866,4.0719655131892,4.156779322130816,3.9225,4.1025,4.1225000000000005,4.1825,0.18000000000000016,0.05999999999999961 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,16,a,4.202499866485596,4.222499847412109,True,5.129190258287841e-13,0.47126689553260803,4.214525556613766,4.23452555661377,4.202500000000001,4.2225,4.2225,4.242500000000001,0.019999999999999574,0.020000000000000462 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,17,"race,",4.522500038146973,4.78249979019165,True,8.075219095834973e-13,0.741946280002594,4.523863360616268,4.783031253821107,4.522500000000001,4.522500000000001,4.782500000000001,4.782500000000001,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,18,and,5.182499885559082,5.322500228881836,True,2.4335565572235207e-13,0.25,5.142079904620651,5.314934950906367,4.8025,5.1825,5.242500000000001,5.3425,0.3799999999999999,0.09999999999999964 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,19,then,5.422500133514404,5.5625,True,1.1495937936398942e-13,0.25,5.406993962189282,5.55731323856257,5.3025,5.4225,5.522500000000001,5.562500000000001,0.1200000000000001,0.040000000000000036 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,20,they,5.582499980926514,5.762499809265137,True,1.4886081950438168e-11,4.0,5.582500046565337,5.7607591757751635,5.5825000000000005,5.5825000000000005,5.742500000000001,5.7625,0.0,0.019999999999999574 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,21,just,5.78249979019165,5.922500133514404,True,1.4744784169670733e-12,1.3547419309616089,5.782499967123763,5.922500201867736,5.782500000000001,5.782500000000001,5.9225,5.9225,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,22,give,5.962500095367432,6.162499904632568,True,2.479647346828595e-13,0.25,5.962426812392603,6.162335078542887,5.9625,5.9625,6.1625000000000005,6.1625000000000005,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,23,up,6.642499923706055,6.702499866485596,True,5.418672780442557e-13,0.49786439538002014,6.61413642460844,6.68734623928928,6.5825000000000005,6.642500000000001,6.6625000000000005,6.702500000000001,0.0600000000000005,0.040000000000000036 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,0,I,0.0024999999441206455,0.02250000089406967,True,8.043445250127679e-12,4.0,0.0032990285590143776,0.023299028559016173,0.0025000000000000005,0.0025000000000000005,0.0225,0.0225,0.0,0.0 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,1,have,0.08250000327825546,0.42250001430511475,True,3.238681241593544e-12,4.0,0.06717380191828314,0.32357469353523305,0.0425,0.1225,0.1825,0.4225,0.07999999999999999,0.24 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,2,been,0.5224999785423279,0.7425000071525574,True,1.3483621771198662e-12,2.748244285583496,0.3951901849667031,0.6275678443671301,0.2225,0.5225,0.4225,0.7424999999999999,0.29999999999999993,0.31999999999999995 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,3,beat,0.7825000286102295,1.3424999713897705,True,1.4721684700284843e-13,0.3000587522983551,0.6899705310710409,1.079888780943723,0.5225,1.1225,0.7424999999999999,1.3425,0.6000000000000001,0.6000000000000001 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,4,up,1.3624999523162842,1.4225000143051147,True,6.7126171158019e-13,1.3681719303131104,1.1659770402041265,1.3613713122992432,0.7825,1.3625,1.2425,1.4224999999999999,0.5800000000000001,0.17999999999999994 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,5,and,1.462499976158142,1.6024999618530273,True,2.956292166362423e-13,0.602554202079773,1.4205737927255522,1.5482913645398126,1.3225,1.4625,1.4224999999999999,1.6025,0.1399999999999999,0.18000000000000016 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,6,put,1.7424999475479126,1.9424999952316284,True,5.560912516057448e-13,1.1334303617477417,1.6589005375801091,1.842611262769312,1.4625,1.7425,1.6025,2.0025,0.28,0.3999999999999999 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,7,down,2.0425000190734863,2.442500114440918,True,8.62298581605532e-14,0.25,1.9559455563234505,2.310078948483982,1.7425,2.0425,2.0025,2.4425,0.30000000000000004,0.43999999999999995 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,8,and,2.5824999809265137,2.702500104904175,True,1.233855411505308e-13,0.25148555636405945,2.5125746937182574,2.693039630571944,2.3225,2.6025,2.6025,2.7025,0.28000000000000025,0.10000000000000009 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,9,everything,2.8424999713897705,3.9825000762939453,True,5.351290564099484e-14,0.25,2.8684364082492393,4.011810221786433,2.8425,2.8425,3.9825,3.9825,0.0,0.0 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,10,that,4.902500152587891,5.122499942779541,True,5.439115737665423e-12,4.0,4.89797082813463,5.13239484504563,4.902500000000001,4.9625,5.102500000000001,5.1625000000000005,0.05999999999999961,0.05999999999999961 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,11,can,5.162499904632568,5.34250020980835,True,4.8560754619926885e-14,0.25,5.188221176465659,5.361559915166783,5.1625000000000005,5.3825,5.2625,5.5025,0.21999999999999975,0.2400000000000002 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,12,go,5.422500133514404,5.502500057220459,True,1.665226523296301e-13,0.3394080102443695,5.454580933666803,5.539748928566693,5.4225,5.602500000000001,5.5025,5.702500000000001,0.1800000000000006,0.20000000000000018 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,13,"wrong,",5.602499961853027,6.202499866485596,True,4.906267449603097e-13,1.0,5.65194480101253,6.236338881255996,5.602500000000001,6.1225000000000005,6.142500000000001,6.8025,0.5199999999999996,0.6599999999999993 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,14,has,6.702499866485596,6.802499771118164,True,7.809464338225103e-13,1.5917322635650635,6.7180709524906215,6.819550494578619,6.702500000000001,6.942500000000001,6.8025,7.062500000000001,0.2400000000000002,0.2600000000000007 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,15,gone,6.862500190734863,7.0625,True,6.044744240742139e-14,0.25,6.884818614020353,7.0893755044610485,6.862500000000001,7.142500000000001,7.062500000000001,7.4225,0.28000000000000025,0.35999999999999943 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,16,wrong,7.28249979019165,7.582499980926514,True,6.222165794753098e-13,1.2682076692581177,7.27870407791671,7.598153728057217,7.142500000000001,7.482500000000001,7.5825000000000005,7.8025,0.33999999999999986,0.21999999999999975 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,17,for,7.662499904632568,7.802499771118164,True,2.2805403941152103e-13,0.46482187509536743,7.688982736794739,7.83358602588385,7.6625000000000005,8.022499999999999,7.8025,8.202499999999999,0.35999999999999854,0.3999999999999986 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,18,me,8.142499923706055,8.262499809265137,True,6.399445209859245e-12,4.0,8.149809329284016,8.26073298564643,8.1425,8.2425,8.2425,8.362499999999999,0.09999999999999964,0.11999999999999922 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,0,I,0.02250000089406967,0.042500000447034836,True,2.5145097115597537e-10,4.0,0.022581619903897353,0.04258161990389827,0.0225,0.0225,0.0425,0.0425,0.0,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,1,have,0.10249999910593033,0.32249999046325684,True,5.2369983349898064e-11,4.0,0.10275901230479557,0.3234319644678103,0.10250000000000001,0.10250000000000001,0.3225,0.3225,0.0,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,2,lost,0.4025000035762787,0.8025000095367432,True,4.580078720797798e-13,1.0,0.5590338658826747,0.8117400282480208,0.4025,0.6224999999999999,0.7825,0.8624999999999999,0.21999999999999992,0.07999999999999996 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,3,at,0.9024999737739563,1.0425000190734863,True,8.964015510773415e-13,1.9571750164031982,0.937330588114691,1.0553245531559798,0.9025,1.0225,1.0425,1.0825,0.12,0.040000000000000036 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,4,everything,1.3224999904632568,2.0824999809265137,True,2.723942219787917e-13,0.5947369933128357,1.323579559459154,2.0836404722492556,1.3225,1.3425,2.0825,2.0825,0.020000000000000018,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,5,you,2.182499885559082,2.362499952316284,True,1.1392326155785365e-13,0.25,2.166667845312565,2.364468039998308,2.1425,2.1825,2.3625,2.3625,0.040000000000000036,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,6,can,2.5225000381469727,2.702500104904175,True,1.9086615222888882e-14,0.25,2.5339093670209367,2.762821360795334,2.4825,2.6225,2.7025,2.8025,0.14000000000000012,0.10000000000000009 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,7,"imagine,",3.002500057220459,3.382499933242798,True,3.490432942676591e-11,4.0,2.9764326793531897,3.3827679885419752,2.8825,3.0025,3.3825,3.3825,0.1200000000000001,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,8,seriously,3.442500114440918,4.022500038146973,True,2.7026327681888007e-13,0.5900843739509583,3.4400904252806708,4.025070808901028,3.4225,3.4425,4.022500000000001,4.022500000000001,0.020000000000000018,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,0,I,0.02250000089406967,0.042500000447034836,True,6.969980148596733e-11,4.0,0.02678660345931201,0.04678660345934946,0.0225,0.10250000000000001,0.0425,0.1225,0.08000000000000002,0.07999999999999999 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,1,used,0.3824999928474426,0.5425000190734863,True,3.881826110552211e-11,4.0,0.35018109647684703,0.5409254689733906,0.10250000000000001,0.3825,0.5425,0.5425,0.28,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,2,to,0.5625,0.6225000023841858,True,5.419344834001194e-11,4.0,0.5617484066259408,0.6218305196632183,0.5625,0.5625,0.6224999999999999,0.6224999999999999,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,3,work,0.6424999833106995,0.7825000286102295,True,1.2994504560923104e-13,0.25,0.6439510150136057,0.7956763526640003,0.6425,0.6425,0.7825,0.8225,0.0,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,4,for,0.862500011920929,0.9825000166893005,True,5.919409927501729e-13,0.25,0.8703185545526655,0.9934280704856465,0.8624999999999999,0.9025,0.9824999999999999,1.0425,0.040000000000000036,0.06000000000000005 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,5,Warren,1.0425000190734863,1.3624999523162842,True,2.076905487868874e-12,0.55162113904953,1.0499859516799703,1.362546218062043,1.0425,1.0825,1.3625,1.3625,0.040000000000000036,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,6,"Avis,",1.4424999952316284,1.6825000047683716,True,5.145964557251581e-11,4.0,1.441602626222574,1.6811464831337093,1.4425,1.4425,1.6824999999999999,1.6824999999999999,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,7,the,1.7024999856948853,1.9824999570846558,True,7.724032351219545e-12,2.0514845848083496,1.7012855259737578,1.9803489043794023,1.7025,1.7025,1.9825,1.9825,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,8,guy,2.0225000381469727,2.1424999237060547,True,1.3621283788400884e-11,3.6177804470062256,2.0202241581787876,2.141641797729933,2.0225,2.0225,2.1425,2.1425,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,9,who,2.1624999046325684,2.262500047683716,True,1.5639521622201613e-10,4.0,2.1618504322195777,2.2658099313974582,2.1625,2.1625,2.2625,2.2825,0.0,0.020000000000000018 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,10,started,2.302500009536743,2.5625,True,3.76403162111183e-12,0.9997177720069885,2.301007069455703,2.562164512343847,2.3025,2.3025,2.5625,2.5625,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,11,Avis,2.6024999618530273,2.7825000286102295,True,1.6464942256821935e-11,4.0,2.6021628830951244,2.7711338037144446,2.6025,2.6025,2.7425,2.7825,0.0,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,12,Rent,2.802500009536743,3.002500057220459,True,3.706247982127042e-12,0.9843705892562866,2.799981994850341,2.994845622631275,2.7825,2.8225,2.9625,3.0025,0.03999999999999959,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,13,a,3.0225000381469727,3.0425000190734863,True,6.146569798276547e-13,0.25,3.014930168179437,3.034930168179444,2.9825,3.0225,3.0025,3.0425,0.040000000000000036,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,14,"Car,",3.0625,3.1624999046325684,True,6.6249657669492645e-12,1.7595750093460083,3.062430806341528,3.1624378407080567,3.0625,3.0625,3.1625,3.1625,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,15,and,3.182499885559082,3.302500009536743,True,1.0457030207022822e-12,0.27773621678352356,3.1825015875733373,3.2987676736960596,3.1825,3.1825,3.2825,3.3025,0.0,0.020000000000000018 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,16,he,3.3424999713897705,3.4024999141693115,True,1.69514088096262e-12,0.4502253532409668,3.3416142026375746,3.402494144256177,3.3425,3.3425,3.4025,3.4025,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,17,said,3.442500114440918,3.622499942779541,True,7.551881754490342e-13,0.25,3.442500007657619,3.6225138017818206,3.4425,3.4425,3.6225,3.6225,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,18,to,3.702500104904175,3.762500047683716,True,1.4711676497793265e-14,0.25,3.6993621761372792,3.7652132808598355,3.6625,3.7425,3.7625,3.8025,0.08000000000000007,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,19,me,3.882499933242798,3.9625000953674316,True,3.844036200462142e-12,1.0209667682647705,3.882508254724707,3.9624960997761027,3.8825,3.8825,3.9625,3.9625,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,20,one,4.022500038146973,4.182499885559082,True,4.8639130917349505e-12,1.2918436527252197,4.022505987413522,4.182510136166168,4.022500000000001,4.022500000000001,4.1825,4.1825,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,21,"day,",4.922500133514404,5.042500019073486,True,8.347099920980039e-12,2.2169697284698486,4.921261247051621,5.042292247022992,4.9225,4.9225,5.0425,5.0425,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,22,"""Robert,",5.162499904632568,5.442500114440918,True,3.313618097359333e-13,0.25,5.157729190413535,5.437921281142969,5.1225000000000005,5.1625000000000005,5.402500000000001,5.442500000000001,0.040000000000000036,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,23,"""what's",5.502500057220459,5.802499771118164,True,2.250095726061274e-12,0.5976200699806213,5.493928773068351,5.787613456780464,5.4225,5.5025,5.6625000000000005,5.8025,0.08000000000000007,0.13999999999999968 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,24,the,5.822500228881836,5.922500133514404,True,2.3816506492663203e-13,0.25,5.8117797173499195,5.916011727614231,5.7225,5.822500000000001,5.862500000000001,5.9225,0.10000000000000053,0.05999999999999961 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,25,purpose,5.942500114440918,6.28249979019165,True,1.1442309773290749e-11,3.0390501022338867,5.94251287266894,6.286029911061819,5.942500000000001,5.942500000000001,6.282500000000001,6.322500000000001,0.0,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,26,of,6.362500190734863,6.442500114440918,True,3.76615709105077e-12,1.0002822875976562,6.3626166902528665,6.4423161972541,6.362500000000001,6.362500000000001,6.442500000000001,6.442500000000001,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,27,business,6.462500095367432,6.822500228881836,True,2.3635134648036793e-12,0.6277435421943665,6.462403766646239,6.821232931946638,6.4625,6.4625,6.8025,6.822500000000001,0.0,0.020000000000000462 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,0,But,0.042500000447034836,0.2224999964237213,True,6.265210247304032e-13,0.2663658559322357,0.04595421401102171,0.19960157519223576,0.0025000000000000005,0.10250000000000001,0.1225,0.28250000000000003,0.1,0.16000000000000003 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,1,I,0.26249998807907104,0.2824999988079071,True,4.512839754666764e-13,0.25,0.3053911111772659,0.3253911111772663,0.2625,0.6625,0.28250000000000003,0.6825,0.39999999999999997,0.39999999999999997 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,2,just,0.6625000238418579,1.402500033378601,True,9.192134033109145e-12,3.9080424308776855,0.6529190020049375,1.3804359304662248,0.5625,1.0025,1.2825,1.4025,0.43999999999999995,0.1200000000000001 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,3,kept,1.4824999570846558,1.6425000429153442,True,2.3521070074278283e-12,1.0,1.4731671348482018,1.634072464256062,1.3425,1.4825,1.5425,1.6425,0.1399999999999999,0.10000000000000009 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,4,going,1.7024999856948853,2.262500047683716,True,6.741932099402215e-12,2.866337299346924,1.7043089382694716,2.2765034438543106,1.7025,1.7025,2.2625,2.3025,0.0,0.040000000000000036 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,0,"""",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,1,And,0.12250000238418579,0.24250000715255737,True,5.430787416993432e-11,2.974587917327881,0.13211225042789249,0.25671074886264156,0.1225,0.1625,0.2425,0.3425,0.04000000000000001,0.10000000000000003 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,2,I,0.32249999046325684,0.3425000011920929,True,2.400587367779039e-11,1.3148661851882935,0.3668057281520321,0.3868057281532408,0.3225,0.5225,0.3425,0.5425,0.19999999999999996,0.19999999999999996 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,3,"said,",0.5224999785423279,0.7225000262260437,True,2.0516449650287427e-11,1.1237410306930542,0.5449670600283814,0.7418337194054421,0.5225,0.6625,0.6825,0.9025,0.14,0.21999999999999997 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,4,"""Oh,",0.8224999904632568,0.9024999737739563,True,1.968307895144905e-12,0.25,0.8328528775409426,0.9142629965513558,0.7825,0.9824999999999999,0.9025,1.0425,0.19999999999999996,0.14 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,5,make,0.9825000166893005,1.2625000476837158,True,1.3614405436346289e-10,4.0,0.9900671560139473,1.2648433600608693,0.9225,1.0825,1.2625,1.2625,0.16000000000000003,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,6,a,1.3424999713897705,1.3624999523162842,True,1.5998102148584437e-11,0.8762589693069458,1.342202284908815,1.36220228490884,1.3425,1.3425,1.3625,1.3625,0.0,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,7,"profit,",1.3825000524520874,1.8624999523162842,True,1.0078236856170264e-11,0.5520120859146118,1.3882990201214693,1.8605397868103712,1.3825,1.4224999999999999,1.8625,1.8625,0.039999999999999813,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,8,and,1.902500033378601,2.002500057220459,True,2.559467611809585e-12,0.25,1.9024795079759782,2.002565711600564,1.9025,1.9025,2.0025,2.0025,0.0,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,9,all,2.0625,2.1624999046325684,True,1.0053620262684415e-11,0.5506637692451477,2.0632850578056883,2.1639649354818697,2.0625,2.0625,2.1625,2.1625,0.0,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,10,these,2.202500104904175,2.382499933242798,True,1.4980766193523065e-12,0.25,2.2033509669407514,2.3840730034618955,2.2025,2.2025,2.3825,2.3825,0.0,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,11,other,2.4825000762939453,2.742500066757202,True,4.946603543443118e-11,2.7093875408172607,2.4818356624233613,2.7605229751379516,2.4825,2.4825,2.7425,2.7825,0.0,0.040000000000000036 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,12,things,2.762500047683716,3.122499942779541,True,4.533611680512806e-11,2.4831807613372803,2.795081236142752,3.131064936710411,2.7625,2.8425,3.1225,3.1625,0.07999999999999963,0.040000000000000036 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,0,it,0.48249998688697815,0.5625,True,2.2540422653372083e-11,0.62372887134552,0.48282851572821867,0.5627058230843515,0.4825,0.4825,0.5625,0.5625,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,1,comes,0.5824999809265137,0.8224999904632568,True,4.899627231713666e-11,1.3558037281036377,0.6122922089193678,0.8890952566150813,0.5824999999999999,0.6224999999999999,0.8025,1.1025,0.040000000000000036,0.30000000000000004 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,2,with,0.862500011920929,1.1024999618530273,True,5.177407114143051e-11,1.432669758796692,1.0343328058467511,1.2714862000398628,0.8624999999999999,2.0425,1.1025,2.3825,1.1800000000000002,1.2799999999999998 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,3,the,1.162500023841858,2.1424999237060547,True,1.6085549559008427e-11,0.4451123774051666,1.7534948157635741,2.317185306234759,1.1624999999999999,2.5025,2.1425,2.8225,1.34,0.6799999999999997 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,4,assistance,2.242500066757202,3.4625000953674316,True,5.7605586967213185e-11,1.5940369367599487,2.4377709212653507,3.5095971681953815,2.2425,2.9625,3.4025,3.6225,0.7199999999999998,0.2200000000000002 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,5,and,3.4825000762939453,3.5824999809265137,True,1.789636147608853e-11,0.4952203929424286,4.0819872324546465,4.286099413915198,3.4425,4.5425,3.5625,4.8425,1.1000000000000005,1.2800000000000002 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,6,agitation,4.542500019073486,5.402500152587891,True,5.5601932780202645e-11,1.5385926961898804,4.775625557132102,5.481898357717567,4.5425,5.0825000000000005,5.402500000000001,5.6625000000000005,0.54,0.2599999999999998 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,7,of,5.482500076293945,5.602499961853027,True,9.260540945188467e-11,2.5625369548797607,5.806335671074142,5.92265445706831,5.482500000000001,6.482500000000001,5.602500000000001,6.6225000000000005,1.0,1.0199999999999996 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,8,states,6.482500076293945,6.982500076293945,True,5.0344433483173745e-11,1.3931094408035278,6.551695071292202,6.893363711888713,6.482500000000001,6.8025,6.7225,7.0825000000000005,0.3199999999999994,0.3600000000000003 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,9,and,7.102499961853027,7.822500228881836,True,1.104089805692432e-10,3.055189609527588,6.97998107934995,7.432471150361345,6.8025,7.102500000000001,6.982500000000001,7.822500000000001,0.3000000000000007,0.8399999999999999 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,10,"regimes,",7.882500171661377,8.262499809265137,True,5.572348485416434e-11,1.5419561862945557,7.716863671576262,8.18288138010086,7.102500000000001,7.8825,8.1025,8.2625,0.7799999999999994,0.16000000000000014 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,11,Yesterday,8.302499771118164,8.842499732971191,True,9.80091181823628e-12,0.2712065875530243,8.243045019532943,8.802590784018477,8.1825,8.3025,8.782499999999999,8.8425,0.120000000000001,0.0600000000000005 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,12,Iran,9.302499771118164,9.442500114440918,True,9.267232814469395e-12,0.25643885135650635,9.28248466227814,9.428928899971622,9.3025,9.3025,9.442499999999999,9.442499999999999,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,13,announced,9.462499618530273,9.922499656677246,True,4.382011420389631e-11,1.2125712633132935,9.470828595188868,9.948668430727986,9.4625,9.5025,9.9225,10.0625,0.03999999999999915,0.14000000000000057 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,14,that,10.082500457763672,10.22249984741211,True,6.737973894110905e-12,0.25,10.112171472976984,10.317274030543697,10.0825,10.0825,10.2225,10.3425,0.0,0.11999999999999922 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,15,it,11.022500038146973,11.082500457763672,True,1.3952581277842935e-11,0.3860898017883301,10.98394079716456,11.063832396543807,10.9025,11.022499999999999,11.0425,11.1625,0.11999999999999922,0.11999999999999922 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,16,will,11.162500381469727,11.362500190734863,True,4.104548298466959e-11,1.135792851448059,11.193551591725347,11.379601260949299,11.1425,11.2625,11.362499999999999,11.5025,0.11999999999999922,0.14000000000000057 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,17,finance,11.422499656677246,11.762499809265137,True,2.8811382099536154e-11,0.7972561120986938,11.4400876158457,11.820485161778358,11.4225,11.5625,11.7625,11.9225,0.14000000000000057,0.16000000000000014 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,18,the,11.802499771118164,11.922499656677246,True,3.0040948895138087e-11,0.8312801718711853,11.866457503420186,11.978970130729623,11.782499999999999,11.942499999999999,11.9025,12.0825,0.16000000000000014,0.17999999999999972 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,19,families,11.942500114440918,12.382499694824219,True,3.755114622028266e-11,1.039099097251892,12.034518519610577,12.432086350730916,11.942499999999999,12.1025,12.3825,12.6425,0.16000000000000014,0.2599999999999998 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,20,of,12.462499618530273,12.542499542236328,True,2.093970396382927e-12,0.25,12.519979089225814,12.600236932231414,12.4625,12.7225,12.5425,12.8225,0.2599999999999998,0.27999999999999936 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,21,the,12.6225004196167,12.822500228881836,True,1.0107663404174128e-12,0.25,12.683711846600158,12.879735837706036,12.6225,13.6025,12.8225,13.7625,0.9799999999999986,0.9399999999999995 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,22,terrorists,13.582500457763672,14.102499961853027,True,3.2193830934446055e-11,0.890853762626648,13.48057177307044,14.12193960941006,12.9225,13.8025,14.022499999999999,14.3225,0.8800000000000008,0.3000000000000007 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,23,and,14.242500305175781,14.442500114440918,True,6.540654823306014e-11,1.8099017143249512,14.243990294329773,14.439934456951706,14.1625,14.3825,14.3225,14.5025,0.22000000000000064,0.17999999999999972 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,24,murderers,14.602499961853027,15.202500343322754,True,5.3976757091733774e-11,1.4936214685440063,14.574493863698141,15.18575901971097,14.3825,14.6025,15.1625,15.2425,0.21999999999999886,0.08000000000000007 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,25,this,15.282500267028809,15.662500381469727,True,9.564676828333063e-11,2.646696090698242,15.271233298767404,15.507784351694662,15.2225,15.2825,15.4625,15.6625,0.0600000000000005,0.1999999999999993 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,26,shows,15.762499809265137,16.162500381469727,True,1.070308425221711e-10,2.9617111682891846,15.638094435743163,15.937968171367956,15.4825,15.7625,15.7425,16.1625,0.27999999999999936,0.4200000000000017 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,27,that,16.22249984741211,16.422500610351562,True,1.4821713301138573e-10,4.0,16.03565573699944,16.203248310703422,15.7625,16.2225,16.1625,16.4025,0.46000000000000085,0.23999999999999844 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,28,"Iran,",16.502500534057617,16.72249984741211,True,4.210410839422529e-11,1.1650867462158203,16.26544869858648,16.458665519694218,16.2225,16.5025,16.3625,16.7225,0.28000000000000114,0.35999999999999943 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,29,even,17.5625,17.762500762939453,True,9.684803653486895e-11,2.6799371242523193,16.61354563803406,16.827777791718276,16.5025,17.5625,16.7225,17.762500000000003,1.0599999999999987,1.0400000000000027 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,30,after,17.822500228881836,18.002500534057617,True,3.2281885498086638e-12,0.25,17.554003479065504,17.80643906509736,17.5225,17.8225,17.762500000000003,18.0025,0.3000000000000007,0.23999999999999844 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,31,the,18.0625,18.202499389648438,True,1.899481093248223e-11,0.5256162285804749,17.860950102720363,18.01428792989417,17.8225,18.0625,17.962500000000002,18.2025,0.23999999999999844,0.23999999999999844 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,32,nuclear,18.282499313354492,18.622499465942383,True,7.600799330209629e-12,0.25,18.086117188736683,18.544572324433087,18.0625,18.282500000000002,18.5225,18.622500000000002,0.22000000000000242,0.10000000000000142 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,33,"agreement,",18.6825008392334,19.502500534057617,True,1.3309903179603566e-10,3.683058977127075,18.689252254938246,19.452889571682917,18.622500000000002,18.6825,19.422500000000003,19.5025,0.05999999999999872,0.0799999999999983 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,34,is,19.6825008392334,19.762500762939453,True,6.084657951099803e-12,0.25,19.557316484633294,19.728965377547198,19.4825,19.6825,19.7025,19.762500000000003,0.1999999999999993,0.060000000000002274 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,35,continuing,20.202499389648438,20.662500381469727,True,5.529132013348814e-11,1.5299975872039795,20.186404995029186,20.684973994192696,20.2025,20.2025,20.6625,20.742500000000003,0.0,0.08000000000000185 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,36,to,21.502500534057617,21.582500457763672,True,9.673984946445557e-10,4.0,21.502510645887668,21.582509151446565,21.5025,21.5025,21.582500000000003,21.582500000000003,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,37,aid,21.662500381469727,21.782499313354492,True,4.237645789828548e-10,4.0,21.662507714897142,21.788616675529326,21.6625,21.6625,21.782500000000002,21.8225,0.0,0.03999999999999915 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,38,"terrorism,",21.842500686645508,22.342500686645508,True,9.071070283805938e-12,0.2510107457637787,21.838637629170766,22.378070868609647,21.802500000000002,21.8625,22.3425,22.442500000000003,0.05999999999999872,0.10000000000000142 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,39,including,22.6825008392334,23.162500381469727,True,6.269147527493413e-12,0.25,22.67344056762576,23.179853509455537,22.6825,22.6825,23.1625,23.262500000000003,0.0,0.10000000000000142 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,40,Palestinian,23.342500686645508,24.042499542236328,True,1.5516138721083372e-11,0.4293558895587921,23.341317527591546,24.04398988161638,23.3425,23.3425,24.0025,24.082500000000003,0.0,0.08000000000000185 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,41,"terrorism,",24.102500915527344,24.582500457763672,True,2.0350023749449164e-11,0.5631170868873596,24.10249792843023,24.583562825209636,24.102500000000003,24.102500000000003,24.582500000000003,24.582500000000003,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,42,Hezbollah,24.702499389648438,25.262500762939453,True,8.40829662840381e-12,0.25,24.68922904288737,25.2904581805427,24.642500000000002,24.7025,25.2025,25.3825,0.05999999999999872,0.17999999999999972 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,43,terrorism,25.322500228881836,26.002500534057617,True,7.838881627142413e-11,2.1691415309906006,25.346885974875732,26.004277391559278,25.3225,25.422500000000003,26.0025,26.0025,0.10000000000000142,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,44,and,26.842500686645508,26.982500076293945,True,3.270300696911477e-11,0.9049434661865234,26.818193885178502,26.98777704569297,26.802500000000002,26.8425,26.922500000000003,27.0225,0.03999999999999915,0.09999999999999787 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,45,its,27.082500457763672,27.202499389648438,True,3.613817578518308e-11,1.0,27.057459753673502,27.203179205761856,26.962500000000002,27.082500000000003,27.1625,27.242500000000003,0.120000000000001,0.08000000000000185 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,46,assistance,27.282499313354492,28.122499465942383,True,1.4203026255099616e-10,3.9302000999450684,27.289326195794334,28.100023559728623,27.282500000000002,27.282500000000002,28.062500000000004,28.142500000000002,0.0,0.0799999999999983 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,47,to,28.202499389648438,28.282499313354492,True,7.287294032098934e-12,0.25,28.202197475208703,28.282161719627336,28.2025,28.2025,28.282500000000002,28.282500000000002,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,48,Hamas.,28.342500686645508,28.782499313354492,True,2.1801163305190663e-11,0.6032723784446716,28.34016592506999,28.781192489043658,28.302500000000002,28.3425,28.782500000000002,28.782500000000002,0.03999999999999915,0.0 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,0,Yesterday,0.12250000238418579,0.862500011920929,True,2.1098019165055604e-11,0.4256690740585327,0.11640499422426298,0.8237373720723118,0.0625,0.1225,0.7825,0.8624999999999999,0.06,0.07999999999999996 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,1,Eliav,0.9024999737739563,1.162500023841858,True,6.203321190056954e-11,1.2515686750411987,0.8757917552775388,1.1610153192742962,0.8424999999999999,0.9025,1.1225,1.1624999999999999,0.06000000000000005,0.039999999999999813 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,2,Gelman,1.222499966621399,1.662500023841858,True,2.492321973801559e-11,0.5028454661369324,1.2232918082472444,1.6615993583479005,1.2225,1.2225,1.6025,1.6824999999999999,0.0,0.07999999999999985 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,3,lost,2.7225000858306885,2.862499952316284,True,5.466818109312044e-10,4.0,2.709146128467911,2.8661180187391095,2.7025,2.7625,2.8625,2.9225,0.06000000000000005,0.06000000000000005 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,4,his,2.9024999141693115,3.002500057220459,True,3.090692146656693e-10,4.0,2.9108267355157285,3.0414643223665143,2.9025,2.9625,3.0025,3.2425,0.06000000000000005,0.2400000000000002 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,5,life,3.1024999618530273,3.362499952316284,True,1.2600011312091652e-10,2.5421509742736816,3.1401606861304536,3.357841510464017,3.0225,3.3025,3.2425,3.5625,0.28000000000000025,0.31999999999999984 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,6,in,3.442500114440918,3.5625,True,2.6729063407060494e-10,4.0,3.428606886284111,3.532967025626201,3.3025,3.6625,3.3625,3.7225,0.3599999999999999,0.3600000000000003 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,7,another,3.6624999046325684,4.242499828338623,True,3.822804919839662e-11,0.771280825138092,3.6285882996720065,4.14030831047379,3.4425,3.8025,3.9425,4.242500000000001,0.3600000000000003,0.3000000000000007 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,8,terrorist,4.28249979019165,5.242499828338623,True,4.334416506268646e-11,0.8745024800300598,4.227954760286244,5.168522414951293,4.062500000000001,4.3425,4.9625,5.242500000000001,0.27999999999999936,0.28000000000000025 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,9,"attack,",5.34250020980835,5.682499885559082,True,6.46387596225928e-11,1.3041375875473022,5.276098853024842,5.630281070381624,5.1225000000000005,5.3425,5.442500000000001,5.7225,0.21999999999999975,0.27999999999999936 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,10,not,5.78249979019165,5.902500152587891,True,1.2150396140608866e-11,0.25,5.716276151729501,5.852571298442281,5.482500000000001,5.782500000000001,5.6825,5.9225,0.2999999999999998,0.2400000000000002 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,11,by,5.982500076293945,6.082499980926514,True,1.0236080212611132e-11,0.25,5.934587808080619,6.029627616167395,5.782500000000001,5.982500000000001,5.862500000000001,6.0825000000000005,0.20000000000000018,0.21999999999999975 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,12,the,6.122499942779541,6.302499771118164,True,4.95643699693904e-11,1.0,6.089065477376196,6.26458295760959,5.982500000000001,6.1225000000000005,6.2225,6.3025,0.13999999999999968,0.08000000000000007 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,13,students,6.322500228881836,6.802499771118164,True,1.086939913075291e-10,2.192986488342285,6.304983799467197,6.802649601015644,6.282500000000001,6.322500000000001,6.8025,6.8025,0.040000000000000036,0.0 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,14,of,7.422500133514404,7.522500038146973,True,3.4040623098086087e-10,4.0,7.421097526520383,7.5222877816487355,7.4225,7.4225,7.522500000000001,7.522500000000001,0.0,0.0 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,15,"terrorism,",7.662499904632568,8.362500190734863,True,1.7434940643989982e-11,0.3517635762691498,7.651950712497826,8.326166381941334,7.5825000000000005,7.6625000000000005,8.282499999999999,8.362499999999999,0.08000000000000007,0.08000000000000007 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,16,but,8.40250015258789,8.542499542236328,True,4.870383610300344e-12,0.25,8.40232606126986,8.544283059130962,8.4025,8.4025,8.5425,8.5425,0.0,0.0 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,17,by,8.662500381469727,8.72249984741211,True,3.2112475872869695e-11,0.6478943824768066,8.65890888774039,8.725163996226525,8.6425,8.7025,8.7225,8.7625,0.0600000000000005,0.03999999999999915 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,18,those,8.782500267028809,9.162500381469727,True,9.06100472430893e-11,1.828128695487976,8.782524958225315,9.20255260115738,8.782499999999999,8.782499999999999,9.1625,9.3225,0.0,0.16000000000000014 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,19,who,9.202500343322754,9.322500228881836,True,7.350461905808459e-11,1.4830132722854614,9.292884555691698,9.434678222093696,9.2025,9.4225,9.3225,9.5825,0.21999999999999886,0.2599999999999998 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,20,teach,9.422499656677246,9.72249984741211,True,4.777102324826643e-11,0.963817834854126,9.512342365613454,9.798499598139479,9.4025,9.6225,9.7225,9.8825,0.22000000000000064,0.16000000000000014 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,21,"terrorism,",9.822500228881836,10.6225004196167,True,2.364412277078287e-10,4.0,9.885742869369814,10.623797601314282,9.8225,9.9625,10.522499999999999,10.8025,0.14000000000000057,0.28000000000000114 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,22,a,10.842499732971191,10.862500190734863,True,7.060157580091042e-12,0.25,10.76912108115059,10.789121081150874,10.6025,10.8425,10.6225,10.862499999999999,0.2400000000000002,0.23999999999999844 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,23,Palestinian,10.962499618530273,12.522500038146973,True,4.3775850999683286e-11,0.8832120895385742,10.898607984569265,12.05718561330501,10.782499999999999,10.9625,11.6625,12.522499999999999,0.1800000000000015,0.8599999999999994 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,24,teacher.,12.5625,12.962499618530273,True,8.628150971468074e-11,1.7407970428466797,12.515745111313707,12.963414362425844,12.4625,12.6225,12.942499999999999,12.9825,0.16000000000000014,0.040000000000000924 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,0,"Therefore,",0.4025000035762787,0.9424999952316284,True,1.6840784322624813e-10,1.0,0.4024729803370939,0.9336746683054675,0.4025,0.4025,0.9025,0.9624999999999999,0.0,0.05999999999999994 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,1,the,1.0625,1.2024999856948853,True,9.747938567450376e-11,0.5788292288780212,1.0589909815193694,1.1984154783750278,1.0625,1.0625,1.1624999999999999,1.2025,0.0,0.040000000000000036 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,2,world,1.2625000476837158,1.5225000381469727,True,1.6847746808767994e-10,1.000413417816162,1.2558060204353718,1.5185764126331496,1.2225,1.2625,1.5225,1.5225,0.040000000000000036,0.0 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,3,needs,1.5625,1.902500033378601,True,1.0756048135496243e-10,0.6386904716491699,1.5614918157611801,1.8797170301478279,1.5625,1.5625,1.7825,1.9625,0.0,0.17999999999999994 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,4,to,1.9424999952316284,2.002500057220459,True,1.7243546868161985e-10,1.0239158868789673,1.9494528303504337,2.021352098064118,1.8825,2.2425,1.9625,2.3225,0.3600000000000001,0.3599999999999999 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,5,stand,2.0425000190734863,2.4625000953674316,True,1.421072287621783e-10,0.8438278436660767,2.1454380160719135,2.5239863439827293,2.0425,2.3825,2.4625,2.8225,0.33999999999999986,0.3599999999999999 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,6,with,2.5425000190734863,2.822499990463257,True,3.683832960899025e-10,2.1874473094940186,2.627782996043301,2.9132515079477193,2.5425,2.8625,2.8225,3.9025,0.31999999999999984,1.08 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,7,Israel,3.742500066757202,4.122499942779541,True,1.1312620978864985e-10,0.6717395782470703,3.7309032744213915,4.150365024998529,3.7425,3.9425,4.1225000000000005,4.2225,0.19999999999999973,0.09999999999999964 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,8,against,4.242499828338623,4.682499885559082,True,2.678313681947486e-10,1.5903735160827637,4.242938262935196,4.670751606173934,4.242500000000001,4.242500000000001,4.5825000000000005,4.7225,0.0,0.13999999999999968 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,9,this,4.742499828338623,4.902500152587891,True,9.41213912364347e-11,0.5588895678520203,4.724187153540902,4.903113320015298,4.6625000000000005,4.742500000000001,4.862500000000001,4.902500000000001,0.08000000000000007,0.040000000000000036 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,10,"incitement,",5.422500133514404,6.102499961853027,True,6.119732498532926e-11,0.3633876144886017,5.423858436959219,6.115242073345665,5.4225,5.4225,6.102500000000001,6.202500000000001,0.0,0.09999999999999964 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,11,which,6.502500057220459,6.802499771118164,True,2.0067764117115416e-10,1.1916170120239258,6.50248685522129,6.802489095420521,6.5025,6.5025,6.8025,6.8025,0.0,0.0 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,12,is,6.862500190734863,6.942500114440918,True,8.340554358277075e-10,4.0,6.8625014843098695,6.942410921639754,6.862500000000001,6.862500000000001,6.942500000000001,6.942500000000001,0.0,0.0 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,13,the,7.002500057220459,7.182499885559082,True,3.8694789039617206e-10,2.2976832389831543,7.002492295030229,7.182530934830729,7.0025,7.0025,7.1825,7.1825,0.0,0.0 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,14,number,7.402500152587891,7.762499809265137,True,2.0023324664997233e-10,1.1889781951904297,7.378536282253883,7.766816192156913,7.3025,7.402500000000001,7.6825,7.822500000000001,0.10000000000000053,0.14000000000000057 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,15,one,9.782500267028809,9.90250015258789,True,1.6587432816184133e-10,0.9849560856819153,9.77142133456187,9.889990542704302,9.7425,9.782499999999999,9.8425,9.9025,0.03999999999999915,0.0600000000000005 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,16,cause,9.962499618530273,10.162500381469727,True,1.8768611664832235e-10,1.1144737005233765,9.974359250912508,10.190149976930748,9.9625,10.0025,10.1625,10.2225,0.03999999999999915,0.0600000000000005 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,17,of,10.382499694824219,10.442500114440918,True,3.711768045699948e-11,0.25,10.352394778030892,10.418035924396685,10.2025,10.3825,10.2625,10.442499999999999,0.17999999999999972,0.17999999999999972 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,18,terrorism.,10.5024995803833,10.982500076293945,True,1.314886949321803e-10,0.7807753682136536,10.4911182530075,10.981002444124938,10.3825,10.5025,10.9825,10.9825,0.11999999999999922,0.0 diff --git a/final/Q1/pyproject.toml b/final/Q1/pyproject.toml deleted file mode 100644 index f4afaf6..0000000 --- a/final/Q1/pyproject.toml +++ /dev/null @@ -1,24 +0,0 @@ -[project] -name = "math-q1-comparison" -version = "0.1.0" -requires-python = ">=3.14" -dependencies = [ - "matplotlib>=3.11.2", - "mediapipe>=1.0.1", - "numpy>=2.5.2", - "opencv-python-headless>=5.0.0.93", - "openpyxl>=3.1.5", - "pillow>=12.3.0", - "scikit-learn>=1.9.1", - "scipy>=1.17.1", - "torch>=2.14.0", - "transformers>=5.17.0", -] - -[tool.uv.sources] -torch = { index = "pytorch" } - -[[tool.uv.index]] -name = "pytorch" -url = "https://download.pytorch.org/whl/cu130" -explicit = true diff --git a/final/Q1/results/model_comparison/comparison.png b/final/Q1/results/model_comparison/comparison.png deleted file mode 100644 index 556343b..0000000 Binary files a/final/Q1/results/model_comparison/comparison.png and /dev/null differ diff --git a/final/Q1/results/model_comparison/comparison_summary.csv b/final/Q1/results/model_comparison/comparison_summary.csv deleted file mode 100644 index c001e08..0000000 --- a/final/Q1/results/model_comparison/comparison_summary.csv +++ /dev/null @@ -1,6 +0,0 @@ -method,sample_count,video_group_count,oof_accuracy,oof_macro_f1,oof_mae,oof_pearson,fold_accuracy_mean,fold_accuracy_sd,fold_macro_f1_mean,fold_macro_f1_sd,fold_mae_mean,fold_mae_sd,fold_pearson_mean,fold_pearson_sd -B0,100,37,0.6,0.3612633181126332,0.5642678308486938,0.34585652345724877,0.6,0.1274754878398196,0.33701813174429807,0.09810807621270928,0.5642678308486938,0.15030090846290933,0.40788979900767275,0.06001860958324475 -B1,100,37,0.61,0.387431302270012,0.5989421024918556,0.23765809617297712,0.61,0.1341640786499874,0.3493607699490052,0.11905440418044842,0.5989421024918556,0.15750913261096855,0.281166840616461,0.14435440813423514 -B2,100,37,0.58,0.33061794334825517,0.588000754788518,0.2790493470006418,0.5800000000000001,0.14404860290887933,0.3128829195495862,0.08742851668319461,0.588000754788518,0.14204292727229248,0.3331122890661654,0.08879703094318196 -B3,100,37,0.59,0.3587962962962963,0.5997931832820177,0.23011191320162028,0.5900000000000001,0.12942179105544785,0.33257284523004604,0.09564119982991781,0.5997931832820177,0.15746511520951317,0.2803250178248575,0.13217248970127823 -B4,100,37,0.6,0.4222603610475464,0.59276591360569,0.19864510121331094,0.6,0.13693063937629155,0.3976841268630668,0.08882960164587973,0.59276591360569,0.16014796281119822,0.24000288610371706,0.14964617105964673 diff --git a/final/Q1/results/model_comparison/fold_metrics.csv b/final/Q1/results/model_comparison/fold_metrics.csv deleted file mode 100644 index 248683a..0000000 --- a/final/Q1/results/model_comparison/fold_metrics.csv +++ /dev/null @@ -1,26 +0,0 @@ -method,fold,train_samples,valid_samples,train_video_groups,valid_video_groups,accuracy,macro_f1,mae,pearson,feature_dimension -B0,1,80,20,31,6,0.75,0.29411764705882354,0.4224671095609665,0.39024875749849447,4385 -B1,1,80,20,31,6,0.75,0.29411764705882354,0.43092925772070884,0.3768780281745036,4385 -B2,1,80,20,31,6,0.75,0.2857142857142857,0.43804959058761594,0.3386523481377315,4385 -B3,1,80,20,31,6,0.75,0.29411764705882354,0.42625029012560844,0.3688530099245201,4455 -B4,1,80,20,31,6,0.8,0.42745098039215684,0.41526668667793276,0.3438838954822536,8225 -B0,2,80,20,30,7,0.6,0.3639846743295019,0.776566531509161,0.3958411255263523,4385 -B1,2,80,20,30,7,0.6,0.35714285714285715,0.8237437169998885,0.24091732574554836,4385 -B2,2,80,20,30,7,0.6,0.35714285714285715,0.7888965889811516,0.3639766310607314,4385 -B3,2,80,20,30,7,0.6,0.35714285714285715,0.8207762833684683,0.256615486910397,4455 -B4,2,80,20,30,7,0.6,0.3639846743295019,0.8059555269777775,0.19887966518486633,8225 -B0,3,80,20,29,8,0.65,0.44334975369458124,0.6439033597707748,0.4541649022346378,4385 -B1,3,80,20,29,8,0.7,0.5119047619047619,0.6599065795540809,0.3794933845977076,4385 -B2,3,80,20,29,8,0.6,0.35555555555555557,0.660385686159134,0.3833892851036205,4385 -B3,3,80,20,29,8,0.65,0.44334975369458124,0.6637263983488083,0.36870656729912815,4455 -B4,3,80,20,29,8,0.5,0.3956043956043956,0.6777055777609349,0.23098821201949785,8225 -B0,4,80,20,29,8,0.4,0.19047619047619047,0.5507291875779629,0.32319803952574416,4385 -B1,4,80,20,29,8,0.4,0.19047619047619047,0.6101470191031695,0.04446251878558958,4385 -B2,4,80,20,29,8,0.35,0.1728395061728395,0.5764139499515295,0.17960866044359325,4385 -B3,4,80,20,29,8,0.4,0.19047619047619047,0.6146079197525978,0.0589277387406542,4455 -B4,4,80,20,29,8,0.45,0.2792022792022792,0.607093845307827,0.018750134966085342,8225 -B0,5,80,20,29,8,0.6,0.39316239316239315,0.427672965824604,0.4759961702531348,4385 -B1,5,80,20,29,8,0.6,0.39316239316239315,0.46998393908143044,0.3640829457789559,4385 -B2,5,80,20,29,8,0.6,0.39316239316239315,0.4762579582631588,0.3999345205851506,4385 -B3,5,80,20,29,8,0.55,0.37777777777777777,0.4736050248146057,0.34852228624958836,4455 -B4,5,80,20,29,8,0.65,0.5221783047870004,0.45780793130397796,0.4075125228658822,8225 diff --git a/final/Q1/results/model_comparison/group_bootstrap_deltas.csv b/final/Q1/results/model_comparison/group_bootstrap_deltas.csv deleted file mode 100644 index b895fb3..0000000 --- a/final/Q1/results/model_comparison/group_bootstrap_deltas.csv +++ /dev/null @@ -1,17 +0,0 @@ -comparison,metric,reference_oof,method_oof,delta_oof,group_bootstrap_ci95_low,group_bootstrap_ci95_high,bootstrap_repeats -B0-B1,accuracy,0.61,0.6,-0.010000000000000009,-0.029710396039603987,0.0,2000 -B0-B1,macro_f1,0.387431302270012,0.3612633181126332,-0.02616798415737881,-0.08327057744765032,0.0,2000 -B0-B1,mae,0.5989421024918556,0.5642678308486938,-0.034674271643161836,-0.06304207975806683,-0.012443204964559182,2000 -B0-B1,pearson,0.23765809617297712,0.34585652345724877,0.10819842728427165,0.04340079372240924,0.19005024141624596,2000 -B2-B1,accuracy,0.61,0.58,-0.030000000000000027,-0.0648204607046071,0.0,2000 -B2-B1,macro_f1,0.387431302270012,0.33061794334825517,-0.05681335892175682,-0.10940517350156256,-0.0009457159449812041,2000 -B2-B1,mae,0.5989421024918556,0.588000754788518,-0.010941347703337656,-0.027305471624463586,0.004017100735475708,2000 -B2-B1,pearson,0.23765809617297712,0.2790493470006418,0.041391250827664705,-0.009244736549056344,0.10131359014731482,2000 -B3-B1,accuracy,0.61,0.59,-0.020000000000000018,-0.05154639175257736,0.0,2000 -B3-B1,macro_f1,0.387431302270012,0.3587962962962963,-0.02863500597371571,-0.07212177947137302,0.0,2000 -B3-B1,mae,0.5989421024918556,0.5997931832820177,0.000851080790162051,-0.004701075716562739,0.006246566925533936,2000 -B3-B1,pearson,0.23765809617297712,0.23011191320162028,-0.007546182971356841,-0.022226358336772584,0.005402452990031458,2000 -B4-B1,accuracy,0.61,0.6,-0.010000000000000009,-0.08080808080808077,0.06196073008849559,2000 -B4-B1,macro_f1,0.387431302270012,0.4222603610475464,0.034829058777534394,-0.051408708516982864,0.13655330256210496,2000 -B4-B1,mae,0.5989421024918556,0.59276591360569,-0.006176188886165668,-0.03045045264816862,0.015397113913740889,2000 -B4-B1,pearson,0.23765809617297712,0.19864510121331094,-0.039012994959666175,-0.1082683426423725,0.03034022099301614,2000 diff --git a/final/Q1/results/model_comparison/modality_summary.csv b/final/Q1/results/model_comparison/modality_summary.csv deleted file mode 100644 index 71fa296..0000000 --- a/final/Q1/results/model_comparison/modality_summary.csv +++ /dev/null @@ -1,301 +0,0 @@ -sample_id,video_id,clip_id,modality,source_duration_s,observed_duration_s_mean_dimension,native_length,native_dimension,main_grid_length,grid_step_s,grid_dimension,mean_grid_coverage,status,source_video_sha256 --3g5yACwYnA/13,-3g5yACwYnA,13,text,5.5139970779418945,2.946523889899254,15,768,56,0.1,768,0.7015532851219177,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f --3g5yACwYnA/13,-3g5yACwYnA,13,audio,5.5139970779418945,5.445240411887298,540,74,56,0.1,74,0.9882608652114868,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f --3g5yACwYnA/13,-3g5yACwYnA,13,vision,5.5139970779418945,5.5139970779418945,43,35,56,0.1,35,1.0,ok,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f --3g5yACwYnA/3,-3g5yACwYnA,3,text,14.388997077941895,7.041063531115653,29,768,144,0.1,768,0.6343300342559814,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d --3g5yACwYnA/3,-3g5yACwYnA,3,audio,14.388997077941895,14.260037878559858,1433,74,144,0.1,74,0.9912122488021851,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d --3g5yACwYnA/3,-3g5yACwYnA,3,vision,14.388997077941895,14.388997077941895,103,35,144,0.1,35,1.0,ok,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d --3g5yACwYnA/2,-3g5yACwYnA,2,text,9.394009590148926,5.1430321040563305,14,768,94,0.1,768,0.7563282251358032,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7 --3g5yACwYnA/2,-3g5yACwYnA,2,audio,9.394009590148926,9.331671435768538,924,74,94,0.1,74,0.9936367273330688,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7 --3g5yACwYnA/2,-3g5yACwYnA,2,vision,9.394009590148926,9.394009590148926,78,35,94,0.1,35,1.0,ok,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7 --3g5yACwYnA/9,-3g5yACwYnA,9,text,8.816991806030273,5.6415157541632714,21,768,89,0.1,768,0.6637077927589417,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016 --3g5yACwYnA/9,-3g5yACwYnA,9,audio,8.816991806030273,8.735654204761659,870,74,89,0.1,74,0.9912108778953552,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016 --3g5yACwYnA/9,-3g5yACwYnA,9,vision,8.816991806030273,8.816991806030273,75,35,89,0.1,35,1.0,ok,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016 --3nNcZdcdvU/5,-3nNcZdcdvU,5,text,7.867969036102295,5.002839090395719,18,768,79,0.1,768,0.7357116341590881,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728 --3nNcZdcdvU/5,-3nNcZdcdvU,5,audio,7.867969036102295,7.843361772234375,779,74,79,0.1,74,0.9971166849136353,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728 --3nNcZdcdvU/5,-3nNcZdcdvU,5,vision,7.867969036102295,7.816666602730405,67,35,79,0.1,35,0.9904456734657288,ok,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,text,3.9820311069488525,2.40418455898877,8,768,40,0.1,768,0.6164926290512085,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,audio,3.9820311069488525,3.9192059241395896,392,74,40,0.1,74,0.9881895780563354,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,vision,3.9820311069488525,0.8653643131256102,27,35,40,0.1,35,0.9814813733100891,ok,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,text,5.6529951095581055,3.0192474961280813,10,768,57,0.1,768,0.7021505832672119,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,audio,5.6529951095581055,5.595076320642555,555,74,57,0.1,74,0.9900854229927063,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,vision,5.6529951095581055,1.199999868869782,44,35,57,0.1,35,0.666666567325592,ok,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,text,3.3580079078674316,1.3907382175326355,7,768,34,0.1,768,0.5562952756881714,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,audio,3.3580079078674316,3.306980685486987,326,74,34,0.1,74,0.9901678562164307,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,vision,3.3580079078674316,1.6413411736488344,21,35,34,0.1,35,0.841666579246521,ok,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,text,7.733983993530273,4.492685443162919,22,768,78,0.1,768,0.6705501675605774,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,audio,7.733983993530273,7.691443904670509,764,74,78,0.1,74,0.9946621060371399,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,vision,7.733983993530273,0.0,65,35,78,0.1,35,0.0,ok,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702 --NFrJFQijFE/1,-NFrJFQijFE,1,text,5.745999813079834,4.074365028738975,16,768,58,0.1,768,0.7835317254066467,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f --NFrJFQijFE/1,-NFrJFQijFE,1,audio,5.745999813079834,5.699824074635634,566,74,58,0.1,74,0.9922494292259216,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f --NFrJFQijFE/1,-NFrJFQijFE,1,vision,5.745999813079834,0.0,44,35,58,0.1,35,0.0,ok,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f --NFrJFQijFE/2,-NFrJFQijFE,2,text,6.855999946594238,3.4186642885208136,18,768,69,0.1,768,0.7431879639625549,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1 --NFrJFQijFE/2,-NFrJFQijFE,2,audio,6.855999946594238,6.798472889210726,670,74,69,0.1,74,0.9926568269729614,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1 --NFrJFQijFE/2,-NFrJFQijFE,2,vision,6.855999946594238,0.0,55,35,69,0.1,35,0.0,ok,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1 --THoVjtIkeU/12,-THoVjtIkeU,12,text,14.896029472351074,7.941008102986966,39,768,149,0.1,768,0.6352806687355042,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4 --THoVjtIkeU/12,-THoVjtIkeU,12,audio,14.896029472351074,14.759312417861578,1481,74,149,0.1,74,0.9911767244338989,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4 --THoVjtIkeU/12,-THoVjtIkeU,12,vision,14.896029472351074,14.896029472351074,106,35,149,0.1,35,1.0,ok,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4 --THoVjtIkeU/2,-THoVjtIkeU,2,text,4.2919921875,2.5793988468125453,10,768,43,0.1,768,0.7164996266365051,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b --THoVjtIkeU/2,-THoVjtIkeU,2,audio,4.2919921875,4.247343715932063,424,74,43,0.1,74,0.9895758628845215,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b --THoVjtIkeU/2,-THoVjtIkeU,2,vision,4.2919921875,4.2919921875,31,35,43,0.1,35,1.0,ok,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b --THoVjtIkeU/6,-THoVjtIkeU,6,text,8.097004890441895,5.15853084437549,30,768,81,0.1,768,0.6787540316581726,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64 --THoVjtIkeU/6,-THoVjtIkeU,6,audio,8.097004890441895,8.027356093840018,799,74,81,0.1,74,0.9927164316177368,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64 --THoVjtIkeU/6,-THoVjtIkeU,6,vision,8.097004890441895,8.097004890441895,68,35,81,0.1,35,1.0,ok,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64 --UuX1xuaiiE/1,-UuX1xuaiiE,1,text,10.350000381469727,5.7645069250837,27,768,104,0.1,768,0.6550575494766235,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c --UuX1xuaiiE/1,-UuX1xuaiiE,1,audio,10.350000381469727,10.268040973753543,1027,74,104,0.1,74,0.9925051927566528,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c --UuX1xuaiiE/1,-UuX1xuaiiE,1,vision,10.350000381469727,0.45000076293945285,83,35,104,0.1,35,0.8333339691162109,ok,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c --UuX1xuaiiE/0,-UuX1xuaiiE,0,text,3.6333329677581787,2.204757870733737,9,768,37,0.1,768,0.6484582424163818,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8 --UuX1xuaiiE/0,-UuX1xuaiiE,0,audio,3.6333329677581787,3.6124320519937045,358,74,37,0.1,74,0.994590699672699,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8 --UuX1xuaiiE/0,-UuX1xuaiiE,0,vision,3.6333329677581787,1.700000047683716,25,35,37,0.1,35,0.9444444179534912,ok,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8 --UuX1xuaiiE/3,-UuX1xuaiiE,3,text,4.1300129890441895,2.0997684210538865,9,768,42,0.1,768,0.7240580320358276,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa --UuX1xuaiiE/3,-UuX1xuaiiE,3,audio,4.1300129890441895,4.093458598368876,404,74,42,0.1,74,0.992123007774353,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa --UuX1xuaiiE/3,-UuX1xuaiiE,3,vision,4.1300129890441895,4.03001308441162,29,35,42,0.1,35,0.976190447807312,ok,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa --UuX1xuaiiE/6,-UuX1xuaiiE,6,text,8.113997459411621,4.803536714613438,22,768,82,0.1,768,0.7064024806022644,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532 --UuX1xuaiiE/6,-UuX1xuaiiE,6,audio,8.113997459411621,7.968213647201255,795,74,82,0.1,74,0.984533965587616,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532 --UuX1xuaiiE/6,-UuX1xuaiiE,6,vision,8.113997459411621,7.9306641817092896,68,35,82,0.1,35,0.9776423573493958,ok,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532 --a55Q6RWvTA/3,-a55Q6RWvTA,3,text,22.15397071838379,13.423765965458015,65,768,222,0.1,768,0.6580277681350708,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946 --a55Q6RWvTA/3,-a55Q6RWvTA,3,audio,22.15397071838379,21.95386351814141,2209,74,222,0.1,74,0.9912921786308289,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946 --a55Q6RWvTA/3,-a55Q6RWvTA,3,vision,22.15397071838379,22.15397071838379,142,35,222,0.1,35,1.0,ok,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946 --aNfi7CP8vM/7,-aNfi7CP8vM,7,text,8.694987297058105,5.3913327027112254,18,768,87,0.1,768,0.7001731395721436,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1 --aNfi7CP8vM/7,-aNfi7CP8vM,7,audio,8.694987297058105,8.639379485394505,854,74,87,0.1,74,0.9945195913314819,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1 --aNfi7CP8vM/7,-aNfi7CP8vM,7,vision,8.694987297058105,8.694987297058105,74,35,87,0.1,35,1.0,ok,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,text,10.966667175292969,5.40069461874664,14,768,110,0.1,768,0.71061772108078,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,audio,10.966667175292969,10.855676183829436,1090,74,110,0.1,74,0.9910455346107483,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,vision,10.966667175292969,10.966667175292969,86,35,110,0.1,35,1.0,ok,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548 --dxfTGcXJoc/1,-dxfTGcXJoc,1,text,16.16100311279297,9.47002421617508,36,768,162,0.1,768,0.6812966465950012,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595 --dxfTGcXJoc/1,-dxfTGcXJoc,1,audio,16.16100311279297,15.984151583668348,1602,74,162,0.1,74,0.9900091290473938,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595 --dxfTGcXJoc/1,-dxfTGcXJoc,1,vision,16.16100311279297,16.16100311279297,112,35,162,0.1,35,1.0,ok,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595 --dxfTGcXJoc/0,-dxfTGcXJoc,0,text,20.5,13.566675072815258,41,768,205,0.1,768,0.7333337664604187,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55 --dxfTGcXJoc/0,-dxfTGcXJoc,0,audio,20.5,20.265135636200775,2042,74,205,0.1,74,0.9891952872276306,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55 --dxfTGcXJoc/0,-dxfTGcXJoc,0,vision,20.5,15.983333587646484,134,35,205,0.1,35,0.99895840883255,ok,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55 --dxfTGcXJoc/2,-dxfTGcXJoc,2,text,16.697982788085938,9.495353608578453,34,768,167,0.1,768,0.6593995094299316,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7 --dxfTGcXJoc/2,-dxfTGcXJoc,2,audio,16.697982788085938,16.517766948940555,1664,74,167,0.1,74,0.9901677966117859,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7 --dxfTGcXJoc/2,-dxfTGcXJoc,2,vision,16.697982788085938,16.697982788085938,115,35,167,0.1,35,1.0,ok,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7 --dxfTGcXJoc/6,-dxfTGcXJoc,6,text,12.735026359558105,8.155348146427425,27,768,128,0.1,768,0.715381383895874,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513 --dxfTGcXJoc/6,-dxfTGcXJoc,6,audio,12.735026359558105,12.576174248392515,1263,74,128,0.1,74,0.9889141917228699,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513 --dxfTGcXJoc/6,-dxfTGcXJoc,6,vision,12.735026359558105,12.5,95,35,128,0.1,35,1.0,ok,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513 --egA8-b7-3M/26,-egA8-b7-3M,26,text,6.030990123748779,3.6452820789068947,15,768,61,0.1,768,0.6877890825271606,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685 --egA8-b7-3M/26,-egA8-b7-3M,26,audio,6.030990123748779,5.994409297769134,593,74,61,0.1,74,0.9945786595344543,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685 --egA8-b7-3M/26,-egA8-b7-3M,26,vision,6.030990123748779,6.030990123748779,47,35,61,0.1,35,1.0,ok,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685 --egA8-b7-3M/17,-egA8-b7-3M,17,text,7.271028995513916,3.4275580286979666,16,768,73,0.1,768,0.7292676568031311,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f --egA8-b7-3M/17,-egA8-b7-3M,17,audio,7.271028995513916,7.202622791158187,710,74,73,0.1,74,0.9916234016418457,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f --egA8-b7-3M/17,-egA8-b7-3M,17,vision,7.271028995513916,7.271028995513916,60,35,73,0.1,35,1.0,ok,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f --egA8-b7-3M/18,-egA8-b7-3M,18,text,8.386002540588379,3.9958887480199343,22,768,84,0.1,768,0.6243576407432556,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90 --egA8-b7-3M/18,-egA8-b7-3M,18,audio,8.386002540588379,8.326583495091747,827,74,84,0.1,74,0.9932008385658264,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90 --egA8-b7-3M/18,-egA8-b7-3M,18,vision,8.386002540588379,8.386002540588379,71,35,84,0.1,35,1.0,ok,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90 --egA8-b7-3M/16,-egA8-b7-3M,16,text,5.538021087646484,3.3190146580338484,11,768,56,0.1,768,0.7375588417053223,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c --egA8-b7-3M/16,-egA8-b7-3M,16,audio,5.538021087646484,5.498074575251824,549,74,56,0.1,74,0.9940068125724792,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c --egA8-b7-3M/16,-egA8-b7-3M,16,vision,5.538021087646484,5.538021087646484,43,35,56,0.1,35,1.0,ok,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c --egA8-b7-3M/13,-egA8-b7-3M,13,text,4.18398380279541,2.166685357689858,11,768,42,0.1,768,0.6372604370117188,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c --egA8-b7-3M/13,-egA8-b7-3M,13,audio,4.18398380279541,4.149822072886132,403,74,42,0.1,74,0.9924017786979675,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c --egA8-b7-3M/13,-egA8-b7-3M,13,vision,4.18398380279541,4.18398380279541,29,35,42,0.1,35,1.0,ok,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c --egA8-b7-3M/1,-egA8-b7-3M,1,text,10.266016006469727,5.581577223725616,20,768,103,0.1,768,0.6490206122398376,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b --egA8-b7-3M/1,-egA8-b7-3M,1,audio,10.266016006469727,10.199569694899225,1016,74,103,0.1,74,0.9937204718589783,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b --egA8-b7-3M/1,-egA8-b7-3M,1,vision,10.266016006469727,10.266016006469727,83,35,103,0.1,35,1.0,ok,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b --egA8-b7-3M/6,-egA8-b7-3M,6,text,6.264974117279053,3.582069296762348,12,768,63,0.1,768,0.7164138555526733,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0 --egA8-b7-3M/6,-egA8-b7-3M,6,audio,6.264974117279053,6.2242991206613745,619,74,63,0.1,74,0.9942464828491211,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0 --egA8-b7-3M/6,-egA8-b7-3M,6,vision,6.264974117279053,6.264974117279053,50,35,63,0.1,35,1.0,ok,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0 --egA8-b7-3M/9,-egA8-b7-3M,9,text,5.0899739265441895,2.328742109239101,10,768,51,0.1,768,0.597113311290741,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c --egA8-b7-3M/9,-egA8-b7-3M,9,audio,5.0899739265441895,5.05666382530251,494,74,51,0.1,74,0.9939422011375427,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c --egA8-b7-3M/9,-egA8-b7-3M,9,vision,5.0899739265441895,5.0899739265441895,38,35,51,0.1,35,1.0,ok,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c --egA8-b7-3M/20,-egA8-b7-3M,20,text,6.644987106323242,3.9119302723556753,20,768,67,0.1,768,0.6985589861869812,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8 --egA8-b7-3M/20,-egA8-b7-3M,20,audio,6.644987106323242,6.588095611333847,648,74,67,0.1,74,0.992087185382843,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8 --egA8-b7-3M/20,-egA8-b7-3M,20,vision,6.644987106323242,6.644987106323242,54,35,67,0.1,35,1.0,ok,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8 --iRBcNs9oI8/3,-iRBcNs9oI8,3,text,5.620999813079834,2.037480006366969,8,768,57,0.1,768,0.6791599988937378,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50 --iRBcNs9oI8/3,-iRBcNs9oI8,3,audio,5.620999813079834,5.578905152469068,554,74,57,0.1,74,0.9954468607902527,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50 --iRBcNs9oI8/3,-iRBcNs9oI8,3,vision,5.620999813079834,0.0,28,35,57,0.1,35,0.0,ok,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50 --iRBcNs9oI8/7,-iRBcNs9oI8,7,text,4.104000091552734,2.2118893228471284,9,768,42,0.1,768,0.7135127186775208,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081 --iRBcNs9oI8/7,-iRBcNs9oI8,7,audio,4.104000091552734,4.083554145129952,401,74,42,0.1,74,0.995795726776123,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081 --iRBcNs9oI8/7,-iRBcNs9oI8,7,vision,4.104000091552734,0.0,21,35,42,0.1,35,0.0,ok,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081 --iRBcNs9oI8/6,-iRBcNs9oI8,6,text,2.9030001163482666,1.5899996414780622,5,768,30,0.1,768,0.7227271199226379,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3 --iRBcNs9oI8/6,-iRBcNs9oI8,6,audio,2.9030001163482666,2.892310954429008,283,74,30,0.1,74,0.9973600506782532,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3 --iRBcNs9oI8/6,-iRBcNs9oI8,6,vision,2.9030001163482666,0.0,15,35,30,0.1,35,0.0,ok,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3 --iRBcNs9oI8/9,-iRBcNs9oI8,9,text,3.4159998893737793,1.094375005364418,5,768,35,0.1,768,0.643750011920929,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74 --iRBcNs9oI8/9,-iRBcNs9oI8,9,audio,3.4159998893737793,3.383608031917263,338,74,35,0.1,74,0.993934154510498,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74 --iRBcNs9oI8/9,-iRBcNs9oI8,9,vision,3.4159998893737793,0.0,17,35,35,0.1,35,0.0,ok,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74 --iRBcNs9oI8/8,-iRBcNs9oI8,8,text,8.093000411987305,3.901277539858493,21,768,81,0.1,768,0.6192578673362732,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412 --iRBcNs9oI8/8,-iRBcNs9oI8,8,audio,8.093000411987305,8.037513972489212,803,74,81,0.1,74,0.9947929382324219,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412 --iRBcNs9oI8/8,-iRBcNs9oI8,8,vision,8.093000411987305,0.0,41,35,81,0.1,35,0.0,ok,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412 --lzEya4AM_4/5,-lzEya4AM_4,5,text,7.675000190734863,4.456424816604702,19,768,77,0.1,768,0.7427374720573425,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043 --lzEya4AM_4/5,-lzEya4AM_4,5,audio,7.675000190734863,7.5937163826178855,757,74,77,0.1,74,0.9901386499404907,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043 --lzEya4AM_4/5,-lzEya4AM_4,5,vision,7.675000190734863,7.675000190734863,64,35,77,0.1,35,1.0,ok,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043 --lzEya4AM_4/6,-lzEya4AM_4,6,text,14.7919921875,9.074837291240696,43,768,148,0.1,768,0.7318416833877563,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18 --lzEya4AM_4/6,-lzEya4AM_4,6,audio,14.7919921875,14.634100490929308,1475,74,148,0.1,74,0.9907644391059875,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18 --lzEya4AM_4/6,-lzEya4AM_4,6,vision,14.7919921875,14.7919921875,105,35,148,0.1,35,1.0,ok,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,text,6.103000164031982,1.9617638647556308,13,768,62,0.1,768,0.8174015879631042,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,audio,6.103000164031982,5.938054213652739,606,74,62,0.1,74,1.0,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,vision,6.103000164031982,0.0,31,35,62,0.1,35,0.0,ok,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,text,5.730999946594238,3.5213801980018595,11,768,58,0.1,768,0.9266790151596069,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,audio,5.730999946594238,5.5761080561457455,571,74,58,0.1,74,1.0,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,vision,5.730999946594238,0.0,29,35,58,0.1,35,0.0,ok,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,text,2.256999969482422,1.1722640581429007,5,768,23,0.1,768,0.6512578129768372,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,audio,2.256999969482422,2.2208648198211596,223,74,23,0.1,74,0.9862568378448486,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,vision,2.256999969482422,2.256999969482422,12,35,23,0.1,35,1.0,ok,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,text,7.0329999923706055,3.813062208145856,22,768,71,0.1,768,0.5777366757392883,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,audio,7.0329999923706055,6.9057026652870945,691,74,71,0.1,74,0.9846946001052856,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,vision,7.0329999923706055,6.832799911499023,35,35,71,0.1,35,0.9856857061386108,ok,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,text,4.191999912261963,2.284389768779285,11,768,42,0.1,768,0.6529467701911926,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,audio,4.191999912261963,4.127891841835266,418,74,42,0.1,74,0.9846959114074707,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,vision,4.191999912261963,4.191999912261963,21,35,42,0.1,35,1.0,ok,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc --mqbVkbCndg/0,-mqbVkbCndg,0,text,6.466667175292969,3.891069056466222,12,768,65,0.1,768,0.7074670791625977,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda --mqbVkbCndg/0,-mqbVkbCndg,0,audio,6.466667175292969,6.400540961445988,639,74,65,0.1,74,0.9905118346214294,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda --mqbVkbCndg/0,-mqbVkbCndg,0,vision,6.466667175292969,6.466667175292969,53,35,65,0.1,35,1.0,ok,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda --t217m2on-s/2,-t217m2on-s,2,text,5.6860032081604,2.3235132679343233,12,768,57,0.1,768,0.6279765367507935,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8 --t217m2on-s/2,-t217m2on-s,2,audio,5.6860032081604,5.64178691047269,564,74,57,0.1,74,0.9959543347358704,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8 --t217m2on-s/2,-t217m2on-s,2,vision,5.6860032081604,5.6860032081604,45,35,57,0.1,35,1.0,ok,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8 --t217m2on-s/7,-t217m2on-s,7,text,17.183008193969727,10.16458021551371,45,768,172,0.1,768,0.6867958903312683,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7 --t217m2on-s/7,-t217m2on-s,7,audio,17.183008193969727,17.025629438258505,1703,74,172,0.1,74,0.9928514361381531,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7 --t217m2on-s/7,-t217m2on-s,7,vision,17.183008193969727,17.183008193969727,117,35,172,0.1,35,1.0,ok,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7 --tANM6ETl_M/3,-tANM6ETl_M,3,text,6.758008003234863,3.281156235933303,22,768,68,0.1,768,0.5859207510948181,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6 --tANM6ETl_M/3,-tANM6ETl_M,3,audio,6.758008003234863,6.702588935075579,661,74,68,0.1,74,0.9936580061912537,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6 --tANM6ETl_M/3,-tANM6ETl_M,3,vision,6.758008003234863,6.5333333015441895,54,35,68,0.1,35,0.9898989200592041,ok,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6 --tPCytz4rww/11,-tPCytz4rww,11,text,5.466991901397705,3.309338267147543,17,768,55,0.1,768,0.6753751635551453,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d --tPCytz4rww/11,-tPCytz4rww,11,audio,5.466991901397705,5.402410984844775,538,74,55,0.1,74,0.9890678524971008,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d --tPCytz4rww/11,-tPCytz4rww,11,vision,5.466991901397705,5.466991901397705,43,35,55,0.1,35,1.0,ok,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d --tPCytz4rww/10,-tPCytz4rww,10,text,4.788997173309326,2.2889054998755456,12,768,48,0.1,768,0.5449775457382202,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8 --tPCytz4rww/10,-tPCytz4rww,10,audio,4.788997173309326,4.71733509463233,468,74,48,0.1,74,0.9894654750823975,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8 --tPCytz4rww/10,-tPCytz4rww,10,vision,4.788997173309326,4.788997173309326,35,35,48,0.1,35,1.0,ok,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8 --tPCytz4rww/12,-tPCytz4rww,12,text,11.863997459411621,6.21423171013594,26,768,119,0.1,768,0.654129683971405,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5 --tPCytz4rww/12,-tPCytz4rww,12,audio,11.863997459411621,11.690646183812941,1174,74,119,0.1,74,0.9875938892364502,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5 --tPCytz4rww/12,-tPCytz4rww,12,vision,11.863997459411621,11.863997459411621,91,35,119,0.1,35,1.0,ok,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5 --tPCytz4rww/16,-tPCytz4rww,16,text,7.205989837646484,2.154985956847668,16,768,73,0.1,768,0.4489554166793823,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93 --tPCytz4rww/16,-tPCytz4rww,16,audio,7.205989837646484,7.1144090478484685,714,74,73,0.1,74,0.988937258720398,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93 --tPCytz4rww/16,-tPCytz4rww,16,vision,7.205989837646484,7.205989837646484,59,35,73,0.1,35,1.0,ok,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93 --tPCytz4rww/18,-tPCytz4rww,18,text,6.644987106323242,3.4940090492367735,16,768,67,0.1,768,0.6719247698783875,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776 --tPCytz4rww/18,-tPCytz4rww,18,audio,6.644987106323242,6.553973502565075,657,74,67,0.1,74,0.9872123599052429,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776 --tPCytz4rww/18,-tPCytz4rww,18,vision,6.644987106323242,6.644987106323242,54,35,67,0.1,35,1.0,ok,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776 --vxjVxOeScU/4,-vxjVxOeScU,4,text,9.127017974853516,5.366417751088739,21,768,92,0.1,768,0.6465563774108887,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719 --vxjVxOeScU/4,-vxjVxOeScU,4,audio,9.127017974853516,9.08216616914079,904,74,92,0.1,74,0.9960808753967285,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719 --vxjVxOeScU/4,-vxjVxOeScU,4,vision,9.127017974853516,9.127017974853516,77,35,92,0.1,35,1.0,ok,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719 --wMB_hJL-3o/7,-wMB_hJL-3o,7,text,6.044010162353516,3.555890290439128,22,768,61,0.1,768,0.658498227596283,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7 --wMB_hJL-3o/7,-wMB_hJL-3o,7,audio,6.044010162353516,5.968699067508853,598,74,61,0.1,74,0.9895980954170227,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7 --wMB_hJL-3o/7,-wMB_hJL-3o,7,vision,6.044010162353516,5.944010257720948,48,35,61,0.1,35,0.9836065769195557,ok,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7 --wny0OAz3g8/1,-wny0OAz3g8,1,text,6.844009876251221,4.170902410242706,23,768,69,0.1,768,0.62252277135849,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8 --wny0OAz3g8/1,-wny0OAz3g8,1,audio,6.844009876251221,6.814914991726747,678,74,69,0.1,74,0.995954155921936,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8 --wny0OAz3g8/1,-wny0OAz3g8,1,vision,6.844009876251221,3.327343463897705,56,35,69,0.1,35,0.9950981140136719,ok,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8 --wny0OAz3g8/0,-wny0OAz3g8,0,text,3.3333330154418945,1.6946923546493056,9,768,34,0.1,768,0.6052471995353699,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d --wny0OAz3g8/0,-wny0OAz3g8,0,audio,3.3333330154418945,3.3145942848276446,328,74,34,0.1,74,0.9955413937568665,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d --wny0OAz3g8/0,-wny0OAz3g8,0,vision,3.3333330154418945,2.6999998092651367,21,35,34,0.1,35,0.9999999403953552,ok,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d --wny0OAz3g8/3,-wny0OAz3g8,3,text,6.146028995513916,4.208628869801758,20,768,62,0.1,768,0.7014381289482117,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce --wny0OAz3g8/3,-wny0OAz3g8,3,audio,6.146028995513916,6.13413711335208,610,74,62,0.1,74,0.9980819821357727,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce --wny0OAz3g8/3,-wny0OAz3g8,3,vision,6.146028995513916,1.5293622016906734,49,35,62,0.1,35,0.9895832538604736,ok,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce --wny0OAz3g8/2,-wny0OAz3g8,2,text,6.686978816986084,4.909538650512694,23,768,67,0.1,768,0.7553136348724365,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a --wny0OAz3g8/2,-wny0OAz3g8,2,audio,6.686978816986084,6.656249635122918,653,74,67,0.1,74,0.9957627058029175,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a --wny0OAz3g8/2,-wny0OAz3g8,2,vision,6.686978816986084,5.4166669845581055,54,35,67,0.1,35,0.9848485589027405,ok,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a --wny0OAz3g8/5,-wny0OAz3g8,5,text,6.9119791984558105,3.941009076312184,20,768,70,0.1,768,0.6063091158866882,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0 --wny0OAz3g8/5,-wny0OAz3g8,5,audio,6.9119791984558105,6.879425679509704,675,74,70,0.1,74,0.9961636066436768,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0 --wny0OAz3g8/5,-wny0OAz3g8,5,vision,6.9119791984558105,1.4953122138977046,56,35,70,0.1,35,0.9895831346511841,ok,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0 --wny0OAz3g8/7,-wny0OAz3g8,7,text,8.06796932220459,5.293590973317621,29,768,81,0.1,768,0.6700748205184937,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd --wny0OAz3g8/7,-wny0OAz3g8,7,audio,8.06796932220459,8.030726903515895,792,74,81,0.1,74,0.9956274628639221,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd --wny0OAz3g8/7,-wny0OAz3g8,7,vision,8.06796932220459,7.06796932220459,68,35,81,0.1,35,1.0,ok,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd --wny0OAz3g8/9,-wny0OAz3g8,9,text,6.2130208015441895,4.012809376046059,20,768,63,0.1,768,0.6688015460968018,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136 --wny0OAz3g8/9,-wny0OAz3g8,9,audio,6.2130208015441895,6.190979680177328,605,74,63,0.1,74,0.9969837069511414,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136 --wny0OAz3g8/9,-wny0OAz3g8,9,vision,6.2130208015441895,5.8130205273628235,49,35,63,0.1,35,0.9672130942344666,ok,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136 --571d8cVauQ/0,-571d8cVauQ,0,text,5.066667079925537,3.21007038205862,12,768,51,0.1,768,0.6978414058685303,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088 --571d8cVauQ/0,-571d8cVauQ,0,audio,5.066667079925537,5.013243622071034,500,74,51,0.1,74,0.9903978705406189,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088 --571d8cVauQ/0,-571d8cVauQ,0,vision,5.066667079925537,4.9666670799255375,39,35,51,0.1,35,1.0,ok,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088 --571d8cVauQ/5,-571d8cVauQ,5,text,15.272981643676758,9.052198391593993,43,768,153,0.1,768,0.7017207741737366,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e --571d8cVauQ/5,-571d8cVauQ,5,audio,15.272981643676758,15.142900936426344,1512,74,153,0.1,74,0.9918006062507629,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e --571d8cVauQ/5,-571d8cVauQ,5,vision,15.272981643676758,15.272981643676758,108,35,153,0.1,35,1.0,ok,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e --I_e4mIh0yE/1,-I_e4mIh0yE,1,text,7.622004985809326,3.535097924340517,18,768,77,0.1,768,0.6546477675437927,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df --I_e4mIh0yE/1,-I_e4mIh0yE,1,audio,7.622004985809326,7.497626475627357,745,74,77,0.1,74,0.985649049282074,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df --I_e4mIh0yE/1,-I_e4mIh0yE,1,vision,7.622004985809326,7.622004985809326,63,35,77,0.1,35,1.0,ok,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df --I_e4mIh0yE/3,-I_e4mIh0yE,3,text,9.163021087646484,4.626299023628236,22,768,92,0.1,768,0.6251755356788635,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e --I_e4mIh0yE/3,-I_e4mIh0yE,3,audio,9.163021087646484,9.017804326318405,900,74,92,0.1,74,0.9852646589279175,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e --I_e4mIh0yE/3,-I_e4mIh0yE,3,vision,9.163021087646484,9.163021087646484,77,35,92,0.1,35,1.0,ok,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e --UacrmKiTn4/10,-UacrmKiTn4,10,text,7.361979007720947,2.732618988305329,18,768,74,0.1,768,0.47940683364868164,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360 --UacrmKiTn4/10,-UacrmKiTn4,10,audio,7.361979007720947,7.319425493478775,725,74,74,0.1,74,0.9955651164054871,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360 --UacrmKiTn4/10,-UacrmKiTn4,10,vision,7.361979007720947,7.361979007720947,60,35,74,0.1,35,1.0,ok,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360 --UacrmKiTn4/4,-UacrmKiTn4,4,text,4.741015911102295,3.0301611527800563,14,768,48,0.1,768,0.7214669585227966,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84 --UacrmKiTn4/4,-UacrmKiTn4,4,audio,4.741015911102295,4.704772258610339,469,74,48,0.1,74,0.9926760792732239,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84 --UacrmKiTn4/4,-UacrmKiTn4,4,vision,4.741015911102295,4.741015911102295,35,35,48,0.1,35,1.0,ok,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84 --hnBHBN8p5A/7,-hnBHBN8p5A,7,text,7.372000217437744,4.009185888338835,13,768,74,0.1,768,0.657243549823761,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb --hnBHBN8p5A/7,-hnBHBN8p5A,7,audio,7.372000217437744,7.281608330236899,731,74,74,0.1,74,0.9905768632888794,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb --hnBHBN8p5A/7,-hnBHBN8p5A,7,vision,7.372000217437744,0.0,37,35,74,0.1,35,0.0,ok,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb --hnBHBN8p5A/6,-hnBHBN8p5A,6,text,6.401000022888184,3.580871792882682,13,768,65,0.1,768,0.7618876099586487,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7 --hnBHBN8p5A/6,-hnBHBN8p5A,6,audio,6.401000022888184,6.346108104731585,634,74,65,0.1,74,0.9932082295417786,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7 --hnBHBN8p5A/6,-hnBHBN8p5A,6,vision,6.401000022888184,0.0,32,35,65,0.1,35,0.0,ok,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7 --qDkUB0GgYY/6,-qDkUB0GgYY,6,text,4.677995204925537,2.927082145679743,16,768,47,0.1,768,0.6504627466201782,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b --qDkUB0GgYY/6,-qDkUB0GgYY,6,audio,4.677995204925537,4.653116947730401,462,74,47,0.1,74,0.9946085214614868,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b --qDkUB0GgYY/6,-qDkUB0GgYY,6,vision,4.677995204925537,4.677995204925537,35,35,47,0.1,35,1.0,ok,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b --uywlfIYOS8/4,-uywlfIYOS8,4,text,6.044987201690674,3.7997015411034236,18,768,61,0.1,768,0.6908547878265381,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9 --uywlfIYOS8/4,-uywlfIYOS8,4,audio,6.044987201690674,6.015325370511492,589,74,61,0.1,74,0.9957761168479919,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9 --uywlfIYOS8/4,-uywlfIYOS8,4,vision,6.044987201690674,6.044987201690674,48,35,61,0.1,35,1.0,ok,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,text,12.805012702941895,6.537867438234387,34,768,129,0.1,768,0.63474440574646,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,audio,12.805012702941895,12.672918005408468,1274,74,129,0.1,74,0.9899758696556091,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,vision,12.805012702941895,12.805012702941895,95,35,129,0.1,35,1.0,ok,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae --9y-fZ3swSY/0,-9y-fZ3swSY,0,text,6.8333330154418945,3.536603624001146,22,768,69,0.1,768,0.5440928339958191,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c --9y-fZ3swSY/0,-9y-fZ3swSY,0,audio,6.8333330154418945,6.794324037513217,676,74,69,0.1,74,0.9948647022247314,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c --9y-fZ3swSY/0,-9y-fZ3swSY,0,vision,6.8333330154418945,6.8333330154418945,57,35,69,0.1,35,1.0,ok,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c --9y-fZ3swSY/4,-9y-fZ3swSY,4,text,2.8210289478302,1.160462707281113,9,768,29,0.1,768,0.5274830460548401,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc --9y-fZ3swSY/4,-9y-fZ3swSY,4,audio,2.8210289478302,2.7937038478819103,267,74,29,0.1,74,0.9926167726516724,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc --9y-fZ3swSY/4,-9y-fZ3swSY,4,vision,2.8210289478302,2.7210289478302006,15,35,29,0.1,35,1.0,ok,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc --9y-fZ3swSY/8,-9y-fZ3swSY,8,text,4.988996982574463,2.1943973237220367,12,768,50,0.1,768,0.562736988067627,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256 --9y-fZ3swSY/8,-9y-fZ3swSY,8,audio,4.988996982574463,4.9497673825665816,492,74,50,0.1,74,0.9931800365447998,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256 --9y-fZ3swSY/8,-9y-fZ3swSY,8,vision,4.988996982574463,3.688997030258178,37,35,50,0.1,35,0.9487179517745972,ok,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,text,22.511003494262695,12.134252551943066,41,768,226,0.1,768,0.6386448740959167,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,audio,22.511003494262695,22.31367928599184,2241,74,226,0.1,74,0.9917553663253784,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,vision,22.511003494262695,22.511003494262695,144,35,226,0.1,35,1.0,ok,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b --HeZS2-Prhc/2,-HeZS2-Prhc,2,text,8.261979103088379,3.552112135011702,16,768,83,0.1,768,0.6830984950065613,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c --HeZS2-Prhc/2,-HeZS2-Prhc,2,audio,8.261979103088379,8.205439110059995,816,74,83,0.1,74,0.9933875799179077,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c --HeZS2-Prhc/2,-HeZS2-Prhc,2,vision,8.261979103088379,8.261979103088379,70,35,83,0.1,35,1.0,ok,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c --MeTTeMJBNc/0,-MeTTeMJBNc,0,text,9.300000190734863,4.416411150246859,21,768,94,0.1,768,0.6400595903396606,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe --MeTTeMJBNc/0,-MeTTeMJBNc,0,audio,9.300000190734863,9.216486695006088,922,74,94,0.1,74,0.9939717650413513,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe --MeTTeMJBNc/0,-MeTTeMJBNc,0,vision,9.300000190734863,9.200000190734862,78,35,94,0.1,35,1.0,ok,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe --MeTTeMJBNc/13,-MeTTeMJBNc,13,text,5.430013179779053,2.997564935684203,14,768,55,0.1,768,0.6661255359649658,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44 --MeTTeMJBNc/13,-MeTTeMJBNc,13,audio,5.430013179779053,5.368323606413764,526,74,55,0.1,74,0.9918515682220459,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44 --MeTTeMJBNc/13,-MeTTeMJBNc,13,vision,5.430013179779053,5.430013179779053,41,35,55,0.1,35,1.0,ok,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44 --MeTTeMJBNc/7,-MeTTeMJBNc,7,text,10.51699161529541,6.728291415888818,31,768,106,0.1,768,0.7313360571861267,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a --MeTTeMJBNc/7,-MeTTeMJBNc,7,audio,10.51699161529541,10.441329748243898,1042,74,106,0.1,74,0.9934103488922119,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a --MeTTeMJBNc/7,-MeTTeMJBNc,7,vision,10.51699161529541,10.51699161529541,84,35,106,0.1,35,1.0,ok,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a --RfYyzHpjk4/11,-RfYyzHpjk4,11,text,5.238996982574463,3.062753976881504,17,768,53,0.1,768,0.6960803866386414,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904 --RfYyzHpjk4/11,-RfYyzHpjk4,11,audio,5.238996982574463,5.202267333462432,508,74,53,0.1,74,0.9958056807518005,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904 --RfYyzHpjk4/11,-RfYyzHpjk4,11,vision,5.238996982574463,5.238996982574463,40,35,53,0.1,35,1.0,ok,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904 --RfYyzHpjk4/8,-RfYyzHpjk4,8,text,4.588996887207031,2.8058770607668286,17,768,46,0.1,768,0.684955358505249,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c --RfYyzHpjk4/8,-RfYyzHpjk4,8,audio,4.588996887207031,4.553010454316745,454,74,46,0.1,74,0.994469404220581,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c --RfYyzHpjk4/8,-RfYyzHpjk4,8,vision,4.588996887207031,4.588996887207031,33,35,46,0.1,35,1.0,ok,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c --RfYyzHpjk4/2,-RfYyzHpjk4,2,text,5.516016006469727,3.372718970850109,25,768,56,0.1,768,0.7175998091697693,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f --RfYyzHpjk4/2,-RfYyzHpjk4,2,audio,5.516016006469727,5.478826378849712,540,74,56,0.1,74,0.9948742389678955,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f --RfYyzHpjk4/2,-RfYyzHpjk4,2,vision,5.516016006469727,5.416016006469727,43,35,56,0.1,35,1.0,ok,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f --UUCSKoHeMA/0,-UUCSKoHeMA,0,text,7.800000190734863,4.133793779369442,18,768,79,0.1,768,0.666740894317627,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898 --UUCSKoHeMA/0,-UUCSKoHeMA,0,audio,7.800000190734863,7.750270468963159,774,74,79,0.1,74,0.9943835139274597,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898 --UUCSKoHeMA/0,-UUCSKoHeMA,0,vision,7.800000190734863,7.700000190734862,66,35,79,0.1,35,1.0,ok,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898 --ri04Z7vwnc/0,-ri04Z7vwnc,0,text,2.806999921798706,2.1131306469440463,8,768,29,0.1,768,0.7826409935951233,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b --ri04Z7vwnc/0,-ri04Z7vwnc,0,audio,2.806999921798706,2.8018783078000356,277,74,29,0.1,74,0.9982975125312805,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b --ri04Z7vwnc/0,-ri04Z7vwnc,0,vision,2.806999921798706,0.0,14,35,29,0.1,35,0.0,ok,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b --ri04Z7vwnc/2,-ri04Z7vwnc,2,text,6.0269999504089355,3.9384737918153405,16,768,61,0.1,768,0.7431082725524902,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148 --ri04Z7vwnc/2,-ri04Z7vwnc,2,audio,6.0269999504089355,6.008905370493193,592,74,61,0.1,74,0.9971521496772766,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148 --ri04Z7vwnc/2,-ri04Z7vwnc,2,vision,6.0269999504089355,2.106416702270508,30,35,61,0.1,35,0.9906440377235413,ok,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148 --ri04Z7vwnc/5,-ri04Z7vwnc,5,text,3.5450000762939453,1.8027290821075441,9,768,36,0.1,768,0.721091628074646,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736 --ri04Z7vwnc/5,-ri04Z7vwnc,5,audio,3.5450000762939453,3.534527108637063,344,74,36,0.1,74,0.9974266886711121,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736 --ri04Z7vwnc/5,-ri04Z7vwnc,5,vision,3.5450000762939453,3.5450000762939453,18,35,36,0.1,35,1.0,ok,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,text,4.7919921875,2.610484106093645,11,768,48,0.1,768,0.7055363059043884,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,audio,4.7919921875,4.72247887628304,465,74,48,0.1,74,0.9860281348228455,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,vision,4.7919921875,4.7919921875,35,35,48,0.1,35,1.0,ok,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,text,6.800000190734863,3.6206328462809334,24,768,69,0.1,768,0.6351987719535828,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,audio,6.800000190734863,6.715675868979983,672,74,69,0.1,74,0.9885490536689758,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,vision,6.800000190734863,6.199999928474425,56,35,69,0.1,35,0.984375,ok,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,text,8.561002731323242,3.4511515218298876,19,768,86,0.1,768,0.5150972604751587,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,audio,8.561002731323242,8.46259727510246,840,74,86,0.1,74,0.9896790981292725,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,vision,8.561002731323242,8.561002731323242,72,35,86,0.1,35,1.0,ok,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,text,4.427018165588379,2.2374289706349377,9,768,45,0.1,768,0.6215080618858337,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,audio,4.427018165588379,4.367301462550421,425,74,45,0.1,74,0.9892621040344238,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,vision,4.427018165588379,3.027018189430237,31,35,45,0.1,35,0.96875,ok,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,text,6.966015815734863,3.9709798723459264,28,768,70,0.1,768,0.661829948425293,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,audio,6.966015815734863,6.848488390969263,684,74,70,0.1,74,0.9849807620048523,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,vision,6.966015815734863,4.0493486523628235,57,35,70,0.1,35,0.9496123194694519,ok,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,text,2.4749999046325684,1.6227161765098577,5,768,25,0.1,768,0.8113580942153931,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,audio,2.4749999046325684,2.4289188214250514,232,74,25,0.1,74,0.9844902753829956,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,vision,2.4749999046325684,1.8999999761581425,14,35,25,0.1,35,1.0,ok,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,text,3.2949869632720947,1.9395141303539274,13,768,33,0.1,768,0.6465047001838684,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,audio,3.2949869632720947,3.253163003921509,319,74,33,0.1,74,0.9880942702293396,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,vision,3.2949869632720947,3.016666531562805,20,35,33,0.1,35,0.9731181859970093,ok,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,text,29.288021087646484,15.053765958920112,49,768,293,0.1,768,0.6873865723609924,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,audio,29.288021087646484,28.759709144524624,2911,74,293,0.1,74,0.9836021065711975,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,vision,29.288021087646484,27.471354246139526,178,35,293,0.1,35,0.999393880367279,ok,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,text,13.169010162353516,8.127011682093151,25,768,132,0.1,768,0.7192046046257019,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,audio,13.169010162353516,12.971401820754682,1305,74,132,0.1,74,0.9867846369743347,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,vision,13.169010162353516,13.169010162353516,97,35,132,0.1,35,1.0,ok,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,text,11.241994857788086,4.740236129239202,19,768,113,0.1,768,0.6869907975196838,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,audio,11.241994857788086,11.070711010679133,1125,74,113,0.1,74,0.9854583740234375,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,vision,11.241994857788086,11.241994857788086,87,35,113,0.1,35,1.0,ok,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989 diff --git a/final/Q1/results/model_comparison/oof_predictions.csv b/final/Q1/results/model_comparison/oof_predictions.csv deleted file mode 100644 index b866563..0000000 --- a/final/Q1/results/model_comparison/oof_predictions.csv +++ /dev/null @@ -1,101 +0,0 @@ -sample_id,video_id,clip_id,true_polarity,true_polarity_name,true_sentiment,B0_predicted_polarity,B1_predicted_polarity,B2_predicted_polarity,B3_predicted_polarity,B4_predicted_polarity,B0_predicted_sentiment,B1_predicted_sentiment,B2_predicted_sentiment,B3_predicted_sentiment,B4_predicted_sentiment --3g5yACwYnA/13,-3g5yACwYnA,13,2,positive,0.6666666865348816,2,2,2,2,2,0.13452544808387756,0.16021420061588287,0.14872726798057556,0.16442018747329712,0.229058176279068 --3g5yACwYnA/3,-3g5yACwYnA,3,1,neutral,0.0,2,2,2,2,2,0.3026959300041199,0.33461102843284607,0.42589470744132996,0.31887251138687134,0.29829922318458557 --3g5yACwYnA/2,-3g5yACwYnA,2,1,neutral,0.0,2,2,2,2,2,0.3966212272644043,0.46180492639541626,0.48163092136383057,0.43820273876190186,0.5120640397071838 --3g5yACwYnA/9,-3g5yACwYnA,9,2,positive,0.6666666865348816,2,2,2,2,2,0.6833762526512146,0.7414957880973816,0.7930513024330139,0.7018600106239319,0.6162992715835571 --3nNcZdcdvU/5,-3nNcZdcdvU,5,1,neutral,0.0,0,0,0,0,0,-0.7614330053329468,-0.6502371430397034,-0.6979431509971619,-0.6381849050521851,-0.5865846872329712 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,1,neutral,0.0,2,2,2,2,2,0.6054284572601318,0.5735942721366882,0.6148816347122192,0.5838509798049927,0.8063328266143799 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,0,negative,-1.0,0,0,2,2,2,0.08644700050354004,-0.01681867241859436,0.054207056760787964,0.008860379457473755,0.08198326826095581 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,1,neutral,0.0,2,2,2,2,2,0.31941771507263184,0.4352392554283142,0.45426928997039795,0.40970659255981445,0.5301598310470581 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,0,negative,-0.3333333432674408,0,0,0,0,0,-0.07338133454322815,-0.14210763573646545,-0.13199511170387268,-0.13720062375068665,-0.2480643391609192 --NFrJFQijFE/1,-NFrJFQijFE,1,2,positive,0.3333333432674408,2,2,2,2,1,-0.029091298580169678,0.04221409559249878,0.023884594440460205,0.038062989711761475,-0.10948124527931213 --NFrJFQijFE/2,-NFrJFQijFE,2,1,neutral,0.0,2,2,2,2,2,0.5534588098526001,0.6096377372741699,0.5728316307067871,0.5841389894485474,0.6658575534820557 --THoVjtIkeU/12,-THoVjtIkeU,12,2,positive,2.0,2,2,2,2,2,0.1105184555053711,-0.015041038393974304,0.01798933744430542,0.013076543807983398,0.05507504940032959 --THoVjtIkeU/2,-THoVjtIkeU,2,2,positive,1.3333333730697632,2,2,2,2,2,0.14952212572097778,-0.03578713536262512,0.21528872847557068,-0.047229185700416565,0.07181030511856079 --THoVjtIkeU/6,-THoVjtIkeU,6,2,positive,2.6666667461395264,2,2,2,2,2,0.28439241647720337,0.10436569154262543,0.19625744223594666,0.109825998544693,0.11878173053264618 --UuX1xuaiiE/1,-UuX1xuaiiE,1,2,positive,1.6666666269302368,2,2,2,2,2,0.05641767382621765,0.05484718084335327,0.02277640998363495,0.05113929510116577,0.011383742094039917 --UuX1xuaiiE/0,-UuX1xuaiiE,0,2,positive,2.6666667461395264,2,2,2,2,2,0.6992747783660889,0.6104735136032104,0.7187632918357849,0.6213169097900391,0.5144999027252197 --UuX1xuaiiE/3,-UuX1xuaiiE,3,2,positive,0.3333333432674408,2,2,2,2,2,0.66947340965271,0.5266174077987671,0.5624967217445374,0.5222238898277283,0.45163899660110474 --UuX1xuaiiE/6,-UuX1xuaiiE,6,0,negative,-0.3333333432674408,2,2,2,2,2,0.12534436583518982,0.002005934715270996,-0.03466030955314636,-0.01757238805294037,0.03905734419822693 --a55Q6RWvTA/3,-a55Q6RWvTA,3,2,positive,0.6666666865348816,2,2,2,2,2,0.24422422051429749,0.13229034841060638,0.1552799940109253,0.1265096515417099,0.17101362347602844 --aNfi7CP8vM/7,-aNfi7CP8vM,7,1,neutral,0.0,2,2,2,2,2,0.7103666663169861,0.8981346487998962,0.7498640418052673,0.908927321434021,0.7986767292022705 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,0,negative,-2.0,2,2,2,2,2,0.14777150750160217,0.2721712589263916,0.20749390125274658,0.26992136240005493,0.2557317018508911 --dxfTGcXJoc/1,-dxfTGcXJoc,1,2,positive,1.6666666269302368,2,2,2,2,2,0.4534122347831726,0.15173421800136566,0.23860448598861694,0.08927315473556519,0.17231377959251404 --dxfTGcXJoc/0,-dxfTGcXJoc,0,2,positive,1.0,2,2,2,1,2,0.30912163853645325,0.18118944764137268,0.1967533975839615,0.17624947428703308,0.20736707746982574 --dxfTGcXJoc/2,-dxfTGcXJoc,2,2,positive,0.6666666865348816,2,2,2,2,2,0.5904080867767334,0.34547320008277893,0.4100586771965027,0.3144497573375702,0.33179861307144165 --dxfTGcXJoc/6,-dxfTGcXJoc,6,2,positive,0.6666666865348816,2,2,2,2,2,0.40664565563201904,0.07521255314350128,0.0900307297706604,0.107652947306633,0.09203940629959106 --egA8-b7-3M/26,-egA8-b7-3M,26,2,positive,1.6666666269302368,2,2,2,2,2,0.8926460146903992,0.8544832468032837,0.7031165957450867,0.8596105575561523,0.6629092693328857 --egA8-b7-3M/17,-egA8-b7-3M,17,2,positive,0.3333333432674408,2,2,2,2,2,0.780834972858429,0.8010746240615845,0.7224219441413879,0.7491263151168823,0.7902140617370605 --egA8-b7-3M/18,-egA8-b7-3M,18,2,positive,1.0,2,2,2,2,2,0.8284801244735718,0.8428431153297424,0.7629272937774658,0.8139240741729736,0.759922981262207 --egA8-b7-3M/16,-egA8-b7-3M,16,2,positive,0.6666666865348816,2,2,2,2,2,1.1456997394561768,1.103100299835205,0.9217796921730042,1.0922913551330566,0.9465919733047485 --egA8-b7-3M/13,-egA8-b7-3M,13,2,positive,0.3333333432674408,2,2,2,2,2,0.29149097204208374,0.2857578694820404,0.29504942893981934,0.2710241973400116,0.433204710483551 --egA8-b7-3M/1,-egA8-b7-3M,1,2,positive,0.6666666865348816,2,2,2,2,2,0.49869486689567566,0.5516833662986755,0.4600910544395447,0.5341068506240845,0.5454411506652832 --egA8-b7-3M/6,-egA8-b7-3M,6,2,positive,0.6666666865348816,2,2,2,2,2,0.8187044262886047,0.8529066443443298,0.7433335781097412,0.8614661693572998,0.7032537460327148 --egA8-b7-3M/9,-egA8-b7-3M,9,2,positive,0.3333333432674408,2,2,2,2,2,0.746141791343689,0.755176305770874,0.7585423588752747,0.7326465845108032,0.7642356157302856 --egA8-b7-3M/20,-egA8-b7-3M,20,2,positive,0.3333333432674408,2,2,2,2,2,0.75407874584198,0.783722460269928,0.7880091667175293,0.7994571924209595,0.7963440418243408 --iRBcNs9oI8/3,-iRBcNs9oI8,3,2,positive,0.6666666865348816,2,2,2,2,2,0.41121792793273926,0.26391637325286865,0.16125774383544922,0.30018502473831177,0.22292546927928925 --iRBcNs9oI8/7,-iRBcNs9oI8,7,2,positive,0.6666666865348816,2,2,2,2,2,0.6934136152267456,0.5427778959274292,0.5866559743881226,0.5839468240737915,0.5824995636940002 --iRBcNs9oI8/6,-iRBcNs9oI8,6,2,positive,0.3333333432674408,2,2,2,2,2,0.4273873567581177,0.3189065456390381,0.3010154962539673,0.3516676127910614,0.32149577140808105 --iRBcNs9oI8/9,-iRBcNs9oI8,9,2,positive,1.3333333730697632,2,2,1,2,2,0.01312372088432312,-0.0918610543012619,0.1977950930595398,-0.06410297751426697,-0.14683973789215088 --iRBcNs9oI8/8,-iRBcNs9oI8,8,2,positive,1.0,2,2,2,2,2,0.45394086837768555,0.2737142741680145,0.34107109904289246,0.34081369638442993,0.36690521240234375 --lzEya4AM_4/5,-lzEya4AM_4,5,0,negative,-0.6666666865348816,2,1,1,1,2,0.003696233034133911,0.07954774796962738,-0.02835392951965332,0.06377251446247101,0.0930773913860321 --lzEya4AM_4/6,-lzEya4AM_4,6,0,negative,-0.3333333432674408,1,1,1,1,1,-0.08578968048095703,-0.05591443181037903,-0.16103187203407288,-0.07273416221141815,-0.03954911231994629 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,1,neutral,0.0,2,2,2,2,1,0.32054486870765686,0.31638309359550476,0.4096267521381378,0.32599377632141113,0.11944134533405304 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,1,neutral,0.0,2,2,2,2,1,0.3205912411212921,0.2857860326766968,0.32545098662376404,0.2961588501930237,0.07219411432743073 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,2,positive,0.3333333432674408,2,2,2,2,2,0.34881988167762756,0.2555202841758728,0.19199120998382568,0.22443163394927979,0.11697705090045929 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,1,neutral,0.0,2,2,2,2,2,0.49129533767700195,0.509657621383667,0.40949302911758423,0.49832504987716675,0.3923254609107971 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,0,negative,-0.3333333432674408,0,0,0,0,0,0.033514365553855896,-0.12224322557449341,0.015729933977127075,-0.13064196705818176,-0.10159948468208313 --mqbVkbCndg/0,-mqbVkbCndg,0,1,neutral,0.0,2,2,2,2,2,-0.09232228994369507,-0.27456536889076233,-0.23857474327087402,-0.2712118625640869,-0.06543052196502686 --t217m2on-s/2,-t217m2on-s,2,2,positive,0.6666666865348816,2,2,2,2,2,0.4828706979751587,0.4535056948661804,0.48303860425949097,0.4338659644126892,0.29441648721694946 --t217m2on-s/7,-t217m2on-s,7,0,negative,-0.6666666865348816,2,2,2,2,2,0.03153911232948303,0.12483498454093933,0.23645390570163727,0.14657872915267944,0.2676320970058441 --tANM6ETl_M/3,-tANM6ETl_M,3,2,positive,0.3333333432674408,2,2,2,2,2,0.13329337537288666,0.27301740646362305,0.20109084248542786,0.23898470401763916,0.2801833152770996 --tPCytz4rww/11,-tPCytz4rww,11,1,neutral,0.0,2,2,2,2,2,0.39490315318107605,0.41642117500305176,0.5103898644447327,0.40319329500198364,0.5221617221832275 --tPCytz4rww/10,-tPCytz4rww,10,1,neutral,0.0,2,2,2,2,2,0.45396602153778076,0.48757827281951904,0.39305949211120605,0.48652955889701843,0.5163553357124329 --tPCytz4rww/12,-tPCytz4rww,12,1,neutral,0.0,2,2,2,2,2,0.4888620376586914,0.47673290967941284,0.523354709148407,0.47867047786712646,0.5164443254470825 --tPCytz4rww/16,-tPCytz4rww,16,1,neutral,0.0,2,2,2,2,2,0.16386589407920837,0.1889176368713379,0.15202002227306366,0.20772001147270203,0.10384060442447662 --tPCytz4rww/18,-tPCytz4rww,18,1,neutral,0.0,2,2,2,2,2,0.39335885643959045,0.48845911026000977,0.4801775813102722,0.49252307415008545,0.5596518516540527 --vxjVxOeScU/4,-vxjVxOeScU,4,2,positive,1.0,2,2,2,2,2,1.300539255142212,1.3340058326721191,1.3243328332901,1.3665581941604614,1.3101918697357178 --wMB_hJL-3o/7,-wMB_hJL-3o,7,2,positive,0.3333333432674408,2,2,2,2,2,0.43083706498146057,0.3697093427181244,0.3281114399433136,0.3639947175979614,0.4309353828430176 --wny0OAz3g8/1,-wny0OAz3g8,1,2,positive,0.3333333432674408,2,2,2,2,2,-0.029849499464035034,-0.0726122260093689,-0.024748489260673523,-0.07751013338565826,0.01515229046344757 --wny0OAz3g8/0,-wny0OAz3g8,0,2,positive,0.3333333432674408,1,1,1,1,1,-0.26475363969802856,-0.4248991906642914,-0.3667675256729126,-0.43525469303131104,-0.2892696261405945 --wny0OAz3g8/3,-wny0OAz3g8,3,0,negative,-1.3333333730697632,2,2,2,2,2,-0.03229987621307373,0.011097535490989685,-0.044357895851135254,0.014330193400382996,0.1011495590209961 --wny0OAz3g8/2,-wny0OAz3g8,2,0,negative,-0.3333333432674408,2,2,2,2,2,0.0680823028087616,0.020685836672782898,0.1548982560634613,0.049939438700675964,0.12679153680801392 --wny0OAz3g8/5,-wny0OAz3g8,5,2,positive,1.0,2,2,2,2,2,0.4779983460903168,0.42994704842567444,0.3525465130805969,0.4249609410762787,0.4561484158039093 --wny0OAz3g8/7,-wny0OAz3g8,7,1,neutral,0.0,2,2,2,2,2,0.14757554233074188,0.11216723173856735,0.15002727508544922,0.10439274460077286,0.08023504912853241 --wny0OAz3g8/9,-wny0OAz3g8,9,2,positive,0.6666666865348816,2,2,2,2,2,0.18945443630218506,0.2243257611989975,0.255195677280426,0.2078581154346466,0.26747772097587585 --571d8cVauQ/0,-571d8cVauQ,0,1,neutral,0.0,1,1,1,1,1,0.1813371479511261,0.26319703459739685,0.20845845341682434,0.2597145736217499,0.2923925817012787 --571d8cVauQ/5,-571d8cVauQ,5,2,positive,0.3333333432674408,2,2,2,2,2,0.7947983145713806,0.8263474702835083,0.8470317125320435,0.8056427836418152,0.7695794105529785 --I_e4mIh0yE/1,-I_e4mIh0yE,1,0,negative,-0.6666666865348816,1,1,2,1,2,-0.053890615701675415,0.05120442807674408,0.14258822798728943,0.058895498514175415,-0.0020049959421157837 --I_e4mIh0yE/3,-I_e4mIh0yE,3,2,positive,0.6666666865348816,2,2,2,2,2,0.20941728353500366,0.2584538459777832,0.3930857181549072,0.27197059988975525,0.3383082151412964 --UacrmKiTn4/10,-UacrmKiTn4,10,0,negative,-0.6666666865348816,2,2,2,2,2,-0.007775545120239258,0.10976105183362961,0.2304498553276062,0.1546456217765808,0.1571420133113861 --UacrmKiTn4/4,-UacrmKiTn4,4,0,negative,-1.3333333730697632,2,2,2,2,2,0.24989673495292664,0.44462817907333374,0.4752929210662842,0.4392329156398773,0.5184646844863892 --hnBHBN8p5A/7,-hnBHBN8p5A,7,2,positive,0.6666666865348816,2,2,2,2,2,0.25469517707824707,0.267363965511322,0.26611560583114624,0.2937210500240326,0.4176241457462311 --hnBHBN8p5A/6,-hnBHBN8p5A,6,2,positive,1.0,2,2,2,2,2,0.7116098403930664,0.6368920803070068,0.8011423349380493,0.6396559476852417,0.7429358959197998 --qDkUB0GgYY/6,-qDkUB0GgYY,6,2,positive,1.3333333730697632,2,2,2,2,2,1.4230821132659912,1.456520915031433,1.482230544090271,1.4008221626281738,1.3085699081420898 --uywlfIYOS8/4,-uywlfIYOS8,4,2,positive,0.3333333432674408,2,2,2,2,2,0.6702563762664795,0.7197655439376831,0.6297314763069153,0.7727823257446289,0.7619336843490601 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,0,negative,-1.0,2,2,2,2,2,0.23289291560649872,0.10650955140590668,-0.048172906041145325,0.1659325510263443,0.07517096400260925 --9y-fZ3swSY/0,-9y-fZ3swSY,0,2,positive,1.0,2,2,2,2,2,0.8935344815254211,0.981560468673706,1.0655876398086548,0.9575732946395874,0.960616946220398 --9y-fZ3swSY/4,-9y-fZ3swSY,4,1,neutral,0.0,2,2,2,2,1,0.7359274625778198,0.8043063282966614,0.8141930103302002,0.8211376667022705,0.801345944404602 --9y-fZ3swSY/8,-9y-fZ3swSY,8,2,positive,1.6666666269302368,2,2,2,2,2,0.0990075170993805,0.17485348880290985,0.26351824402809143,0.15360157191753387,0.2083582729101181 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,2,positive,0.6666666865348816,2,2,2,2,2,0.9318890571594238,1.1048165559768677,1.1751078367233276,1.091732382774353,0.7978025078773499 --HeZS2-Prhc/2,-HeZS2-Prhc,2,2,positive,0.6666666865348816,2,2,2,2,2,0.42690128087997437,0.37649014592170715,0.3945169448852539,0.3838789463043213,0.30630016326904297 --MeTTeMJBNc/0,-MeTTeMJBNc,0,2,positive,0.3333333432674408,2,2,2,2,2,0.5873996019363403,0.4888506829738617,0.520802915096283,0.4796018600463867,0.48370563983917236 --MeTTeMJBNc/13,-MeTTeMJBNc,13,2,positive,0.3333333432674408,2,2,2,2,2,0.681941032409668,0.7230368852615356,0.6091403365135193,0.7292149662971497,0.745182454586029 --MeTTeMJBNc/7,-MeTTeMJBNc,7,2,positive,1.3333333730697632,2,2,2,2,2,0.5029646158218384,0.47240275144577026,0.5074828863143921,0.46745914220809937,0.47605717182159424 --RfYyzHpjk4/11,-RfYyzHpjk4,11,2,positive,2.0,2,2,2,2,2,0.5564365983009338,0.42342162132263184,0.5021690726280212,0.43483608961105347,0.43543678522109985 --RfYyzHpjk4/8,-RfYyzHpjk4,8,2,positive,0.3333333432674408,2,2,2,2,2,0.3994223177433014,0.3921676576137543,0.2823079228401184,0.40095123648643494,0.42251622676849365 --RfYyzHpjk4/2,-RfYyzHpjk4,2,2,positive,0.3333333432674408,2,2,2,2,1,0.365897536277771,0.38750067353248596,0.23726055026054382,0.3845585286617279,0.21417461335659027 --UUCSKoHeMA/0,-UUCSKoHeMA,0,1,neutral,0.0,2,2,2,2,2,0.5638669729232788,0.5825111269950867,0.6482444405555725,0.5764590501785278,0.5579362511634827 --ri04Z7vwnc/0,-ri04Z7vwnc,0,1,neutral,0.0,2,2,2,2,1,0.26682645082473755,0.1752839833498001,0.11657405644655228,0.17440180480480194,-0.12501448392868042 --ri04Z7vwnc/2,-ri04Z7vwnc,2,0,negative,-0.3333333432674408,2,2,2,2,1,0.41648173332214355,0.39698678255081177,0.31453409790992737,0.42282843589782715,-0.06992921233177185 --ri04Z7vwnc/5,-ri04Z7vwnc,5,2,positive,0.3333333432674408,2,2,2,2,2,0.7894954681396484,0.9298861026763916,0.8353837132453918,0.9478996992111206,0.9307202100753784 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,2,positive,0.6666666865348816,2,2,2,2,2,0.8451777696609497,0.7406796216964722,0.6926915645599365,0.7564423084259033,0.7351438403129578 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,0,negative,-0.3333333432674408,2,2,2,2,0,0.08492019772529602,0.08116327226161957,0.09997616708278656,0.09164288640022278,-0.18887805938720703 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,0,negative,-2.0,2,0,2,0,2,0.25190818309783936,0.23056381940841675,0.42701321840286255,0.2685755491256714,0.35108593106269836 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,1,neutral,0.0,2,2,2,2,2,0.8093756437301636,0.6910800933837891,0.8017246723175049,0.6906447410583496,0.7730451822280884 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,2,positive,1.0,2,2,2,2,1,0.04829922318458557,-0.06272962689399719,0.04885163903236389,-0.056273818016052246,-0.3453366756439209 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,1,neutral,0.0,2,2,2,2,2,-0.11959794163703918,0.12442992627620697,0.22614140808582306,0.12353259325027466,0.05900001525878906 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,2,positive,1.3333333730697632,2,2,2,2,1,0.5094736218452454,0.4484489858150482,0.6055055856704712,0.42063793540000916,0.0980398952960968 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,1,neutral,0.0,0,0,0,0,0,-0.39470893144607544,-0.5162537693977356,-0.5732794404029846,-0.5318436622619629,-0.47799739241600037 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,0,negative,-0.6666666865348816,1,1,1,1,1,-0.31519562005996704,-0.4650754928588867,-0.48384714126586914,-0.4728321433067322,-0.4012225568294525 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,1,neutral,0.0,0,0,0,0,0,-0.3192596435546875,-0.48287540674209595,-0.5565515160560608,-0.4418525695800781,-0.5206273794174194 diff --git a/final/Q1/results/model_comparison/report.md b/final/Q1/results/model_comparison/report.md deleted file mode 100644 index 2250570..0000000 --- a/final/Q1/results/model_comparison/report.md +++ /dev/null @@ -1,41 +0,0 @@ -# 闂涓€ B0-B4 妯″瀷瀵规瘮缁撴灉 - -鏈姤鍛婃寜 `final/Q1/闂涓€.pdf` 绗?4.8.3 鑺傚疄鏂戒簲缁勫崟鍥犵礌瀵圭収銆傚叏閮?100 鏉￠檮浠朵竴瑙嗛鍧囦娇鐢ㄥ悓涓€濂楀喕缁撶壒寰佹娊鍙栧櫒銆?.1 绉掍富鏃堕棿缃戞牸銆佽娴嬫帺鐮佷笌鎸?`video_id` 鍒嗙粍鐨勪簲鎶樺垝鍒嗐€傛儏鎰熸爣绛句粎鐢ㄤ簬鎶樺唴鐨勮交閲忛娴嬫帰閽堬紝涓嶈繘鍏ユ椂闂村榻愩€丆TC 鍚庨獙鎴栧姩鎬佺鍚嶈绠椼€? -## 瀹為檯鐗瑰緛鍜屾瘮杈冪増鏈? -| 鐗堟湰 | 瀹炴柦鍐呭 | -| --- | --- | -| B0 | CTC Viterbi 璇嶅尯闂翠笌闊抽/瑙嗚鐗╃悊鏃堕棿鍖洪棿鎶曞奖锛涜缁冩姌鍧囧€?鏍囧噯宸缉鏀撅紱瑙嗚鏃嬭浆鍚戦噺鍜岃绾垮悜閲忎綔鏅€氬垎閲忓潎鍊笺€?| -| B1 | 鍦?B0 涓婃敼鐢ㄨ缁冩姌涓綅鏁颁笌 MAD 鐨勭ǔ鍋ュ昂搴︼紙MAD 涓洪浂鏃跺洖閫€鍒拌缁冩姌鏍囧噯宸級锛屽苟瀵硅瑙夋棆杞敤 SO(3) 鍧囧€笺€佽绾跨敤褰掍竴鍖栧悜閲忓潎鍊笺€?| -| B2 | 鍦?B1 涓婂彧灏嗘枃鏈瘝鍚戦噺鐨勭‖杈圭晫鎶曞奖鏇挎崲涓哄浐瀹氳浆鍐?CTC 鐘舵€佸浘鐨勫墠鍚戔€撳悗鍚戝崰鎹鐜囨姇褰憋紱濯掍綋鏃堕棿鎴充粛鎸夌墿鐞嗘椂闂淬€?| -| B3 | 鍦?B1 涓婁粎闄勫姞闊抽 log-F0/鑳介噺涓庤瑙?jaw-open/鍢撮儴寮€鍚堢巼鐨勬椂闂村骞夸竴銆佷簩闃惰矾寰勭鍚嶏紱缂烘祴鐐逛箣闂翠笉杩炵嚎銆?| -| B4 | 鍦?B1 涓婁粎闄勫姞鍐荤粨 Wav2Vec2-base-960h 鏈€鍚庡洓灞傜殑鏃堕棿鎶曞奖琛ㄧず銆?| - -闄勪欢涓€鐜版湁鏍囩琛ㄥ拰鍘熷瑙嗛琚洿鎺ヤ娇鐢ㄣ€傛枃鏈负 768 缁?BERT-base-uncased 鏈洓灞傚潎鍊硷紱闊抽涓?74 缁达紙log-Mel 40銆丮FCC 13銆佄擬FCC 13銆侀煹寰?璋辩粺璁?8锛夛紱瑙嗚涓?35 缁达紙17 涓?MediaPipe blendshape 浠ｇ悊銆? 缁村ご濮裤€? 缁磋繎浼艰绾裤€? 缁撮潰閮ㄦ瘮渚嬪嚑浣曪級銆傝瑙変唬鐞嗗拰鍑犱綍绱㈠紩瀹氫箟瑙?`compare_models.py`锛屽畠浠笌 OpenFace AU 瀹氫箟骞朵笉绛夊悓锛涜闄愬埗椤诲湪璁烘枃涓槑绀恒€? -## 缁撴灉 - -鍒嗙被鎸夎繛缁爣绛剧殑涓ユ牸绗﹀彿鏋勯€?Negative/Neutral/Positive锛屽己搴﹂娴嬮檺骞呭埌 [-3, 3]銆傛瘡鎶樿缁冩姌鍗曠嫭鎷熷悎鏍囧噯鍖栧櫒銆侀€昏緫鍥炲綊锛圕=0.05锛変笌 Ridge锛坅lpha=25锛夛紱鍥哄畾浜旀姌鐢?GroupKFold 鎸夊師濮?`video_id` 鍒嗙粍銆傛€讳綋 OOF 鎸囨爣鎸?100 鏉＄暀缁勯娴嬭绠椼€? -| 鏂规硶 | OOF Accuracy | OOF Macro-F1 | OOF MAE | OOF Pearson | Macro-F1 鎶樺潎鍊悸盨D | -| --- | ---: | ---: | ---: | ---: | ---: | -| B0 | 0.600 | 0.361 | 0.564 | 0.346 | 0.337 卤 0.098 | -| B1 | 0.610 | 0.387 | 0.599 | 0.238 | 0.349 卤 0.119 | -| B2 | 0.580 | 0.331 | 0.588 | 0.279 | 0.313 卤 0.087 | -| B3 | 0.590 | 0.359 | 0.600 | 0.230 | 0.333 卤 0.096 | -| B4 | 0.600 | 0.422 | 0.593 | 0.199 | 0.398 卤 0.089 | - -鎸夊綋鍓?OOF 鎺㈤拡锛孧acro-F1 鏈€楂樼殑鏄?B4锛?.422锛夛紝MAE 鏈€浣庣殑鏄?B0锛?.564锛夈€傜浉瀵?B1锛孊4 鐨?Macro-F1 宸负 +0.035锛岃棰戠粍 Bootstrap 95% 鍖洪棿 [-0.051, +0.137]锛屽尯闂磋法杩囬浂銆侭0 鐨?MAE 宸负 -0.035锛屽尯闂?[-0.063, -0.012]锛孭earson 宸负 +0.108銆侭2 鐨?Macro-F1 宸负 -0.057锛屽尯闂?[-0.109, -0.001]锛孧AE 宸负 -0.011锛汢3 鐨?Macro-F1 宸负 -0.029锛屽尯闂?[-0.072, +0.000]銆傜粍 Bootstrap 鍙弽鏄犲綋鍓?37 涓潵婧愯棰戜笂鐨勬娊鏍蜂笉纭畾鎬э紝鏈綔澶氶噸姣旇緝鏍℃銆? -## 瑕嗙洊涓庡榻愭牳楠? -鏍锋湰鏁帮細100锛涙爣绛捐〃杩炵画鏍囩涓庢瀬鎬т竴鑷?100/100锛汣TC 纭榻愬畬鏁寸殑鏍锋湰 91/100銆侭0 纭尯闂村钩鍧囨枃鏈鐩栫巼涓?0.673锛孊2 骞冲潎璇嶅崰鎹悗楠岃川閲忎负 0.229锛堣繖鏄湡鏈涘崰鎹巼锛屼笉鏄鐩栫巼锛夛紱瑙嗚妫€娴嬭鐩栫巼锛堟寜 0.1 绉掔綉鏍煎钩鍧囷級涓?0.859銆傚悗楠屽唴閮?90% 杈圭晫鍖洪棿骞冲潎瀹藉害涓鸿捣鐐?0.131 绉掋€佺粓鐐?0.111 绉掋€傛病鏈変汉宸ヨ竟鐣屽瓙闆嗭紝鍥犳杩欎簺瀹藉害鍙槸妯″瀷鍐呴儴涓嶇‘瀹氭€ф憳瑕侊紝涓嶆槸缁忛獙鏍″噯鐜囨垨杈圭晫璇樊銆? -B0鈥揃4 鐨勭壒寰佹潵婧愩€佹湁鏁堟帺鐮併€佽繛缁鐩栫巼銆佽瘝杈圭晫鍚庨獙瀹藉害銆佹姌鍐呴娴嬪強瑙嗛鍝堝笇鍧囧垎鍒繚瀛樺湪閰嶅 CSV/JSON 涓€傛病鏈変汉宸ヨ瘝杈圭晫鏃朵笉鎶ュ憡 IoU/MATE锛屼篃涓嶆妸涓嶅悓鏂规硶鐨勯娴嬫帰閽堝垎鏁拌В閲婁负瀵归綈鐪熷€笺€? -## 鏂囦欢 - -- `comparison_summary.csv`锛氫簲绉嶆柟娉曟€讳綋 OOF 涓庢姌鍧囧€兼寚鏍囥€?- `fold_metrics.csv`锛氭瘡鎶樺垎绫?鍥炲綊鎸囨爣涓庣壒寰佺淮鏁般€?- `oof_predictions.csv`锛氭瘡鏉℃牱鏈殑鐣欑粍棰勬祴銆?- `group_bootstrap_deltas.csv`锛氱浉瀵?B1 鐨勮棰戠粍閰嶅 Bootstrap 95% 鍖洪棿銆?- `sample_alignment_summary.csv`锛?00 鏉℃牱鏈€佺‖/姒傜巼鏂囨湰瑕嗙洊鍜屽悗楠岃竟鐣屽搴︺€?- `modality_summary.csv`锛?00 琛屾牱鏈?妯℃€佹槑缁嗐€?- `word_alignment_posterior.csv`锛氶€愯瘝纭竟鐣屻€佸疄闄呯浉瀵硅川閲忔潈閲嶅強鍚庨獙杈圭晫鍖洪棿銆?- `split_assignments.csv`锛氶€愭牱鏈姌鍙枫€?- `comparison.png`锛氭牳蹇?OOF 鎸囨爣鍥俱€?- `typical_alignment_example.png`锛氫腑浣嶆椂闀挎牱鏈殑璇嶈竟鐣屻€佽鐩栫巼銆佸０瀛?瑙嗚杞ㄨ抗涓庤棰戝抚鏍搁獙鍥俱€?- `run_manifest.json`锛氱幆澧冦€佹ā鍨?revision銆佸弬鏁般€佸搱甯屼笌杩愯鏃堕棿銆? -## 澶嶇幇 - -鍦ㄤ粨搴撴牴鐩綍鎵ц锛? -```bash -cd final/Q1 -uv sync -uv run python compare_models.py -``` - -鐗瑰緛缂撳瓨鍐欏湪 `final/Q1/cache/native/`锛屾寮忕粨鏋滃彧鍐欏湪 `final/Q1/results/model_comparison/`銆傚垹闄ょ紦瀛樺悗浼氫粠闄勪欢涓€閲嶆柊鎻愬彇銆傞娆¤繍琛岄渶瑕佷笅杞?BERT銆乄av2Vec2 鍜?MediaPipe Face Landmarker 鏉冮噸銆? diff --git a/final/Q1/results/model_comparison/sample_alignment_summary.csv b/final/Q1/results/model_comparison/sample_alignment_summary.csv deleted file mode 100644 index 75d637f..0000000 --- a/final/Q1/results/model_comparison/sample_alignment_summary.csv +++ /dev/null @@ -1,101 +0,0 @@ -sample_id,video_id,clip_id,source_video_sha256,duration_s,word_count,hard_aligned_word_count,unlocated_word_count,posterior_usable_word_count,mean_start_interval_width90_s,mean_end_interval_width90_s,text_hard_grid_coverage,text_posterior_grid_occupancy,audio_grid_coverage,vision_grid_coverage,audio_native_rows,vision_native_rows,status --3g5yACwYnA/13,-3g5yACwYnA,13,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,5.5139970779418945,15,15,0,15,0.037333333333333406,0.020000000000000004,0.7015532851219177,0.24074573814868927,0.9882608652114868,1.0,540,43,ok --3g5yACwYnA/3,-3g5yACwYnA,3,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,14.388997077941895,29,29,0,29,0.2793103448275863,0.25931034482758614,0.6343300342559814,0.19729532301425934,0.9912122488021851,1.0,1433,103,ok --3g5yACwYnA/2,-3g5yACwYnA,2,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,9.394009590148926,14,14,0,14,0.2828571428571428,0.19142857142857145,0.7563282251358032,0.15269099175930023,0.9936367273330688,1.0,924,78,ok --3g5yACwYnA/9,-3g5yACwYnA,9,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,8.816991806030273,21,21,0,21,0.22095238095238093,0.1942857142857142,0.6637077927589417,0.26207172870635986,0.9912108778953552,1.0,870,75,ok --3nNcZdcdvU/5,-3nNcZdcdvU,5,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,7.867969036102295,18,18,0,18,0.03555555555555544,0.012222222222222258,0.7357116341590881,0.22051197290420532,0.9971166849136353,0.9904456734657288,779,67,ok --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,3.9820311069488525,8,7,1,7,0.12285714285714287,0.12285714285714275,0.6164926290512085,0.18007618188858032,0.9881895780563354,0.9814813733100891,392,27,ok --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,5.6529951095581055,10,9,1,9,0.037777777777777764,0.040000000000000036,0.7021505832672119,0.22856950759887695,0.9900854229927063,0.666666567325592,555,44,ok --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,3.3580079078674316,7,7,0,7,0.048571428571428564,0.03428571428571422,0.5562952756881714,0.1454559713602066,0.9901678562164307,0.841666579246521,326,21,ok --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,7.733983993530273,22,22,0,22,0.16363636363636375,0.15181818181818188,0.6705501675605774,0.25460928678512573,0.9946621060371399,0.0,764,65,ok --NFrJFQijFE/1,-NFrJFQijFE,1,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,5.745999813079834,16,16,0,16,0.46625000000000005,0.47125000000000006,0.7835317254066467,0.27368244528770447,0.9922494292259216,0.0,566,44,ok --NFrJFQijFE/2,-NFrJFQijFE,2,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,6.855999946594238,18,18,0,18,0.17888888888888899,0.1544444444444445,0.7431879639625549,0.24775999784469604,0.9926568269729614,0.0,670,55,ok --THoVjtIkeU/12,-THoVjtIkeU,12,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,14.896029472351074,39,39,0,39,0.05999999999999991,0.05282051282051286,0.6352806687355042,0.23379945755004883,0.9911767244338989,1.0,1481,106,ok --THoVjtIkeU/2,-THoVjtIkeU,2,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,4.2919921875,10,10,0,10,0.016000000000000004,0.007999999999999985,0.7164996266365051,0.2465074211359024,0.9895758628845215,1.0,424,31,ok --THoVjtIkeU/6,-THoVjtIkeU,6,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,8.097004890441895,30,30,0,30,0.053333333333333295,0.05733333333333333,0.6787540316581726,0.30513063073158264,0.9927164316177368,1.0,799,68,ok --UuX1xuaiiE/1,-UuX1xuaiiE,1,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,10.350000381469727,27,27,0,27,0.09555555555555556,0.060000000000000046,0.6550575494766235,0.22912898659706116,0.9925051927566528,0.8333339691162109,1027,83,ok --UuX1xuaiiE/0,-UuX1xuaiiE,0,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,3.6333329677581787,9,9,0,9,0.03777777777777775,0.04666666666666665,0.6484582424163818,0.2555619180202484,0.994590699672699,0.9444444179534912,358,25,ok --UuX1xuaiiE/3,-UuX1xuaiiE,3,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,4.1300129890441895,9,9,0,9,0.07777777777777779,0.0600000000000001,0.7240580320358276,0.16585363447666168,0.992123007774353,0.976190447807312,404,29,ok --UuX1xuaiiE/6,-UuX1xuaiiE,6,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,8.113997459411621,22,22,0,22,0.06363636363636366,0.0736363636363637,0.7064024806022644,0.25750458240509033,0.984533965587616,0.9776423573493958,795,68,ok --a55Q6RWvTA/3,-a55Q6RWvTA,3,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,22.15397071838379,65,65,0,65,0.2215384615384614,0.18892307692307703,0.6580277681350708,0.23805321753025055,0.9912921786308289,1.0,2209,142,ok --aNfi7CP8vM/7,-aNfi7CP8vM,7,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,8.694987297058105,18,18,0,18,0.2511111111111111,0.24000000000000002,0.7001731395721436,0.23255421221256256,0.9945195913314819,1.0,854,74,ok --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,10.966667175292969,14,13,1,13,0.22461538461538455,0.08615384615384586,0.71061772108078,0.1247716099023819,0.9910455346107483,1.0,1090,86,ok --dxfTGcXJoc/1,-dxfTGcXJoc,1,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,16.16100311279297,36,36,0,36,0.07777777777777778,0.033333333333333375,0.6812966465950012,0.2449914664030075,0.9900091290473938,1.0,1602,112,ok --dxfTGcXJoc/0,-dxfTGcXJoc,0,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,20.5,41,38,3,38,0.09315789473684201,0.06789473684210537,0.7333337664604187,0.209048330783844,0.9891952872276306,0.99895840883255,2042,134,ok --dxfTGcXJoc/2,-dxfTGcXJoc,2,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,16.697982788085938,34,32,2,32,0.048124999999999946,0.03624999999999996,0.6593995094299316,0.2266717106103897,0.9901677966117859,1.0,1664,115,ok --dxfTGcXJoc/6,-dxfTGcXJoc,6,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,12.735026359558105,27,27,0,27,0.05851851851851849,0.05407407407407409,0.715381383895874,0.25197115540504456,0.9889141917228699,1.0,1263,95,ok --egA8-b7-3M/26,-egA8-b7-3M,26,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,6.030990123748779,15,15,0,15,0.08133333333333341,0.02533333333333337,0.6877890825271606,0.22332797944545746,0.9945786595344543,1.0,593,47,ok --egA8-b7-3M/17,-egA8-b7-3M,17,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,7.271028995513916,16,16,0,16,0.017499999999999995,0.019999999999999962,0.7292676568031311,0.24067606031894684,0.9916234016418457,1.0,710,60,ok --egA8-b7-3M/18,-egA8-b7-3M,18,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,8.386002540588379,22,22,0,22,0.052727272727272734,0.021818181818181796,0.6243576407432556,0.26077014207839966,0.9932008385658264,1.0,827,71,ok --egA8-b7-3M/16,-egA8-b7-3M,16,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,5.538021087646484,11,11,0,11,0.010909090909090908,0.0181818181818182,0.7375588417053223,0.23636400699615479,0.9940068125724792,1.0,549,43,ok --egA8-b7-3M/13,-egA8-b7-3M,13,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,4.18398380279541,11,11,0,11,0.23818181818181816,0.23090909090909092,0.6372604370117188,0.2536529004573822,0.9924017786979675,1.0,403,29,ok --egA8-b7-3M/1,-egA8-b7-3M,1,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,10.266016006469727,20,19,1,19,1.0621052631578947,1.0526315789473684,0.6490206122398376,0.19601602852344513,0.9937204718589783,1.0,1016,83,ok --egA8-b7-3M/6,-egA8-b7-3M,6,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,6.264974117279053,12,12,0,12,0.08833333333333332,0.03666666666666665,0.7164138555526733,0.2193591147661209,0.9942464828491211,1.0,619,50,ok --egA8-b7-3M/9,-egA8-b7-3M,9,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,5.0899739265441895,10,10,0,10,0.007999999999999962,0.011999999999999966,0.597113311290741,0.2590937316417694,0.9939422011375427,1.0,494,38,ok --egA8-b7-3M/20,-egA8-b7-3M,20,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,6.644987106323242,20,20,0,20,0.077,0.021000000000000008,0.6985589861869812,0.2369300276041031,0.992087185382843,1.0,648,54,ok --iRBcNs9oI8/3,-iRBcNs9oI8,3,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,5.620999813079834,8,8,0,8,0.10250000000000009,0.07500000000000001,0.6791599988937378,0.1166667565703392,0.9954468607902527,0.0,554,28,ok --iRBcNs9oI8/7,-iRBcNs9oI8,7,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,4.104000091552734,9,9,0,9,0.10222222222222223,0.02666666666666669,0.7135127186775208,0.18537555634975433,0.995795726776123,0.0,401,21,ok --iRBcNs9oI8/6,-iRBcNs9oI8,6,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,2.9030001163482666,5,5,0,5,0.03999999999999999,0.04399999999999998,0.7227271199226379,0.12799954414367676,0.9973600506782532,0.0,283,15,ok --iRBcNs9oI8/9,-iRBcNs9oI8,9,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,3.4159998893737793,5,5,0,5,0.20800000000000002,0.016000000000000014,0.643750011920929,0.14118166267871857,0.993934154510498,0.0,338,17,ok --iRBcNs9oI8/8,-iRBcNs9oI8,8,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,8.093000411987305,21,21,0,21,0.23238095238095247,0.2228571428571429,0.6192578673362732,0.18252728879451752,0.9947929382324219,0.0,803,41,ok --lzEya4AM_4/5,-lzEya4AM_4,5,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,7.675000190734863,19,19,0,19,0.12000000000000004,0.09473684210526319,0.7427374720573425,0.2366195172071457,0.9901386499404907,1.0,757,64,ok --lzEya4AM_4/6,-lzEya4AM_4,6,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,14.7919921875,43,43,0,43,0.05023255813953489,0.024651162790697716,0.7318416833877563,0.2524191737174988,0.9907644391059875,1.0,1475,105,ok --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,6.103000164031982,13,13,0,13,0.733846153846154,0.7399999999999998,0.8174015879631042,0.1573781669139862,1.0,0.0,606,31,ok --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,5.730999946594238,11,11,0,11,0.4636363636363635,0.46181818181818185,0.9266790151596069,0.2666672170162201,1.0,0.0,571,29,ok --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,2.256999969482422,5,5,0,5,0.11199999999999996,0.06400000000000003,0.6512578129768372,0.15656699240207672,0.9862568378448486,1.0,223,12,ok --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,7.0329999923706055,22,22,0,22,0.11999999999999994,0.09454545454545453,0.5777366757392883,0.25714901089668274,0.9846946001052856,0.9856857061386108,691,35,ok --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,4.191999912261963,11,11,0,11,0.14363636363636367,0.06363636363636366,0.6529467701911926,0.26233649253845215,0.9846959114074707,1.0,418,21,ok --mqbVkbCndg/0,-mqbVkbCndg,0,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,6.466667175292969,12,12,0,12,0.06166666666666665,0.03333333333333339,0.7074670791625977,0.20000411570072174,0.9905118346214294,1.0,639,53,ok --t217m2on-s/2,-t217m2on-s,2,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,5.6860032081604,12,12,0,12,0.21666666666666665,0.2333333333333333,0.6279765367507935,0.16491496562957764,0.9959543347358704,1.0,564,45,ok --t217m2on-s/7,-t217m2on-s,7,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,17.183008193969727,45,45,0,45,0.048444444444444436,0.017777777777777892,0.6867958903312683,0.23202678561210632,0.9928514361381531,1.0,1703,117,ok --tANM6ETl_M/3,-tANM6ETl_M,3,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,6.758008003234863,22,22,0,22,0.030909090909090896,0.021818181818181792,0.5859207510948181,0.2757546901702881,0.9936580061912537,0.9898989200592041,661,54,ok --tPCytz4rww/11,-tPCytz4rww,11,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,5.466991901397705,17,17,0,17,0.09764705882352945,0.08588235294117644,0.6753751635551453,0.28518491983413696,0.9890678524971008,1.0,538,43,ok --tPCytz4rww/10,-tPCytz4rww,10,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,4.788997173309326,12,12,0,12,0.04499999999999999,0.00833333333333334,0.5449775457382202,0.225633442401886,0.9894654750823975,1.0,468,35,ok --tPCytz4rww/12,-tPCytz4rww,12,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,11.863997459411621,26,26,0,26,0.14461538461538456,0.08769230769230774,0.654129683971405,0.22372934222221375,0.9875938892364502,1.0,1174,91,ok --tPCytz4rww/16,-tPCytz4rww,16,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,7.205989837646484,16,16,0,16,0.05125000000000004,0.013749999999999984,0.4489554166793823,0.17846456170082092,0.988937258720398,1.0,714,59,ok --tPCytz4rww/18,-tPCytz4rww,18,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,6.644987106323242,16,16,0,16,0.16375,0.1275,0.6719247698783875,0.22222484648227692,0.9872123599052429,1.0,657,54,ok --vxjVxOeScU/4,-vxjVxOeScU,4,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,9.127017974853516,21,21,0,21,0.06380952380952383,0.037142857142857144,0.6465563774108887,0.2219761461019516,0.9960808753967285,1.0,904,77,ok --wMB_hJL-3o/7,-wMB_hJL-3o,7,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,6.044010162353516,22,22,0,22,0.012727272727272738,0.021818181818181816,0.658498227596283,0.31481292843818665,0.9895980954170227,0.9836065769195557,598,48,ok --wny0OAz3g8/1,-wny0OAz3g8,1,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,6.844009876251221,23,22,1,22,0.013636363636363648,0.011818181818181823,0.62252277135849,0.3443901240825653,0.995954155921936,0.9950981140136719,678,56,ok --wny0OAz3g8/0,-wny0OAz3g8,0,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,3.3333330154418945,9,9,0,9,0.32000000000000006,0.2866666666666667,0.6052471995353699,0.2736770212650299,0.9955413937568665,0.9999999403953552,328,21,ok --wny0OAz3g8/3,-wny0OAz3g8,3,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,6.146028995513916,20,20,0,20,0.022999999999999986,0.02399999999999995,0.7014381289482117,0.3213139474391937,0.9980819821357727,0.9895832538604736,610,49,ok --wny0OAz3g8/2,-wny0OAz3g8,2,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,6.686978816986084,23,23,0,23,0.02869565217391302,0.026086956521739167,0.7553136348724365,0.33939871191978455,0.9957627058029175,0.9848485589027405,653,54,ok --wny0OAz3g8/5,-wny0OAz3g8,5,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,6.9119791984558105,20,20,0,20,0.004999999999999982,0.004999999999999982,0.6063091158866882,0.3181923031806946,0.9961636066436768,0.9895831346511841,675,56,ok --wny0OAz3g8/7,-wny0OAz3g8,7,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,8.06796932220459,29,29,0,29,0.0186206896551724,0.017241379310344838,0.6700748205184937,0.3224986493587494,0.9956274628639221,1.0,792,68,ok --wny0OAz3g8/9,-wny0OAz3g8,9,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,6.2130208015441895,20,20,0,20,0.025999999999999975,0.027000000000000024,0.6688015460968018,0.3180274963378906,0.9969837069511414,0.9672130942344666,605,49,ok --571d8cVauQ/0,-571d8cVauQ,0,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,5.066667079925537,12,12,0,12,0.07500000000000001,0.03000000000000001,0.6978414058685303,0.2360049933195114,0.9903978705406189,1.0,500,39,ok --571d8cVauQ/5,-571d8cVauQ,5,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,15.272981643676758,43,43,0,43,0.14604651162790694,0.11534883720930222,0.7017207741737366,0.23815667629241943,0.9918006062507629,1.0,1512,108,ok --I_e4mIh0yE/1,-I_e4mIh0yE,1,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,7.622004985809326,18,18,0,18,0.061111111111111116,0.02333333333333332,0.6546477675437927,0.2152525633573532,0.985649049282074,1.0,745,63,ok --I_e4mIh0yE/3,-I_e4mIh0yE,3,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,9.163021087646484,22,22,0,22,0.13,0.11545454545454543,0.6251755356788635,0.24003465473651886,0.9852646589279175,1.0,900,77,ok --UacrmKiTn4/10,-UacrmKiTn4,10,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,7.361979007720947,18,18,0,18,0.04555555555555555,0.07222222222222224,0.47940683364868164,0.20824021100997925,0.9955651164054871,1.0,725,60,ok --UacrmKiTn4/4,-UacrmKiTn4,4,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,4.741015911102295,14,14,0,14,0.06714285714285716,0.09857142857142863,0.7214669585227966,0.246806800365448,0.9926760792732239,1.0,469,35,ok --hnBHBN8p5A/7,-hnBHBN8p5A,7,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,7.372000217437744,13,13,0,13,0.01846153846153847,0.021538461538461593,0.657243549823761,0.1729772984981537,0.9905768632888794,0.0,731,37,ok --hnBHBN8p5A/6,-hnBHBN8p5A,6,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,6.401000022888184,13,13,0,13,0.04307692307692312,0.012307692307692353,0.7618876099586487,0.22187285125255585,0.9932082295417786,0.0,634,32,ok --qDkUB0GgYY/6,-qDkUB0GgYY,6,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,4.677995204925537,16,16,0,16,0.033749999999999974,0.02250000000000002,0.6504627466201782,0.24682332575321198,0.9946085214614868,1.0,462,35,ok --uywlfIYOS8/4,-uywlfIYOS8,4,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,6.044987201690674,18,18,0,18,0.0433333333333333,0.04333333333333334,0.6908547878265381,0.25089454650878906,0.9957761168479919,1.0,589,48,ok --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,12.805012702941895,34,34,0,34,0.06470588235294118,0.034705882352941086,0.63474440574646,0.22114422917366028,0.9899758696556091,1.0,1274,95,ok --9y-fZ3swSY/0,-9y-fZ3swSY,0,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,6.8333330154418945,22,22,0,22,0.023636363636363688,0.019999999999999997,0.5440928339958191,0.2264895737171173,0.9948647022247314,1.0,676,57,ok --9y-fZ3swSY/4,-9y-fZ3swSY,4,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,2.8210289478302,9,9,0,9,0.1711111111111111,0.15999999999999998,0.5274830460548401,0.1851968914270401,0.9926167726516724,1.0,267,15,ok --9y-fZ3swSY/8,-9y-fZ3swSY,8,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,4.988996982574463,12,12,0,12,0.07333333333333339,0.05333333333333331,0.562736988067627,0.18345493078231812,0.9931800365447998,0.9487179517745972,492,37,ok --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,22.511003494262695,41,41,0,41,0.6434146341463415,0.6102439024390245,0.6386448740959167,0.19374850392341614,0.9917553663253784,1.0,2241,144,ok --HeZS2-Prhc/2,-HeZS2-Prhc,2,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,8.261979103088379,16,16,0,16,0.14125000000000004,0.08125000000000016,0.6830984950065613,0.15250585973262787,0.9933875799179077,1.0,816,70,ok --MeTTeMJBNc/0,-MeTTeMJBNc,0,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,9.300000190734863,21,21,0,21,0.03714285714285711,0.026666666666666637,0.6400595903396606,0.22926528751850128,0.9939717650413513,1.0,922,78,ok --MeTTeMJBNc/13,-MeTTeMJBNc,13,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,5.430013179779053,14,14,0,14,0.042857142857142864,0.00714285714285715,0.6661255359649658,0.2075468748807907,0.9918515682220459,1.0,526,41,ok --MeTTeMJBNc/7,-MeTTeMJBNc,7,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,10.51699161529541,31,31,0,31,0.03225806451612902,0.02387096774193552,0.7313360571861267,0.269477516412735,0.9934103488922119,1.0,1042,84,ok --RfYyzHpjk4/11,-RfYyzHpjk4,11,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,5.238996982574463,17,17,0,17,0.018823529411764742,0.025882352941176467,0.6960803866386414,0.27842891216278076,0.9958056807518005,1.0,508,40,ok --RfYyzHpjk4/8,-RfYyzHpjk4,8,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,4.588996887207031,17,17,0,17,0.04588235294117646,0.005882352941176463,0.684955358505249,0.27527740597724915,0.994469404220581,1.0,454,33,ok --RfYyzHpjk4/2,-RfYyzHpjk4,2,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,5.516016006469727,25,25,0,25,0.12960000000000005,0.12880000000000008,0.7175998091697693,0.3444570004940033,0.9948742389678955,1.0,540,43,ok --UUCSKoHeMA/0,-UUCSKoHeMA,0,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,7.800000190734863,18,18,0,18,0.23000000000000004,0.20999999999999996,0.666740894317627,0.22424933314323425,0.9943835139274597,1.0,774,66,ok --ri04Z7vwnc/0,-ri04Z7vwnc,0,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,2.806999921798706,8,8,0,8,0.35999999999999993,0.3525,0.7826409935951233,0.25714799761772156,0.9982975125312805,0.0,277,14,ok --ri04Z7vwnc/2,-ri04Z7vwnc,2,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,6.0269999504089355,16,16,0,16,0.23374999999999996,0.22875,0.7431082725524902,0.26666611433029175,0.9971521496772766,0.9906440377235413,592,30,ok --ri04Z7vwnc/5,-ri04Z7vwnc,5,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,3.5450000762939453,9,8,1,8,0.21249999999999986,0.23999999999999994,0.721091628074646,0.21714359521865845,0.9974266886711121,1.0,344,18,ok --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,4.7919921875,11,11,0,11,0.01636363636363641,0.021818181818181816,0.7055363059043884,0.16956037282943726,0.9860281348228455,1.0,465,35,ok --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,6.800000190734863,24,24,0,24,0.056666666666666664,0.029999999999999957,0.6351987719535828,0.2500007152557373,0.9885490536689758,0.984375,672,56,ok --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,8.561002731323242,19,19,0,19,0.25578947368421046,0.26210526315789456,0.5150972604751587,0.16428017616271973,0.9896790981292725,1.0,840,72,ok --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,4.427018165588379,9,9,0,9,0.07555555555555557,0.024444444444444453,0.6215080618858337,0.20000608265399933,0.9892621040344238,0.96875,425,31,ok --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,6.966015815734863,28,28,0,28,0.029285714285714304,0.02428571428571429,0.661829948425293,0.29855138063430786,0.9849807620048523,0.9496123194694519,684,57,ok --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,2.4749999046325684,5,5,0,5,0.21599999999999997,0.16400000000000006,0.8113580942153931,0.14165958762168884,0.9844902753829956,1.0,232,14,ok --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,3.2949869632720947,13,12,1,12,0.07166666666666661,0.06166666666666667,0.6465047001838684,0.2687399089336395,0.9880942702293396,0.9731181859970093,319,20,ok --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,29.288021087646484,49,49,0,49,0.2922448979591836,0.2689795918367347,0.6873865723609924,0.18630558252334595,0.9836021065711975,0.999393880367279,2911,178,ok --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,13.169010162353516,25,25,0,25,0.14880000000000007,0.18799999999999997,0.7192046046257019,0.19541765749454498,0.9867846369743347,1.0,1305,97,ok --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,11.241994857788086,19,19,0,19,0.09578947368421038,0.15263157894736842,0.6869907975196838,0.16814078390598297,0.9854583740234375,1.0,1125,87,ok diff --git a/final/Q1/results/model_comparison/split_assignments.csv b/final/Q1/results/model_comparison/split_assignments.csv deleted file mode 100644 index ef7b811..0000000 --- a/final/Q1/results/model_comparison/split_assignments.csv +++ /dev/null @@ -1,101 +0,0 @@ -sample_id,video_id,fold,split --3g5yACwYnA/13,-3g5yACwYnA,1,valid_oof --3g5yACwYnA/2,-3g5yACwYnA,1,valid_oof --3g5yACwYnA/3,-3g5yACwYnA,1,valid_oof --3g5yACwYnA/9,-3g5yACwYnA,1,valid_oof --3nNcZdcdvU/5,-3nNcZdcdvU,5,valid_oof --571d8cVauQ/0,-571d8cVauQ,2,valid_oof --571d8cVauQ/5,-571d8cVauQ,2,valid_oof --6rXp3zJ3kc/8,-6rXp3zJ3kc,4,valid_oof --9y-fZ3swSY/0,-9y-fZ3swSY,1,valid_oof --9y-fZ3swSY/4,-9y-fZ3swSY,1,valid_oof --9y-fZ3swSY/8,-9y-fZ3swSY,1,valid_oof --AUZQgSxyPQ/2,-AUZQgSxyPQ,3,valid_oof --HeZS2-Prhc/2,-HeZS2-Prhc,2,valid_oof --HwX2H8Z4hY/2,-HwX2H8Z4hY,3,valid_oof --HwX2H8Z4hY/5,-HwX2H8Z4hY,3,valid_oof --HwX2H8Z4hY/6,-HwX2H8Z4hY,3,valid_oof --HwX2H8Z4hY/9,-HwX2H8Z4hY,3,valid_oof --I_e4mIh0yE/1,-I_e4mIh0yE,1,valid_oof --I_e4mIh0yE/3,-I_e4mIh0yE,1,valid_oof --MeTTeMJBNc/0,-MeTTeMJBNc,5,valid_oof --MeTTeMJBNc/13,-MeTTeMJBNc,5,valid_oof --MeTTeMJBNc/7,-MeTTeMJBNc,5,valid_oof --NFrJFQijFE/1,-NFrJFQijFE,5,valid_oof --NFrJFQijFE/2,-NFrJFQijFE,5,valid_oof --RfYyzHpjk4/11,-RfYyzHpjk4,3,valid_oof --RfYyzHpjk4/2,-RfYyzHpjk4,3,valid_oof --RfYyzHpjk4/8,-RfYyzHpjk4,3,valid_oof --THoVjtIkeU/12,-THoVjtIkeU,2,valid_oof --THoVjtIkeU/2,-THoVjtIkeU,2,valid_oof --THoVjtIkeU/6,-THoVjtIkeU,2,valid_oof --UUCSKoHeMA/0,-UUCSKoHeMA,1,valid_oof --UacrmKiTn4/10,-UacrmKiTn4,4,valid_oof --UacrmKiTn4/4,-UacrmKiTn4,4,valid_oof --UuX1xuaiiE/0,-UuX1xuaiiE,2,valid_oof --UuX1xuaiiE/1,-UuX1xuaiiE,2,valid_oof --UuX1xuaiiE/3,-UuX1xuaiiE,2,valid_oof --UuX1xuaiiE/6,-UuX1xuaiiE,2,valid_oof --a55Q6RWvTA/3,-a55Q6RWvTA,5,valid_oof --aNfi7CP8vM/7,-aNfi7CP8vM,4,valid_oof --aqamKhZ1Ec/0,-aqamKhZ1Ec,3,valid_oof --dxfTGcXJoc/0,-dxfTGcXJoc,5,valid_oof --dxfTGcXJoc/1,-dxfTGcXJoc,5,valid_oof --dxfTGcXJoc/2,-dxfTGcXJoc,5,valid_oof --dxfTGcXJoc/6,-dxfTGcXJoc,5,valid_oof --egA8-b7-3M/1,-egA8-b7-3M,1,valid_oof --egA8-b7-3M/13,-egA8-b7-3M,1,valid_oof --egA8-b7-3M/16,-egA8-b7-3M,1,valid_oof --egA8-b7-3M/17,-egA8-b7-3M,1,valid_oof --egA8-b7-3M/18,-egA8-b7-3M,1,valid_oof --egA8-b7-3M/20,-egA8-b7-3M,1,valid_oof --egA8-b7-3M/26,-egA8-b7-3M,1,valid_oof --egA8-b7-3M/6,-egA8-b7-3M,1,valid_oof --egA8-b7-3M/9,-egA8-b7-3M,1,valid_oof --hnBHBN8p5A/6,-hnBHBN8p5A,3,valid_oof --hnBHBN8p5A/7,-hnBHBN8p5A,3,valid_oof --iRBcNs9oI8/3,-iRBcNs9oI8,4,valid_oof --iRBcNs9oI8/6,-iRBcNs9oI8,4,valid_oof --iRBcNs9oI8/7,-iRBcNs9oI8,4,valid_oof --iRBcNs9oI8/8,-iRBcNs9oI8,4,valid_oof --iRBcNs9oI8/9,-iRBcNs9oI8,4,valid_oof --lzEya4AM_4/5,-lzEya4AM_4,2,valid_oof --lzEya4AM_4/6,-lzEya4AM_4,2,valid_oof --mJ2ud6oKI8/1,-mJ2ud6oKI8,5,valid_oof --mJ2ud6oKI8/2,-mJ2ud6oKI8,5,valid_oof --mJ2ud6oKI8/6,-mJ2ud6oKI8,5,valid_oof --mJ2ud6oKI8/8,-mJ2ud6oKI8,5,valid_oof --mJ2ud6oKI8/9,-mJ2ud6oKI8,5,valid_oof --mqbVkbCndg/0,-mqbVkbCndg,2,valid_oof --qDkUB0GgYY/6,-qDkUB0GgYY,1,valid_oof --ri04Z7vwnc/0,-ri04Z7vwnc,4,valid_oof --ri04Z7vwnc/2,-ri04Z7vwnc,4,valid_oof --ri04Z7vwnc/5,-ri04Z7vwnc,4,valid_oof --s9qJ7ATP7w/0,-s9qJ7ATP7w,3,valid_oof --s9qJ7ATP7w/1,-s9qJ7ATP7w,3,valid_oof --s9qJ7ATP7w/4,-s9qJ7ATP7w,3,valid_oof --s9qJ7ATP7w/5,-s9qJ7ATP7w,3,valid_oof --s9qJ7ATP7w/6,-s9qJ7ATP7w,3,valid_oof --s9qJ7ATP7w/7,-s9qJ7ATP7w,3,valid_oof --s9qJ7ATP7w/8,-s9qJ7ATP7w,3,valid_oof --t217m2on-s/2,-t217m2on-s,4,valid_oof --t217m2on-s/7,-t217m2on-s,4,valid_oof --tANM6ETl_M/3,-tANM6ETl_M,5,valid_oof --tPCytz4rww/10,-tPCytz4rww,4,valid_oof --tPCytz4rww/11,-tPCytz4rww,4,valid_oof --tPCytz4rww/12,-tPCytz4rww,4,valid_oof --tPCytz4rww/16,-tPCytz4rww,4,valid_oof --tPCytz4rww/18,-tPCytz4rww,4,valid_oof --uywlfIYOS8/4,-uywlfIYOS8,4,valid_oof --vxjVxOeScU/4,-vxjVxOeScU,3,valid_oof --wMB_hJL-3o/7,-wMB_hJL-3o,3,valid_oof --wny0OAz3g8/0,-wny0OAz3g8,2,valid_oof --wny0OAz3g8/1,-wny0OAz3g8,2,valid_oof --wny0OAz3g8/2,-wny0OAz3g8,2,valid_oof --wny0OAz3g8/3,-wny0OAz3g8,2,valid_oof --wny0OAz3g8/5,-wny0OAz3g8,2,valid_oof --wny0OAz3g8/7,-wny0OAz3g8,2,valid_oof --wny0OAz3g8/9,-wny0OAz3g8,2,valid_oof --yRb-Jum7EQ/1,-yRb-Jum7EQ,5,valid_oof --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,valid_oof --yRb-Jum7EQ/6,-yRb-Jum7EQ,5,valid_oof diff --git a/final/Q1/results/model_comparison/typical_alignment_example.png b/final/Q1/results/model_comparison/typical_alignment_example.png deleted file mode 100644 index d56096b..0000000 Binary files a/final/Q1/results/model_comparison/typical_alignment_example.png and /dev/null differ diff --git a/final/Q1/results/model_comparison/word_alignment_posterior.csv b/final/Q1/results/model_comparison/word_alignment_posterior.csv deleted file mode 100644 index e5714f8..0000000 --- a/final/Q1/results/model_comparison/word_alignment_posterior.csv +++ /dev/null @@ -1,1933 +0,0 @@ -sample_id,video_id,clip_id,word_index,word,hard_start_s,hard_end_s,hard_valid,ctc_quality_score_uncalibrated,relative_quality_weight,start_mean_s,end_mean_s,start_p05_s,start_p95_s,end_p05_s,end_p95_s,start_width90_s,end_width90_s --3g5yACwYnA/13,-3g5yACwYnA,13,0,They've,0.0024999999441206455,0.5625,True,3.1319849491673324e-11,4.0,0.019184879635086593,0.5625060804898443,0.0025000000000000005,0.10250000000000001,0.5625,0.5625,0.1,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,1,been,1.222499966621399,1.3624999523162842,True,5.074469056759456e-12,2.26932692527771,1.2063062407761618,1.38194823633117,1.1425,1.2625,1.3625,1.4224999999999999,0.11999999999999988,0.05999999999999983 --3g5yACwYnA/13,-3g5yACwYnA,13,2,able,1.4424999952316284,1.6024999618530273,True,2.422228541874849e-13,0.25,1.4426709260933925,1.5958374384159129,1.4425,1.4425,1.5825,1.6025,0.0,0.020000000000000018 --3g5yACwYnA/13,-3g5yACwYnA,13,3,to,1.622499942779541,1.7024999856948853,True,1.4663940634679351e-12,0.6557784676551819,1.63353469473686,1.702500930802706,1.6225,1.6425,1.7025,1.7025,0.020000000000000018,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,4,find,1.7625000476837158,2.0225000381469727,True,2.2361119032809906e-12,1.0,1.7625198227574603,2.022487282563605,1.7625,1.7625,2.0225,2.0225,0.0,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,5,solutions,2.1624999046325684,2.6424999237060547,True,7.178173853927827e-12,3.210113763809204,2.162477129647909,2.642508732091731,2.1625,2.1625,2.6425,2.6425,0.0,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,6,or,2.7225000858306885,2.802500009536743,True,1.8666060017102915e-11,4.0,2.7225030011118756,2.8025052458122928,2.7225,2.7225,2.8025,2.8025,0.0,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,7,at,3.0225000381469727,3.122499942779541,True,7.055260780979011e-13,0.3155146539211273,3.0114486420923248,3.1096179351127704,2.9425,3.0225,3.0425,3.1225,0.08000000000000007,0.08000000000000007 --3g5yACwYnA/13,-3g5yACwYnA,13,8,least,3.1624999046325684,3.382499933242798,True,4.716877496230287e-12,2.109410285949707,3.1602214981987653,3.377016650868552,3.1225,3.1625,3.3625,3.3825,0.040000000000000036,0.020000000000000018 --3g5yACwYnA/13,-3g5yACwYnA,13,9,bring,3.4625000953674316,3.7225000858306885,True,5.437177401021454e-13,0.25,3.4542782483017413,3.707435340678973,3.3825,3.4625,3.6825,3.7225,0.08000000000000007,0.040000000000000036 --3g5yACwYnA/13,-3g5yACwYnA,13,10,some,3.762500047683716,3.9625000953674316,True,4.003620351911152e-12,1.7904382944107056,3.7678537500060894,3.962399443995387,3.7625,3.8025,3.9625,3.9625,0.040000000000000036,0.0 --3g5yACwYnA/13,-3g5yACwYnA,13,11,answers,4.002500057220459,4.322500228881836,True,3.1072377743718294e-12,1.3895716667175293,4.002374694620699,4.31993317940175,4.0025,4.0025,4.3025,4.322500000000001,0.0,0.020000000000000462 --3g5yACwYnA/13,-3g5yACwYnA,13,12,to,4.362500190734863,4.422500133514404,True,1.1880822931367718e-13,0.25,4.360161068296207,4.4264563847068485,4.3425,4.402500000000001,4.4225,4.4625,0.0600000000000005,0.040000000000000036 --3g5yACwYnA/13,-3g5yACwYnA,13,13,the,4.462500095367432,4.5625,True,2.673985605692892e-15,0.25,4.469530870157028,4.569707820997299,4.4625,4.482500000000001,4.562500000000001,4.5825000000000005,0.020000000000000462,0.019999999999999574 --3g5yACwYnA/13,-3g5yACwYnA,13,14,table.,4.602499961853027,4.882500171661377,True,1.6668160992064363e-13,0.25,4.602502613481098,4.882542159056495,4.602500000000001,4.602500000000001,4.8825,4.8825,0.0,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,0,We're,0.0024999999441206455,0.16249999403953552,True,1.0417753465441493e-10,4.0,0.01892479479475388,0.3908951550217483,0.0025000000000000005,0.0425,0.1625,0.8624999999999999,0.04,0.7 --3g5yACwYnA/3,-3g5yACwYnA,3,1,a,0.20250000059604645,0.2224999964237213,True,2.0192024051055024e-13,0.25,0.5809306189576965,0.6009306189578986,0.2025,1.2425,0.2225,1.2625,1.04,1.04 --3g5yACwYnA/3,-3g5yACwYnA,3,2,huge,0.6025000214576721,0.862500011920929,True,2.080993537212361e-11,4.0,0.8749699312498749,1.1586938794079154,0.6024999999999999,1.3625,0.8624999999999999,1.6625,0.7600000000000001,0.8000000000000002 --3g5yACwYnA/3,-3g5yACwYnA,3,3,user,1.3624999523162842,1.662500023841858,True,1.8008346550080212e-11,4.0,1.6450011455096585,1.9430967049678605,1.3625,2.2425,1.6625,2.5025,0.8800000000000001,0.8399999999999999 --3g5yACwYnA/3,-3g5yACwYnA,3,4,of,2.422499895095825,2.4825000762939453,True,2.5086363108356435e-12,0.6720010638237,2.3888363472402654,2.5262128094487677,2.1625,2.6025,2.4425,2.6625,0.43999999999999995,0.2200000000000002 --3g5yACwYnA/3,-3g5yACwYnA,3,5,adhesives,2.6424999237060547,3.262500047683716,True,1.9735572551193847e-11,4.0,2.6479228501381638,3.2627808520362973,2.5625,2.7225,3.2625,3.2625,0.16000000000000014,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,6,for,3.302500009536743,3.4825000762939453,True,5.313816608953914e-10,4.0,3.305240723840881,3.485738646070515,3.3025,3.3025,3.4825,3.4825,0.0,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,7,our,3.5625,4.34250020980835,True,1.8105811988577969e-12,0.4850095212459564,3.6584500267844233,4.351316019909387,3.5625,4.202500000000001,4.3425,4.442500000000001,0.6400000000000006,0.10000000000000053 --3g5yACwYnA/3,-3g5yACwYnA,3,8,"operation,",4.582499980926514,5.142499923706055,True,2.16136266870115e-12,0.5789751410484314,4.582841088198986,5.142577850811578,4.5825000000000005,4.5825000000000005,5.1425,5.1425,0.0,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,9,called,5.502500057220459,5.78249979019165,True,7.694975732996934e-12,2.0612919330596924,5.469346469231026,5.787966327255339,5.242500000000001,5.5425,5.782500000000001,5.8425,0.2999999999999998,0.05999999999999961 --3g5yACwYnA/3,-3g5yACwYnA,3,10,"flocking,",5.862500190734863,6.242499828338623,True,3.7366615877887366e-12,1.0009584426879883,5.862416384166256,6.255591073381409,5.862500000000001,5.862500000000001,6.242500000000001,6.2625,0.0,0.019999999999999574 --3g5yACwYnA/3,-3g5yACwYnA,3,11,and,6.602499961853027,6.742499828338623,True,3.1151616659494397e-13,0.25,6.595235466538168,6.747981306446609,6.282500000000001,6.6225000000000005,6.702500000000001,6.742500000000001,0.33999999999999986,0.040000000000000036 --3g5yACwYnA/3,-3g5yACwYnA,3,12,we,7.022500038146973,7.082499980926514,True,3.7330837206195344e-12,1.0,7.025503971831001,7.085534043478729,7.022500000000001,7.022500000000001,7.0825000000000005,7.0825000000000005,0.0,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,13,don't,7.162499904632568,7.34250020980835,True,1.3746884186538466e-10,4.0,7.163477889296987,7.344480228729067,7.142500000000001,7.1625000000000005,7.3425,7.3425,0.019999999999999574,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,14,have,7.522500038146973,7.662499904632568,True,1.6831982923887386e-14,0.25,7.518314039598685,7.660886400412066,7.482500000000001,7.522500000000001,7.6225000000000005,7.702500000000001,0.040000000000000036,0.08000000000000007 --3g5yACwYnA/3,-3g5yACwYnA,3,15,the,7.722499847412109,7.882500171661377,True,8.987861453355062e-13,0.25,7.7243982678780885,7.882159094863079,7.7225,7.7625,7.862500000000001,7.8825,0.040000000000000036,0.019999999999999574 --3g5yACwYnA/3,-3g5yACwYnA,3,16,technical,8.022500038146973,8.662500381469727,True,1.3002188301719508e-12,0.348296195268631,8.011902283844488,8.662006267621967,7.942500000000001,8.0425,8.6625,8.6625,0.09999999999999964,0.0 --3g5yACwYnA/3,-3g5yACwYnA,3,17,inside,8.72249984741211,9.0024995803833,True,2.5177188350648805e-13,0.25,8.71091233648601,9.001311320857447,8.6825,8.7225,8.9825,9.0025,0.040000000000000924,0.019999999999999574 --3g5yACwYnA/3,-3g5yACwYnA,3,18,compounding,9.40250015258789,10.22249984741211,True,6.742292813465001e-13,0.25,9.359030940393758,10.232189826338123,9.022499999999999,9.4025,10.2225,10.3025,0.3800000000000008,0.08000000000000007 --3g5yACwYnA/3,-3g5yACwYnA,3,19,or,10.342499732971191,10.40250015258789,True,2.5491054037561633e-13,0.25,10.398864243814538,10.46224094293215,10.3425,10.5025,10.4025,10.5625,0.16000000000000014,0.16000000000000014 --3g5yACwYnA/3,-3g5yACwYnA,3,20,the,10.5024995803833,10.682499885559082,True,1.6563738770326852e-13,0.25,10.54237820403615,10.694267109284878,10.4625,10.6625,10.5625,10.782499999999999,0.1999999999999993,0.21999999999999886 --3g5yACwYnA/3,-3g5yACwYnA,3,21,technical,10.802499771118164,11.242500305175781,True,3.798831735291053e-11,4.0,10.778501265601966,11.232468921558434,10.5825,10.8425,10.9625,11.3825,0.2599999999999998,0.41999999999999993 --3g5yACwYnA/3,-3g5yACwYnA,3,22,expertise,11.362500190734863,11.90250015258789,True,2.313237492182485e-12,0.6196585893630981,11.339150095742763,11.878519378812266,10.9825,11.4225,11.4825,11.942499999999999,0.4399999999999995,0.4599999999999991 --3g5yACwYnA/3,-3g5yACwYnA,3,23,to,11.962499618530273,12.022500038146973,True,2.884898700136751e-12,0.772792398929596,11.943536876391573,12.008612822373081,11.6025,12.0025,11.6825,12.0825,0.40000000000000036,0.40000000000000036 --3g5yACwYnA/3,-3g5yACwYnA,3,24,do,12.1225004196167,12.202500343322754,True,8.551721136784707e-11,4.0,12.100686304121547,12.180724080264547,11.8225,12.1225,11.9025,12.2025,0.3000000000000007,0.3000000000000007 --3g5yACwYnA/3,-3g5yACwYnA,3,25,these,12.242500305175781,13.162500381469727,True,2.1788269105593727e-11,4.0,12.236831720783304,13.084602136464406,12.0025,12.2425,12.3225,13.1825,0.2400000000000002,0.8599999999999994 --3g5yACwYnA/3,-3g5yACwYnA,3,26,types,13.202500343322754,13.462499618530273,True,1.7226959442284695e-11,4.0,13.170666582865142,13.46226141424027,12.9225,13.2025,13.3825,13.7225,0.28000000000000114,0.33999999999999986 --3g5yACwYnA/3,-3g5yACwYnA,3,27,of,13.702500343322754,13.802499771118164,True,6.332336998510213e-12,1.6962751150131226,13.783998039741556,13.89545682407746,13.4625,13.9225,13.7225,14.0025,0.4599999999999991,0.27999999999999936 --3g5yACwYnA/3,-3g5yACwYnA,3,28,things.,13.922499656677246,14.262499809265137,True,1.0484706852720294e-11,2.808591365814209,13.996225018186697,14.291213862031034,13.9225,14.0625,14.2625,14.3225,0.14000000000000057,0.0600000000000005 --3g5yACwYnA/2,-3g5yACwYnA,2,0,Key,0.12250000238418579,0.2824999988079071,True,1.2059077694748233e-12,0.46602973341941833,0.12004656147855662,0.2754928971448747,0.0025000000000000005,0.1825,0.2025,0.28250000000000003,0.18,0.08000000000000002 --3g5yACwYnA/2,-3g5yACwYnA,2,1,Polymer,1.4225000143051147,1.9824999570846558,True,1.2241609635699202e-11,4.0,1.2243119624106564,1.9746772787439282,0.2625,1.4224999999999999,1.5225,2.0225,1.16,0.5 --3g5yACwYnA/2,-3g5yACwYnA,2,2,brings,2.0425000190734863,2.302500009536743,True,1.1930802483114955e-12,0.46107247471809387,2.0715362022919037,2.346903905400217,1.8625,2.1025,2.2425,2.5225,0.24,0.2799999999999998 --3g5yACwYnA/2,-3g5yACwYnA,2,3,a,2.502500057220459,2.5225000381469727,True,2.7638483949404824e-12,1.0681045055389404,2.5343152915969336,2.5543152915969847,2.5025,2.6425,2.5225,2.6625,0.14000000000000012,0.14000000000000012 --3g5yACwYnA/2,-3g5yACwYnA,2,4,technical,2.922499895095825,3.682499885559082,True,6.836649302233155e-11,4.0,2.945770103472421,3.662219509655508,2.9225,2.9225,3.5425,3.6825,0.0,0.14000000000000012 --3g5yACwYnA/2,-3g5yACwYnA,2,5,aspect,3.762500047683716,4.102499961853027,True,7.779469071971662e-14,0.25,3.762773828203278,4.127419794909451,3.6625,3.8225000000000002,4.1025,4.1625000000000005,0.16000000000000014,0.0600000000000005 --3g5yACwYnA/2,-3g5yACwYnA,2,6,to,4.382500171661377,4.442500114440918,True,2.0063360695043997e-11,4.0,4.397380835314191,4.461387623157674,4.3825,4.3825,4.442500000000001,4.522500000000001,0.0,0.08000000000000007 --3g5yACwYnA/2,-3g5yACwYnA,2,7,our,4.502500057220459,4.862500190734863,True,9.0564179419661e-12,3.4999029636383057,4.554801215071612,4.891103084451879,4.5025,4.6225000000000005,4.862500000000001,4.862500000000001,0.1200000000000001,0.0 --3g5yACwYnA/2,-3g5yACwYnA,2,8,operation,5.462500095367432,6.142499923706055,True,2.41139139905977e-12,0.9318955540657043,5.434152783262643,6.164972441664202,5.282500000000001,5.5025,6.142500000000001,6.3825,0.21999999999999975,0.23999999999999932 --3g5yACwYnA/2,-3g5yACwYnA,2,9,that,6.222499847412109,6.382500171661377,True,2.55808666350249e-13,0.25,6.2506696287137125,6.425851613401193,6.2225,6.5025,6.3825,6.742500000000001,0.28000000000000025,0.3600000000000003 --3g5yACwYnA/2,-3g5yACwYnA,2,10,we,6.682499885559082,6.742499828338623,True,3.615498471356421e-13,0.25,6.68956208113118,6.750288014655133,6.6825,6.822500000000001,6.742500000000001,6.8825,0.14000000000000057,0.13999999999999968 --3g5yACwYnA/2,-3g5yACwYnA,2,11,don't,6.822500228881836,7.002500057220459,True,1.598295940041794e-10,4.0,6.829173580026902,7.0141967823015605,6.822500000000001,6.9225,6.982500000000001,7.1625000000000005,0.09999999999999964,0.17999999999999972 --3g5yACwYnA/2,-3g5yACwYnA,2,12,have,7.042500019073486,7.202499866485596,True,5.563233250807653e-13,0.25,7.07007014122585,7.245521610851821,7.0425,7.282500000000001,7.202500000000001,7.5425,0.2400000000000002,0.33999999999999986 --3g5yACwYnA/2,-3g5yACwYnA,2,13,internally.,7.28249979019165,9.182499885559082,True,4.959014102134951e-12,1.9164384603500366,7.391144668115456,9.147154329969988,7.282500000000001,8.2625,9.0825,9.2225,0.9799999999999986,0.14000000000000057 --3g5yACwYnA/9,-3g5yACwYnA,9,0,We,0.0024999999441206455,0.0625,True,1.3202740289930404e-10,4.0,0.0037092361625596475,0.06376428622874805,0.0025000000000000005,0.0025000000000000005,0.0625,0.0625,0.0,0.0 --3g5yACwYnA/9,-3g5yACwYnA,9,1,have,0.12250000238418579,0.8824999928474426,True,4.727816987730449e-13,0.4530237317085266,0.24601737150185057,0.9129629357636357,0.1225,0.8025,0.8825,1.0425,0.6799999999999999,0.16000000000000003 --3g5yACwYnA/9,-3g5yACwYnA,9,2,many,0.9225000143051147,1.1024999618530273,True,3.4017011213069437e-13,0.3259541094303131,0.9672695832607601,1.1443447348089129,0.9225,1.1225,1.1025,1.3225,0.20000000000000007,0.21999999999999997 --3g5yACwYnA/9,-3g5yACwYnA,9,3,new,1.1825000047683716,1.3224999904632568,True,3.941063621282215e-12,3.7763631343841553,1.262579923107276,1.4144081895232492,1.1824999999999999,1.6025,1.3225,1.8025,0.42000000000000015,0.48 --3g5yACwYnA/9,-3g5yACwYnA,9,4,opportunities,1.6024999618530273,2.4024999141693115,True,1.088685782149601e-12,1.0431885719299316,1.6586099648067913,2.444467497988983,1.6025,1.8825,2.4025,2.7025,0.28,0.30000000000000027 --3g5yACwYnA/9,-3g5yACwYnA,9,5,through,2.4825000762939453,2.7825000286102295,True,4.15607964167862e-14,0.25,2.5234634994329452,2.8696162143253496,2.4825,2.8025,2.7825,3.4425,0.3200000000000003,0.6599999999999997 --3g5yACwYnA/9,-3g5yACwYnA,9,6,the,2.802500009536743,3.442500114440918,True,1.9428814026362096e-12,1.8616865873336792,2.902911114428035,3.464744278514586,2.8025,3.4825,3.4425,3.6025,0.6799999999999997,0.16000000000000014 --3g5yACwYnA/9,-3g5yACwYnA,9,7,way,3.4825000762939453,3.6024999618530273,True,3.119503787230027e-12,2.9891369342803955,3.5099164031810415,3.63228134889645,3.4825,3.6825,3.6025,3.8225000000000002,0.20000000000000018,0.2200000000000002 --3g5yACwYnA/9,-3g5yACwYnA,9,8,things,3.682499885559082,4.002500057220459,True,6.429745516393914e-13,0.6161040663719177,3.7145892049209013,4.026147698972161,3.6825,3.9225,3.9825,4.2625,0.23999999999999977,0.28000000000000025 --3g5yACwYnA/9,-3g5yACwYnA,9,9,have,4.022500038146973,4.162499904632568,True,1.5793911859775245e-13,0.25,4.060617069421903,4.224488988758646,4.022500000000001,4.3025,4.1625000000000005,4.522500000000001,0.27999999999999936,0.3600000000000003 --3g5yACwYnA/9,-3g5yACwYnA,9,10,changed,4.202499866485596,4.522500038146973,True,2.325777157669018e-12,2.228580951690674,4.267085991580041,4.5723446469859,4.202500000000001,4.5825000000000005,4.522500000000001,4.862500000000001,0.3799999999999999,0.33999999999999986 --3g5yACwYnA/9,-3g5yACwYnA,9,11,through,4.582499980926514,5.582499980926514,True,1.854727959757496e-12,1.7772172689437866,4.6221517393402145,5.554001297056343,4.5825000000000005,4.8825,5.4625,5.6225000000000005,0.2999999999999998,0.16000000000000014 --3g5yACwYnA/9,-3g5yACwYnA,9,12,the,5.622499942779541,5.742499828338623,True,3.626375675441773e-12,3.4748260974884033,5.595031140793692,5.718768123192464,5.522500000000001,5.6625000000000005,5.6225000000000005,5.8025,0.13999999999999968,0.17999999999999972 --3g5yACwYnA/9,-3g5yACwYnA,9,13,"years,",5.78249979019165,5.982500076293945,True,3.45286983954949e-14,0.25,5.757698982112918,5.970677998071189,5.6425,5.8425,5.902500000000001,6.0425,0.20000000000000018,0.13999999999999968 --3g5yACwYnA/9,-3g5yACwYnA,9,14,looking,6.042500019073486,6.442500114440918,True,1.2439189218602098e-12,1.1919344663619995,6.0297255832126435,6.383957853422528,5.9225,6.0825000000000005,6.282500000000001,6.442500000000001,0.16000000000000014,0.16000000000000014 --3g5yACwYnA/9,-3g5yACwYnA,9,15,for,6.462500095367432,6.5625,True,1.4023155466696968e-12,1.343711495399475,6.419190410852593,6.540302343999718,6.3425,6.4625,6.482500000000001,6.562500000000001,0.1200000000000001,0.08000000000000007 --3g5yACwYnA/9,-3g5yACwYnA,9,16,new,6.642499923706055,6.84250020980835,True,1.043613546275468e-12,1.0,6.635691555353821,6.833978589755013,6.642500000000001,6.642500000000001,6.8425,6.8425,0.0,0.0 --3g5yACwYnA/9,-3g5yACwYnA,9,17,niche,6.862500190734863,7.0625,True,5.4846457372493065e-14,0.25,6.854528859600319,7.055299959028502,6.862500000000001,6.862500000000001,7.062500000000001,7.062500000000001,0.0,0.0 --3g5yACwYnA/9,-3g5yACwYnA,9,18,"high-end,",7.082499980926514,7.462500095367432,True,7.439023897749808e-13,0.7128140330314636,7.0769385969042,7.45691194296881,7.0825000000000005,7.0825000000000005,7.4625,7.4625,0.0,0.0 --3g5yACwYnA/9,-3g5yACwYnA,9,19,value-added,7.5625,8.082500457763672,True,5.798322705221487e-14,0.25,7.552404120714275,8.074980337515674,7.522500000000001,7.562500000000001,8.0425,8.0825,0.040000000000000036,0.03999999999999915 --3g5yACwYnA/9,-3g5yACwYnA,9,20,products.,8.102499961853027,8.662500381469727,True,2.1248971752616495e-14,0.25,8.098459094614249,8.634617109598796,8.1025,8.1025,8.5425,8.6825,0.0,0.1399999999999988 --3nNcZdcdvU/5,-3nNcZdcdvU,5,0,Noncompliance,0.5425000190734863,1.5425000190734863,True,3.009791851513177e-13,4.0,0.5421899065110246,1.5434444401076381,0.5225,0.5425,1.5425,1.5425,0.020000000000000018,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,1,can,1.6825000047683716,1.8624999523162842,True,4.914635593020883e-14,0.9133073091506958,1.682499728861315,1.8625016311882543,1.6824999999999999,1.6824999999999999,1.8625,1.8625,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,2,result,1.9824999570846558,2.502500057220459,True,5.090972131707727e-13,4.0,1.9826826486872584,2.502627463381576,1.9825,1.9825,2.5025,2.5025,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,3,in,2.6024999618530273,2.702500104904175,True,1.6868163089196406e-14,0.3134681284427643,2.602500402040593,2.7114411198718202,2.6025,2.6025,2.7025,2.7225,0.0,0.020000000000000018 --3nNcZdcdvU/5,-3nNcZdcdvU,5,4,legal,2.8424999713897705,3.122499942779541,True,1.6298654049659578e-13,3.0288469791412354,2.8319148446350573,3.108045806654291,2.8025,2.8425,3.0625,3.1225,0.03999999999999959,0.06000000000000005 --3nNcZdcdvU/5,-3nNcZdcdvU,5,5,liability,3.1424999237060547,3.802500009536743,True,4.397796688447586e-13,4.0,3.1425001068073284,3.8025137671985,3.1425,3.1425,3.8025,3.8025,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,6,to,3.9825000762939453,4.142499923706055,True,2.3713054631870067e-13,4.0,3.982500462639652,4.142499970675897,3.9825,3.9825,4.1425,4.1425,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,7,you,4.262499809265137,4.522500038146973,True,9.180395498160389e-15,0.25,4.256718628077809,4.512681988741261,4.202500000000001,4.2625,4.4225,4.522500000000001,0.05999999999999961,0.10000000000000053 --3nNcZdcdvU/5,-3nNcZdcdvU,5,8,and,4.5625,5.142499923706055,True,9.738124342279983e-14,1.8096764087677002,4.610781303553796,5.143268143927736,4.562500000000001,5.0425,5.1425,5.1425,0.47999999999999954,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,9,to,5.242499828338623,5.322500228881836,True,1.2475421086802219e-13,2.318359613418579,5.242284573869038,5.322497097245752,5.242500000000001,5.242500000000001,5.322500000000001,5.322500000000001,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,10,the,5.422500133514404,5.522500038146973,True,5.847647127806e-14,1.0866926908493042,5.420521893684469,5.521005673422197,5.402500000000001,5.4225,5.522500000000001,5.522500000000001,0.019999999999999574,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,11,school,5.542500019073486,5.802499771118164,True,5.076406095748951e-15,0.25,5.541309582381759,5.801309274139924,5.5425,5.5425,5.8025,5.8025,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,12,district,5.822500228881836,6.182499885559082,True,2.3873014813431616e-16,0.25,5.822409113735198,6.18241082307903,5.822500000000001,5.822500000000001,6.1825,6.1825,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,13,in,6.202499866485596,6.262499809265137,True,3.187962021713249e-15,0.25,6.202413110773365,6.2624131946284285,6.202500000000001,6.202500000000001,6.2625,6.2625,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,14,which,6.28249979019165,6.462500095367432,True,3.9412292157998865e-16,0.25,6.282413518600741,6.46250329822077,6.282500000000001,6.282500000000001,6.4625,6.4625,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,15,you,6.482500076293945,6.622499942779541,True,4.329610265143072e-14,0.804589569568634,6.482504276688287,6.617728718552279,6.482500000000001,6.482500000000001,6.5825000000000005,6.6225000000000005,0.0,0.040000000000000036 --3nNcZdcdvU/5,-3nNcZdcdvU,5,16,are,6.642499923706055,6.742499828338623,True,4.5697071750531656e-14,0.8492078185081482,6.642361698991119,6.743794056590974,6.642500000000001,6.642500000000001,6.742500000000001,6.742500000000001,0.0,0.0 --3nNcZdcdvU/5,-3nNcZdcdvU,5,17,employed.,6.84250020980835,7.28249979019165,True,6.300834149135931e-14,1.170910358428955,6.83813239815432,7.282623043355807,6.822500000000001,6.8425,7.282500000000001,7.282500000000001,0.019999999999999574,0.0 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,0,”,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,1,[President,0.10249999910593033,0.762499988079071,True,8.245064109539332e-14,4.0,0.10250473972383489,0.7625327724371536,0.10250000000000001,0.10250000000000001,0.7625,0.7625,0.0,0.0 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,2,Ronald,0.8224999904632568,1.8025000095367432,True,5.068065997592048e-16,0.25,0.854274069167481,1.7270714047834743,0.8225,1.0225,1.3625,2.0425,0.19999999999999996,0.6799999999999999 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,3,Reagan:],1.8624999523162842,2.802500009536743,True,1.1842246509850916e-14,1.0,1.9694596577449979,2.8033453188270716,1.6625,2.2825,2.8025,2.8225,0.6200000000000001,0.019999999999999574 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,4,"""Look",2.882499933242798,3.0225000381469727,True,2.492327701099535e-14,2.104607105255127,2.881426774537937,3.0216973335913506,2.8825,2.8825,3.0225,3.0225,0.0,0.0 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,5,at,3.1024999618530273,3.1624999046325684,True,2.696593126813346e-15,0.25,3.1034472692660957,3.16550226599371,3.1025,3.1425,3.1625,3.2225,0.040000000000000036,0.06000000000000005 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,6,the,3.242500066757202,3.3424999713897705,True,2.8035099876354622e-14,2.367380142211914,3.2418077886934986,3.342347080125065,3.2425,3.2425,3.3425,3.3425,0.0,0.0 --HwX2H8Z4hY/2,-HwX2H8Z4hY,2,7,record,3.422499895095825,3.9024999141693115,True,5.635536779396251e-16,0.25,3.421806202848189,3.881243156703797,3.4225,3.4225,3.8025,3.9025,0.0,0.09999999999999964 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,0,"""",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,1,President,0.8025000095367432,1.2424999475479126,True,1.1130892147120756e-14,0.25,0.7991785910278454,1.249418363734453,0.8025,0.8025,1.2425,1.2625,0.0,0.020000000000000018 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,2,Ronald,1.3224999904632568,1.662500023841858,True,6.521055939158471e-13,3.265170097351074,1.3182879263108291,1.657037437672821,1.2625,1.3225,1.6025,1.6625,0.06000000000000005,0.06000000000000005 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,3,Reagan,1.7024999856948853,2.122499942779541,True,1.9971566447059969e-13,1.0,1.7002046832597368,2.1152442116829513,1.7025,1.7025,2.0625,2.1225,0.0,0.06000000000000005 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,4,blamed,2.202500104904175,2.5625,True,1.681951561534334e-13,0.8421730995178223,2.1942407816196767,2.5565269463501545,2.1225,2.2025,2.5025,2.5625,0.08000000000000007,0.06000000000000005 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,5,the,2.6624999046325684,2.762500047683716,True,1.4306970931858738e-14,0.25,2.643355740531605,2.752176231037962,2.6025,2.6625,2.7225,2.7625,0.06000000000000005,0.040000000000000036 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,6,professional,2.7825000286102295,3.422499895095825,True,1.6846922891011057e-14,0.25,2.7774452139102097,3.4217504683401407,2.7425,2.7825,3.4225,3.4225,0.040000000000000036,0.0 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,7,bureaucracy,3.4625000953674316,4.202499866485596,True,5.344916132951627e-13,2.676262855529785,3.4615893755070632,4.158197134262134,3.4625,3.4625,4.1425,4.202500000000001,0.0,0.0600000000000005 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,8,of,4.302499771118164,4.382500171661377,True,8.464278540218362e-13,4.0,4.265456447231751,4.360272046146851,4.202500000000001,4.3025,4.322500000000001,4.3825,0.09999999999999964,0.05999999999999961 --HwX2H8Z4hY/5,-HwX2H8Z4hY,5,9,education,4.462500095367432,5.082499980926514,True,6.528327141028245e-13,3.268810749053955,4.462466272651668,5.082501771556385,4.4625,4.4625,5.0825000000000005,5.0825000000000005,0.0,0.0 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,0,And,0.0024999999441206455,0.6424999833106995,True,9.006679205073899e-16,0.25,0.049993577900290236,0.6401664878744063,0.0025000000000000005,0.2425,0.6224999999999999,0.6425,0.24,0.020000000000000018 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,1,in,0.6825000047683716,0.762499988079071,True,1.0384882278386635e-14,0.25,0.6755059356561959,0.7452473237909909,0.6625,0.6825,0.7224999999999999,0.7625,0.020000000000000018,0.040000000000000036 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,2,"part,",0.8424999713897705,1.1825000047683716,True,4.603873096013615e-14,0.3257012665271759,0.8366663344434105,1.175919532391122,0.7625,0.8424999999999999,1.1025,1.1824999999999999,0.07999999999999996,0.07999999999999985 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,3,Al,1.5425000190734863,1.622499942779541,True,1.3575890628683696e-12,4.0,1.542340600155332,1.622350825487453,1.5425,1.5425,1.6225,1.6225,0.0,0.0 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,4,Shanker,1.722499966621399,2.1624999046325684,True,1.41352627844836e-13,1.0,1.7221858829602623,2.1581962257056984,1.7225,1.7225,2.1025,2.1625,0.0,0.06000000000000005 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,5,did,2.262500047683716,2.4024999141693115,True,8.53964848054195e-13,4.0,2.2624871936417077,2.405549344077747,2.2625,2.2625,2.4025,2.4025,0.0,0.0 --HwX2H8Z4hY/6,-HwX2H8Z4hY,6,6,too,2.682499885559082,2.822499990463257,True,9.367995962294984e-12,4.0,2.678787842175452,2.8245599587910464,2.6825,2.6825,2.8025,2.8425,0.0,0.03999999999999959 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,0,It,0.5625,0.6424999833106995,True,9.704259118015809e-15,0.25,0.3968797872067635,0.6168155228213217,0.0625,0.5824999999999999,0.5824999999999999,0.6425,0.5199999999999999,0.06000000000000005 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,1,is,0.6825000047683716,0.7425000071525574,True,1.4060067942810867e-13,0.25,0.6826864336953968,0.7429342737517168,0.6825,0.6825,0.7424999999999999,0.7424999999999999,0.0,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,2,a,0.8424999713897705,0.862500011920929,True,3.836665414622825e-13,0.2704606354236603,0.8431059245975748,0.863105924597575,0.8424999999999999,0.8424999999999999,0.8624999999999999,0.8624999999999999,0.0,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,3,word,0.9225000143051147,1.162500023841858,True,2.349814407723999e-13,0.25,0.9232465709824628,1.1633531719563064,0.9225,0.9225,1.1624999999999999,1.1624999999999999,0.0,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,4,which,1.2625000476837158,1.5625,True,5.4473098801870526e-14,0.25,1.2682948092150728,1.5630203083225693,1.2625,1.3225,1.5625,1.5625,0.06000000000000005,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,5,the,1.722499966621399,1.8224999904632568,True,8.272986245713709e-13,0.5831931829452515,1.6909958021630012,1.7986297285033759,1.6225,1.7225,1.7225,1.8225,0.09999999999999987,0.10000000000000009 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,6,administrator,1.8825000524520874,2.622499942779541,True,7.934183908833714e-13,0.559309720993042,1.8591177881897383,2.7274044722357593,1.8025,1.8825,2.6225,3.1625,0.08000000000000007,0.54 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,7,uses,2.9024999141693115,3.1624999046325684,True,2.1700481309897685e-13,0.25,2.968049678105385,3.2345771687388063,2.8825,3.2425,3.1625,3.5025,0.3600000000000003,0.33999999999999986 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,8,when,3.242500066757202,3.442500114440918,True,4.379272010128499e-13,0.3087109625339508,3.333607512728239,3.547610849410154,3.2425,3.6825,3.4425,3.9025,0.43999999999999995,0.45999999999999996 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,9,he,3.502500057220459,3.5625,True,1.9151127623844022e-13,0.25,3.6091286554749216,3.6789170759396757,3.5025,3.9625,3.5625,4.022500000000001,0.45999999999999996,0.46000000000000085 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,10,turns,3.6424999237060547,3.9024999141693115,True,2.0098356572767484e-12,1.416806936264038,3.750671620423674,3.986732770438924,3.6425,4.062500000000001,3.9025,4.2625,0.4200000000000008,0.3600000000000003 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,11,around,3.9625000953674316,4.262499809265137,True,3.172354393066179e-11,4.0,4.042443095688754,4.331695960114718,3.9625,4.3025,4.2625,4.5425,0.3400000000000003,0.28000000000000025 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,12,anything,4.302499771118164,5.002500057220459,True,4.246567507359966e-12,2.993561267852783,4.397334396251662,5.0187542042154245,4.3025,4.562500000000001,5.0025,5.1225000000000005,0.2600000000000007,0.1200000000000001 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,13,which,5.202499866485596,5.482500076293945,True,1.0097850507151396e-11,4.0,5.1998952268885485,5.481770232068269,5.1825,5.202500000000001,5.482500000000001,5.482500000000001,0.020000000000000462,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,14,he,5.542500019073486,5.602499961853027,True,3.1471665009295824e-13,0.25,5.541826700619963,5.601893321673665,5.5425,5.5425,5.602500000000001,5.602500000000001,0.0,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,15,doesn't,5.662499904632568,6.002500057220459,True,4.0290708269719033e-11,4.0,5.664100729682788,6.0016396736273805,5.6625000000000005,5.702500000000001,6.0025,6.0025,0.040000000000000036,0.0 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,16,like,6.042500019073486,6.182499885559082,True,2.3577168863087028e-12,1.662041187286377,6.0421210584664955,6.251301613627984,6.0425,6.0425,6.1825,6.322500000000001,0.0,0.14000000000000057 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,17,that,6.242499828338623,6.402500152587891,True,9.430432129642341e-12,4.0,6.31569748562974,6.490365143363123,6.242500000000001,6.402500000000001,6.402500000000001,6.602500000000001,0.16000000000000014,0.20000000000000018 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,18,the,6.462500095367432,6.602499961853027,True,3.4823832706365465e-10,4.0,6.546923966814362,6.670741888996065,6.4625,6.6625000000000005,6.602500000000001,6.7625,0.20000000000000018,0.15999999999999925 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,19,teacher,6.662499904632568,7.0625,True,3.151389912914304e-11,4.0,6.7149365146030195,7.083825396014788,6.6625000000000005,6.782500000000001,7.062500000000001,7.1225000000000005,0.1200000000000001,0.05999999999999961 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,20,is,7.162499904632568,7.262499809265137,True,2.1730292912303106e-11,4.0,7.167127941420237,7.250665423256899,7.1625000000000005,7.1825,7.2225,7.2625,0.019999999999999574,0.040000000000000036 --HwX2H8Z4hY/9,-HwX2H8Z4hY,9,21,doing,7.322500228881836,7.582499980926514,True,2.088004595612869e-11,4.0,7.323569851270532,7.58443002944152,7.322500000000001,7.322500000000001,7.5825000000000005,7.602500000000001,0.0,0.020000000000000462 --NFrJFQijFE/1,-NFrJFQijFE,1,0,Structural,0.0024999999441206455,0.7225000262260437,True,3.124166550461105e-11,0.801537275314331,0.029607444381030804,0.6871020932567873,0.0025000000000000005,0.10250000000000001,0.4825,0.9225,0.1,0.44 --NFrJFQijFE/1,-NFrJFQijFE,1,1,mounts,1.122499942779541,1.7825000286102295,True,3.382181687494601e-11,0.8677337765693665,0.7741501022267345,1.168869413851003,0.5425,1.0425,0.9025,1.4625,0.5,0.5599999999999999 --NFrJFQijFE/1,-NFrJFQijFE,1,2,inside,1.8424999713897705,2.242500066757202,True,3.242017765359151e-11,0.8317732214927673,1.259193099460418,1.646948133757638,0.9824999999999999,1.5625,1.3625,1.9425,0.5800000000000001,0.5799999999999998 --NFrJFQijFE/1,-NFrJFQijFE,1,3,have,2.382499933242798,2.7825000286102295,True,3.251355781830334e-11,0.8341690301895142,1.7308771690731846,1.958774697749872,1.4625,2.0225,1.6824999999999999,2.2625,0.56,0.5800000000000003 --NFrJFQijFE/1,-NFrJFQijFE,1,4,to,3.0625,3.122499942779541,True,3.055768832416206e-11,0.7839891314506531,2.040202773527604,2.1314457321513656,1.7625,2.3625,1.8425,2.4425,0.5999999999999999,0.5999999999999999 --NFrJFQijFE/1,-NFrJFQijFE,1,5,be,3.1624999046325684,3.2225000858306885,True,2.7543876901514608e-11,0.7066667079925537,2.2165689766668426,2.3095375530237123,1.9224999999999999,2.5225,2.0025,2.6225,0.6000000000000001,0.6200000000000001 --NFrJFQijFE/1,-NFrJFQijFE,1,6,properly,3.242500066757202,3.6624999046325684,True,3.094473635667505e-11,0.7939192652702332,2.398161975532348,2.939312401374726,2.0825,2.7025,2.5825,3.2225,0.6200000000000001,0.6400000000000001 --NFrJFQijFE/1,-NFrJFQijFE,1,7,stress,3.7825000286102295,4.042500019073486,True,4.0694180664102575e-11,1.0440514087677002,3.024703822620534,3.3952124304109335,2.6625,3.3025,3.0825,3.6825,0.6400000000000001,0.6000000000000001 --NFrJFQijFE/1,-NFrJFQijFE,1,8,analyzed,4.0625,4.482500076293945,True,5.706426650653462e-11,1.4640429019927979,3.4734230005814695,3.95665797695199,3.1625,3.7625,3.6625,4.202500000000001,0.6000000000000001,0.5400000000000005 --NFrJFQijFE/1,-NFrJFQijFE,1,9,so,4.502500057220459,4.5625,True,3.726018105165707e-11,0.955948531627655,4.019371413642997,4.09926351081791,3.7625,4.2625,3.8425,4.322500000000001,0.5,0.48000000000000087 --NFrJFQijFE/1,-NFrJFQijFE,1,10,we,4.582499980926514,4.682499885559082,True,4.315695370515904e-11,1.107236385345459,4.158343759812449,4.2382602103512115,3.9225,4.3825,4.0025,4.4625,0.4600000000000004,0.45999999999999996 --NFrJFQijFE/1,-NFrJFQijFE,1,11,make,4.702499866485596,4.862500190734863,True,5.602865393861123e-11,1.437473177909851,4.297877403832084,4.498025124451961,4.062500000000001,4.5025,4.2625,4.6825,0.4399999999999995,0.41999999999999993 --NFrJFQijFE/1,-NFrJFQijFE,1,12,sure,4.922500133514404,5.0625,True,5.5529952164290464e-11,1.4246784448623657,4.5595462645139255,4.759348019483343,4.322500000000001,4.742500000000001,4.5425,4.9225,0.41999999999999993,0.3799999999999999 --NFrJFQijFE/1,-NFrJFQijFE,1,13,all,5.082499980926514,5.182499885559082,True,4.774074538471673e-11,1.2248382568359375,4.817843713841208,4.9557359749111045,4.6425,4.9625,4.782500000000001,5.102500000000001,0.3200000000000003,0.3200000000000003 --NFrJFQijFE/1,-NFrJFQijFE,1,14,that,5.202499866485596,5.34250020980835,True,5.2850689102879045e-11,1.3559391498565674,5.012945479106424,5.209031399250358,4.8425,5.1425,5.062500000000001,5.322500000000001,0.2999999999999998,0.2599999999999998 --NFrJFQijFE/1,-NFrJFQijFE,1,15,happens,5.362500190734863,5.642499923706055,True,5.486522347553091e-11,1.4076241254806519,5.265253840657979,5.6252282574922905,5.1425,5.362500000000001,5.5825000000000005,5.6425,0.22000000000000064,0.05999999999999961 --NFrJFQijFE/2,-NFrJFQijFE,2,0,So,1.122499942779541,1.2424999475479126,True,2.0999696732503133e-14,0.3740757703781128,0.7017702038054376,0.8164313921794365,0.6625,0.8225,0.7224999999999999,0.9624999999999999,0.16000000000000003,0.24 --NFrJFQijFE/2,-NFrJFQijFE,2,1,we,1.2825000286102295,1.3825000524520874,True,1.8122491829137936e-14,0.3228229880332947,0.9301739611043676,1.0355676938406366,0.7825,1.1225,0.8825,1.2425,0.3400000000000001,0.36 --NFrJFQijFE/2,-NFrJFQijFE,2,2,coordinate,1.6425000429153442,2.0824999809265137,True,2.99360789792185e-14,0.5332630276679993,1.1385153786376387,1.8848721368518628,0.9624999999999999,1.3425,1.6824999999999999,2.0425,0.3800000000000001,0.3600000000000001 --NFrJFQijFE/2,-NFrJFQijFE,2,3,with,2.1024999618530273,2.242500066757202,True,6.905049177840378e-14,1.2300232648849487,1.955838283594859,2.1544054792982386,1.7625,2.1025,1.9825,2.2625,0.3400000000000001,0.28000000000000025 --NFrJFQijFE/2,-NFrJFQijFE,2,4,all,2.262500047683716,2.362499952316284,True,5.794130230945757e-14,1.0321309566497803,2.2032026778324427,2.3225781228227205,2.0625,2.3025,2.2025,2.4225,0.2400000000000002,0.21999999999999975 --NFrJFQijFE/2,-NFrJFQijFE,2,5,the,2.382499933242798,2.4825000762939453,True,5.4333795763365084e-14,0.9678690433502197,2.3595980088641553,2.4785183487033846,2.2425,2.4625,2.3625,2.5825,0.21999999999999975,0.2200000000000002 --NFrJFQijFE/2,-NFrJFQijFE,2,6,proper,2.5625,2.7825000286102295,True,4.613511653327011e-14,0.8218227624893188,2.5235697929220096,2.7911478799564198,2.3825,2.6425,2.6825,2.9025,0.26000000000000023,0.21999999999999975 --NFrJFQijFE/2,-NFrJFQijFE,2,7,technical,2.802500009536743,3.242500066757202,True,2.8803697572693174e-14,0.5130914449691772,2.826699438084622,3.324501218236374,2.7225,2.9425,3.2025,3.5625,0.21999999999999975,0.3599999999999999 --NFrJFQijFE/2,-NFrJFQijFE,2,8,fields,3.622499942779541,3.9625000953674316,True,3.771829203136345e-12,4.0,3.567640087175455,3.9341314603260984,3.3425,3.6225,3.8825,3.9625,0.28000000000000025,0.08000000000000007 --NFrJFQijFE/2,-NFrJFQijFE,2,9,to,4.102499961853027,4.162499904632568,True,2.0974448035598198e-13,3.736259937286377,4.002873380071735,4.076475277349137,3.9025,4.1025,3.9625,4.1625000000000005,0.20000000000000018,0.20000000000000062 --NFrJFQijFE/2,-NFrJFQijFE,2,10,get,4.182499885559082,4.322500228881836,True,2.997963680149811e-14,0.5340389609336853,4.131802025449857,4.282693539325499,4.1025,4.242500000000001,4.2625,4.3825,0.14000000000000057,0.1200000000000001 --NFrJFQijFE/2,-NFrJFQijFE,2,11,ready,4.34250020980835,4.642499923706055,True,1.2902478564724706e-12,4.0,4.348985787804194,4.638174202884239,4.3425,4.4225,4.6225000000000005,4.6425,0.08000000000000007,0.019999999999999574 --NFrJFQijFE/2,-NFrJFQijFE,2,12,for,4.702499866485596,4.802499771118164,True,3.9598769955347807e-14,0.7053883075714111,4.69689650444899,4.800728795198717,4.6625000000000005,4.702500000000001,4.782500000000001,4.822500000000001,0.040000000000000036,0.040000000000000036 --NFrJFQijFE/2,-NFrJFQijFE,2,13,the,4.822500228881836,4.962500095367432,True,2.13347828986743e-12,4.0,4.832524953971189,4.960742471002819,4.822500000000001,4.8425,4.9625,4.9625,0.019999999999999574,0.0 --NFrJFQijFE/2,-NFrJFQijFE,2,14,instrument,4.982500076293945,5.582499980926514,True,5.864949639226152e-14,1.0447462797164917,4.981819918289069,5.583200022323766,4.982500000000001,4.982500000000001,5.5825000000000005,5.5825000000000005,0.0,0.0 --NFrJFQijFE/2,-NFrJFQijFE,2,15,to,6.0625,6.142499923706055,True,2.7822413291430856e-14,0.4956114590167999,6.008112967011355,6.1294094160023525,5.7625,6.062500000000001,6.0825000000000005,6.142500000000001,0.3000000000000007,0.0600000000000005 --NFrJFQijFE/2,-NFrJFQijFE,2,16,arrive,6.202499866485596,6.502500057220459,True,1.3836344179771198e-12,4.0,6.202900777167125,6.502181568618448,6.202500000000001,6.202500000000001,6.5025,6.5025,0.0,0.0 --NFrJFQijFE/2,-NFrJFQijFE,2,17,here,6.542500019073486,6.682499885559082,True,7.368213569662607e-13,4.0,6.542190924116508,6.682470262173858,6.5425,6.5425,6.6825,6.6825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,0,What,0.0024999999441206455,0.8025000095367432,True,5.184272455771577e-11,4.0,0.0025000003549047693,0.8035597524626871,0.0025000000000000005,0.0025000000000000005,0.8025,0.8025,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,1,ever,1.6425000429153442,1.9225000143051147,True,4.004269246911385e-13,4.0,1.6529561096015108,1.911600849839455,1.6425,1.7225,1.8825,1.9224999999999999,0.07999999999999985,0.039999999999999813 --THoVjtIkeU/12,-THoVjtIkeU,12,2,we,2.002500057220459,2.0625,True,3.836271849234213e-14,0.6021820306777954,2.0016582942541135,2.061671197113673,2.0025,2.0025,2.0625,2.0625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,3,turn,2.1424999237060547,2.302500009536743,True,1.717639139068043e-15,0.25,2.1415002094996507,2.301430511112869,2.1425,2.1425,2.3025,2.3025,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,4,our,2.322499990463257,2.442500114440918,True,6.326280374124166e-14,0.9930402636528015,2.3216422600531423,2.441643863547024,2.3225,2.3225,2.4425,2.4425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,5,attention,2.4825000762939453,2.882499933242798,True,1.9333507962838092e-15,0.25,2.4818262028628437,2.882207397831878,2.4825,2.4825,2.8825,2.8825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,6,to,2.922499895095825,3.0625,True,6.509613540480602e-14,1.0218181610107422,2.9226896623999803,3.0626401735658586,2.9225,2.9225,3.0625,3.0625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,7,is,3.182499885559082,3.2825000286102295,True,3.3738997381495273e-13,4.0,3.1823176206620607,3.2825267449727344,3.1825,3.1825,3.2825,3.2825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,8,what,3.322499990463257,3.502500057220459,True,1.615317071995779e-14,0.25355735421180725,3.3225591942568835,3.5025797752496106,3.3225000000000002,3.3225000000000002,3.5025,3.5025,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,9,grows,3.5225000381469727,3.882499933242798,True,3.4992076239619835e-14,0.5492728352546692,3.522784014159437,3.8827429304851,3.5225,3.5225,3.8825,3.8825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,10,and,4.362500190734863,4.482500076293945,True,9.108250991537309e-14,1.4297280311584473,4.362460354649206,4.482600317036873,4.362500000000001,4.362500000000001,4.482500000000001,4.482500000000001,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,11,so,4.542500019073486,4.642499923706055,True,1.8818568665174285e-12,4.0,4.542616593028849,4.642609848982019,4.5425,4.5425,4.6425,4.6425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,12,if,4.682499885559082,4.78249979019165,True,6.370618144341603e-14,1.0,4.73088135814891,4.8068325905395515,4.6825,4.7625,4.7625,4.822500000000001,0.08000000000000007,0.0600000000000005 --THoVjtIkeU/12,-THoVjtIkeU,12,13,we,4.882500171661377,4.962500095367432,True,6.87291274782155e-14,1.078845500946045,4.882564385938244,4.962677005890677,4.8825,4.8825,4.9625,4.9625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,14,believe,5.022500038146973,5.522500038146973,True,2.0615089225527877e-13,3.235963821411133,5.0245719541936245,5.522680156939918,5.022500000000001,5.022500000000001,5.522500000000001,5.522500000000001,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,15,that,5.582499980926514,5.762499809265137,True,2.316116514818555e-15,0.25,5.582652937970199,5.762661710917487,5.5825000000000005,5.5825000000000005,5.7625,5.7625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,16,anything,5.78249979019165,6.122499942779541,True,1.0723705621683722e-14,0.25,5.7857325380603175,6.128300725814943,5.782500000000001,5.8025,6.1225000000000005,6.1225000000000005,0.019999999999999574,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,17,is,6.182499885559082,6.262499809265137,True,5.072557499909147e-13,4.0,6.186674312374733,6.2654187095178235,6.1825,6.1825,6.242500000000001,6.2625,0.0,0.019999999999999574 --THoVjtIkeU/12,-THoVjtIkeU,12,18,"possible,",6.302499771118164,6.742499828338623,True,4.263187974298463e-15,0.25,6.30960288496603,6.754539570207679,6.3025,6.3025,6.742500000000001,6.742500000000001,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,19,if,6.78249979019165,6.84250020980835,True,1.6299522766650282e-14,0.25585463643074036,6.813844945293333,6.877896626746542,6.782500000000001,6.782500000000001,6.8425,6.8425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,20,we,6.902500152587891,7.042500019073486,True,8.570621968205516e-13,4.0,6.945868750376086,7.086394513895723,6.902500000000001,6.902500000000001,7.0425,7.0425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,21,believe,7.5625,8.142499923706055,True,4.034537461061749e-13,4.0,7.495042199965977,8.16664224609275,7.142500000000001,7.562500000000001,8.0825,8.1425,0.41999999999999993,0.0600000000000005 --THoVjtIkeU/12,-THoVjtIkeU,12,22,that,8.542499542236328,8.802499771118164,True,9.425311009100823e-14,1.4794970750808716,8.552403409813959,8.814869143102763,8.5425,8.5825,8.8025,8.862499999999999,0.03999999999999915,0.05999999999999872 --THoVjtIkeU/12,-THoVjtIkeU,12,23,social,8.90250015258789,9.542499542236328,True,5.2791052779221914e-12,4.0,8.926572150764354,9.554650222126032,8.8425,9.0825,9.282499999999999,9.6825,0.2400000000000002,0.40000000000000036 --THoVjtIkeU/12,-THoVjtIkeU,12,24,justice,9.602499961853027,10.082500457763672,True,3.2318182418417107e-12,4.0,9.622228193294516,10.075158284652437,9.4625,9.782499999999999,9.8825,10.1225,0.3199999999999985,0.2400000000000002 --THoVjtIkeU/12,-THoVjtIkeU,12,25,can,10.182499885559082,10.322500228881836,True,9.062416344695484e-14,1.4225332736968994,10.163757654882376,10.305245554736647,10.022499999999999,10.1825,10.1225,10.3225,0.16000000000000014,0.1999999999999993 --THoVjtIkeU/12,-THoVjtIkeU,12,26,be,10.422499656677246,10.482500076293945,True,1.1201589785944654e-12,4.0,10.406784621799975,10.467071219758102,10.2625,10.4225,10.3225,10.4825,0.16000000000000014,0.16000000000000014 --THoVjtIkeU/12,-THoVjtIkeU,12,27,achieved,10.522500038146973,10.942500114440918,True,2.057594986591433e-14,0.3229820132255554,10.510673160622034,10.934793906175887,10.4225,10.522499999999999,10.942499999999999,10.942499999999999,0.09999999999999964,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,28,and,10.982500076293945,11.082500457763672,True,7.772953694541382e-15,0.25,10.975281306995331,11.075634516296919,10.9825,10.9825,11.0825,11.0825,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,29,if,11.102499961853027,11.162500381469727,True,1.2482614429403481e-14,0.25,11.096370572360204,11.156537360099648,11.1025,11.1025,11.1625,11.1625,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,30,we,11.182499885559082,11.242500305175781,True,1.2027428749131208e-13,1.8879531621932983,11.177150273118796,11.237494385446567,11.1825,11.1825,11.2425,11.2425,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,31,believe,11.322500228881836,11.822500228881836,True,1.71007942507094e-13,2.6843225955963135,11.317170351965828,11.81544774843011,11.3225,11.3225,11.8225,11.8225,0.0,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,32,in,11.842499732971191,11.962499618530273,True,1.5597131875974557e-13,2.448291778564453,11.874989445701733,11.986233154258349,11.8425,11.942499999999999,11.9625,12.0425,0.09999999999999964,0.08000000000000007 --THoVjtIkeU/12,-THoVjtIkeU,12,33,"youth,",12.102499961853027,12.40250015258789,True,3.8225936223887716e-14,0.6000349521636963,12.07514111375613,12.372758250652677,12.0625,12.1025,12.362499999999999,12.4025,0.03999999999999915,0.040000000000000924 --THoVjtIkeU/12,-THoVjtIkeU,12,34,they,12.882499694824219,13.042499542236328,True,3.4154286115885973e-14,0.5361220240592957,12.580522630946723,13.026778540456233,12.4025,12.9025,12.9825,13.0425,0.5,0.0600000000000005 --THoVjtIkeU/12,-THoVjtIkeU,12,35,will,13.0625,13.202500343322754,True,1.877775482306797e-15,0.25,13.049451388170713,13.195483131860904,13.022499999999999,13.0625,13.1825,13.2225,0.040000000000000924,0.040000000000000924 --THoVjtIkeU/12,-THoVjtIkeU,12,36,believe,13.242500305175781,13.542499542236328,True,3.5739823372928775e-14,0.5610103011131287,13.23451659139245,13.54107337938477,13.2225,13.2425,13.5425,13.5425,0.019999999999999574,0.0 --THoVjtIkeU/12,-THoVjtIkeU,12,37,in,13.602499961853027,13.662500381469727,True,4.70323768402307e-15,0.25,13.59477173018874,13.655624982027442,13.5825,13.6025,13.6425,13.6625,0.019999999999999574,0.019999999999999574 --THoVjtIkeU/12,-THoVjtIkeU,12,38,themselves.,13.682499885559082,14.682499885559082,True,1.3947767975850713e-14,0.25,13.682050784224838,14.477877429780559,13.6825,13.6825,14.1425,14.7225,0.0,0.5800000000000001 --THoVjtIkeU/2,-THoVjtIkeU,2,0,I,0.042500000447034836,0.0625,True,3.5587171095174605e-14,0.5238187909126282,0.042747574508858015,0.06274757450885804,0.0425,0.0425,0.0625,0.0625,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,1,think,0.16249999403953552,0.3824999928474426,True,2.8180750918579967e-13,4.0,0.16252283449036006,0.38258277339076985,0.1625,0.1625,0.3825,0.3825,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,2,social,0.6825000047683716,1.4424999952316284,True,2.6086730974157757e-13,3.8397881984710693,0.6959264616025735,1.4393716808823362,0.6825,0.8025,1.4025,1.4425,0.12,0.039999999999999813 --THoVjtIkeU/2,-THoVjtIkeU,2,3,justice,1.462499976158142,1.7625000476837158,True,5.4383574195609324e-14,0.8004889488220215,1.462500073806679,1.7625047152212947,1.4625,1.4625,1.7625,1.7625,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,4,is,1.8025000095367432,1.8825000524520874,True,4.2815872917413567e-13,4.0,1.8025460129344417,1.8825969079606335,1.8025,1.8025,1.8825,1.8825,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,5,extremely,1.9424999952316284,2.362499952316284,True,7.994530737279529e-15,0.25,1.9425053363639069,2.362500000000592,1.9425,1.9425,2.3625,2.3625,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,6,important,2.382499933242798,2.822499990463257,True,5.668493222011316e-14,0.8343633413314819,2.3825001908451227,2.8223862620997116,2.3825,2.3825,2.8225,2.8225,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,7,for,2.862499952316284,2.9825000762939453,True,4.5019671042305365e-13,4.0,2.8625263658238085,2.98251959707777,2.8625,2.8625,2.9825,2.9825,0.0,0.0 --THoVjtIkeU/2,-THoVjtIkeU,2,8,young,3.0625,3.242500066757202,True,8.062027404714435e-15,0.25,3.0776871893499806,3.257933224930891,3.0625,3.1025,3.2425,3.2825,0.040000000000000036,0.040000000000000036 --THoVjtIkeU/2,-THoVjtIkeU,2,9,people.,3.322499990463257,3.702500104904175,True,7.919095338249776e-14,1.1656365394592285,3.3220047688676053,3.702550181798884,3.3225000000000002,3.3225000000000002,3.7025,3.7025,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,0,You,0.0024999999441206455,0.10249999910593033,True,7.510342987706448e-15,0.4623764753341675,0.008626349453761577,0.16667783381217036,0.0025000000000000005,0.0625,0.10250000000000001,0.5425,0.06,0.43999999999999995 --THoVjtIkeU/6,-THoVjtIkeU,6,1,know,0.12250000238418579,0.5425000190734863,True,6.107723236777041e-16,0.25,0.1987221878267382,0.5696078762787643,0.1225,0.5824999999999999,0.5425,0.7224999999999999,0.4599999999999999,0.17999999999999994 --THoVjtIkeU/6,-THoVjtIkeU,6,2,that,0.5824999809265137,0.7425000071525574,True,1.469799013943153e-14,0.9048861265182495,0.6074026626126879,0.7695639834093579,0.5824999999999999,0.7424999999999999,0.7424999999999999,0.9225,0.16000000000000003,0.18000000000000005 --THoVjtIkeU/6,-THoVjtIkeU,6,3,I,0.7825000286102295,0.8025000095367432,True,1.0772212982734786e-11,4.0,0.8124906763620903,0.8324906763620635,0.7825,0.9824999999999999,0.8025,1.0025,0.19999999999999996,0.19999999999999996 --THoVjtIkeU/6,-THoVjtIkeU,6,4,think,0.9225000143051147,1.3224999904632568,True,5.037423183120761e-14,3.101304531097412,0.9372821593564936,1.3304404827120164,0.9025,1.0425,1.3225,1.3825,0.14,0.06000000000000005 --THoVjtIkeU/6,-THoVjtIkeU,6,5,listening,1.3825000524520874,1.8825000524520874,True,9.660665720352419e-15,0.5947617292404175,1.3928878982518418,1.8981742756703237,1.3825,1.4825,1.8825,1.9224999999999999,0.09999999999999987,0.039999999999999813 --THoVjtIkeU/6,-THoVjtIkeU,6,6,to,1.9424999952316284,2.002500057220459,True,1.2283348234496012e-14,0.756227970123291,1.942528648184741,2.0025308908655695,1.9425,1.9425,2.0025,2.0025,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,7,young,2.0625,2.262500047683716,True,1.2662656576298303e-15,0.25,2.0621304027330667,2.2621369025721645,2.0625,2.0625,2.2625,2.2625,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,8,"people,",2.302500009536743,2.5625,True,1.7007364483748177e-14,1.0470634698867798,2.3024999785176608,2.5634430580912624,2.3025,2.3025,2.5625,2.5625,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,9,taking,2.622499942779541,3.0225000381469727,True,2.2397052067786236e-14,1.3788810968399048,2.6227230997220414,3.0213912292627607,2.6225,2.6225,3.0225,3.0225,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,10,in,3.0425000190734863,3.1024999618530273,True,1.287970262804335e-14,0.7929427027702332,3.0563697916002726,3.123298588516466,3.0425,3.0825,3.1025,3.1625,0.040000000000000036,0.06000000000000005 --THoVjtIkeU/6,-THoVjtIkeU,6,11,what,3.2225000858306885,3.382499933242798,True,1.399858132353782e-16,0.25,3.2090641191027367,3.368759455696855,3.1625,3.2225,3.3225000000000002,3.3825,0.06000000000000005,0.05999999999999961 --THoVjtIkeU/6,-THoVjtIkeU,6,12,they,3.422499895095825,3.5625,True,1.5478470013954165e-14,0.952936589717865,3.422107918188843,3.5624991299962057,3.4225,3.4225,3.5625,3.5625,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,13,have,3.6024999618530273,3.742500066757202,True,9.16368692624285e-15,0.5641651153564453,3.6024997152443112,3.749687033398412,3.6025,3.6025,3.7425,3.7625,0.0,0.020000000000000018 --THoVjtIkeU/6,-THoVjtIkeU,6,14,to,3.7825000286102295,3.862499952316284,True,1.177859367793065e-14,0.7251526117324829,3.7825058915301977,3.8625039279875057,3.7825,3.7825,3.8625,3.8625,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,15,say,3.942500114440918,4.122499942779541,True,1.6323537844770836e-13,4.0,3.942500199874739,4.122500127971544,3.9425,3.9425,4.1225000000000005,4.1225000000000005,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,16,because,4.162499904632568,4.482500076293945,True,1.3337907980772584e-13,4.0,4.166683924893797,4.48249963454062,4.1625000000000005,4.1825,4.482500000000001,4.482500000000001,0.019999999999999574,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,17,what,4.542500019073486,4.942500114440918,True,7.24388929238982e-17,0.25,4.554594493809835,4.895420128655968,4.522500000000001,4.6425,4.702500000000001,4.942500000000001,0.11999999999999922,0.2400000000000002 --THoVjtIkeU/6,-THoVjtIkeU,6,18,we,5.002500057220459,5.0625,True,2.4732789465553226e-14,1.522681474685669,4.958055151596716,5.01805986357333,4.8825,5.0025,4.942500000000001,5.062500000000001,0.1200000000000001,0.1200000000000001 --THoVjtIkeU/6,-THoVjtIkeU,6,19,have,5.082499980926514,5.222499847412109,True,1.3268368778088669e-14,0.8168710470199585,5.043438433626083,5.2030129096654605,5.0025,5.0825000000000005,5.1825,5.2225,0.08000000000000007,0.040000000000000036 --THoVjtIkeU/6,-THoVjtIkeU,6,20,seen,5.242499828338623,5.422500133514404,True,3.088271961293812e-14,1.901303768157959,5.242158090563371,5.422237301298598,5.242500000000001,5.242500000000001,5.4225,5.4225,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,21,all,5.482500076293945,5.682499885559082,True,1.7910698016940577e-14,1.1026774644851685,5.482469297811507,5.681582380617955,5.482500000000001,5.482500000000001,5.6825,5.6825,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,22,around,5.702499866485596,5.962500095367432,True,4.47852407828124e-14,2.757216691970825,5.702103166805097,5.957460999624755,5.702500000000001,5.702500000000001,5.9225,5.9625,0.0,0.040000000000000036 --THoVjtIkeU/6,-THoVjtIkeU,6,23,the,5.982500076293945,6.082499980926514,True,1.5442178252054344e-15,0.25,5.980183708565806,6.080183708652749,5.942500000000001,5.982500000000001,6.0425,6.0825000000000005,0.040000000000000036,0.040000000000000036 --THoVjtIkeU/6,-THoVjtIkeU,6,24,world,6.102499961853027,6.322500228881836,True,1.8824873148341357e-15,0.25,6.102498364639328,6.324249512896174,6.102500000000001,6.102500000000001,6.322500000000001,6.322500000000001,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,25,is,6.402500152587891,6.522500038146973,True,3.4449125409924397e-13,4.0,6.402505356574926,6.5225017704465404,6.402500000000001,6.402500000000001,6.522500000000001,6.522500000000001,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,26,the,6.582499980926514,6.682499885559082,True,4.761372435105153e-14,2.9313530921936035,6.582500199985874,6.682500449272542,6.5825000000000005,6.5825000000000005,6.6825,6.6825,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,27,power,6.742499828338623,7.042500019073486,True,2.462818089656732e-14,1.516241192817688,6.74240651506444,7.042512615514087,6.742500000000001,6.742500000000001,7.0425,7.0425,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,28,of,7.082499980926514,7.142499923706055,True,2.6027791033556014e-12,4.0,7.083613699835981,7.143614692086718,7.0825000000000005,7.0825000000000005,7.142500000000001,7.142500000000001,0.0,0.0 --THoVjtIkeU/6,-THoVjtIkeU,6,29,youth.,7.242499828338623,7.522500038146973,True,5.518415246790441e-14,3.3974287509918213,7.2424925780003235,7.522504128690015,7.242500000000001,7.242500000000001,7.522500000000001,7.522500000000001,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,0,^And,0.4625000059604645,0.5625,True,7.444570879352151e-15,0.25,0.43347005061479676,0.5584540097468289,0.1825,0.4625,0.5225,0.5625,0.28,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,1,all,0.5824999809265137,0.7825000286102295,True,4.728579723958792e-13,4.0,0.6187218539498627,0.7942601619890411,0.5824999999999999,0.6625,0.7825,0.8225,0.08000000000000007,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,2,of,0.8424999713897705,0.9024999737739563,True,2.301460346083861e-13,2.2389814853668213,0.8424948712648224,0.9027736800678561,0.8424999999999999,0.8424999999999999,0.9025,0.9025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,3,them,0.9424999952316284,1.122499942779541,True,1.480158904514431e-14,0.25,0.9432060795990252,1.113068420763404,0.9425,0.9425,1.1025,1.1225,0.0,0.020000000000000018 --UuX1xuaiiE/1,-UuX1xuaiiE,1,4,are,1.2024999856948853,1.3224999904632568,True,2.4575976561962143e-13,2.3908801078796387,1.1923363969861287,1.3174479199245037,1.1225,1.2025,1.2825,1.3225,0.07999999999999985,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,5,really,1.3825000524520874,1.662500023841858,True,2.800404426037649e-15,0.25,1.3814252978084791,1.6624772229432363,1.3825,1.3825,1.6625,1.6625,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,6,being,1.6825000047683716,1.9225000143051147,True,1.952734679413603e-14,0.25,1.6824843176111908,1.9214195095315,1.6824999999999999,1.6824999999999999,1.9025,1.9224999999999999,0.0,0.019999999999999796 --UuX1xuaiiE/1,-UuX1xuaiiE,1,7,able,2.5625,2.762500047683716,True,9.968488843626125e-14,0.9697869420051575,2.3964615747960076,2.7372177674963947,2.0225,2.5625,2.7225,2.7625,0.54,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,8,to,2.7825000286102295,2.8424999713897705,True,1.889153675887264e-14,0.25,2.782500876058673,2.842501166223785,2.7825,2.7825,2.8425,2.8425,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,9,explore,2.882499933242798,3.5225000381469727,True,4.779727503546882e-13,4.0,2.9052813661038304,3.531178920856283,2.8825,2.9825,3.5225,3.5825,0.10000000000000009,0.06000000000000005 --UuX1xuaiiE/1,-UuX1xuaiiE,1,10,^these,3.622499942779541,4.242499828338623,True,6.823929984446642e-13,4.0,3.627667145170375,4.240710937512301,3.6225,3.6225,4.242500000000001,4.242500000000001,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,11,different,4.262499809265137,4.802499771118164,True,2.766109756069217e-14,0.26910167932510376,4.382878667595825,4.964671088009417,4.2625,4.522500000000001,4.8025,5.1825,0.2600000000000007,0.3799999999999999 --UuX1xuaiiE/1,-UuX1xuaiiE,1,12,areas,4.902500152587891,5.182499885559082,True,3.5131094675055e-14,0.34177374839782715,5.074588703285389,5.393802822040885,4.902500000000001,5.322500000000001,5.1825,5.6425,0.41999999999999993,0.45999999999999996 --UuX1xuaiiE/1,-UuX1xuaiiE,1,13,in,5.542500019073486,5.862500190734863,True,1.8041015729941545e-12,4.0,5.605517378896713,5.7680139174769876,5.242500000000001,5.8025,5.562500000000001,5.862500000000001,0.5599999999999996,0.2999999999999998 --UuX1xuaiiE/1,-UuX1xuaiiE,1,14,depth,5.962500095367432,6.34250020980835,True,3.677016915774878e-13,3.577195167541504,5.942366997981639,6.342105138567985,5.8425,6.062500000000001,6.3425,6.3425,0.22000000000000064,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,15,^in,6.382500171661377,6.582499980926514,True,7.172436472871468e-13,4.0,6.382663094054936,6.584390946636888,6.3825,6.3825,6.5825000000000005,6.602500000000001,0.0,0.020000000000000462 --UuX1xuaiiE/1,-UuX1xuaiiE,1,16,a,6.682499885559082,6.702499866485596,True,1.4643733274588566e-12,4.0,6.68250055881938,6.702500558819382,6.6825,6.6825,6.702500000000001,6.702500000000001,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,17,way,6.762499809265137,6.922500133514404,True,1.0279050424418304e-13,1.0,6.762495012667477,6.939677273518233,6.7625,6.7625,6.9225,6.9625,0.0,0.040000000000000036 --UuX1xuaiiE/1,-UuX1xuaiiE,1,18,that,7.022500038146973,7.202499866485596,True,1.8719922801561234e-14,0.25,7.021126903366722,7.195559932352289,7.022500000000001,7.022500000000001,7.1825,7.202500000000001,0.0,0.020000000000000462 --UuX1xuaiiE/1,-UuX1xuaiiE,1,19,really,7.222499847412109,7.522500038146973,True,4.071806385580511e-15,0.25,7.2194584454033,7.522500219334755,7.202500000000001,7.2225,7.522500000000001,7.522500000000001,0.019999999999999574,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,20,wasn't,7.5625,7.802499771118164,True,2.0166210281530317e-12,4.0,7.562499676953594,7.801666052689677,7.562500000000001,7.562500000000001,7.8025,7.8025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,21,available,7.84250020980835,8.322500228881836,True,4.036739475674067e-14,0.3927152156829834,7.842499475900292,8.322500361921357,7.8425,7.8425,8.3225,8.3225,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,22,^before,8.362500190734863,8.702500343322754,True,3.3737571655638454e-13,3.28216814994812,8.362539013059672,8.702503611878011,8.362499999999999,8.362499999999999,8.7025,8.7025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,23,the,8.742500305175781,8.842499732971191,True,2.0222062888683272e-14,0.25,8.74366559409698,8.844477233896045,8.7425,8.7425,8.8425,8.8425,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,24,center,8.862500190734863,9.302499771118164,True,2.695884422816708e-13,2.6226978302001953,8.86645495473147,9.305803311848454,8.862499999999999,8.862499999999999,9.3025,9.3025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,25,was,9.442500114440918,9.602499961853027,True,3.3444106587853656e-14,0.32536181807518005,9.447406086810131,9.607217398306826,9.442499999999999,9.442499999999999,9.6025,9.6025,0.0,0.0 --UuX1xuaiiE/1,-UuX1xuaiiE,1,26,here,9.922499656677246,10.102499961853027,True,9.340148446335128e-12,4.0,9.927156903525098,10.156147021362736,9.9225,9.942499999999999,10.1025,10.2425,0.019999999999999574,0.14000000000000057 --UuX1xuaiiE/0,-UuX1xuaiiE,0,0,^it's,0.0625,0.18250000476837158,True,5.385001797773847e-13,4.0,0.03084138853451605,0.18886906847383023,0.0025000000000000005,0.0825,0.14250000000000002,0.2425,0.08,0.09999999999999998 --UuX1xuaiiE/0,-UuX1xuaiiE,0,1,amazing,0.30250000953674316,0.8025000095367432,True,2.427161661759658e-13,4.0,0.2978772205761982,0.7982895129975586,0.2425,0.3025,0.7224999999999999,0.8025,0.06,0.08000000000000007 --UuX1xuaiiE/0,-UuX1xuaiiE,0,2,to,0.8824999928474426,0.9624999761581421,True,5.4123535080802254e-14,1.0,0.8823922464530151,0.962499782349916,0.8825,0.8825,0.9624999999999999,0.9624999999999999,0.0,0.0 --UuX1xuaiiE/0,-UuX1xuaiiE,0,3,see,1.0625,1.2424999475479126,True,3.0656412738222505e-13,4.0,1.0624987865229019,1.2425008786156162,1.0625,1.0625,1.2425,1.2425,0.0,0.0 --UuX1xuaiiE/0,-UuX1xuaiiE,0,4,their,1.3224999904632568,1.5225000381469727,True,7.977060365105054e-16,0.25,1.3222094811963274,1.5226780527244042,1.3225,1.3225,1.5225,1.5225,0.0,0.0 --UuX1xuaiiE/0,-UuX1xuaiiE,0,5,enthusiasm,1.5625,2.322499990463257,True,1.264410261605023e-13,2.336156129837036,1.55597757995693,2.3073898823403156,1.5425,1.5625,2.2225,2.3225,0.020000000000000018,0.09999999999999964 --UuX1xuaiiE/0,-UuX1xuaiiE,0,6,and,2.442500114440918,2.5425000190734863,True,3.3708023419493256e-15,0.25,2.4114640999737245,2.5267290614733597,2.3025,2.4425,2.4625,2.5425,0.13999999999999968,0.08000000000000007 --UuX1xuaiiE/0,-UuX1xuaiiE,0,7,their,2.5824999809265137,2.9825000762939453,True,1.4151743673944915e-14,0.261471152305603,2.5825076917084684,2.9839370714614866,2.5825,2.5825,2.9825,2.9825,0.0,0.0 --UuX1xuaiiE/0,-UuX1xuaiiE,0,8,passion,3.122499942779541,3.502500057220459,True,6.910157649427136e-17,0.25,3.1188937037274393,3.5345878746530324,3.0825,3.1225,3.5025,3.5625,0.040000000000000036,0.06000000000000005 --UuX1xuaiiE/3,-UuX1xuaiiE,3,0,My,0.12250000238418579,0.32249999046325684,True,8.870484347056617e-10,4.0,0.12261680417447868,0.32700275790598615,0.1225,0.1225,0.3225,0.3825,0.0,0.06 --UuX1xuaiiE/3,-UuX1xuaiiE,3,1,goal,0.5625,0.8424999713897705,True,2.5603030112675285e-11,1.5297502279281616,0.5562232336384092,0.8381385156691515,0.5625,0.5625,0.8424999999999999,0.8424999999999999,0.0,0.0 --UuX1xuaiiE/3,-UuX1xuaiiE,3,2,is,1.222499966621399,1.2825000286102295,True,1.1648832939914477e-12,0.25,1.146023235323786,1.2660061071273174,1.0425,1.2225,1.2425,1.3425,0.17999999999999994,0.10000000000000009 --UuX1xuaiiE/3,-UuX1xuaiiE,3,3,to,1.462499976158142,1.6425000429153442,True,2.4778476492848256e-10,4.0,1.4593958501682065,1.6262894351115438,1.4625,1.4825,1.5625,1.6625,0.020000000000000018,0.10000000000000009 --UuX1xuaiiE/3,-UuX1xuaiiE,3,4,become,1.722499966621399,2.0425000190734863,True,1.1037502509192443e-11,0.6594774723052979,1.7086218803776163,2.042267428813497,1.6025,1.7225,2.0425,2.0425,0.11999999999999988,0.0 --UuX1xuaiiE/3,-UuX1xuaiiE,3,5,a,2.622499942779541,2.6424999237060547,True,2.320204575342877e-12,0.25,2.6157081677890304,2.6357081677890317,2.6225,2.6225,2.6425,2.6425,0.0,0.0 --UuX1xuaiiE/3,-UuX1xuaiiE,3,6,human,2.702500104904175,2.9024999141693115,True,2.8239372645844085e-12,0.25,2.707741696294927,2.923241916792613,2.7025,2.7625,2.9025,3.0625,0.06000000000000005,0.16000000000000014 --UuX1xuaiiE/3,-UuX1xuaiiE,3,7,rights,2.9825000762939453,3.4825000762939453,True,2.520619477031083e-11,1.5060398578643799,3.020026811992114,3.50541435590898,2.9825,3.2425,3.4825,3.5825,0.26000000000000023,0.10000000000000009 --UuX1xuaiiE/3,-UuX1xuaiiE,3,8,lawyer,3.622499942779541,4.002500057220459,True,1.673673873103798e-11,1.0,3.636799554936652,4.012098400623447,3.6225,3.6825,4.0025,4.022500000000001,0.06000000000000005,0.020000000000000462 --UuX1xuaiiE/6,-UuX1xuaiiE,6,0,I,0.5824999809265137,0.6025000214576721,True,1.787539005704275e-10,4.0,0.270109773646166,0.2901097736461649,0.0225,0.5824999999999999,0.0425,0.6024999999999999,0.5599999999999999,0.5599999999999999 --UuX1xuaiiE/6,-UuX1xuaiiE,6,1,ended,0.6225000023841858,0.8025000095367432,True,2.451882826545043e-13,0.36168426275253296,0.5396114880489867,0.7337382133486879,0.5225,0.6224999999999999,0.7224999999999999,0.8025,0.09999999999999998,0.08000000000000007 --UuX1xuaiiE/6,-UuX1xuaiiE,6,2,up,0.8424999713897705,0.9225000143051147,True,9.941535442092864e-13,1.466504454612732,0.7558957635136302,0.8178524341981249,0.7424999999999999,0.8424999999999999,0.8025,0.9225,0.09999999999999998,0.12 --UuX1xuaiiE/6,-UuX1xuaiiE,6,3,going,0.9424999952316284,1.122499942779541,True,8.119074226679065e-15,0.25,0.8555654063033344,1.1208553058000463,0.8424999999999999,0.9425,1.1225,1.1225,0.10000000000000009,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,4,to,1.162500023841858,1.222499966621399,True,1.4474453062453436e-14,0.25,1.1624981375345769,1.2224993879065165,1.1624999999999999,1.1624999999999999,1.2225,1.2225,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,5,work,1.2625000476837158,1.462499976158142,True,4.01744846970694e-14,0.25,1.2624994413657256,1.4591329219296074,1.2625,1.2625,1.4224999999999999,1.4825,0.0,0.06000000000000005 --UuX1xuaiiE/6,-UuX1xuaiiE,6,6,for,1.5625,1.7424999475479126,True,5.021742028286924e-13,0.7407715916633606,1.5621988027396478,1.7420246341274674,1.5625,1.5625,1.7425,1.7425,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,7,the,1.8224999904632568,1.9824999570846558,True,2.22696115563005e-13,0.32850542664527893,1.8241144657810688,1.9721595686699893,1.8225,1.8225,1.9224999999999999,2.0025,0.0,0.08000000000000007 --UuX1xuaiiE/6,-UuX1xuaiiE,6,8,US,2.1624999046325684,2.242500066757202,True,7.930261482214096e-12,4.0,2.1214027566976656,2.197309546467254,1.9625,2.1625,2.0225,2.2425,0.20000000000000018,0.2200000000000002 --UuX1xuaiiE/6,-UuX1xuaiiE,6,9,government,2.262500047683716,2.7225000858306885,True,1.3203499606856146e-14,0.25,2.2378999774402657,2.7214863527895146,2.1625,2.2625,2.7225,2.7225,0.10000000000000009,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,10,as,2.8424999713897705,2.9024999141693115,True,7.30077456823075e-12,4.0,2.842739371428413,2.902744261495768,2.8425,2.8425,2.9025,2.9025,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,11,an,2.9825000762939453,3.0425000190734863,True,1.1879888349095036e-12,1.7524363994598389,2.972215429222952,3.0322589391232513,2.9425,2.9825,3.0025,3.0425,0.040000000000000036,0.040000000000000036 --UuX1xuaiiE/6,-UuX1xuaiiE,6,12,intelligence,3.0824999809265137,3.7225000858306885,True,5.813590169163885e-13,0.8575793504714966,3.082955856583384,3.7232939342321854,3.0825,3.0825,3.7225,3.7225,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,13,"analyst,",3.762500047683716,4.822500228881836,True,2.2186831365178517e-12,3.2728431224823,3.7644551139300924,4.80955242831551,3.7625,3.7625,4.7625,4.8425,0.0,0.08000000000000007 --UuX1xuaiiE/6,-UuX1xuaiiE,6,14,specializing,4.862500190734863,5.622499942779541,True,1.4901028544747619e-12,2.1980934143066406,4.88489165691581,5.637407196149477,4.862500000000001,4.982500000000001,5.6225000000000005,5.702500000000001,0.1200000000000001,0.08000000000000007 --UuX1xuaiiE/6,-UuX1xuaiiE,6,15,in,5.722499847412109,5.802499771118164,True,6.507028531450854e-13,0.959870457649231,5.719573747226648,5.80167959979051,5.7225,5.7225,5.8025,5.822500000000001,0.0,0.020000000000000462 --UuX1xuaiiE/6,-UuX1xuaiiE,6,16,war,5.882500171661377,6.002500057220459,True,2.1248417522018448e-12,3.1344151496887207,5.878821325527894,5.999303897122543,5.8825,5.8825,6.0025,6.0025,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,17,crimes,6.042500019073486,6.34250020980835,True,1.829281355298501e-13,0.2698425352573395,6.033983176986125,6.344507453138877,6.022500000000001,6.0425,6.3425,6.3425,0.019999999999999574,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,18,and,6.382500171661377,6.482500076293945,True,1.405874928191858e-13,0.25,6.384966204069522,6.485061293202971,6.3825,6.3825,6.482500000000001,6.482500000000001,0.0,0.0 --UuX1xuaiiE/6,-UuX1xuaiiE,6,19,human,6.502500057220459,6.702499866485596,True,7.051110455062737e-13,1.040129542350769,6.505737929966874,6.709886966565923,6.5025,6.5025,6.702500000000001,6.7225,0.0,0.019999999999999574 --UuX1xuaiiE/6,-UuX1xuaiiE,6,20,rights,6.722499847412109,6.982500076293945,True,4.419648241132945e-12,4.0,6.7350518605513106,7.002116245068988,6.7225,6.782500000000001,6.982500000000001,7.022500000000001,0.0600000000000005,0.040000000000000036 --UuX1xuaiiE/6,-UuX1xuaiiE,6,21,issues,7.042500019073486,7.34250020980835,True,1.0247198922730405e-11,4.0,7.047294136122448,7.364531168340181,7.0425,7.0425,7.3425,7.562500000000001,0.0,0.22000000000000064 --a55Q6RWvTA/3,-a55Q6RWvTA,3,0,And,0.02250000089406967,0.8224999904632568,True,1.8799320281905063e-14,0.25,0.119519558806842,0.8420489822368266,0.0225,0.7625,0.8225,0.9225,0.74,0.09999999999999998 --a55Q6RWvTA/3,-a55Q6RWvTA,3,1,if,1.0425000190734863,1.122499942779541,True,1.864498572895501e-12,4.0,1.0396889744500983,1.1189426038956152,1.0425,1.0425,1.1025,1.1225,0.0,0.020000000000000018 --a55Q6RWvTA/3,-a55Q6RWvTA,3,2,you,1.1825000047683716,1.3825000524520874,True,8.737685922022287e-12,4.0,1.1823567191989515,1.3813940509165903,1.1824999999999999,1.1824999999999999,1.3825,1.3825,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,3,clip,1.4424999952316284,1.6825000047683716,True,4.2130695091478765e-13,1.162251591682434,1.4420208862780637,1.6970771282427792,1.4425,1.4425,1.6824999999999999,1.7425,0.0,0.06000000000000005 --a55Q6RWvTA/3,-a55Q6RWvTA,3,4,"coupons,",1.7625000476837158,2.5425000190734863,True,1.3545874491538434e-12,3.736875057220459,1.7613384775974255,2.544447333196721,1.7625,1.7625,2.5425,2.5825,0.0,0.040000000000000036 --a55Q6RWvTA/3,-a55Q6RWvTA,3,5,if,2.5824999809265137,2.742500066757202,True,1.0346320154785982e-12,2.854219913482666,2.591940711601199,2.7392036510285633,2.5825,2.7025,2.7225,2.7825,0.1200000000000001,0.06000000000000005 --a55Q6RWvTA/3,-a55Q6RWvTA,3,6,you,2.802500009536743,2.9825000762939453,True,4.206752676240433e-13,1.1605089902877808,2.805593026690063,2.980874186453363,2.8025,2.8425,2.9825,2.9825,0.03999999999999959,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,7,don't,3.0425000190734863,3.242500066757202,True,6.30838103976572e-10,4.0,3.0425001275429757,3.2477120612251564,3.0425,3.0425,3.2425,3.2825,0.0,0.040000000000000036 --a55Q6RWvTA/3,-a55Q6RWvTA,3,8,go,3.3424999713897705,3.442500114440918,True,1.436209983406156e-12,3.962045431137085,3.342501925342056,3.44250103576293,3.3425,3.3425,3.4425,3.4425,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,9,out,3.5625,3.702500104904175,True,1.3597915924767195e-13,0.37512314319610596,3.539193583208049,3.6907864440326263,3.4825,3.5625,3.6625,3.7025,0.08000000000000007,0.040000000000000036 --a55Q6RWvTA/3,-a55Q6RWvTA,3,10,for,3.742500066757202,3.862499952316284,True,5.18228004725476e-14,0.25,3.7424883769630815,3.862544926610272,3.7425,3.7425,3.8625,3.8625,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,11,dinner,3.942500114440918,4.382500171661377,True,1.2317580224026348e-13,0.3398027718067169,3.9419675211510485,4.380234510158347,3.9425,3.9425,4.3825,4.3825,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,12,and,4.462500095367432,4.5625,True,9.629341594930008e-14,0.26564285159111023,4.462225306627924,4.562438974723767,4.4625,4.4625,4.562500000000001,4.562500000000001,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,13,movies,4.662499904632568,5.142499923706055,True,1.1253193689386709e-11,4.0,4.655136187997946,5.1260571327776345,4.5825000000000005,4.6625000000000005,4.9625,5.1425,0.08000000000000007,0.17999999999999972 --a55Q6RWvTA/3,-a55Q6RWvTA,3,14,and,5.182499885559082,5.322500228881836,True,2.892920007279559e-13,0.7980644106864929,5.167784439597989,5.302560216276301,5.022500000000001,5.1825,5.1425,5.322500000000001,0.15999999999999925,0.1800000000000006 --a55Q6RWvTA/3,-a55Q6RWvTA,3,15,things,5.34250020980835,5.602499961853027,True,2.486937793286931e-13,0.6860668659210205,5.327936327257898,5.5895131003357275,5.1825,5.3425,5.4625,5.602500000000001,0.16000000000000014,0.14000000000000057 --a55Q6RWvTA/3,-a55Q6RWvTA,3,16,of,5.622499942779541,5.682499885559082,True,2.622747722128005e-12,4.0,5.610183745847421,5.671949375451719,5.482500000000001,5.6225000000000005,5.562500000000001,5.6825,0.13999999999999968,0.11999999999999922 --a55Q6RWvTA/3,-a55Q6RWvTA,3,17,that,5.702499866485596,5.84250020980835,True,2.375747710538223e-13,0.6553930640220642,5.696998378867096,5.8405395591316625,5.6625000000000005,5.702500000000001,5.8425,5.8425,0.040000000000000036,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,18,nature,5.902500152587891,6.28249979019165,True,2.4568303121086377e-12,4.0,5.901516263387521,6.276519742983215,5.902500000000001,5.902500000000001,6.242500000000001,6.322500000000001,0.0,0.08000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,19,and,6.362500190734863,7.022500038146973,True,7.170564597820672e-13,1.9781302213668823,6.404674142671651,7.0242655162952055,6.362500000000001,6.8825,7.022500000000001,7.022500000000001,0.5199999999999996,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,20,try,7.082499980926514,7.302499771118164,True,9.657237032625887e-13,2.664123773574829,7.0846341886283755,7.303463941016041,7.0825000000000005,7.0825000000000005,7.3025,7.3025,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,21,to,7.402500152587891,7.462500095367432,True,4.870198465838733e-15,0.25,7.39206605640134,7.460182285553983,7.3825,7.402500000000001,7.442500000000001,7.4625,0.020000000000000462,0.019999999999999574 --a55Q6RWvTA/3,-a55Q6RWvTA,3,22,take,7.502500057220459,7.662499904632568,True,2.18452897237471e-14,0.25,7.49860130593234,7.661219461951071,7.4625,7.5025,7.6225000000000005,7.702500000000001,0.040000000000000036,0.08000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,23,up,7.742499828338623,7.802499771118164,True,1.7658331665382915e-13,0.4871370792388916,7.742432282304045,7.802925440257224,7.742500000000001,7.742500000000001,7.8025,7.8025,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,24,things,7.962500095367432,8.302499771118164,True,4.2784580132210204e-13,1.1802902221679688,7.952760213706006,8.299296605177526,7.862500000000001,7.9625,8.2625,8.3025,0.09999999999999964,0.040000000000000924 --a55Q6RWvTA/3,-a55Q6RWvTA,3,25,in,8.362500190734863,8.462499618530273,True,1.1459259105853214e-12,3.1612443923950195,8.362640924084438,8.462201634766528,8.362499999999999,8.362499999999999,8.4625,8.4625,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,26,life,8.542499542236328,8.782500267028809,True,3.432629072479265e-13,0.9469529390335083,8.542499652125546,8.782508521681644,8.5425,8.5425,8.782499999999999,8.782499999999999,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,27,"that,",8.842499732971191,9.102499961853027,True,5.165082771207974e-13,1.4248816967010498,8.8325292619187,9.086651659124355,8.8025,8.862499999999999,9.022499999999999,9.1025,0.05999999999999872,0.08000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,28,"well,",9.22249984741211,9.362500190734863,True,5.009049273453636e-13,1.381837010383606,9.193693502270262,9.36246372511127,9.0825,9.2225,9.362499999999999,9.362499999999999,0.14000000000000057,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,29,they,9.382499694824219,9.582500457763672,True,1.9582944342727907e-14,0.25,9.400462764831364,9.591486656966236,9.3825,9.4625,9.5825,9.6225,0.08000000000000007,0.040000000000000924 --a55Q6RWvTA/3,-a55Q6RWvTA,3,30,always,9.642499923706055,9.922499656677246,True,1.198839747092173e-13,0.3307216763496399,9.642498995341569,9.922498654780092,9.6425,9.6425,9.9225,9.9225,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,31,"say,",9.962499618530273,10.1225004196167,True,4.1787146659588714e-12,4.0,9.962500002041113,10.122500003597148,9.9625,9.9625,10.1225,10.1225,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,32,"""the",10.182499885559082,10.282500267028809,True,1.0157119015221511e-13,0.2802025377750397,10.18358703675862,10.283588063519483,10.1825,10.1825,10.282499999999999,10.282499999999999,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,33,best,10.302499771118164,10.522500038146973,True,4.5164518419670355e-14,0.25,10.323673882731844,10.535599687450915,10.3025,10.3425,10.522499999999999,10.5625,0.03999999999999915,0.040000000000000924 --a55Q6RWvTA/3,-a55Q6RWvTA,3,34,things,10.542499542236328,10.822500228881836,True,5.324179072336947e-14,0.25,10.564405953112635,10.834780913875006,10.5425,10.6025,10.782499999999999,10.862499999999999,0.05999999999999872,0.08000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,35,in,10.842499732971191,10.922499656677246,True,4.024187463835295e-14,0.25,10.922409797559588,10.99278094836726,10.8025,11.0625,10.862499999999999,11.1225,0.2599999999999998,0.26000000000000156 --a55Q6RWvTA/3,-a55Q6RWvTA,3,36,life,10.982500076293945,11.182499885559082,True,2.4966956128393003e-13,0.6887587308883667,11.052018990177505,11.236140451775867,10.942499999999999,11.1825,11.1225,11.3425,0.2400000000000002,0.21999999999999886 --a55Q6RWvTA/3,-a55Q6RWvTA,3,37,are,11.242500305175781,11.342499732971191,True,1.2090145508000805e-12,3.3352856636047363,11.315824128651643,11.482355839813513,11.2425,11.442499999999999,11.3425,11.7225,0.1999999999999993,0.3800000000000008 --a55Q6RWvTA/3,-a55Q6RWvTA,3,38,"free"".",11.442500114440918,11.72249984741211,True,1.108621183677945e-13,0.30583325028419495,11.80240737192858,12.06420304908739,11.442499999999999,12.4225,11.7225,12.6625,0.9800000000000004,0.9399999999999995 --a55Q6RWvTA/3,-a55Q6RWvTA,3,39,Reading,11.782500267028809,12.72249984741211,True,7.512862402504761e-14,0.25,12.147773825953134,12.878409906681043,11.782499999999999,12.7825,12.6825,13.1625,1.0000000000000018,0.4800000000000004 --a55Q6RWvTA/3,-a55Q6RWvTA,3,40,a,12.782500267028809,12.802499771118164,True,9.634311577688681e-12,4.0,12.954937272825967,12.974937272826114,12.7825,13.2025,12.8025,13.2225,0.41999999999999993,0.41999999999999993 --a55Q6RWvTA/3,-a55Q6RWvTA,3,41,"book,",12.90250015258789,13.162500381469727,True,3.839624798662733e-12,4.0,13.294036123397236,13.538265691133404,12.9025,14.0625,13.1625,14.2025,1.1600000000000001,1.040000000000001 --a55Q6RWvTA/3,-a55Q6RWvTA,3,42,going,14.0625,14.542499542236328,True,3.271775645546926e-12,4.0,13.899637684976911,14.479520424724328,13.2025,14.2225,14.1625,14.6025,1.0199999999999996,0.4399999999999995 --a55Q6RWvTA/3,-a55Q6RWvTA,3,43,for,14.642499923706055,14.782500267028809,True,5.69680546767988e-14,0.25,14.57912428176785,14.724128175889916,14.3825,14.6425,14.5425,14.7825,0.2599999999999998,0.2400000000000002 --a55Q6RWvTA/3,-a55Q6RWvTA,3,44,a,14.802499771118164,14.822500228881836,True,1.7280364639216117e-11,4.0,14.777518286815813,14.797518286816297,14.7025,14.8025,14.7225,14.8225,0.09999999999999964,0.09999999999999964 --a55Q6RWvTA/3,-a55Q6RWvTA,3,45,nice,14.962499618530273,15.1225004196167,True,2.8319247988295437e-12,4.0,14.930842492182515,15.106118105713016,14.8825,14.9625,15.0825,15.1225,0.08000000000000007,0.040000000000000924 --a55Q6RWvTA/3,-a55Q6RWvTA,3,46,"walk,",15.182499885559082,15.462499618530273,True,1.6164060107765055e-12,4.0,15.18135379782965,15.461013343972695,15.1825,15.1825,15.4625,15.4625,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,47,"and,",15.5024995803833,15.602499961853027,True,2.406427210006456e-14,0.25,15.501115550027231,15.601660253222741,15.5025,15.5025,15.6025,15.6025,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,48,of,15.6225004196167,15.682499885559082,True,7.75856375673234e-12,4.0,15.621856150264364,15.682008493386707,15.6225,15.6225,15.6825,15.6825,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,49,"course,",15.702500343322754,15.942500114440918,True,5.6591589189325733e-14,0.25,15.702154879797067,15.942473582145436,15.7025,15.7025,15.942499999999999,15.942499999999999,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,50,combining,15.962499618530273,16.542499542236328,True,3.2498599623030644e-13,0.8965327739715576,15.962615579554416,16.54258848862367,15.9625,15.9625,16.5425,16.5425,0.0,0.0 --a55Q6RWvTA/3,-a55Q6RWvTA,3,51,exercise,16.5625,17.162500381469727,True,7.296909387485839e-13,2.0129847526550293,16.56451128516722,17.16727364513594,16.5625,16.5625,17.1625,17.2025,0.0,0.03999999999999915 --a55Q6RWvTA/3,-a55Q6RWvTA,3,52,and,17.202499389648438,17.302499771118164,True,1.9747090857576317e-14,0.25,17.212519549668535,17.31286686273573,17.2025,17.242500000000003,17.302500000000002,17.3425,0.0400000000000027,0.03999999999999915 --a55Q6RWvTA/3,-a55Q6RWvTA,3,53,various,17.322500228881836,17.622499465942383,True,9.008352603364495e-14,0.25,17.33331033428284,17.632382986564632,17.3225,17.3625,17.602500000000003,17.6625,0.03999999999999915,0.05999999999999872 --a55Q6RWvTA/3,-a55Q6RWvTA,3,54,things,17.642499923706055,17.88249969482422,True,2.6505485428555443e-14,0.25,17.65600623163198,17.90537406876171,17.642500000000002,17.7025,17.8825,18.0025,0.05999999999999872,0.120000000000001 --a55Q6RWvTA/3,-a55Q6RWvTA,3,55,of,17.90250015258789,17.962499618530273,True,3.624920459285863e-13,1.0,17.93835276685364,18.001385025457154,17.902500000000003,18.0425,17.962500000000002,18.102500000000003,0.13999999999999702,0.14000000000000057 --a55Q6RWvTA/3,-a55Q6RWvTA,3,56,that,18.002500534057617,18.142499923706055,True,1.8815473484812273e-11,4.0,18.04488132385217,18.219963675608064,17.9825,18.1825,18.142500000000002,18.462500000000002,0.1999999999999993,0.3200000000000003 --a55Q6RWvTA/3,-a55Q6RWvTA,3,57,nature,18.1825008392334,18.5625,True,2.4222108693794375e-12,4.0,18.28547153085806,18.701281830499646,18.1825,18.582500000000003,18.5625,19.142500000000002,0.40000000000000213,0.5800000000000018 --a55Q6RWvTA/3,-a55Q6RWvTA,3,58,where,18.582500457763672,19.042499542236328,True,1.1723989698985901e-14,0.25,18.726882158154776,19.134524101321563,18.582500000000003,19.2025,19.0425,19.442500000000003,0.6199999999999974,0.40000000000000213 --a55Q6RWvTA/3,-a55Q6RWvTA,3,59,you're,19.0625,19.262500762939453,True,6.019562018982905e-12,4.0,19.18884854699223,19.43021445240018,19.0625,19.5625,19.262500000000003,19.962500000000002,0.5,0.6999999999999993 --a55Q6RWvTA/3,-a55Q6RWvTA,3,60,not,19.282499313354492,19.40250015258789,True,2.795208725939191e-13,0.7711089849472046,19.521225771376137,19.66507505485541,19.282500000000002,20.282500000000002,19.402500000000003,20.5025,1.0,1.0999999999999979 --a55Q6RWvTA/3,-a55Q6RWvTA,3,61,spending,19.4424991607666,20.082500457763672,True,1.305721616983238e-13,0.3602069616317749,19.728286447221432,20.32897058236136,19.442500000000003,20.642500000000002,20.0225,21.1625,1.1999999999999993,1.1400000000000006 --a55Q6RWvTA/3,-a55Q6RWvTA,3,62,money,20.282499313354492,20.602500915527344,True,2.586418492572795e-13,0.7135103940963745,20.500230104664706,20.79165190936027,20.282500000000002,21.2225,20.602500000000003,21.402500000000003,0.9399999999999977,0.8000000000000007 --a55Q6RWvTA/3,-a55Q6RWvTA,3,63,per,20.842500686645508,21.0625,True,4.991007607202391e-13,1.3768599033355713,20.973787732360663,21.205024207243827,20.8425,21.422500000000003,21.0625,21.5425,0.5800000000000018,0.4800000000000004 --a55Q6RWvTA/3,-a55Q6RWvTA,3,64,se.,21.22249984741211,21.582500457763672,True,1.484069464541271e-11,4.0,21.35793839975314,21.641898876505973,21.2225,21.5625,21.582500000000003,21.942500000000003,0.33999999999999986,0.35999999999999943 --aNfi7CP8vM/7,-aNfi7CP8vM,7,0,Maybe,0.5625,0.7425000071525574,True,1.3992611323093707e-12,4.0,0.5699168353417414,0.7684721379873901,0.5625,0.6224999999999999,0.7424999999999999,0.8624999999999999,0.05999999999999994,0.12 --aNfi7CP8vM/7,-aNfi7CP8vM,7,1,they,0.8025000095367432,1.0625,True,7.763144375385819e-14,0.6851189732551575,0.8381540024758877,1.1033643669058124,0.8025,1.0025,1.0625,1.4224999999999999,0.19999999999999996,0.3599999999999999 --aNfi7CP8vM/7,-aNfi7CP8vM,7,2,have,1.0824999809265137,1.2625000476837158,True,4.901391250332343e-13,4.0,1.1800089747111384,1.4216257555081657,1.0825,1.5825,1.2625,1.7425,0.5,0.48 --aNfi7CP8vM/7,-aNfi7CP8vM,7,3,no,1.4225000143051147,1.502500057220459,True,2.19147440537297e-13,1.9340368509292603,1.5502325525781857,1.6303863815472515,1.4025,1.8225,1.4625,1.8825,0.41999999999999993,0.42000000000000015 --aNfi7CP8vM/7,-aNfi7CP8vM,7,4,political,1.5824999809265137,2.0625,True,9.636221535340786e-14,0.8504232168197632,1.7219728765180327,2.217611584075496,1.5825,2.0425,2.0625,2.5825,0.45999999999999996,0.52 --aNfi7CP8vM/7,-aNfi7CP8vM,7,5,party,2.182499885559082,2.4625000953674316,True,4.943981964274091e-13,4.0,2.3189574948447387,2.597655834164009,2.1425,2.7025,2.3825,3.0225,0.56,0.6400000000000001 --aNfi7CP8vM/7,-aNfi7CP8vM,7,6,"affiliation,",2.5625,3.202500104904175,True,3.149363365581581e-13,2.77940034866333,2.6754966075215223,3.384642765575778,2.4425,3.0625,3.1625,3.8825,0.6200000000000001,0.7199999999999998 --aNfi7CP8vM/7,-aNfi7CP8vM,7,7,they're,3.322499990463257,3.882499933242798,True,6.927912236942557e-12,4.0,3.5162592580884375,3.9951804657104297,3.2625,4.2625,3.8825,4.482500000000001,1.0,0.600000000000001 --aNfi7CP8vM/7,-aNfi7CP8vM,7,8,not,4.262499809265137,4.422500133514404,True,9.374659794107731e-14,0.8273397088050842,4.3249445299839655,4.4702296803616,4.2625,4.5025,4.4225,4.6225000000000005,0.2400000000000002,0.20000000000000018 --aNfi7CP8vM/7,-aNfi7CP8vM,7,9,interested,4.502500057220459,5.142499923706055,True,1.493164993775567e-13,1.3177595138549805,4.536229501246246,5.153956404348874,4.5025,4.6625000000000005,5.1425,5.202500000000001,0.16000000000000014,0.0600000000000005 --aNfi7CP8vM/7,-aNfi7CP8vM,7,10,in,5.202499866485596,5.262499809265137,True,3.058207373863968e-14,0.2698952853679657,5.20525492933666,5.265296308868387,5.202500000000001,5.242500000000001,5.2625,5.3025,0.040000000000000036,0.040000000000000036 --aNfi7CP8vM/7,-aNfi7CP8vM,7,11,"politics,",5.34250020980835,5.922500133514404,True,3.0942337179897667e-14,0.2730746865272522,5.342027017595276,5.92221771234432,5.3425,5.3425,5.9225,5.9225,0.0,0.0 --aNfi7CP8vM/7,-aNfi7CP8vM,7,12,they,6.002500057220459,6.142499923706055,True,4.5508526451637586e-15,0.25,5.995617953652946,6.139102854390898,5.942500000000001,6.0025,6.102500000000001,6.142500000000001,0.05999999999999961,0.040000000000000036 --aNfi7CP8vM/7,-aNfi7CP8vM,7,13,could,6.162499904632568,6.502500057220459,True,1.133866492451091e-13,1.0006686449050903,6.165296514612912,6.5001556714207736,6.1625000000000005,6.1625000000000005,6.482500000000001,6.5025,0.0,0.019999999999999574 --aNfi7CP8vM/7,-aNfi7CP8vM,7,14,care,6.5625,6.822500228881836,True,1.1323512521524068e-13,0.9993313550949097,6.562667537244219,6.822789861817852,6.562500000000001,6.562500000000001,6.822500000000001,6.822500000000001,0.0,0.0 --aNfi7CP8vM/7,-aNfi7CP8vM,7,15,less,6.922500133514404,7.122499942779541,True,2.815684697118209e-13,2.484919786453247,6.9223711358609785,7.12232906665508,6.9225,6.9225,7.1225000000000005,7.1225000000000005,0.0,0.0 --aNfi7CP8vM/7,-aNfi7CP8vM,7,16,about,7.142499923706055,7.402500152587891,True,2.719624249110722e-14,0.25,7.14313662311112,7.4259741476470245,7.142500000000001,7.142500000000001,7.402500000000001,7.482500000000001,0.0,0.08000000000000007 --aNfi7CP8vM/7,-aNfi7CP8vM,7,17,politics.,7.462500095367432,8.522500038146973,True,5.850625973274903e-14,0.5163339376449585,7.499265627423223,8.521439814756093,7.4625,7.6625000000000005,8.5025,8.522499999999999,0.20000000000000018,0.019999999999999574 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,0,In,0.36250001192092896,0.48249998688697815,True,2.7085831407619443e-13,0.44671571254730225,0.3661229464627679,0.4824918267992842,0.3625,0.4025,0.4825,0.4825,0.040000000000000036,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,1,"2008,",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,2,there,0.6225000023841858,1.0225000381469727,True,4.3837511203535795e-13,0.7229943871498108,0.644216758746706,1.2320726340392565,0.6024999999999999,0.7424999999999999,1.0225,1.4825,0.14,0.45999999999999996 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,3,were,2.182499885559082,2.922499895095825,True,3.5700947406680505e-12,4.0,2.098391009917989,3.0003518244192926,1.3825,2.6425,2.7625,3.0825,1.26,0.31999999999999984 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,4,very,3.262500047683716,3.502500057220459,True,4.024087920523334e-13,0.6636766195297241,3.2628512483329692,3.5029909181345595,3.2625,3.2625,3.5025,3.5025,0.0,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,5,serious,3.6424999237060547,4.102499961853027,True,1.2576761463864439e-13,0.25,3.604995371759258,4.081283859552203,3.5425,3.6425,4.0425,4.1025,0.10000000000000009,0.05999999999999961 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,6,tribal,4.162499904632568,4.582499980926514,True,6.287949761568656e-12,4.0,4.164219364401541,4.584062047007865,4.1625000000000005,4.1625000000000005,4.5825000000000005,4.5825000000000005,0.0,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,7,warfare,4.722499847412109,5.202499866485596,True,1.5909093432821958e-13,0.26238226890563965,4.706837861170518,5.205690278227914,4.6425,4.7225,5.202500000000001,5.202500000000001,0.08000000000000007,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,8,in,6.142499923706055,6.202499866485596,True,1.019524534240368e-10,4.0,6.1186756160246425,6.21318732554233,6.062500000000001,6.1825,6.202500000000001,6.2625,0.11999999999999922,0.05999999999999961 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,9,Kenya,7.082499980926514,8.0024995803833,True,6.063326381776368e-13,1.0,7.055643839797694,8.001751852174415,6.3425,7.482500000000001,8.0025,8.0025,1.1400000000000006,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,10,following,8.162500381469727,9.282500267028809,True,1.3233601497616987e-12,2.1825644969940186,8.167307698703715,9.274023163387671,8.1625,8.1625,9.2025,9.282499999999999,0.0,0.0799999999999983 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,11,a,9.40250015258789,9.422499656677246,True,3.7868968377763323e-11,4.0,9.40242807987088,9.422428079870883,9.4025,9.4025,9.4225,9.4225,0.0,0.0 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,12,disputed,9.462499618530273,9.90250015258789,True,2.7071137757676833e-13,0.4464733600616455,9.47686688744534,9.925606046827598,9.4625,9.5025,9.9025,9.9825,0.03999999999999915,0.08000000000000007 --aqamKhZ1Ec/0,-aqamKhZ1Ec,0,13,election.,10.022500038146973,10.802499771118164,True,3.659902676061977e-12,4.0,10.029209911460025,10.806518969512238,10.022499999999999,10.022499999999999,10.8025,10.862499999999999,0.0,0.05999999999999872 --dxfTGcXJoc/1,-dxfTGcXJoc,1,0,For,0.5625,0.7425000071525574,True,2.275428977183136e-12,4.0,0.3649672540175232,0.656470491487688,0.0225,0.5625,0.5824999999999999,0.7424999999999999,0.54,0.16000000000000003 --dxfTGcXJoc/1,-dxfTGcXJoc,1,1,the,0.8025000095367432,0.9424999952316284,True,3.523209827181739e-13,2.672248363494873,0.7190080419087704,0.8384369833953639,0.6425,0.8025,0.7424999999999999,0.9425,0.16000000000000003,0.20000000000000007 --dxfTGcXJoc/1,-dxfTGcXJoc,1,2,10th,1.002500057220459,1.0625,True,7.89121092892287e-16,0.25,0.9201663747546199,0.991584920117422,0.8025,1.0025,0.9025,1.0825,0.19999999999999996,0.18000000000000005 --dxfTGcXJoc/1,-dxfTGcXJoc,1,3,straight,1.0824999809265137,1.462499976158142,True,5.292460907187473e-15,0.25,1.0590410591480313,1.4525213757665048,0.9225,1.1225,1.4025,1.4625,0.20000000000000007,0.05999999999999983 --dxfTGcXJoc/1,-dxfTGcXJoc,1,4,"year,",1.5824999809265137,1.7625000476837158,True,4.7748890480642583e-14,0.36216092109680176,1.5743957871409902,1.7569251246340676,1.4825,1.5825,1.6824999999999999,1.7625,0.10000000000000009,0.08000000000000007 --dxfTGcXJoc/1,-dxfTGcXJoc,1,5,some,2.262500047683716,2.5425000190734863,True,1.943253283981372e-12,4.0,2.2612920543236856,2.5424015098644395,2.2625,2.2625,2.5425,2.5425,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,6,of,2.5824999809265137,2.6624999046325684,True,3.9846300087589825e-13,3.022221565246582,2.5853655723810456,2.6652560546039608,2.5825,2.6025,2.6625,2.6625,0.020000000000000018,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,7,Kentucky’s,2.762500047683716,3.262500047683716,True,1.15754682315089e-12,4.0,2.750634717027689,3.2556658742957265,2.7225,2.7625,3.2425,3.2625,0.040000000000000036,0.020000000000000018 --dxfTGcXJoc/1,-dxfTGcXJoc,1,8,best,3.422499895095825,3.762500047683716,True,8.019150258111243e-14,0.608228325843811,3.4022852380664403,3.72717692242951,3.2825,3.4225,3.7025,3.7625,0.13999999999999968,0.06000000000000005 --dxfTGcXJoc/1,-dxfTGcXJoc,1,9,and,3.922499895095825,4.022500038146973,True,1.6227658797339332e-14,0.25,3.9223628956324568,4.022449556910346,3.9225,3.9225,4.022500000000001,4.022500000000001,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,10,most,4.082499980926514,4.262499809265137,True,9.945570164952497e-14,0.7543414831161499,4.082499603629278,4.262509829162647,4.0825000000000005,4.0825000000000005,4.2625,4.2625,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,11,innovative,4.442500114440918,5.122499942779541,True,1.133854512017085e-12,4.0,4.4410250441935455,5.11803027501793,4.442500000000001,4.442500000000001,5.102500000000001,5.1225000000000005,0.0,0.019999999999999574 --dxfTGcXJoc/1,-dxfTGcXJoc,1,12,biotech,5.162499904632568,5.502500057220459,True,1.5729797564305315e-13,1.193057656288147,5.155285740506763,5.500234169729445,5.1225000000000005,5.1625000000000005,5.4625,5.5025,0.040000000000000036,0.040000000000000036 --dxfTGcXJoc/1,-dxfTGcXJoc,1,13,companies,5.542500019073486,6.022500038146973,True,8.639043798906731e-13,4.0,5.54199486653639,6.021684045459382,5.5425,5.5425,6.022500000000001,6.022500000000001,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,14,and,6.082499980926514,6.182499885559082,True,4.0721878045166596e-14,0.30886316299438477,6.081297587982795,6.181694894313973,6.0825000000000005,6.0825000000000005,6.1825,6.1825,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,15,technologies,6.222499847412109,6.922500133514404,True,2.050334592862066e-13,1.555116891860962,6.222158853837142,6.937339063734151,6.2225,6.2225,6.9225,6.9625,0.0,0.040000000000000036 --dxfTGcXJoc/1,-dxfTGcXJoc,1,16,were,6.942500114440918,7.482500076293945,True,3.483278661169098e-14,0.26419615745544434,7.130313396095074,7.4978267357210475,6.942500000000001,7.322500000000001,7.4625,7.522500000000001,0.3799999999999999,0.0600000000000005 --dxfTGcXJoc/1,-dxfTGcXJoc,1,17,on,7.502500057220459,7.702499866485596,True,1.5179640530660343e-11,4.0,7.540963992421254,7.71163558566263,7.5025,7.6825,7.702500000000001,7.782500000000001,0.17999999999999972,0.08000000000000007 --dxfTGcXJoc/1,-dxfTGcXJoc,1,18,display,7.84250020980835,8.40250015258789,True,7.89802034537046e-13,4.0,7.842515193459164,8.403630710839492,7.8425,7.8425,8.4025,8.4025,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,19,at,8.662500381469727,8.72249984741211,True,2.0993258435071053e-15,0.25,8.662467828886165,8.7226922086402,8.6625,8.6625,8.7225,8.7225,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,20,the,8.822500228881836,8.942500114440918,True,1.2593050246253318e-13,0.9551448225975037,8.822210482336128,8.942231664275779,8.8225,8.8225,8.942499999999999,8.942499999999999,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,21,annual,9.082500457763672,9.382499694824219,True,5.38172479670751e-13,4.0,9.082499874861758,9.382500529056607,9.0825,9.0825,9.3825,9.3825,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,22,BIO,9.662500381469727,9.90250015258789,True,6.07335223778982e-11,4.0,9.578064211583579,9.880240503537104,9.442499999999999,9.6625,9.8225,9.9025,0.22000000000000064,0.08000000000000007 --dxfTGcXJoc/1,-dxfTGcXJoc,1,23,International,9.922499656677246,10.682499885559082,True,7.311739511376911e-14,0.5545733571052551,9.908699669202857,10.700666650198874,9.8825,9.942499999999999,10.6825,10.7425,0.05999999999999872,0.0600000000000005 --dxfTGcXJoc/1,-dxfTGcXJoc,1,24,"Convention,",10.72249984741211,11.262499809265137,True,1.0889742883476994e-13,0.8259541392326355,10.747097081493896,11.267687896899911,10.7225,10.8025,11.2625,11.3025,0.08000000000000007,0.040000000000000924 --dxfTGcXJoc/1,-dxfTGcXJoc,1,25,which,11.322500228881836,12.082500457763672,True,4.037281915573489e-14,0.30621564388275146,11.364414611412695,12.082394236045653,11.3225,11.7425,12.0825,12.0825,0.41999999999999993,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,26,I,12.382499694824219,12.40250015258789,True,1.2588138864622067e-10,4.0,12.382510769537037,12.402510769537036,12.3825,12.3825,12.4025,12.4025,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,27,and,12.482500076293945,12.642499923706055,True,1.533161313562511e-14,0.25,12.48280965262064,12.642572691239474,12.4825,12.4825,12.6425,12.6425,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,28,other,12.702500343322754,12.962499618530273,True,9.549927173927233e-14,0.7243331670761108,12.702509718675014,12.962495484432637,12.7025,12.7025,12.9625,12.9625,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,29,state,12.982500076293945,13.242500305175781,True,6.808946852523978e-14,0.5164380669593811,12.98288355950477,13.242183987541857,12.9825,12.9825,13.2425,13.2425,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,30,officials,13.262499809265137,13.702500343322754,True,2.7725016360957873e-14,0.25,13.26382449943722,13.702492000013828,13.2625,13.2825,13.7025,13.7025,0.02000000000000135,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,31,attended,13.72249984741211,14.162500381469727,True,7.640498070381543e-15,0.25,13.724172197793889,14.162500196491765,13.7225,13.7225,14.1625,14.1625,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,32,this,14.22249984741211,14.362500190734863,True,1.377583214601935e-13,1.0448552370071411,14.222454748006289,14.362454751756776,14.2225,14.2225,14.362499999999999,14.362499999999999,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,33,week,14.422499656677246,14.6225004196167,True,4.912310792512531e-13,3.725839376449585,14.422499999645678,14.622500000213078,14.4225,14.4225,14.6225,14.6225,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,34,in,14.662500381469727,14.882499694824219,True,1.148558245039899e-12,4.0,14.664907397502905,14.88327589515385,14.6625,14.6625,14.8825,14.8825,0.0,0.0 --dxfTGcXJoc/1,-dxfTGcXJoc,1,35,Chicago.,14.962499618530273,15.522500038146973,True,7.882481017733312e-13,4.0,14.962501636791323,15.51671348154772,14.9625,14.9625,15.5025,15.522499999999999,0.0,0.019999999999999574 --dxfTGcXJoc/0,-dxfTGcXJoc,0,0,This,0.20250000059604645,0.6625000238418579,True,1.436269511187623e-15,0.25,0.18800400140906554,0.6712783209637588,0.0225,0.3825,0.6625,0.7025,0.36,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,1,is,0.7225000262260437,0.8025000095367432,True,1.321620655796163e-12,4.0,0.7296080131653702,0.8073423085081691,0.7224999999999999,0.7825,0.8025,0.8424999999999999,0.06000000000000005,0.039999999999999925 --dxfTGcXJoc/0,-dxfTGcXJoc,0,2,Governor,0.9024999737739563,1.2424999475479126,True,1.0927341930616619e-12,4.0,0.887471538448594,1.2277552870491857,0.8624999999999999,0.9025,1.2025,1.2425,0.040000000000000036,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,3,Steve,1.2825000286102295,1.5625,True,7.474941753895722e-14,0.2840318977832794,1.281864992925297,1.5529176310112593,1.2825,1.2825,1.5425,1.5625,0.0,0.020000000000000018 --dxfTGcXJoc/0,-dxfTGcXJoc,0,4,Beshear,1.6024999618530273,1.9824999570846558,True,7.465651361004966e-13,2.8367886543273926,1.60314666497837,2.0731438341617148,1.6025,1.6025,1.9825,2.7625,0.0,0.7800000000000002 --dxfTGcXJoc/0,-dxfTGcXJoc,0,5,...,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/0,-dxfTGcXJoc,0,6,About,2.0824999809265137,2.8424999713897705,True,3.1875823053133245e-13,1.21121346950531,2.23390625717333,2.906884357988618,2.0825,2.9425,2.8425,3.3825,0.8599999999999999,0.54 --dxfTGcXJoc/0,-dxfTGcXJoc,0,7,Kentucky,2.942500114440918,4.482500076293945,True,1.9611760132542955e-12,4.0,3.0234051678294898,4.527675130658362,2.9425,3.4225,4.482500000000001,4.862500000000001,0.48,0.3799999999999999 --dxfTGcXJoc/0,-dxfTGcXJoc,0,8,Our,4.78249979019165,5.002500057220459,True,2.0286934025032233e-12,4.0,4.766380309692605,4.975474037119736,4.6225000000000005,4.9225,4.862500000000001,5.0425,0.2999999999999998,0.17999999999999972 --dxfTGcXJoc/0,-dxfTGcXJoc,0,9,state,5.142499923706055,5.482500076293945,True,2.2418547053482119e-13,0.8518571257591248,5.102480756287364,5.457607935480797,4.982500000000001,5.1425,5.3825,5.482500000000001,0.15999999999999925,0.10000000000000053 --dxfTGcXJoc/0,-dxfTGcXJoc,0,10,is,5.542500019073486,5.622499942779541,True,2.1595016898821873e-13,0.8205647468566895,5.542502282433472,5.62249050879839,5.5425,5.5425,5.6225000000000005,5.6225000000000005,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,11,home,5.702499866485596,5.902500152587891,True,7.612381997543563e-14,0.2892543375492096,5.6969075990261855,5.905562707339487,5.6425,5.702500000000001,5.862500000000001,5.902500000000001,0.0600000000000005,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,12,to,5.922500133514404,6.042500019073486,True,7.356884334586491e-14,0.27954596281051636,5.937102992383272,6.056635431575253,5.9225,5.9225,6.0425,6.0425,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,13,more,6.222499847412109,6.622499942779541,True,6.24342970986097e-13,2.372370481491089,6.277575214162152,6.631543457001568,6.2225,6.322500000000001,6.6225000000000005,6.6625000000000005,0.10000000000000053,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,14,than,6.802499771118164,7.102499961853027,True,9.57974747705509e-14,0.3640100359916687,6.803191204364817,7.107020835049525,6.8025,6.8025,7.102500000000001,7.142500000000001,0.0,0.040000000000000036 --dxfTGcXJoc/0,-dxfTGcXJoc,0,15,"1,500",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/0,-dxfTGcXJoc,0,16,bioscience,7.28249979019165,8.0625,True,5.816785312966199e-13,2.210254669189453,7.267452880689412,8.062370269304813,7.1825,7.282500000000001,8.0625,8.0625,0.10000000000000053,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,17,and,8.102499961853027,8.342499732971191,True,8.574386453673657e-14,0.32580846548080444,8.103254544239489,8.342748976407043,8.1025,8.1025,8.3425,8.3425,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,18,lifescience,8.462499618530273,9.0625,True,9.144152449125365e-13,3.474583387374878,8.440910172928174,9.06317123654407,8.3825,8.4625,9.0625,9.0625,0.08000000000000007,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,19,companies,9.102499961853027,10.102499961853027,True,1.1046472981127153e-12,4.0,9.103525367338998,10.10579297477803,9.1025,9.1025,10.1025,10.1225,0.0,0.02000000000000135 --dxfTGcXJoc/0,-dxfTGcXJoc,0,20,and,10.22249984741211,10.362500190734863,True,6.748803447510776e-14,0.2564401924610138,10.222497809790337,10.356745416243506,10.2225,10.2225,10.3425,10.362499999999999,0.0,0.019999999999999574 --dxfTGcXJoc/0,-dxfTGcXJoc,0,21,more,10.442500114440918,10.602499961853027,True,2.3733483711305126e-13,0.9018219113349915,10.414533955030066,10.579202864997653,10.3825,10.442499999999999,10.5625,10.6025,0.05999999999999872,0.03999999999999915 --dxfTGcXJoc/0,-dxfTGcXJoc,0,22,than,10.72249984741211,10.962499618530273,True,6.653097669138963e-13,2.5280356407165527,10.7225013520628,10.963060296230047,10.7225,10.7225,10.9625,10.9625,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,23,"100,000",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/0,-dxfTGcXJoc,0,24,people,11.102499961853027,11.5024995803833,True,1.0843516568600359e-13,0.41203057765960693,11.102483038622685,11.502500341848476,11.1025,11.1025,11.5025,11.5025,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,25,who,11.5625,11.702500343322754,True,1.174416358433461e-12,4.0,11.576823297591739,11.714007237913536,11.5625,11.6225,11.7025,11.7425,0.0600000000000005,0.03999999999999915 --dxfTGcXJoc/0,-dxfTGcXJoc,0,26,work,11.90250015258789,12.162500381469727,True,7.076656020075026e-14,0.2688978910446167,11.905745763990348,12.163903512245955,11.9025,11.9025,12.1625,12.1625,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,27,for,12.262499809265137,12.40250015258789,True,6.21533965787513e-13,2.36169695854187,12.262508738077944,12.402008549180124,12.2625,12.2625,12.4025,12.4025,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,28,these,12.5024995803833,12.72249984741211,True,5.1970743247115037e-14,0.25,12.50070802564183,12.722592853346624,12.5025,12.5025,12.7225,12.7225,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,29,"firms,",12.822500228881836,13.202500343322754,True,1.987435586383538e-14,0.25,12.810238807852192,13.202497205526639,12.7825,12.8225,13.2025,13.2025,0.03999999999999915,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,30,in,13.22249984741211,13.962499618530273,True,3.6057056862839887e-13,1.370091438293457,13.324914664363726,13.96804775045872,13.2225,13.942499999999999,13.9625,14.0425,0.7199999999999989,0.08000000000000007 --dxfTGcXJoc/0,-dxfTGcXJoc,0,31,areas,14.182499885559082,14.522500038146973,True,1.2572369089813157e-13,0.47772330045700073,14.18246398667518,14.522510488364302,14.1825,14.1825,14.522499999999999,14.522499999999999,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,32,from,14.5625,14.822500228881836,True,8.391539314822356e-15,0.25,14.562750522345475,14.823092307045739,14.5625,14.5625,14.8225,14.8225,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,33,nutrigenomics,15.182499885559082,16.122499465942383,True,1.1121746969558477e-12,4.0,15.177670019934878,16.099262169106762,15.1425,15.1825,16.0225,16.122500000000002,0.03999999999999915,0.10000000000000142 --dxfTGcXJoc/0,-dxfTGcXJoc,0,34,and,16.262500762939453,16.38249969482422,True,4.261740564398195e-14,0.25,16.258652767187964,16.38099827413018,16.262500000000003,16.282500000000002,16.3825,16.3825,0.019999999999999574,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,35,pharmaceuticals,16.422500610351562,17.262500762939453,True,5.213513540151815e-13,1.9810242652893066,16.4224417108849,17.25543344328825,16.422500000000003,16.422500000000003,17.2225,17.262500000000003,0.0,0.0400000000000027 --dxfTGcXJoc/0,-dxfTGcXJoc,0,36,to,17.642499923706055,17.74250030517578,True,4.542984640820008e-13,1.7262375354766846,17.642496491416676,17.742499915475133,17.642500000000002,17.642500000000002,17.742500000000003,17.742500000000003,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,37,nanotechnology,17.782499313354492,18.762500762939453,True,2.8901043342376143e-13,1.0981781482696533,17.782588215090776,18.763284028765124,17.782500000000002,17.782500000000002,18.762500000000003,18.762500000000003,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,38,and,18.822500228881836,18.922500610351562,True,1.1881575943657827e-14,0.25,18.822476509585076,18.922488605782018,18.8225,18.8225,18.922500000000003,18.922500000000003,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,39,medical,18.9424991607666,19.262500762939453,True,1.1681875898474775e-13,0.4438864290714264,18.942492184714514,19.2624987997749,18.942500000000003,18.942500000000003,19.262500000000003,19.262500000000003,0.0,0.0 --dxfTGcXJoc/0,-dxfTGcXJoc,0,40,devices.,19.282499313354492,19.802499771118164,True,1.4210033974157432e-12,4.0,19.282502277797803,19.802471720501764,19.282500000000002,19.282500000000002,19.802500000000002,19.802500000000002,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,0,An,0.5625,0.6424999833106995,True,3.528646423450396e-14,2.5437161922454834,0.5517457553728019,0.6400199589743981,0.5625,0.5625,0.6425,0.6425,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,1,estimated,0.762499988079071,1.2625000476837158,True,3.06941976074501e-15,0.25,0.7624537774900568,1.2747010779553254,0.7625,0.7625,1.2625,1.3025,0.0,0.040000000000000036 --dxfTGcXJoc/2,-dxfTGcXJoc,2,2,"20,000",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/2,-dxfTGcXJoc,2,3,biotech,1.502500057220459,2.1424999237060547,True,5.178755102704102e-13,4.0,1.5016645351351747,2.145477612250111,1.5025,1.5025,2.1425,2.1625,0.0,0.020000000000000018 --dxfTGcXJoc/2,-dxfTGcXJoc,2,4,"scientists,",2.362499952316284,3.2825000286102295,True,3.88290907673422e-13,4.0,2.3601136758402634,3.177040551962975,2.3625,2.3625,2.9225,3.2825,0.0,0.3600000000000003 --dxfTGcXJoc/2,-dxfTGcXJoc,2,5,executives,3.3424999713897705,4.502500057220459,True,4.8946347734439566e-14,3.5284242630004883,3.3090474232848885,4.504003094828398,3.1825,3.4425,4.5025,4.5425,0.2599999999999998,0.040000000000000036 --dxfTGcXJoc/2,-dxfTGcXJoc,2,6,and,4.742499828338623,4.862500190734863,True,7.208249534101149e-15,0.5196253657341003,4.737240676908777,4.860440843264866,4.742500000000001,4.742500000000001,4.862500000000001,4.862500000000001,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,7,industry,5.042500019073486,5.442500114440918,True,6.4237835225287344e-15,0.46307510137557983,5.040880535443564,5.443378707143529,5.0425,5.0425,5.442500000000001,5.442500000000001,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,8,experts,5.582499980926514,6.022500038146973,True,3.06460179498938e-14,2.209197759628296,5.5703212329533915,6.014850126754625,5.482500000000001,5.5825000000000005,5.9625,6.022500000000001,0.09999999999999964,0.0600000000000005 --dxfTGcXJoc/2,-dxfTGcXJoc,2,9,from,6.422500133514404,6.582499980926514,True,9.360125936930621e-16,0.25,6.38109006975023,6.616702439780006,6.022500000000001,6.5025,6.5825000000000005,6.702500000000001,0.47999999999999954,0.1200000000000001 --dxfTGcXJoc/2,-dxfTGcXJoc,2,10,more,6.622499942779541,6.882500171661377,True,9.563321136515576e-14,4.0,6.664792178092532,6.8927378471517535,6.6225000000000005,6.7625,6.8825,6.9225,0.13999999999999968,0.040000000000000036 --dxfTGcXJoc/2,-dxfTGcXJoc,2,11,than,7.002500057220459,7.28249979019165,True,5.0399656372152393e-14,3.6331899166107178,7.0024999794329315,7.282500014929406,7.0025,7.0025,7.282500000000001,7.282500000000001,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,12,50,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --dxfTGcXJoc/2,-dxfTGcXJoc,2,13,nations,7.362500190734863,7.742499828338623,True,3.6337700731715276e-15,0.261949747800827,7.369429892315447,7.749385730355412,7.362500000000001,7.4225,7.742500000000001,7.782500000000001,0.05999999999999961,0.040000000000000036 --dxfTGcXJoc/2,-dxfTGcXJoc,2,14,were,8.0625,8.22249984741211,True,1.6512727735737767e-14,1.190362811088562,8.062279727477186,8.222492308029125,8.0625,8.0625,8.2225,8.2225,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,15,at,8.382499694824219,8.462499618530273,True,2.5949011040230733e-15,0.25,8.382462737272258,8.46255610150032,8.3825,8.3825,8.4625,8.4625,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,16,the,8.522500038146973,8.642499923706055,True,1.0026508991150445e-15,0.25,8.527928748031597,8.648442775581263,8.5025,8.5625,8.6425,8.6825,0.0600000000000005,0.03999999999999915 --dxfTGcXJoc/2,-dxfTGcXJoc,2,17,BIO,8.662500381469727,8.942500114440918,True,7.251367041549717e-12,4.0,8.685797706798342,8.935314419747938,8.6625,8.8225,8.9025,8.9625,0.16000000000000014,0.0600000000000005 --dxfTGcXJoc/2,-dxfTGcXJoc,2,18,conference,9.0024995803833,9.5625,True,1.2436561925687623e-13,4.0,9.002366312686632,9.553113604517561,9.0025,9.0025,9.522499999999999,9.5625,0.0,0.040000000000000924 --dxfTGcXJoc/2,-dxfTGcXJoc,2,19,to,9.602499961853027,9.702500343322754,True,4.1949855590119045e-14,3.024064064025879,9.602500040663637,9.702500020830492,9.6025,9.6025,9.7025,9.7025,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,20,learn,9.802499771118164,10.0625,True,2.779141040325369e-16,0.25,9.80226629806537,10.062706163218133,9.8025,9.8025,10.0625,10.0625,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,21,about,10.202500343322754,10.442500114440918,True,1.123129689158944e-14,0.8096371293067932,10.18387034190013,10.433185442440921,10.1225,10.2025,10.4025,10.442499999999999,0.08000000000000007,0.03999999999999915 --dxfTGcXJoc/2,-dxfTGcXJoc,2,22,the,10.462499618530273,10.5625,True,2.2475309865299534e-15,0.25,10.462392739534936,10.562392741067562,10.4625,10.4625,10.5625,10.5625,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,23,latest,10.582500457763672,10.90250015258789,True,3.027837854573112e-15,0.25,10.582967332999607,10.903517748664571,10.5825,10.5825,10.9025,10.9025,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,24,scientific,10.922499656677246,11.442500114440918,True,9.800098143140554e-16,0.25,10.925212631979305,11.448081060031368,10.9225,10.9625,11.442499999999999,11.522499999999999,0.040000000000000924,0.08000000000000007 --dxfTGcXJoc/2,-dxfTGcXJoc,2,25,"advances,",11.5024995803833,12.042499542236328,True,3.089713950183218e-14,2.2273004055023193,11.505574248933469,12.04758122048326,11.5025,11.5425,12.0425,12.0625,0.040000000000000924,0.019999999999999574 --dxfTGcXJoc/2,-dxfTGcXJoc,2,26,and,12.082500457763672,12.6225004196167,True,2.6814522011229386e-14,1.9329943656921387,12.084524282799858,12.62267486373147,12.0825,12.0825,12.6225,12.6225,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,27,to,12.682499885559082,12.742500305175781,True,4.186913927947634e-15,0.30182453989982605,12.68249796092688,12.742535934060587,12.6825,12.6825,12.7425,12.7425,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,28,exchange,12.862500190734863,13.362500190734863,True,4.882353388631832e-15,0.3519571125507355,12.839402800312625,13.350664314337655,12.7825,12.862499999999999,13.3225,13.362499999999999,0.0799999999999983,0.03999999999999915 --dxfTGcXJoc/2,-dxfTGcXJoc,2,29,ideas,13.482500076293945,13.882499694824219,True,3.945389344004943e-14,2.8441362380981445,13.482486191160033,13.882548812984396,13.4825,13.4825,13.8825,13.8825,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,30,and,13.922499656677246,14.282500267028809,True,2.3429222700387803e-14,1.6889562606811523,13.922678087539742,14.282500215590204,13.9225,13.9225,14.2825,14.2825,0.0,0.0 --dxfTGcXJoc/2,-dxfTGcXJoc,2,31,establish,14.322500228881836,14.762499809265137,True,1.651188557322996e-15,0.25,14.322500022955364,14.773487639175407,14.3225,14.3225,14.7625,14.8425,0.0,0.08000000000000007 --dxfTGcXJoc/2,-dxfTGcXJoc,2,32,business,14.822500228881836,15.242500305175781,True,8.650353789394286e-14,4.0,14.832761792509285,15.240279039373075,14.8225,14.862499999999999,15.2025,15.2425,0.03999999999999915,0.03999999999999915 --dxfTGcXJoc/2,-dxfTGcXJoc,2,33,relationships.,15.262499809265137,16.022499084472656,True,2.0376777268147532e-15,0.25,15.26250556174122,16.02250089371535,15.2625,15.2625,16.0225,16.0225,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,0,Kentucky’s,0.42250001430511475,0.9225000143051147,True,5.297549725280515e-12,4.0,0.4224967278309293,0.9225000000223426,0.4225,0.4225,0.9225,0.9225,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,1,participation,0.9424999952316284,1.7825000286102295,True,3.977016199002703e-14,0.8985112905502319,0.9465664649478172,1.7885813004286177,0.9425,0.9824999999999999,1.7825,1.8425,0.039999999999999925,0.06000000000000005 --dxfTGcXJoc/6,-dxfTGcXJoc,6,2,at,1.8424999713897705,1.9225000143051147,True,2.1943811513974038e-15,0.25,1.8533036193237216,1.9368632112892796,1.8425,1.9025,1.9224999999999999,2.0025,0.06000000000000005,0.08000000000000007 --dxfTGcXJoc/6,-dxfTGcXJoc,6,3,the,2.0225000381469727,2.1624999046325684,True,1.865988171812806e-15,0.25,2.020929687527187,2.151507239832155,1.9825,2.0225,2.0825,2.1625,0.040000000000000036,0.08000000000000007 --dxfTGcXJoc/6,-dxfTGcXJoc,6,4,annual,2.3424999713897705,2.682499885559082,True,2.7859146738661023e-13,4.0,2.308788690749572,2.6643921880715054,2.1625,2.3425,2.6225,2.6825,0.17999999999999972,0.06000000000000005 --dxfTGcXJoc/6,-dxfTGcXJoc,6,5,BIO,2.742500066757202,2.942500114440918,True,1.0218228173319854e-11,4.0,2.739909203795047,2.971583070334784,2.7425,2.7425,2.9425,3.0425,0.0,0.10000000000000009 --dxfTGcXJoc/6,-dxfTGcXJoc,6,6,International,2.9825000762939453,3.7225000858306885,True,4.252274124179681e-14,0.9606992602348328,3.0381234983409264,3.7414172328758766,2.9825,3.1225,3.7225,3.7625,0.14000000000000012,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,7,Convention,3.802500009536743,4.882500171661377,True,1.806679534709782e-13,4.0,3.7986267028834164,4.640981911105811,3.7625,3.8025,4.322500000000001,4.8825,0.040000000000000036,0.5599999999999996 --dxfTGcXJoc/6,-dxfTGcXJoc,6,8,is,4.902500152587891,4.962500095367432,True,1.016744997961764e-15,0.25,4.882875855533999,4.9563961056449015,4.862500000000001,4.902500000000001,4.9225,4.9625,0.040000000000000036,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,9,the,5.0625,5.162499904632568,True,1.3455526298201959e-13,3.0399529933929443,5.055186037952798,5.159575162330058,4.9625,5.062500000000001,5.1225000000000005,5.1625000000000005,0.10000000000000053,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,10,primary,5.202499866485596,5.702499866485596,True,1.5114196718723875e-14,0.3414689898490906,5.2024999401486784,5.710515431236941,5.202500000000001,5.202500000000001,5.702500000000001,5.742500000000001,0.0,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,11,means,5.802499771118164,6.142499923706055,True,4.23735550228628e-14,0.957328736782074,5.802486364345739,6.142863221968487,5.8025,5.8025,6.142500000000001,6.142500000000001,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,12,by,6.262499809265137,6.382500171661377,True,7.462917436806826e-12,4.0,6.262499999696703,6.382500000022064,6.2625,6.2625,6.3825,6.3825,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,13,which,6.422500133514404,6.682499885559082,True,4.42622826411776e-14,1.0,6.449507150222789,6.7000271550535855,6.4225,6.482500000000001,6.6825,6.7225,0.0600000000000005,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,14,our,7.002500057220459,7.162499904632568,True,5.836931144583696e-14,1.3187144994735718,6.956397673938863,7.150806240348821,6.7225,7.022500000000001,7.102500000000001,7.1625000000000005,0.3000000000000007,0.05999999999999961 --dxfTGcXJoc/6,-dxfTGcXJoc,6,15,high-tech,7.222499847412109,7.662499904632568,True,1.0861458217201192e-12,4.0,7.2149935824394245,7.665882070120274,7.1625000000000005,7.2225,7.6625000000000005,7.702500000000001,0.05999999999999961,0.040000000000000036 --dxfTGcXJoc/6,-dxfTGcXJoc,6,16,companies,7.762499809265137,8.202500343322754,True,2.5332166919689314e-13,4.0,7.762376133681743,8.20258015845541,7.7625,7.7625,8.202499999999999,8.202499999999999,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,17,and,8.282500267028809,8.40250015258789,True,5.609055226036587e-14,1.2672313451766968,8.25984785590537,8.380906738814469,8.2225,8.282499999999999,8.362499999999999,8.4025,0.05999999999999872,0.040000000000000924 --dxfTGcXJoc/6,-dxfTGcXJoc,6,18,institutions,8.422499656677246,9.142499923706055,True,5.389259832399695e-15,0.25,8.422248950513739,9.142531347225114,8.4225,8.4225,9.1425,9.1425,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,19,can,9.242500305175781,9.5024995803833,True,2.1174656893472666e-15,0.25,9.282007274928727,9.511500731685478,9.1625,9.362499999999999,9.4825,9.5625,0.1999999999999993,0.08000000000000007 --dxfTGcXJoc/6,-dxfTGcXJoc,6,20,reach,9.5625,9.822500228881836,True,1.6722176960737127e-14,0.3777974247932434,9.569936347326685,9.826579649667691,9.5625,9.6425,9.8225,9.8825,0.08000000000000007,0.0600000000000005 --dxfTGcXJoc/6,-dxfTGcXJoc,6,21,out,9.882499694824219,10.1225004196167,True,3.801270076161413e-14,0.8588057160377502,9.905369645603065,10.128871358968683,9.8825,10.022499999999999,10.1225,10.1625,0.1399999999999988,0.03999999999999915 --dxfTGcXJoc/6,-dxfTGcXJoc,6,22,to,10.262499809265137,10.342499732971191,True,1.2812964393909415e-13,2.8947815895080566,10.262491452605754,10.342524144191376,10.2625,10.2625,10.3425,10.3425,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,23,the,10.422499656677246,10.522500038146973,True,1.0252761232240962e-14,0.25,10.420831128496536,10.52083710974172,10.4225,10.4225,10.522499999999999,10.522499999999999,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,24,global,10.542499542236328,10.90250015258789,True,4.665796625468767e-14,1.0541247129440308,10.54248245260101,10.903017817334318,10.5425,10.5425,10.9025,10.9025,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,25,scientific,10.942500114440918,11.582500457763672,True,1.1520418035607539e-14,0.260276198387146,10.943268176515302,11.582229395041422,10.942499999999999,10.942499999999999,11.5825,11.5825,0.0,0.0 --dxfTGcXJoc/6,-dxfTGcXJoc,6,26,community.,11.602499961853027,12.102499961853027,True,2.5937306230745805e-13,4.0,11.612190715637311,12.102458094905488,11.6025,11.6425,12.1025,12.1025,0.040000000000000924,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,0,We're,0.0024999999441206455,0.5824999809265137,True,2.7304946498274418e-11,4.0,0.04607508111870628,0.5898032740548333,0.0025000000000000005,0.5225,0.5824999999999999,0.6625,0.52,0.08000000000000007 --egA8-b7-3M/26,-egA8-b7-3M,26,1,updating,0.6025000214576721,1.1425000429153442,True,1.190343722028142e-12,4.0,0.6201202747881431,1.1412705985929916,0.6024999999999999,0.7025,1.1225,1.1624999999999999,0.10000000000000009,0.039999999999999813 --egA8-b7-3M/26,-egA8-b7-3M,26,2,it,1.2024999856948853,1.2625000476837158,True,6.882085098200777e-15,0.25,1.2068630134194995,1.2669326558135239,1.2025,1.2425,1.2625,1.3025,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/26,-egA8-b7-3M,26,3,all,1.3224999904632568,1.5225000381469727,True,3.317236825397911e-14,1.0,1.3240528697525118,1.5232767925449109,1.3225,1.3225,1.5225,1.5225,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,4,the,1.5824999809265137,1.6825000047683716,True,1.9559788579532342e-15,0.25,1.5769041980110663,1.6835013931160456,1.5625,1.5825,1.6824999999999999,1.6824999999999999,0.020000000000000018,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,5,time,1.7024999856948853,1.9824999570846558,True,4.720274798845024e-16,0.25,1.7062997103515074,1.984997575047607,1.7025,1.7425,1.9825,2.0225,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/26,-egA8-b7-3M,26,6,and,2.0225000381469727,2.5625,True,6.023966183732812e-15,0.25,2.0364141891598666,2.5625021914273565,2.0225,2.1825,2.5625,2.5625,0.16000000000000014,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,7,looking,2.5824999809265137,2.942500114440918,True,5.855222312859884e-14,1.7650902271270752,2.582682866295387,2.9398393089929113,2.5825,2.5825,2.9025,2.9425,0.0,0.040000000000000036 --egA8-b7-3M/26,-egA8-b7-3M,26,8,forward,3.002500057220459,3.3424999713897705,True,3.392760824073946e-15,0.25,2.999731412922151,3.3424460140552243,3.0025,3.0025,3.3425,3.3425,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,9,to,3.382499933242798,3.4625000953674316,True,1.8818388687613652e-14,0.5672910809516907,3.3825160972871147,3.462510902065862,3.3825,3.3825,3.4625,3.4625,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,10,your,3.5225000381469727,3.742500066757202,True,6.742193371796993e-15,0.25,3.522994713898922,3.743594339737526,3.5225,3.5225,3.7425,3.7425,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,11,comments,3.7825000286102295,4.302499771118164,True,6.467650850647266e-14,1.9497103691101074,3.78386669880374,4.305125915713463,3.7825,3.7825,4.3025,4.3025,0.0,0.0 --egA8-b7-3M/26,-egA8-b7-3M,26,12,on,4.462500095367432,4.5625,True,3.0199590658058773e-12,4.0,4.438194697536424,4.541530148943003,4.3425,4.4625,4.482500000000001,4.562500000000001,0.1200000000000001,0.08000000000000007 --egA8-b7-3M/26,-egA8-b7-3M,26,13,our,4.602499961853027,4.742499828338623,True,1.3194816766981532e-11,4.0,4.597614970972109,4.7378090732319125,4.5425,4.602500000000001,4.6825,4.742500000000001,0.0600000000000005,0.0600000000000005 --egA8-b7-3M/26,-egA8-b7-3M,26,14,videos.,4.882500171661377,5.242499828338623,True,7.06815682371964e-12,4.0,4.839578380641243,5.242501309845208,4.7225,4.8825,5.242500000000001,5.242500000000001,0.16000000000000014,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,0,As,0.6225000023841858,0.6825000047683716,True,6.329900796228638e-13,4.0,0.6225000000896067,0.682500000365255,0.6224999999999999,0.6224999999999999,0.6825,0.6825,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,1,you,0.7825000286102295,0.9424999952316284,True,1.412991631252053e-13,4.0,0.7823893755411854,0.9310866364147513,0.7825,0.7825,0.8825,0.9425,0.0,0.06000000000000005 --egA8-b7-3M/17,-egA8-b7-3M,17,2,have,0.9624999761581421,1.1024999618530273,True,5.60062928109048e-15,0.25,0.9529266344489182,1.098373411090721,0.9225,0.9624999999999999,1.0625,1.1225,0.039999999999999925,0.06000000000000005 --egA8-b7-3M/17,-egA8-b7-3M,17,3,more,1.1825000047683716,1.3424999713897705,True,7.51511771243012e-16,0.25,1.1824028515775171,1.3425010470509353,1.1824999999999999,1.1824999999999999,1.3425,1.3425,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,4,"experience,",1.3825000524520874,2.122499942779541,True,1.3858544574505555e-13,4.0,1.3873374469946451,2.1217125144118096,1.3825,1.4625,2.1225,2.1225,0.07999999999999985,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,5,you,3.2225000858306885,3.3424999713897705,True,3.083890429264255e-14,1.0576536655426025,3.213691251973658,3.354710556297301,3.2225,3.2225,3.3225000000000002,3.3825,0.0,0.05999999999999961 --egA8-b7-3M/17,-egA8-b7-3M,17,6,have,3.362499952316284,3.5425000190734863,True,3.8175747827697004e-14,1.3092786073684692,3.377375456438648,3.5414414297293204,3.3625,3.4025,3.5425,3.5425,0.040000000000000036,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,7,more,3.6624999046325684,3.802500009536743,True,4.685770594101265e-15,0.25,3.6478162339869873,3.7964527218527744,3.5825,3.6625,3.7625,3.8025,0.08000000000000007,0.040000000000000036 --egA8-b7-3M/17,-egA8-b7-3M,17,8,times,3.8424999713897705,4.182499885559082,True,1.295994798836738e-14,0.4444754421710968,3.8424998444900877,4.182500874910728,3.8425,3.8425,4.1825,4.1825,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,9,"exhibiting,",4.222499847412109,4.882500171661377,True,6.736042387940622e-14,2.3101987838745117,4.223205191074807,4.882471799434598,4.2225,4.2225,4.8825,4.8825,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,10,you,5.242499828338623,5.34250020980835,True,1.6752002473117609e-15,0.25,5.242488668737627,5.3432239190882544,5.242500000000001,5.242500000000001,5.3425,5.3425,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,11,get,5.402500152587891,5.522500038146973,True,2.7476793355765e-14,0.9423463940620422,5.402500228003155,5.522500551047494,5.402500000000001,5.402500000000001,5.522500000000001,5.522500000000001,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,12,to,5.582499980926514,5.642499923706055,True,4.209141343033008e-15,0.25,5.582587451647595,5.642588721621551,5.5825000000000005,5.5825000000000005,5.6425,5.6425,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,13,know,5.702499866485596,5.922500133514404,True,6.87243773174473e-14,2.3569769859313965,5.683751382141159,5.890393221205199,5.6625000000000005,5.702500000000001,5.822500000000001,5.9225,0.040000000000000036,0.09999999999999964 --egA8-b7-3M/17,-egA8-b7-3M,17,14,more,5.942500114440918,6.102499961853027,True,3.703195011110858e-15,0.25,5.942366821871721,6.103246214752875,5.942500000000001,5.942500000000001,6.102500000000001,6.102500000000001,0.0,0.0 --egA8-b7-3M/17,-egA8-b7-3M,17,15,people.,6.182499885559082,6.542500019073486,True,1.1336075036571386e-13,3.8878297805786133,6.182499922873765,6.542499989348836,6.1825,6.1825,6.5425,6.5425,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,0,It,0.5824999809265137,0.6825000047683716,True,5.045397490099392e-14,1.2702128887176514,0.4498708134808255,0.6283878986425523,0.0825,0.5824999999999999,0.6024999999999999,0.6825,0.4999999999999999,0.08000000000000007 --egA8-b7-3M/18,-egA8-b7-3M,18,1,becomes,0.7024999856948853,1.122499942779541,True,1.2237954891819707e-14,0.30809876322746277,0.6740202091094792,1.123904883788223,0.6625,0.7025,1.1225,1.1425,0.040000000000000036,0.020000000000000018 --egA8-b7-3M/18,-egA8-b7-3M,18,2,easier,1.1825000047683716,1.7424999475479126,True,5.1457912231397604e-15,0.25,1.2593575459889583,1.7659632994273018,1.1824999999999999,1.3825,1.7425,1.8225,0.20000000000000018,0.08000000000000007 --egA8-b7-3M/18,-egA8-b7-3M,18,3,at,1.8424999713897705,1.9824999570846558,True,1.3661030043733008e-13,3.439256429672241,1.8446638169367537,1.9840793573308473,1.8425,1.8425,1.9825,1.9825,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,4,each,2.182499885559082,2.362499952316284,True,2.2053683545694182e-14,0.555216372013092,2.175812096254957,2.353765489008425,2.0625,2.1825,2.3225,2.3625,0.1200000000000001,0.040000000000000036 --egA8-b7-3M/18,-egA8-b7-3M,18,5,one,2.4825000762939453,2.6024999618530273,True,8.226489693445582e-14,2.0710742473602295,2.465061924003497,2.5964168548924764,2.3625,2.4825,2.5625,2.6025,0.1200000000000001,0.040000000000000036 --egA8-b7-3M/18,-egA8-b7-3M,18,6,of,2.622499942779541,2.682499885559082,True,2.7406403223782273e-14,0.6899747252464294,2.6223798933822544,2.68237989421122,2.6225,2.6225,2.6825,2.6825,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,7,these,2.702500104904175,2.882499933242798,True,2.363750047882111e-15,0.25,2.702441638482025,2.882768890312429,2.7025,2.7025,2.8825,2.8825,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,8,conferences,2.942500114440918,3.6024999618530273,True,9.357525334024314e-14,2.3558201789855957,2.940330155669175,3.593531353090126,2.9025,2.9425,3.5825,3.6025,0.040000000000000036,0.020000000000000018 --egA8-b7-3M/18,-egA8-b7-3M,18,9,to,4.102499961853027,4.202499866485596,True,2.403003740002879e-13,4.0,4.10248789549808,4.202500336033287,4.1025,4.1025,4.202500000000001,4.202500000000001,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,10,connect,4.222499847412109,4.582499980926514,True,2.2258525589086836e-13,4.0,4.226862793204726,4.588976822861837,4.2225,4.2625,4.5825000000000005,4.6425,0.040000000000000036,0.05999999999999961 --egA8-b7-3M/18,-egA8-b7-3M,18,11,with,4.622499942779541,4.78249979019165,True,3.549643396124941e-15,0.25,4.643047295040439,4.802463991137171,4.6225000000000005,4.6625000000000005,4.782500000000001,4.822500000000001,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/18,-egA8-b7-3M,18,12,people,4.882500171661377,5.162499904632568,True,1.767017623530414e-14,0.4448586106300354,4.882395800547012,5.162499794327169,4.8825,4.8825,5.1625000000000005,5.1625000000000005,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,13,that,5.202499866485596,5.362500190734863,True,5.594892829864539e-17,0.25,5.2024996957554865,5.3532616793556365,5.202500000000001,5.202500000000001,5.3425,5.362500000000001,0.0,0.020000000000000462 --egA8-b7-3M/18,-egA8-b7-3M,18,14,you've,5.382500171661377,5.602499961853027,True,1.3271654608626449e-11,4.0,5.382502593463899,5.602570477903483,5.3825,5.3825,5.602500000000001,5.602500000000001,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,15,met,5.682499885559082,5.882500171661377,True,1.9963176077497646e-13,4.0,5.679981733714091,5.879140283703421,5.6825,5.6825,5.8825,5.8825,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,16,or,6.422500133514404,6.482500076293945,True,7.505055469236507e-14,1.8894484043121338,6.397987073919746,6.459058711645056,6.4225,6.4225,6.482500000000001,6.482500000000001,0.0,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,17,that,6.522500038146973,6.662499904632568,True,7.683182416404072e-17,0.25,6.51310953711065,6.660509407784024,6.522500000000001,6.522500000000001,6.6625000000000005,6.6825,0.0,0.019999999999999574 --egA8-b7-3M/18,-egA8-b7-3M,18,18,you're,6.682499885559082,6.862500190734863,True,3.4061416881447926e-12,4.0,6.681564351164622,6.866491034969756,6.6825,6.702500000000001,6.862500000000001,6.8825,0.020000000000000462,0.019999999999999574 --egA8-b7-3M/18,-egA8-b7-3M,18,19,doing,6.902500152587891,7.142499923706055,True,2.8987788325317634e-14,0.7297871112823486,6.902497657128809,7.133894547298574,6.902500000000001,6.902500000000001,7.102500000000001,7.142500000000001,0.0,0.040000000000000036 --egA8-b7-3M/18,-egA8-b7-3M,18,20,business,7.182499885559082,7.5625,True,2.4658436913443244e-14,0.620792806148529,7.175063793795647,7.562567369568958,7.142500000000001,7.1825,7.562500000000001,7.562500000000001,0.03999999999999915,0.0 --egA8-b7-3M/18,-egA8-b7-3M,18,21,with.,7.622499942779541,7.882500171661377,True,1.9961424874148648e-11,4.0,7.622509877955194,7.882500128054946,7.6225000000000005,7.6225000000000005,7.8825,7.8825,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,0,"Third,",0.48249998688697815,0.762499988079071,True,1.2783299267217496e-14,0.45957401394844055,0.4823735110546779,0.765816467639421,0.4825,0.4825,0.7625,0.8225,0.0,0.06000000000000005 --egA8-b7-3M/16,-egA8-b7-3M,16,1,another,0.8424999713897705,1.2825000286102295,True,4.566552146731233e-14,1.6417269706726074,0.8410692456432085,1.2830330828196779,0.8225,0.8424999999999999,1.2825,1.3025,0.019999999999999907,0.020000000000000018 --egA8-b7-3M/16,-egA8-b7-3M,16,2,big,1.3224999904632568,1.4824999570846558,True,1.7119000038064505e-14,0.6154473423957825,1.3225007758015552,1.4825008899204788,1.3225,1.3225,1.4825,1.4825,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,3,benefit,1.5625,1.962499976158142,True,4.657146047385048e-14,1.674296498298645,1.5624781565144765,1.962500288652209,1.5625,1.5625,1.9625,1.9625,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,4,of,2.0225000381469727,2.1024999618530273,True,2.248744234171953e-14,0.808448851108551,2.022781738748026,2.097683776003884,2.0225,2.0225,2.0825,2.1025,0.0,0.020000000000000018 --egA8-b7-3M/16,-egA8-b7-3M,16,5,exhibiting,2.122499942779541,2.7225000858306885,True,2.7815540466096834e-14,1.0,2.1197486749478904,2.722499622656068,2.1025,2.1225,2.7225,2.7225,0.020000000000000018,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,6,is,2.822499990463257,2.882499933242798,True,7.896127192852029e-14,2.8387465476989746,2.822455644522566,2.8825844839545818,2.8225,2.8225,2.8825,2.8825,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,7,connecting,2.922499895095825,3.4825000762939453,True,5.372833745970066e-15,0.25,2.9224863158974808,3.4838534184456353,2.9225,2.9225,3.4625,3.5225,0.0,0.06000000000000005 --egA8-b7-3M/16,-egA8-b7-3M,16,8,with,3.502500057220459,3.6624999046325684,True,8.081755225572461e-16,0.25,3.5063061035173675,3.66635899435173,3.5025,3.5425,3.6625,3.7025,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/16,-egA8-b7-3M,16,9,existing,3.742500066757202,4.302499771118164,True,7.100570131868267e-14,2.552734851837158,3.743222429243274,4.303965535822457,3.7425,3.7425,4.3025,4.3025,0.0,0.0 --egA8-b7-3M/16,-egA8-b7-3M,16,10,clients.,4.362500190734863,4.822500228881836,True,1.5700299133697415e-13,4.0,4.359248951566402,4.822504151992989,4.322500000000001,4.362500000000001,4.822500000000001,4.822500000000001,0.040000000000000036,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,0,What,0.24250000715255737,0.6225000023841858,True,1.5400974069637363e-16,0.25,0.2141840485714987,0.6218597133402548,0.0225,0.4225,0.6224999999999999,0.6224999999999999,0.39999999999999997,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,1,are,0.6424999833106995,0.7425000071525574,True,8.498745056231133e-14,2.449159622192383,0.6421815628124172,0.742500011758617,0.6425,0.6425,0.7424999999999999,0.7424999999999999,0.0,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,2,you,0.762499988079071,0.862500011920929,True,8.022830108047295e-15,0.25,0.7653580882059453,0.8659613972267409,0.7625,0.7825,0.8624999999999999,0.8825,0.020000000000000018,0.020000000000000018 --egA8-b7-3M/13,-egA8-b7-3M,13,3,doing,0.9024999737739563,1.122499942779541,True,2.871552483101579e-14,0.827521026134491,0.9025000213152364,1.1225003642958045,0.9025,0.9025,1.1225,1.1225,0.0,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,4,that's,1.1425000429153442,1.3624999523162842,True,1.606225569217301e-11,4.0,1.1429115027852526,1.3625002174895329,1.1425,1.1425,1.3625,1.3625,0.0,0.0 --egA8-b7-3M/13,-egA8-b7-3M,13,5,"different?""",1.3825000524520874,1.7825000286102295,True,1.846142698078406e-14,0.5320194959640503,1.4034251715517392,2.3733903913393863,1.3825,1.4224999999999999,1.7825,2.4425,0.039999999999999813,0.6599999999999999 --egA8-b7-3M/13,-egA8-b7-3M,13,6,They,1.8025000095367432,2.4625000953674316,True,1.3074539687669626e-14,0.3767807185649872,2.441006479437174,2.718606976840083,1.8025,2.5025,2.4825,2.7425,0.7,0.26000000000000023 --egA8-b7-3M/13,-egA8-b7-3M,13,7,always,2.502500057220459,2.7825000286102295,True,5.6516907988432216e-14,1.6286983489990234,2.7741362125960785,3.0562366014397764,2.5025,2.8025,2.7825,3.0825,0.30000000000000027,0.2999999999999998 --egA8-b7-3M/13,-egA8-b7-3M,13,8,want,2.802500009536743,2.9825000762939453,True,3.4700659636314676e-14,1.0,3.1022008047455936,3.3186257301717013,2.8025,3.2025,2.9825,3.4425,0.3999999999999999,0.45999999999999996 --egA8-b7-3M/13,-egA8-b7-3M,13,9,an,3.0625,3.122499942779541,True,3.821534492391525e-14,1.1012858152389526,3.4092592789422267,3.47028094392638,3.0625,3.4625,3.1225,3.5225,0.3999999999999999,0.3999999999999999 --egA8-b7-3M/13,-egA8-b7-3M,13,10,answer.,3.202500104904175,3.5625,True,1.833897183417532e-12,4.0,3.5131397249203156,3.9093662996538545,3.2025,3.5625,3.5625,4.0025,0.3599999999999999,0.4400000000000004 --egA8-b7-3M/1,-egA8-b7-3M,1,0,I,0.0024999999441206455,0.02250000089406967,True,3.349408424224709e-12,4.0,0.00807213853936062,0.02807213853936155,0.0025000000000000005,0.0825,0.0225,0.10250000000000001,0.08,0.08000000000000002 --egA8-b7-3M/1,-egA8-b7-3M,1,1,started,0.042500000447034836,0.30250000953674316,True,2.7094884495759697e-13,0.8343841433525085,0.08285976140362887,0.392640622001406,0.0425,0.6224999999999999,0.3025,1.6225,0.58,1.32 --egA8-b7-3M/1,-egA8-b7-3M,1,2,a,0.6225000023841858,0.6424999833106995,True,1.6140776215589625e-11,4.0,0.7018397959792573,0.7218397959792763,0.6224999999999999,1.7625,0.6425,1.7825,1.1400000000000001,1.1400000000000001 --egA8-b7-3M/1,-egA8-b7-3M,1,3,successful,0.762499988079071,1.622499942779541,True,4.594923345892926e-14,0.25,0.8340952262394151,1.6983804921435448,0.7025,1.8425,1.6225,2.7025,1.1400000000000001,1.08 --egA8-b7-3M/1,-egA8-b7-3M,1,4,Legal,1.8424999713897705,2.382499933242798,True,9.063408118632765e-13,2.791066884994507,1.909492493181274,2.4335311441149527,1.8425,2.7825,2.3825,3.1025,0.9400000000000002,0.7200000000000002 --egA8-b7-3M/1,-egA8-b7-3M,1,5,Nurse,2.4625000953674316,2.702500104904175,True,3.247291487356446e-13,1.0,2.5228978831850655,2.7700245247650632,2.4625,3.2025,2.7025,3.4625,0.7400000000000002,0.7599999999999998 --egA8-b7-3M/1,-egA8-b7-3M,1,6,Consulting,2.7825000286102295,3.4825000762939453,True,9.766556053475428e-13,3.007600784301758,2.8444628086268335,3.550414946085584,2.7825,3.5425,3.4625,4.562500000000001,0.7599999999999998,1.100000000000001 --egA8-b7-3M/1,-egA8-b7-3M,1,7,Business,3.5425000190734863,3.9825000762939453,True,1.635268933068354e-13,0.5035793781280518,3.6237951499738292,4.086650313197705,3.5425,4.702500000000001,3.9825,5.402500000000001,1.1600000000000006,1.4200000000000008 --egA8-b7-3M/1,-egA8-b7-3M,1,8,in,4.002500057220459,4.5625,True,9.467710767934942e-14,0.2915571630001068,4.122795613570893,4.643007217848893,4.0025,5.6225000000000005,4.562500000000001,5.6825,1.62,1.1199999999999992 --egA8-b7-3M/1,-egA8-b7-3M,1,9,1989.,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --egA8-b7-3M/1,-egA8-b7-3M,1,10,I,4.702499866485596,4.722499847412109,True,3.378953340000754e-14,0.25,4.822565464327348,4.842565464327365,4.702500000000001,6.3825,4.7225,6.402500000000001,1.6799999999999997,1.6800000000000006 --egA8-b7-3M/1,-egA8-b7-3M,1,11,mentor,4.802499771118164,5.102499961853027,True,1.5290421923400133e-14,0.25,4.9231409745934025,5.229286423895843,4.8025,6.482500000000001,5.102500000000001,6.8425,1.6800000000000006,1.7399999999999993 --egA8-b7-3M/1,-egA8-b7-3M,1,12,nurses,5.142499923706055,5.402500152587891,True,3.649005231277317e-15,0.25,5.285193221719072,5.554316901085585,5.1425,6.902500000000001,5.402500000000001,7.3025,1.7600000000000007,1.8999999999999995 --egA8-b7-3M/1,-egA8-b7-3M,1,13,who,5.622499942779541,5.722499847412109,True,1.0704910807617096e-13,0.32965660095214844,5.754514660120223,5.858585417759601,5.6225000000000005,7.402500000000001,5.7225,7.522500000000001,1.7800000000000002,1.8000000000000007 --egA8-b7-3M/1,-egA8-b7-3M,1,14,want,6.382500171661377,6.682499885559082,True,5.1592024923052815e-12,4.0,6.4705962452594825,6.762408602745925,6.3825,7.562500000000001,6.6825,7.7625,1.1800000000000006,1.08 --egA8-b7-3M/1,-egA8-b7-3M,1,15,to,6.762499809265137,6.84250020980835,True,2.8021755922591485e-12,4.0,6.843815901061743,6.922242147418595,6.7625,7.8425,6.8425,7.902500000000001,1.08,1.0600000000000005 --egA8-b7-3M/1,-egA8-b7-3M,1,16,become,6.942500114440918,7.462500095367432,True,5.2748408939373714e-12,4.0,7.017877169431938,7.522635350083966,6.942500000000001,7.9225,7.4625,8.2425,0.9799999999999995,0.7799999999999994 --egA8-b7-3M/1,-egA8-b7-3M,1,17,Legal,7.5625,7.902500152587891,True,1.0792557711240824e-13,0.33235567808151245,7.620066755669658,7.950217895020635,7.562500000000001,8.3225,7.902500000000001,8.5425,0.7599999999999989,0.6399999999999997 --egA8-b7-3M/1,-egA8-b7-3M,1,18,Nurse,7.962500095367432,8.242500305175781,True,1.0314321011867245e-12,3.1762843132019043,8.00911242193168,8.283288744907443,7.9625,8.5825,8.2425,8.782499999999999,0.6199999999999992,0.5399999999999991 --egA8-b7-3M/1,-egA8-b7-3M,1,19,Consultants.,8.382499694824219,9.5024995803833,True,3.968895958411656e-12,4.0,8.410315918310687,9.492776884061083,8.3225,8.8225,9.4625,9.5025,0.5,0.03999999999999915 --egA8-b7-3M/6,-egA8-b7-3M,6,0,First,0.5224999785423279,0.8025000095367432,True,2.200081683132425e-15,0.25,0.4853864311485953,0.8069054596275058,0.2025,0.5225,0.8025,0.8624999999999999,0.31999999999999995,0.05999999999999994 --egA8-b7-3M/6,-egA8-b7-3M,6,1,exhibiting,0.8424999713897705,1.5625,True,3.04314916094086e-14,1.037160038948059,0.8581546005495158,1.5735363871558865,0.8424999999999999,1.0425,1.5625,1.6025,0.20000000000000007,0.040000000000000036 --egA8-b7-3M/6,-egA8-b7-3M,6,2,is,1.6425000429153442,1.722499966621399,True,2.2094691798802552e-13,4.0,1.6424968655880368,1.7224987665687466,1.6425,1.6425,1.7225,1.7225,0.0,0.0 --egA8-b7-3M/6,-egA8-b7-3M,6,3,a,1.8025000095367432,1.8224999904632568,True,1.2546567951243759e-11,4.0,1.8023980684775633,1.8223980684775658,1.8025,1.8025,1.8225,1.8225,0.0,0.0 --egA8-b7-3M/6,-egA8-b7-3M,6,4,wonderful,1.9424999952316284,2.4625000953674316,True,2.6952779811077916e-14,0.9185992479324341,1.8748230106996233,2.4290915945601004,1.8425,1.9425,2.3825,2.4625,0.09999999999999987,0.08000000000000007 --egA8-b7-3M/6,-egA8-b7-3M,6,5,opportunity,2.5425000190734863,3.202500104904175,True,1.6584008946381006e-14,0.5652128458023071,2.505562645229575,3.206492567731367,2.4625,2.5425,3.2025,3.2225,0.08000000000000007,0.020000000000000018 --egA8-b7-3M/6,-egA8-b7-3M,6,6,to,3.2825000286102295,3.382499933242798,True,4.802218396700829e-14,1.636682391166687,3.282497056715122,3.3825000470074538,3.2825,3.2825,3.3825,3.3825,0.0,0.0 --egA8-b7-3M/6,-egA8-b7-3M,6,7,network,3.4825000762939453,3.922499895095825,True,2.825085780274513e-14,0.9628400802612305,3.4783378131246514,3.9006160511864714,3.4225,3.4825,3.8625,3.9225,0.06000000000000005,0.06000000000000005 --egA8-b7-3M/6,-egA8-b7-3M,6,8,with,3.9625000953674316,4.122499942779541,True,5.249891572357314e-15,0.25,3.9591645135049784,4.119164923234471,3.9225,3.9625,4.0825000000000005,4.1225000000000005,0.040000000000000036,0.040000000000000036 --egA8-b7-3M/6,-egA8-b7-3M,6,9,other,4.262499809265137,4.502500057220459,True,3.9747078648148457e-14,1.354651927947998,4.241709003588393,4.488159328074618,4.1225000000000005,4.2625,4.442500000000001,4.5025,0.13999999999999968,0.05999999999999961 --egA8-b7-3M/6,-egA8-b7-3M,6,10,legal,4.602499961853027,4.922500133514404,True,2.4844702846676253e-14,0.8467521667480469,4.572833735083782,4.898411272791733,4.482500000000001,4.602500000000001,4.8425,4.9225,0.1200000000000001,0.08000000000000007 --egA8-b7-3M/6,-egA8-b7-3M,6,11,vendors.,4.962500095367432,5.442500114440918,True,5.481644191297416e-14,1.8682429790496826,4.962452113281357,5.442494524553728,4.9625,4.9625,5.442500000000001,5.442500000000001,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,0,"Secondly,",0.5625,1.0824999809265137,True,1.8873178844400207e-13,1.530295729637146,0.5556694643132206,1.0816803932159271,0.5625,0.5625,1.0825,1.0825,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,1,another,1.122499942779541,1.5824999809265137,True,1.2623711483691208e-13,1.0235695838928223,1.1289479746884983,1.585168118194489,1.1225,1.1225,1.5825,1.5825,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,2,goal,1.6825000047683716,1.8825000524520874,True,1.2042343305266462e-13,0.9764304161071777,1.6824995293756553,1.8825667309258272,1.6824999999999999,1.6824999999999999,1.8825,1.8825,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,3,of,1.9824999570846558,2.0425000190734863,True,9.055312055750164e-12,4.0,1.9823969628656442,2.0423972940717987,1.9825,1.9825,2.0425,2.0425,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,4,exhibiting,2.1024999618530273,2.622499942779541,True,2.0145135356413636e-14,0.25,2.102419345902239,2.6224487529212688,2.1025,2.1025,2.6225,2.6225,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,5,is,2.742500066757202,2.8424999713897705,True,2.4083781843819985e-12,4.0,2.742313009892268,2.8279796871804743,2.7425,2.7425,2.8025,2.8425,0.0,0.03999999999999959 --egA8-b7-3M/9,-egA8-b7-3M,9,6,building,2.862499952316284,3.2825000286102295,True,2.3681746938886833e-14,0.25,2.8559528332329007,3.282546562084778,2.8425,2.8625,3.2825,3.2825,0.020000000000000018,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,7,your,3.322499990463257,3.502500057220459,True,8.219796608502968e-15,0.25,3.322499735080081,3.5015646541237775,3.3225000000000002,3.3225000000000002,3.5025,3.5025,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,8,company,3.5225000381469727,3.9024999141693115,True,3.6006809513156046e-13,2.919543504714966,3.5224999329970728,3.9025043103914396,3.5225,3.5225,3.9025,3.9025,0.0,0.0 --egA8-b7-3M/9,-egA8-b7-3M,9,9,image.,4.042500019073486,4.302499771118164,True,5.5716044266033906e-15,0.25,4.064112624999909,4.345916642191695,4.0425,4.1025,4.3025,4.3825,0.05999999999999961,0.08000000000000007 --egA8-b7-3M/20,-egA8-b7-3M,20,0,Now,0.5824999809265137,0.7425000071525574,True,3.1301773916999134e-14,0.2761642634868622,0.4356619142255531,0.7065679669073096,0.0625,0.5824999999999999,0.6825,0.7424999999999999,0.5199999999999999,0.05999999999999994 --egA8-b7-3M/20,-egA8-b7-3M,20,1,you've,0.762499988079071,0.9624999761581421,True,1.005963715106084e-11,4.0,0.7493537559098281,0.9559724070717779,0.7424999999999999,0.7625,0.9425,0.9624999999999999,0.020000000000000018,0.019999999999999907 --egA8-b7-3M/20,-egA8-b7-3M,20,2,got,0.9825000166893005,1.1825000047683716,True,2.447966891585933e-14,0.25,0.9825000739198075,1.1663853893456297,0.9824999999999999,0.9824999999999999,1.1425,1.1824999999999999,0.0,0.039999999999999813 --egA8-b7-3M/20,-egA8-b7-3M,20,3,a,1.2024999856948853,1.222499966621399,True,5.193426851762828e-12,4.0,1.2024999656061082,1.2224999656061082,1.2025,1.2025,1.2225,1.2225,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,4,face,1.2424999475479126,1.5425000190734863,True,1.842966155119613e-13,1.6259825229644775,1.243189139356799,1.5429338149669567,1.2425,1.2425,1.5425,1.5425,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,5,that,1.6024999618530273,1.7825000286102295,True,4.151051823510308e-15,0.25,1.6025000546147912,1.782485139751137,1.6025,1.6025,1.7825,1.7825,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,6,you,1.8224999904632568,1.9225000143051147,True,2.3299396690012715e-15,0.25,1.8158877928657156,1.9213270670153828,1.8025,1.8225,1.9025,1.9224999999999999,0.020000000000000018,0.019999999999999796 --egA8-b7-3M/20,-egA8-b7-3M,20,7,can,1.962499976158142,2.0625,True,4.888492497086283e-13,4.0,1.9624941818187185,2.062500087680068,1.9625,1.9625,2.0625,2.0625,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,8,connect,2.1024999618530273,2.4625000953674316,True,1.8919876787221873e-13,1.6692323684692383,2.108301743869357,2.4709835100351327,2.1025,2.1425,2.4625,2.5225,0.040000000000000036,0.06000000000000005 --egA8-b7-3M/20,-egA8-b7-3M,20,9,with,2.5225000381469727,2.702500104904175,True,5.3128519548432e-15,0.25,2.5457953478717408,2.7183556023547792,2.5225,2.5825,2.7025,2.7425,0.06000000000000005,0.040000000000000036 --egA8-b7-3M/20,-egA8-b7-3M,20,10,a,2.7825000286102295,2.802500009536743,True,4.2030949033711185e-12,4.0,2.782499639676574,2.8024996396765736,2.7825,2.7825,2.8025,2.8025,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,11,"voice,",2.822499990463257,3.202500104904175,True,2.7150111586021763e-12,4.0,2.83914372742463,3.220260657530159,2.8225,2.8825,3.2025,3.2825,0.06000000000000005,0.08000000000000007 --egA8-b7-3M/20,-egA8-b7-3M,20,12,and,3.2825000286102295,4.022500038146973,True,1.7373433150013468e-13,1.5327953100204468,3.3491538445964393,4.026596308706412,3.2825,3.9425,4.022500000000001,4.062500000000001,0.6599999999999997,0.040000000000000036 --egA8-b7-3M/20,-egA8-b7-3M,20,13,that,4.122499942779541,4.28249979019165,True,4.251538560768285e-14,0.3750979006290436,4.110459146562859,4.271831829133859,4.0425,4.1225000000000005,4.2225,4.282500000000001,0.08000000000000007,0.0600000000000005 --egA8-b7-3M/20,-egA8-b7-3M,20,14,makes,4.302499771118164,4.502500057220459,True,9.645896006851146e-15,0.25,4.295467501888811,4.502881881025231,4.2625,4.3025,4.5025,4.5025,0.040000000000000036,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,15,you,4.542500019073486,4.682499885559082,True,3.1286988109871863e-14,0.27603378891944885,4.5424999966891395,4.682497906328179,4.5425,4.5425,4.6825,4.6825,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,16,closer,4.722499847412109,5.102499961853027,True,1.2777630245108113e-13,1.1273242235183716,4.722500016785279,5.102793700421638,4.7225,4.7225,5.102500000000001,5.102500000000001,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,17,to,5.202499866485596,5.302499771118164,True,9.8913234645049e-14,0.8726757764816284,5.202467187444825,5.302500007946147,5.202500000000001,5.202500000000001,5.3025,5.3025,0.0,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,18,that,5.362500190734863,5.5625,True,5.875642854202834e-13,4.0,5.356749432746811,5.56137750147641,5.322500000000001,5.362500000000001,5.562500000000001,5.562500000000001,0.040000000000000036,0.0 --egA8-b7-3M/20,-egA8-b7-3M,20,19,client.,5.582499980926514,6.002500057220459,True,8.170173522101254e-14,0.7208248972892761,5.582466544181751,6.002488319140519,5.5825000000000005,5.5825000000000005,6.0025,6.0025,0.0,0.0 --iRBcNs9oI8/3,-iRBcNs9oI8,3,0,"Well,",0.4424999952316284,0.6225000023841858,True,4.203285289272607e-12,1.5519766807556152,0.44251731066523176,0.6402426472765932,0.4425,0.4425,0.6224999999999999,0.7025,0.0,0.08000000000000007 --iRBcNs9oI8/3,-iRBcNs9oI8,3,1,it,0.9024999737739563,0.9825000166893005,True,1.378709565133876e-13,0.25,0.9026934341895801,0.9877443085068495,0.9025,0.9025,0.9824999999999999,1.0025,0.0,0.020000000000000018 --iRBcNs9oI8/3,-iRBcNs9oI8,3,2,is,1.162500023841858,1.2424999475479126,True,2.6363368546599222e-14,0.25,1.1766154327924332,1.258820351938083,1.1624999999999999,1.2225,1.2425,1.3025,0.06000000000000005,0.06000000000000005 --iRBcNs9oI8/3,-iRBcNs9oI8,3,3,the,1.9824999570846558,2.302500009536743,True,7.262065948587804e-12,2.681368589401245,1.9861770102307585,2.318400811512359,1.9825,1.9825,2.3025,2.5425,0.0,0.23999999999999977 --iRBcNs9oI8/3,-iRBcNs9oI8,3,4,secret,3.322499990463257,3.622499942779541,True,1.9209831208372163e-12,0.7092835307121277,3.1189257501802254,3.604293380599604,2.6225,3.3225000000000002,3.5825,3.6225,0.7000000000000002,0.040000000000000036 --iRBcNs9oI8/3,-iRBcNs9oI8,3,5,of,3.7225000858306885,4.102499961853027,True,2.4477631293401414e-12,0.9037861824035645,3.7260529538165423,4.095743694350303,3.7225,3.7225,4.1025,4.1025,0.0,0.0 --iRBcNs9oI8/3,-iRBcNs9oI8,3,6,power,4.402500152587891,4.902500152587891,True,4.5697900324936924e-11,4.0,4.409120587918154,4.902042298636173,4.402500000000001,4.402500000000001,4.902500000000001,4.902500000000001,0.0,0.0 --iRBcNs9oI8/3,-iRBcNs9oI8,3,7,pose,5.042500019073486,5.382500171661377,True,2.968922850621336e-12,1.096213698387146,5.047806403743758,5.364777216140973,5.0425,5.102500000000001,5.2225,5.3825,0.0600000000000005,0.16000000000000014 --iRBcNs9oI8/7,-iRBcNs9oI8,7,0,"Well,",0.10249999910593033,0.36250001192092896,True,2.920177391996931e-13,3.763028383255005,0.10136593814621651,0.3623925371058929,0.10250000000000001,0.10250000000000001,0.3625,0.3625,0.0,0.0 --iRBcNs9oI8/7,-iRBcNs9oI8,7,1,for,1.3224999904632568,1.4424999952316284,True,7.760179082444071e-14,1.0,0.9651913170585473,1.426231262055861,0.4425,1.3225,1.4025,1.4425,0.88,0.039999999999999813 --iRBcNs9oI8/7,-iRBcNs9oI8,7,2,"starters,",1.4824999570846558,1.9424999952316284,True,7.344413299097477e-14,0.9464231729507446,1.482722124857738,1.9316510145653845,1.4825,1.4825,1.8825,1.9425,0.0,0.05999999999999983 --iRBcNs9oI8/7,-iRBcNs9oI8,7,3,let's,1.9824999570846558,2.5625,True,5.298965259636912e-12,4.0,1.9825266210685708,2.562499980618171,1.9825,1.9825,2.5625,2.5625,0.0,0.0 --iRBcNs9oI8/7,-iRBcNs9oI8,7,4,look,2.622499942779541,2.882499933242798,True,8.900332185667048e-14,1.146923542022705,2.621435781036787,2.883659047511814,2.6225,2.6225,2.8825,2.9025,0.0,0.020000000000000018 --iRBcNs9oI8/7,-iRBcNs9oI8,7,5,at,2.9825000762939453,3.0625,True,3.4943981708874736e-14,0.45029863715171814,2.9829116244373175,3.062499663832645,2.9825,2.9825,3.0625,3.0625,0.0,0.0 --iRBcNs9oI8/7,-iRBcNs9oI8,7,6,the,3.1624999046325684,3.322499990463257,True,1.0356679977594624e-13,1.3345929384231567,3.156277465682998,3.3100716739224545,3.1225,3.1625,3.2425,3.3225000000000002,0.040000000000000036,0.08000000000000007 --iRBcNs9oI8/7,-iRBcNs9oI8,7,7,word,3.3424999713897705,3.682499885559082,True,3.161334693983363e-15,0.25,3.3424933417526446,3.682494353615535,3.3425,3.3425,3.6825,3.6825,0.0,0.0 --iRBcNs9oI8/7,-iRBcNs9oI8,7,8,"""power""",3.702500104904175,4.002500057220459,True,2.9010224534024506e-14,0.3738344609737396,3.7025010532407543,3.9910909636389107,3.7025,3.7025,3.9625,4.0025,0.0,0.04000000000000048 --iRBcNs9oI8/6,-iRBcNs9oI8,6,0,What,0.042500000447034836,0.2224999964237213,True,6.827441173182236e-13,1.0,0.05154200673577662,0.2727785157094661,0.0425,0.0825,0.2225,0.3025,0.04,0.07999999999999999 --iRBcNs9oI8/6,-iRBcNs9oI8,6,1,is,0.4625000059604645,0.6025000214576721,True,1.1234021118954399e-13,0.25,0.4747218729776408,0.5963319673877671,0.4625,0.5225,0.5425,0.6024999999999999,0.05999999999999994,0.05999999999999994 --iRBcNs9oI8/6,-iRBcNs9oI8,6,2,a,0.7425000071525574,0.762499988079071,True,2.809957263616783e-14,0.25,0.7333161148797499,0.7533161148798755,0.6825,0.7424999999999999,0.7025,0.7625,0.05999999999999994,0.05999999999999994 --iRBcNs9oI8/6,-iRBcNs9oI8,6,3,power,0.9825000166893005,1.722499966621399,True,3.0594309637782535e-12,4.0,0.9841119228812493,1.722488496427188,0.9824999999999999,0.9824999999999999,1.7225,1.7225,0.0,0.0 --iRBcNs9oI8/6,-iRBcNs9oI8,6,4,pose,2.1024999618530273,2.6024999618530273,True,4.7897163665822085e-12,4.0,2.1029123141162605,2.5993707527881207,2.0625,2.1025,2.5825,2.6025,0.040000000000000036,0.020000000000000018 --iRBcNs9oI8/9,-iRBcNs9oI8,9,0,Power,0.0024999999441206455,0.2224999964237213,True,8.225095646620991e-15,0.25,0.006262492974741897,0.22425165857230403,0.0025000000000000005,0.0225,0.2225,0.2225,0.019999999999999997,0.0 --iRBcNs9oI8/9,-iRBcNs9oI8,9,1,is,0.2824999988079071,0.3425000011920929,True,4.88303009138491e-11,4.0,0.2828602076691416,0.3461376424479182,0.28250000000000003,0.28250000000000003,0.3425,0.3425,0.0,0.0 --iRBcNs9oI8/9,-iRBcNs9oI8,9,2,similar,1.3025000095367432,1.722499966621399,True,1.3116465277459872e-14,0.25,1.1245273514934184,1.7144200210143659,0.4625,1.3025,1.7025,1.7425,0.84,0.040000000000000036 --iRBcNs9oI8/9,-iRBcNs9oI8,9,3,to,1.7424999475479126,1.8025000095367432,True,3.081003117563763e-12,4.0,1.7386692485023425,1.7996332226989926,1.7225,1.7625,1.7825,1.8225,0.040000000000000036,0.040000000000000036 --iRBcNs9oI8/9,-iRBcNs9oI8,9,4,strength,1.9225000143051147,2.5824999809265137,True,4.50202212749079e-13,1.0,1.8981114999728592,2.5831794475236496,1.8025,1.9425,2.5825,2.5825,0.1399999999999999,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,0,I've,0.08250000327825546,0.24250000715255737,True,1.9600367336114477e-12,4.0,0.08247995998981052,0.24164600371421696,0.0825,0.0825,0.2425,0.2425,0.0,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,1,drawn,0.32249999046325684,0.6625000238418579,True,1.3570146525573867e-12,4.0,0.3229530278849659,0.6657272327837613,0.3225,0.3225,0.6625,0.7025,0.0,0.040000000000000036 --iRBcNs9oI8/8,-iRBcNs9oI8,8,2,this,1.0225000381469727,1.3424999713897705,True,7.521722367133041e-14,0.3343006670475006,1.0222614949808086,1.342502502681455,1.0225,1.0225,1.3425,1.3425,0.0,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,3,man,1.5225000381469727,1.6825000047683716,True,7.045138262920872e-14,0.3131190240383148,1.5236198190614774,1.6824829413710227,1.5225,1.5425,1.6824999999999999,1.6824999999999999,0.020000000000000018,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,4,"here,",1.902500033378601,2.0824999809265137,True,2.3500652988829758e-14,0.25,1.894543622342994,2.0867907583195966,1.7025,1.9825,2.0425,2.2025,0.28,0.16000000000000014 --iRBcNs9oI8/8,-iRBcNs9oI8,8,5,and,2.122499942779541,2.262500047683716,True,2.832734047331087e-13,1.2590000629425049,2.1331121793088736,2.2719190723194225,2.1225,2.2825,2.2625,2.3825,0.16000000000000014,0.11999999999999966 --iRBcNs9oI8/8,-iRBcNs9oI8,8,6,if,2.322499990463257,2.382499933242798,True,5.582629085572333e-13,2.48118257522583,2.33868003750859,2.4031823336609714,2.2825,2.5625,2.3825,2.6425,0.2799999999999998,0.26000000000000023 --iRBcNs9oI8/8,-iRBcNs9oI8,8,7,you,2.4825000762939453,2.6424999237060547,True,9.512268766718665e-14,0.4227698743343353,2.5072316046245557,2.6582997433161406,2.4825,2.7425,2.6425,2.8425,0.26000000000000023,0.19999999999999973 --iRBcNs9oI8/8,-iRBcNs9oI8,8,8,can,2.742500066757202,2.8424999713897705,True,4.3768065343882667e-13,1.945258378982544,2.75386187499682,2.937324736774701,2.7425,2.8825,2.8425,4.0425,0.13999999999999968,1.2000000000000006 --iRBcNs9oI8/8,-iRBcNs9oI8,8,9,"tell,",2.882499933242798,4.082499980926514,True,2.2499873058440256e-13,1.0,2.980365531712314,4.098930060097728,2.8825,4.1225000000000005,4.0825000000000005,4.282500000000001,1.2400000000000007,0.20000000000000018 --iRBcNs9oI8/8,-iRBcNs9oI8,8,10,he,4.122499942779541,4.182499885559082,True,4.699343873564542e-14,0.25,4.143350252178951,4.2046936964374835,4.1025,4.402500000000001,4.1825,4.4625,0.3000000000000007,0.28000000000000025 --iRBcNs9oI8/8,-iRBcNs9oI8,8,11,has,4.222499847412109,4.34250020980835,True,1.2339205991609287e-13,0.5484122633934021,4.245734427287916,4.369364316926948,4.2225,4.522500000000001,4.3425,4.6825,0.3000000000000007,0.33999999999999986 --iRBcNs9oI8/8,-iRBcNs9oI8,8,12,a,4.402500152587891,4.422500133514404,True,4.446784954148519e-12,4.0,4.431854037932112,4.451854037932103,4.402500000000001,4.742500000000001,4.4225,4.7625,0.33999999999999986,0.33999999999999986 --iRBcNs9oI8/8,-iRBcNs9oI8,8,13,lot,4.522500038146973,4.682499885559082,True,2.9134436833541666e-14,0.25,4.5482175681919905,4.7054606013575935,4.522500000000001,4.8425,4.6825,4.9625,0.3199999999999994,0.28000000000000025 --iRBcNs9oI8/8,-iRBcNs9oI8,8,14,of,4.742499828338623,4.802499771118164,True,5.46444290031446e-15,0.25,4.767774255389696,4.82777455421578,4.742500000000001,5.062500000000001,4.8025,5.1225000000000005,0.3200000000000003,0.3200000000000003 --iRBcNs9oI8/8,-iRBcNs9oI8,8,15,big,4.84250020980835,4.962500095367432,True,2.147364317611755e-12,4.0,4.875985463534146,5.002288285727535,4.8425,5.202500000000001,4.9625,5.3425,0.3600000000000003,0.3799999999999999 --iRBcNs9oI8/8,-iRBcNs9oI8,8,16,"muscles,",5.0625,5.462500095367432,True,1.9685501397915563e-14,0.25,5.087760129561801,5.490328924230847,5.062500000000001,5.3825,5.4625,5.8025,0.3199999999999994,0.33999999999999986 --iRBcNs9oI8/8,-iRBcNs9oI8,8,17,he,5.682499885559082,5.802499771118164,True,2.0166227628765077e-12,4.0,5.678916934830031,5.797729507728853,5.6225000000000005,5.862500000000001,5.702500000000001,5.9225,0.2400000000000002,0.21999999999999975 --iRBcNs9oI8/8,-iRBcNs9oI8,8,18,looks,6.502500057220459,6.762499809265137,True,4.851144984850675e-13,2.156076669692993,6.49249963470516,6.761737275706995,6.5025,6.5025,6.7625,6.7625,0.0,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,19,very,7.202499866485596,7.402500152587891,True,6.853084722965863e-13,3.045832633972168,7.2021361104523285,7.402531057662287,7.202500000000001,7.202500000000001,7.402500000000001,7.402500000000001,0.0,0.0 --iRBcNs9oI8/8,-iRBcNs9oI8,8,20,strong,7.602499961853027,8.0024995803833,True,4.99631972350912e-14,0.25,7.602283377792524,8.002472983869318,7.602500000000001,7.602500000000001,8.0025,8.0025,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,0,If,0.5224999785423279,0.5824999809265137,True,3.035739655531458e-14,0.25,0.5224777847512705,0.5825087523727438,0.5225,0.5225,0.5824999999999999,0.5824999999999999,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,1,it,0.6625000238418579,0.7425000071525574,True,9.317088658748751e-14,0.5512282848358154,0.6439698113695347,0.7142330631919445,0.6224999999999999,0.6625,0.6825,0.7424999999999999,0.040000000000000036,0.05999999999999994 --lzEya4AM_4/5,-lzEya4AM_4,5,2,falls,0.762499988079071,1.0225000381469727,True,1.690241371345158e-13,1.0,0.7456461893744665,1.0056523214029987,0.7224999999999999,0.7625,0.9824999999999999,1.0225,0.040000000000000036,0.040000000000000036 --lzEya4AM_4/5,-lzEya4AM_4,5,3,into,1.0824999809265137,1.2625000476837158,True,9.029643623526679e-14,0.5342221260070801,1.06263615289812,1.2547635900885998,1.0225,1.0825,1.1624999999999999,1.2625,0.06000000000000005,0.10000000000000009 --lzEya4AM_4/5,-lzEya4AM_4,5,4,oh,1.2825000286102295,1.3424999713897705,True,5.106823641807916e-14,0.30213576555252075,1.3017148062167647,1.3783215908484356,1.1824999999999999,1.3825,1.2625,1.4625,0.20000000000000018,0.19999999999999996 --lzEya4AM_4/5,-lzEya4AM_4,5,5,any,1.3825000524520874,1.4824999570846558,True,2.4951281275484294e-13,1.4761962890625,1.416671421638377,1.5684959034922972,1.2825,1.5025,1.4625,1.7425,0.21999999999999997,0.28 --lzEya4AM_4/5,-lzEya4AM_4,5,6,of,1.6425000429153442,1.7424999475479126,True,2.3230219831082977e-11,4.0,1.6744825900566362,1.7571266553083897,1.6425,1.7825,1.7425,1.8425,0.1399999999999999,0.10000000000000009 --lzEya4AM_4/5,-lzEya4AM_4,5,7,those,1.8025000095367432,2.0425000190734863,True,3.8556565709266244e-13,2.281127691268921,1.813751078471044,2.042598142283778,1.8025,1.8625,2.0425,2.0425,0.06000000000000005,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,8,three,2.0824999809265137,2.2825000286102295,True,9.28581078132526e-14,0.5493777990341187,2.082500668233339,2.282500675241747,2.0825,2.0825,2.2825,2.2825,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,9,"categories,",2.322499990463257,2.9024999141693115,True,7.862823212618705e-14,0.4651893675327301,2.3225014444100087,2.9024997494292215,2.3225,2.3225,2.9025,2.9025,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,10,inducing,3.6424999237060547,4.202499866485596,True,1.9956248025615464e-12,4.0,3.6424999115902814,4.202500006501488,3.6425,3.6425,4.202500000000001,4.202500000000001,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,11,nausea,4.262499809265137,5.382500171661377,True,1.1790844993073146e-12,4.0,4.463467536769676,5.383299142343135,4.2625,5.022500000000001,5.3825,5.3825,0.7600000000000007,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,12,and,5.422500133514404,5.542500019073486,True,8.983205347125323e-13,4.0,5.424328417539918,5.544325310866087,5.4225,5.4225,5.5425,5.5425,0.0,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,13,vomiting,5.5625,5.982500076293945,True,5.396987652790675e-13,3.1930277347564697,5.576995124703589,5.9820157407403665,5.562500000000001,5.602500000000001,5.982500000000001,5.982500000000001,0.040000000000000036,0.0 --lzEya4AM_4/5,-lzEya4AM_4,5,14,would,6.022500038146973,6.382500171661377,True,4.5605273707840024e-14,0.2698151469230652,6.023217173328293,6.409607625661401,6.022500000000001,6.022500000000001,6.3825,6.4225,0.0,0.040000000000000036 --lzEya4AM_4/5,-lzEya4AM_4,5,15,be,6.402500152587891,6.462500095367432,True,7.951032101427113e-16,0.25,6.483258350281619,6.549422500484275,6.402500000000001,6.522500000000001,6.4625,6.602500000000001,0.1200000000000001,0.14000000000000057 --lzEya4AM_4/5,-lzEya4AM_4,5,16,your,6.502500057220459,6.642499923706055,True,8.889075456611217e-15,0.25,6.592861811750548,6.7463621054641605,6.482500000000001,6.642500000000001,6.6225000000000005,6.8025,0.16000000000000014,0.17999999999999972 --lzEya4AM_4/5,-lzEya4AM_4,5,17,next,6.682499885559082,6.822500228881836,True,8.763239699866032e-13,4.0,6.774462248977959,6.919159147895202,6.642500000000001,6.862500000000001,6.8025,7.0425,0.21999999999999975,0.2400000000000002 --lzEya4AM_4/5,-lzEya4AM_4,5,18,step.,6.862500190734863,7.102499961853027,True,8.070761398602799e-13,4.0,6.9531891126610725,7.268405006346323,6.862500000000001,7.0825000000000005,7.102500000000001,7.522500000000001,0.21999999999999975,0.41999999999999993 --lzEya4AM_4/6,-lzEya4AM_4,6,0,If,0.6225000023841858,0.7024999856948853,True,3.9581296682085487e-13,4.0,0.6218706464646045,0.7023365326741986,0.6224999999999999,0.6224999999999999,0.7025,0.7025,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,1,the,0.7425000071525574,0.8424999713897705,True,7.399572254029266e-15,0.4287492334842682,0.7424768243763704,0.8424768481110348,0.7424999999999999,0.7424999999999999,0.8424999999999999,0.8424999999999999,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,2,product,0.862500011920929,1.2424999475479126,True,1.7258508058543183e-14,1.0,0.8624984023485787,1.2425003773706746,0.8624999999999999,0.8624999999999999,1.2425,1.2425,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,3,or,1.2825000286102295,1.6024999618530273,True,2.486088049834245e-13,4.0,1.282676981052881,1.6025512390288499,1.2825,1.2825,1.6025,1.6025,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,4,poison,1.662500023841858,2.1024999618530273,True,4.366728604144471e-14,2.530189037322998,1.6673157302049542,2.0989654347339073,1.6625,1.7225,2.0625,2.1025,0.05999999999999983,0.040000000000000036 --lzEya4AM_4/6,-lzEya4AM_4,6,5,is,2.202500104904175,2.262500047683716,True,8.725329588346858e-15,0.5055668354034424,2.180319198328119,2.2460606071930846,2.1425,2.2025,2.2225,2.2625,0.06000000000000005,0.040000000000000036 --lzEya4AM_4/6,-lzEya4AM_4,6,6,some,2.2825000286102295,2.442500114440918,True,2.255789346007446e-15,0.25,2.278221478596384,2.4349226931964756,2.2625,2.2825,2.4225,2.4425,0.020000000000000018,0.020000000000000018 --lzEya4AM_4/6,-lzEya4AM_4,6,7,type,2.4625000953674316,2.682499885559082,True,1.0200491167879078e-13,4.0,2.462554132308005,2.682496700492244,2.4625,2.4625,2.6825,2.6825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,8,of,2.702500104904175,2.762500047683716,True,5.058627467109146e-14,2.9310920238494873,2.7039424007353188,2.7639755466185556,2.7025,2.7025,2.7625,2.7625,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,9,petroleum,2.882499933242798,3.3424999713897705,True,4.413881640828038e-13,4.0,2.8775856658907264,3.3593962724351547,2.8225,2.8825,3.3425,3.4225,0.06000000000000005,0.08000000000000007 --lzEya4AM_4/6,-lzEya4AM_4,6,10,product,3.4024999141693115,3.822499990463257,True,5.6658426865127676e-14,3.2829272747039795,3.4179361032192093,3.8208257166363166,3.4025,3.4425,3.8225000000000002,3.8225000000000002,0.040000000000000036,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,11,or,4.222499847412109,4.302499771118164,True,2.7757481439760236e-14,1.608336091041565,4.12428872001621,4.2612516688364535,3.8825,4.2225,4.242500000000001,4.3025,0.3400000000000003,0.05999999999999961 --lzEya4AM_4/6,-lzEya4AM_4,6,12,corrosive,4.362500190734863,4.84250020980835,True,4.3797563774490567e-13,4.0,4.329747394910065,4.842412625596829,4.3025,4.362500000000001,4.8425,4.8425,0.0600000000000005,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,13,"product,",4.862500190734863,5.262499809265137,True,3.187660435632379e-14,1.8470081090927124,4.862499669555491,5.262370885994928,4.862500000000001,4.862500000000001,5.2625,5.2625,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,14,you,5.802499771118164,5.902500152587891,True,1.5641947796290719e-15,0.25,5.53509435686845,5.867162990568692,5.322500000000001,5.8025,5.862500000000001,5.902500000000001,0.47999999999999954,0.040000000000000036 --lzEya4AM_4/6,-lzEya4AM_4,6,15,would,5.922500133514404,6.122499942779541,True,1.0120089614679573e-14,0.5863826274871826,5.922112572905214,6.122047150371503,5.9225,5.9225,6.1225000000000005,6.1225000000000005,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,16,not,6.142499923706055,6.262499809265137,True,6.352139781784071e-15,0.368058443069458,6.142500764751273,6.2625701809161365,6.142500000000001,6.142500000000001,6.2625,6.2625,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,17,attempt,6.302499771118164,6.822500228881836,True,1.9684304540361092e-15,0.25,6.318155035522648,6.821948412854423,6.3025,6.4625,6.822500000000001,6.822500000000001,0.16000000000000014,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,18,to,7.182499885559082,7.28249979019165,True,1.797134727002988e-14,1.0413036346435547,7.182534654775597,7.28252803343239,7.1825,7.1825,7.282500000000001,7.282500000000001,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,19,induce,7.382500171661377,7.922500133514404,True,4.838478183106891e-14,2.803532123565674,7.414923567572577,7.934817600865357,7.3825,7.5025,7.9225,7.9625,0.1200000000000001,0.040000000000000036 --lzEya4AM_4/6,-lzEya4AM_4,6,20,vomiting,7.962500095367432,8.382499694824219,True,8.089727889307174e-13,4.0,7.989677049757497,8.384180167319787,7.9625,8.0025,8.3825,8.3825,0.03999999999999915,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,21,and,8.602499961853027,8.702500343322754,True,7.461027882484355e-15,0.4323101341724396,8.558395721540947,8.70051005570173,8.4225,8.6025,8.6825,8.7025,0.17999999999999972,0.02000000000000135 --lzEya4AM_4/6,-lzEya4AM_4,6,22,"nausea,",8.72249984741211,9.142499923706055,True,1.0365191344119395e-11,4.0,8.722975493432113,9.139562814034488,8.7225,8.7225,9.1225,9.1425,0.0,0.019999999999999574 --lzEya4AM_4/6,-lzEya4AM_4,6,23,as,9.642499923706055,9.702500343322754,True,8.344037537733745e-14,4.0,9.641942999273425,9.701962157826868,9.6425,9.6425,9.7025,9.7025,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,24,as,9.762499809265137,9.822500228881836,True,5.40865399342727e-12,4.0,9.762383360924126,9.822383364858918,9.7625,9.7625,9.8225,9.8225,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,25,the,9.842499732971191,9.942500114440918,True,6.742547008052472e-15,0.39067959785461426,9.842500125440687,9.942500469035092,9.8425,9.8425,9.942499999999999,9.942499999999999,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,26,product,10.0024995803833,10.342499732971191,True,2.207761392052001e-14,1.2792307138442993,9.999605801336743,10.338683442149089,9.9625,10.0025,10.282499999999999,10.3425,0.03999999999999915,0.0600000000000005 --lzEya4AM_4/6,-lzEya4AM_4,6,27,burned,10.362500190734863,10.682499885559082,True,1.995170183038096e-14,1.156050205230713,10.362784650428713,10.68227166700739,10.362499999999999,10.362499999999999,10.6825,10.6825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,28,on,10.702500343322754,10.782500267028809,True,2.3671003850610217e-13,4.0,10.702523528497984,10.782508642676058,10.7025,10.7025,10.782499999999999,10.782499999999999,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,29,the,10.822500228881836,10.922499656677246,True,9.76079348504735e-15,0.5655641555786133,10.823841751296246,10.923854219050918,10.8225,10.8425,10.9225,10.942499999999999,0.019999999999999574,0.019999999999999574 --lzEya4AM_4/6,-lzEya4AM_4,6,30,way,11.0024995803833,11.1225004196167,True,9.980518066729852e-15,0.5782955288887024,10.97704504414781,11.089484271552267,10.942499999999999,11.0025,11.0625,11.1225,0.0600000000000005,0.0600000000000005 --lzEya4AM_4/6,-lzEya4AM_4,6,31,down,11.142499923706055,11.382499694824219,True,1.0334919192959893e-14,0.5988304018974304,11.139335546006153,11.392634807234638,11.1025,11.1425,11.3825,11.4225,0.040000000000000924,0.03999999999999915 --lzEya4AM_4/6,-lzEya4AM_4,6,32,it,11.422499656677246,11.822500228881836,True,4.624125230265992e-15,0.26793307065963745,11.473483985557554,11.813451673400056,11.4225,11.7225,11.7625,11.8225,0.3000000000000007,0.0600000000000005 --lzEya4AM_4/6,-lzEya4AM_4,6,33,could,11.842499732971191,12.022500038146973,True,3.321526835517517e-15,0.25,11.841404836972417,12.022500039924171,11.8225,11.8425,12.022499999999999,12.022499999999999,0.019999999999999574,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,34,cause,12.0625,12.322500228881836,True,5.640726550264078e-15,0.3268374502658844,12.06249996265842,12.322485847197163,12.0625,12.0625,12.3225,12.3225,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,35,damage,12.362500190734863,12.702500343322754,True,1.301325600690283e-14,0.7540197372436523,12.362499995548271,12.702500000019468,12.362499999999999,12.362499999999999,12.7025,12.7025,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,36,and,12.742500305175781,12.842499732971191,True,3.552262157300055e-16,0.25,12.742469306485088,12.842550373760359,12.7425,12.7425,12.8425,12.8425,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,37,burning,12.882499694824219,13.482500076293945,True,1.0955535659435203e-13,4.0,12.882500009396367,13.474533767589687,12.8825,12.8825,13.4825,13.4825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,38,on,13.5625,13.6225004196167,True,6.547006318156912e-14,3.793494939804077,13.558178654761617,13.618923926660116,13.5625,13.5625,13.6225,13.6225,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,39,the,13.642499923706055,13.762499809265137,True,5.965204543566466e-15,0.3456384837627411,13.653632540597108,13.769364286143006,13.6425,13.7025,13.7625,13.8025,0.0600000000000005,0.040000000000000924 --lzEya4AM_4/6,-lzEya4AM_4,6,40,way,13.822500228881836,13.982500076293945,True,1.5995148253318027e-14,0.9267978668212891,13.822149170618747,13.981798894446957,13.8225,13.8225,13.9825,13.9825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,41,back,14.022500038146973,14.182499885559082,True,3.619833416573879e-15,0.25,14.022029813684714,14.182729174722772,14.022499999999999,14.022499999999999,14.1825,14.1825,0.0,0.0 --lzEya4AM_4/6,-lzEya4AM_4,6,42,up.,14.202500343322754,14.542499542236328,True,1.867283073430921e-16,0.25,14.203948102653264,14.521972037563804,14.2025,14.2025,14.2825,14.7025,0.0,0.41999999999999993 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,0,There,0.0024999999441206455,0.48249998688697815,True,8.852508309731277e-11,1.0036022663116455,0.0698258865699199,0.5209542854210507,0.0025000000000000005,0.2225,0.3025,0.8225,0.22,0.52 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,1,are,0.5024999976158142,0.6025000214576721,True,8.811709695244474e-11,0.9989769458770752,0.6773580062375331,0.9126256507986633,0.4025,1.0225,0.6024999999999999,1.2825,0.6199999999999999,0.68 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,2,a,0.6225000023841858,0.6424999833106995,True,8.820733726766505e-11,1.0,1.0689133910196154,1.0889133910215332,0.7224999999999999,1.4825,0.7424999999999999,1.5025,0.76,0.76 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,3,dozen,0.6625000238418579,0.8424999713897705,True,8.838636073038586e-11,1.002029538154602,1.2452262918671384,1.6960141945305303,0.8624999999999999,1.6625,1.2825,2.1625,0.8000000000000002,0.8800000000000001 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,4,or,0.862500011920929,0.9225000143051147,True,8.844412008324198e-11,1.0026843547821045,1.8525124174329262,1.9802406363476905,1.4224999999999999,2.3225,1.5425,2.4625,0.8999999999999999,0.9199999999999999 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,5,more,0.9424999952316284,1.0824999809265137,True,8.837957449214784e-11,1.0019526481628418,2.13674030925521,2.479925987685495,1.6824999999999999,2.6225,2.0025,2.9625,0.9400000000000002,0.96 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,6,hands,1.1024999618530273,1.2825000286102295,True,8.822227670624017e-11,1.0001693964004517,2.636425683706365,3.0873399364897796,2.1625,3.1225,2.6025,3.5825,0.96,0.98 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,7,on,5.002500057220459,5.0625,True,8.802221451720271e-11,0.9979012608528137,3.2438396325030245,3.3715681957004184,2.7425,3.7225,2.8825,3.8625,0.98,0.98 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,8,techniques,5.082499980926514,5.462500095367432,True,8.812522239720622e-11,0.999069094657898,3.5280678917076664,4.517627610187784,3.0425,4.0025,4.062500000000001,4.9225,0.9600000000000004,0.8599999999999994 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,9,that,5.482500076293945,5.622499942779541,True,8.802477496905325e-11,0.9979302883148193,4.674114102900365,5.017234219415555,4.242500000000001,5.062500000000001,4.6225000000000005,5.362500000000001,0.8200000000000003,0.7400000000000002 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,10,we,5.78249979019165,5.84250020980835,True,8.77886582872911e-11,0.9952534437179565,5.173628548001558,5.301276562327708,4.8025,5.482500000000001,4.942500000000001,5.602500000000001,0.6800000000000006,0.6600000000000001 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,11,can,5.862500190734863,5.962500095367432,True,8.818036578706057e-11,0.9996942281723022,5.4575589081038185,5.692827986897282,5.1425,5.702500000000001,5.4225,5.8825,0.5600000000000005,0.45999999999999996 --mJ2ud6oKI8/1,-mJ2ud6oKI8,1,12,do,5.982500076293945,6.042500019073486,True,9.066991601969221e-11,1.0279181003570557,5.849047962874614,5.976323928323296,5.6425,5.982500000000001,5.822500000000001,6.0425,0.34000000000000075,0.21999999999999975 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,0,So,0.0024999999441206455,0.0625,True,9.096196018631986e-11,1.0093817710876465,0.032718039902220324,0.12302393990958412,0.0025000000000000005,0.10250000000000001,0.0625,0.2225,0.1,0.16 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,1,physical,0.08250000327825546,0.7425000071525574,True,9.035900500053984e-11,1.0026909112930298,0.20484162269624073,0.7183170211432781,0.10250000000000001,0.3425,0.5425,0.9425,0.24000000000000002,0.4 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,2,therapists,0.762499988079071,1.1425000429153442,True,9.044751059228417e-11,1.0036730766296387,0.7999759850899307,1.454059627827414,0.6024999999999999,1.0225,1.2025,1.7225,0.42000000000000004,0.52 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,3,and,1.162500023841858,1.2625000476837158,True,9.025457464728603e-11,1.0015320777893066,1.5358937446088856,1.6969050105096142,1.2825,1.8225,1.4224999999999999,1.9825,0.54,0.56 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,4,chiropractors,1.3025000095367432,1.8025000095367432,True,9.00861468755565e-11,0.9996631145477295,1.7787420065370874,2.644814585780178,1.5025,2.0625,2.3425,2.9425,0.56,0.6000000000000001 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,5,will,1.8224999904632568,1.962499976158142,True,9.00861468755565e-11,0.9996631145477295,2.72665159286188,2.9581697385244805,2.4225,3.0225,2.6625,3.2625,0.6000000000000001,0.6000000000000001 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,6,do,1.9824999570846558,2.0425000190734863,True,9.00861468755565e-11,0.9996631145477295,3.0400067456061746,3.1305127941614,2.7425,3.3425,2.8225,3.4225,0.5999999999999996,0.6000000000000001 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,7,joint,2.0625,2.242500066757202,True,9.00861468755565e-11,0.9996631145477295,3.2123498012430876,3.51437399545938,2.9225,3.5025,3.2225,3.8025,0.5800000000000001,0.5800000000000001 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,8,manipulations,2.262500047683716,2.762500047683716,True,9.00861468755565e-11,0.9996631145477295,3.5962110025410743,4.462293761119333,3.3025,3.8825,4.202500000000001,4.702500000000001,0.5799999999999996,0.5 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,9,and,4.722499847412109,4.822500228881836,True,9.011650453638609e-11,1.0,4.544132032136779,4.705125324259879,4.3025,4.7625,4.4625,4.9225,0.45999999999999996,0.45999999999999996 --mJ2ud6oKI8/2,-mJ2ud6oKI8,2,10,mobilizations,4.84250020980835,5.682499885559082,True,9.054999805524488e-11,1.0048103332519531,4.786928814409058,5.65272602695609,4.562500000000001,4.982500000000001,5.5825000000000005,5.6825,0.41999999999999993,0.09999999999999964 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,0,They,0.02250000089406967,0.3824999928474426,True,5.596440533759718e-14,0.25,0.07685901928112787,0.3990715972593789,0.0225,0.14250000000000002,0.3825,0.4425,0.12000000000000002,0.06 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,1,are,0.4424999952316284,0.7024999856948853,True,6.72760512663434e-13,1.0,0.5160575050547208,0.7227117404819162,0.4425,0.6024999999999999,0.7025,0.7424999999999999,0.15999999999999992,0.039999999999999925 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,2,all,0.7825000286102295,0.9424999952316284,True,2.1975723649668433e-13,0.3266500234603882,0.7935273972149047,0.9526167018557699,0.7424999999999999,0.8424999999999999,0.8624999999999999,0.9425,0.09999999999999998,0.08000000000000007 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,3,very,1.0824999809265137,1.3825000524520874,True,3.322413177908601e-11,4.0,1.1101423300401905,1.4078282823092163,1.0625,1.0825,1.3825,1.3825,0.020000000000000018,0.0 --mJ2ud6oKI8/6,-mJ2ud6oKI8,6,4,safe,1.8224999904632568,2.1624999046325684,True,4.374835129578036e-12,4.0,1.859322265183538,2.141602476798535,1.7825,1.9425,2.0225,2.1625,0.15999999999999992,0.14000000000000012 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,0,It's,0.0625,0.26249998807907104,True,1.3943171617292194e-10,4.0,0.047669511258957874,0.2484408818378565,0.0025000000000000005,0.0625,0.1825,0.28250000000000003,0.06,0.10000000000000003 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,1,safe,0.4025000035762787,0.6225000023841858,True,4.5447569319012615e-12,4.0,0.3360747022548775,0.5170328362234636,0.2025,0.4025,0.4225,0.6224999999999999,0.2,0.19999999999999996 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,2,and,0.6424999833106995,0.7425000071525574,True,2.003821819912999e-15,0.25,0.5856019452028779,0.7191063800421992,0.4825,0.6425,0.7025,0.7424999999999999,0.15999999999999998,0.039999999999999925 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,3,the,0.8424999713897705,0.9624999761581421,True,1.243637679816667e-12,2.812455415725708,0.8453878094860923,0.9667462538582235,0.8424999999999999,0.8424999999999999,0.9624999999999999,0.9624999999999999,0.0,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,4,person,1.122499942779541,1.3825000524520874,True,1.8701285366216208e-13,0.4229249060153961,1.0165126982997161,1.308275716440458,0.9824999999999999,1.1225,1.2825,1.3825,0.14000000000000012,0.10000000000000009 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,5,will,1.462499976158142,1.6425000429153442,True,3.865140087329355e-13,0.8740917444229126,1.3867817686280777,1.5999738769213134,1.3625,1.4625,1.5225,1.7225,0.09999999999999987,0.19999999999999996 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,6,feel,1.7024999856948853,1.9225000143051147,True,2.057110043288471e-13,0.46521028876304626,1.6644042882905339,1.8580207414522327,1.5625,1.8425,1.7625,2.0825,0.28,0.32000000000000006 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,7,better,2.0625,2.3424999713897705,True,2.55443778109099e-12,4.0,1.9039668350534087,2.269341184316521,1.8425,2.1225,2.2225,2.3825,0.28,0.1599999999999997 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,8,later,2.382499933242798,2.742500066757202,True,6.280226447392956e-13,1.4202574491500854,2.3391000804104283,2.610403111609425,2.3225,2.4025,2.5225,2.7425,0.08000000000000007,0.2200000000000002 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,9,"on,",2.922499895095825,3.002500057220459,True,4.978645534031712e-13,1.1259082555770874,2.736678888486408,2.8013639750269204,2.6825,2.9225,2.7425,3.0025,0.23999999999999977,0.2599999999999998 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,10,but,3.0225000381469727,3.1424999237060547,True,5.492410657683376e-15,0.25,2.853221959313782,3.0344224455998434,2.8025,3.0225,3.0025,3.1425,0.21999999999999975,0.14000000000000012 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,11,it's,3.182499885559082,3.2825000286102295,True,7.276466755523625e-12,4.0,3.110513745542075,3.269832850162292,3.0625,3.1825,3.2625,3.2825,0.1200000000000001,0.020000000000000018 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,12,sort,3.302500009536743,3.4625000953674316,True,1.0976491254550275e-13,0.25,3.30240099690659,3.461609169642533,3.3025,3.3025,3.4425,3.4825,0.0,0.040000000000000036 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,13,of,3.5425000190734863,3.622499942779541,True,2.677345107768292e-12,4.0,3.5413547723387526,3.6216566418041243,3.5425,3.5425,3.6225,3.6225,0.0,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,14,like,3.6624999046325684,3.8424999713897705,True,8.644191455296421e-14,0.25,3.666813689753435,3.838871048201672,3.6625,3.7025,3.8225000000000002,3.8625,0.040000000000000036,0.03999999999999959 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,15,flossing,3.882499933242798,4.34250020980835,True,1.1762174621874483e-13,0.26599863171577454,3.8838651729144584,4.3424120629860194,3.8825,3.8825,4.3425,4.3425,0.0,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,16,your,4.362500190734863,5.0625,True,3.292849730406923e-14,0.25,4.41819147818272,5.111481728190935,4.362500000000001,4.9625,5.022500000000001,5.1625000000000005,0.5999999999999996,0.13999999999999968 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,17,teeth,5.102499961853027,5.922500133514404,True,1.063016455459323e-13,0.25,5.148201109665343,5.921581298640506,5.102500000000001,5.202500000000001,5.9225,5.9225,0.09999999999999964,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,18,for,5.962500095367432,6.0625,True,6.951333768583784e-14,0.25,5.954329937943031,6.058653654509152,5.942500000000001,5.9625,6.0425,6.062500000000001,0.019999999999999574,0.020000000000000462 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,19,the,6.082499980926514,6.202499866485596,True,4.954940624468662e-11,4.0,6.082503363629208,6.202503801804082,6.0825000000000005,6.0825000000000005,6.202500000000001,6.202500000000001,0.0,0.0 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,20,first,6.262499809265137,6.502500057220459,True,5.828269290797383e-12,4.0,6.26250188369506,6.513630284736709,6.2625,6.2625,6.5025,6.562500000000001,0.0,0.0600000000000005 --mJ2ud6oKI8/9,-mJ2ud6oKI8,9,21,time,6.662499904632568,6.882500171661377,True,1.4780298296032668e-12,3.342527389526367,6.662037112250419,6.879055062501529,6.6625000000000005,6.6625000000000005,6.862500000000001,6.8825,0.0,0.019999999999999574 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,0,Sometimes,0.7825000286102295,1.162500023841858,True,6.672226790169211e-13,0.2790868580341339,0.4509397771605163,1.1624976149860125,0.0625,0.7825,1.1624999999999999,1.1624999999999999,0.72,0.0 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,1,we,1.222499966621399,1.3624999523162842,True,1.8925133682401452e-10,4.0,1.2224980141426944,1.3624753576252613,1.2225,1.2225,1.3625,1.3625,0.0,0.0 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,2,have,1.4225000143051147,1.6425000429153442,True,1.5286633721009468e-12,0.6394115090370178,1.422078617604423,1.6204572489346851,1.4025,1.4425,1.5825,1.6425,0.039999999999999813,0.06000000000000005 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,3,to,1.7024999856948853,1.8025000095367432,True,2.3907347455887074e-12,1.0,1.7059088038846568,1.799902197420372,1.7025,1.7025,1.7825,1.8025,0.0,0.020000000000000018 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,4,stretch,1.9824999570846558,2.262500047683716,True,5.604740629938654e-12,2.3443591594696045,1.932309459892086,2.2160627365081274,1.8825,1.9825,2.1625,2.2625,0.09999999999999987,0.10000000000000009 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,5,that,2.302500009536743,2.442500114440918,True,2.0492338295013957e-12,0.85715651512146,2.2511005842143135,2.4180146282719335,2.2025,2.3025,2.4025,2.4425,0.10000000000000009,0.040000000000000036 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,6,joint,2.4825000762939453,2.8424999713897705,True,6.914385296957759e-12,2.8921592235565186,2.4558962302559877,2.8143964443535863,2.4225,2.4825,2.7825,2.8425,0.06000000000000005,0.05999999999999961 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,7,that,2.922499895095825,3.182499885559082,True,2.6334511828152163e-12,1.101523756980896,2.889294160998902,3.104634416094476,2.8225,2.9425,3.0625,3.1825,0.1200000000000001,0.1200000000000001 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,8,is,3.2225000858306885,3.2825000286102295,True,1.5851010825088108e-12,0.6630184054374695,3.128230706539492,3.2041105692463807,3.0825,3.2225,3.1825,3.2825,0.14000000000000012,0.10000000000000009 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,9,already,3.302500009536743,3.702500104904175,True,1.9910991258531574e-11,4.0,3.240455573466579,3.5472033751853838,3.2225,3.3025,3.5025,3.7025,0.08000000000000007,0.20000000000000018 --mJ2ud6oKI8/8,-mJ2ud6oKI8,8,10,irritated,3.742500066757202,4.142499923706055,True,5.874412826838149e-13,0.25,3.594318712702423,4.142499418606456,3.5225,3.7425,4.1425,4.1425,0.2200000000000002,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,0,"How,",0.16249999403953552,0.36250001192092896,True,7.513402335186659e-13,3.2922470569610596,0.16295956092843808,0.4110743546178127,0.1625,0.1625,0.3625,0.4625,0.0,0.10000000000000003 --mqbVkbCndg/0,-mqbVkbCndg,0,1,at,0.4625000059604645,0.9024999737739563,True,5.430498715638943e-13,2.3795535564422607,0.5327332294690263,0.9094743295388469,0.4625,0.8825,0.9025,0.9624999999999999,0.41999999999999993,0.05999999999999994 --mqbVkbCndg/0,-mqbVkbCndg,0,2,a,0.9825000166893005,1.002500057220459,True,2.976669952192701e-11,4.0,0.9824999384988177,1.0024999384988176,0.9824999999999999,0.9824999999999999,1.0025,1.0025,0.0,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,3,particular,1.0225000381469727,1.5625,True,1.1336772991719923e-13,0.4967584013938904,1.035725275560386,1.5800902946906012,1.0225,1.0625,1.5625,1.6025,0.040000000000000036,0.040000000000000036 --mqbVkbCndg/0,-mqbVkbCndg,0,4,moment,1.6024999618530273,1.9824999570846558,True,1.4489467568476466e-13,0.6349042057991028,1.6349622213695993,2.0086853034404504,1.6025,1.6625,1.9825,2.0425,0.06000000000000005,0.06000000000000005 --mqbVkbCndg/0,-mqbVkbCndg,0,5,in,2.0225000381469727,2.122499942779541,True,1.2200440310805583e-13,0.5346028804779053,2.0643636679285398,2.153146501312387,2.0225,2.1025,2.1025,2.1825,0.08000000000000007,0.08000000000000007 --mqbVkbCndg/0,-mqbVkbCndg,0,6,time—our,2.242500066757202,2.942500114440918,True,1.081351371293171e-12,4.0,2.2427627508097463,2.94254615237048,2.2425,2.2425,2.9425,2.9425,0.0,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,7,moment,3.002500057220459,3.4024999141693115,True,2.059274381875295e-13,0.9023395776748657,3.032264713672813,3.4035362198847228,3.0025,3.0625,3.4025,3.4025,0.06000000000000005,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,8,in,3.442500114440918,3.5425000190734863,True,4.4287579788367115e-13,1.9406076669692993,3.4443813754599724,3.5444835777278967,3.4425,3.4425,3.5425,3.5425,0.0,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,9,time—are,3.682499885559082,4.622499942779541,True,1.3476970461921006e-13,0.5905382633209229,3.679404616920505,4.624780510292684,3.6225,3.6825,4.6225000000000005,4.6625000000000005,0.06000000000000005,0.040000000000000036 --mqbVkbCndg/0,-mqbVkbCndg,0,10,they,4.722499847412109,4.922500133514404,True,7.855013568845021e-14,0.34419354796409607,4.718384341212022,4.920950495392551,4.702500000000001,4.7225,4.9225,4.9225,0.019999999999999574,0.0 --mqbVkbCndg/0,-mqbVkbCndg,0,11,limited,5.422500133514404,5.902500152587891,True,2.5050260802315927e-13,1.0976605415344238,5.42248890408293,5.900145834505198,5.4225,5.4225,5.8825,5.902500000000001,0.0,0.020000000000000462 --t217m2on-s/2,-t217m2on-s,2,0,But,0.5224999785423279,0.7024999856948853,True,4.395274508933733e-12,0.7390152812004089,0.5225184924573115,0.725752175317485,0.5225,0.5225,0.7025,0.7424999999999999,0.0,0.039999999999999925 --t217m2on-s/2,-t217m2on-s,2,1,a,0.7225000262260437,0.7425000071525574,True,3.8194742507657864e-11,4.0,0.8000684223499559,0.820068422350041,0.7224999999999999,0.9025,0.7424999999999999,0.9225,0.18000000000000005,0.18000000000000005 --t217m2on-s/2,-t217m2on-s,2,2,good,0.862500011920929,1.002500057220459,True,7.123131945396821e-12,1.1976734399795532,0.9991130601109262,1.1892688242883607,0.7825,1.3625,1.0025,1.5625,0.5800000000000001,0.56 --t217m2on-s/2,-t217m2on-s,2,3,thing,1.0625,1.5625,True,4.771816188714473e-12,0.802326500415802,1.2388210425890023,1.644624686370855,1.0625,1.6025,1.5625,1.8225,0.54,0.26 --t217m2on-s/2,-t217m2on-s,2,4,to,1.6425000429153442,1.7024999856948853,True,1.0024173158207361e-10,4.0,1.7111086033802672,1.7718470918463047,1.6425,1.8625,1.7025,1.9224999999999999,0.21999999999999997,0.21999999999999997 --t217m2on-s/2,-t217m2on-s,2,5,do,1.722499966621399,1.8224999904632568,True,1.4645716280362042e-12,0.25,1.8248417371760293,1.899259041877084,1.7225,2.0025,1.8225,2.0625,0.28,0.24 --t217m2on-s/2,-t217m2on-s,2,6,is,1.8624999523162842,1.9225000143051147,True,2.995276249251333e-11,4.0,1.9473941160787953,2.048927727382598,1.8625,2.1225,1.9224999999999999,2.3225,0.26,0.3999999999999999 --t217m2on-s/2,-t217m2on-s,2,7,"yawning,",2.002500057220459,2.5225000381469727,True,2.0731873249385524e-12,0.34858280420303345,2.126425467276586,2.7815458651990497,2.0025,2.4025,2.5225,3.3825,0.3999999999999999,0.8599999999999999 --t217m2on-s/2,-t217m2on-s,2,8,chewing,3.322499990463257,3.862499952316284,True,8.043577089111853e-12,1.352435827255249,3.3678403420855934,3.876952620924111,3.3225000000000002,3.4625,3.8625,3.9025,0.13999999999999968,0.040000000000000036 --t217m2on-s/2,-t217m2on-s,2,9,gum,4.002500057220459,4.182499885559082,True,4.4149128797243975e-12,0.742317259311676,4.002468735954162,4.1826709100986985,4.0025,4.0025,4.1825,4.1825,0.0,0.0 --t217m2on-s/2,-t217m2on-s,2,10,is,4.302499771118164,4.402500152587891,True,4.180251630958587e-11,4.0,4.302498326491306,4.402498558458009,4.3025,4.3025,4.402500000000001,4.402500000000001,0.0,0.0 --t217m2on-s/2,-t217m2on-s,2,11,effective.,4.422500133514404,5.042500019073486,True,2.2316224389251627e-12,0.3752218782901764,4.423601965630909,5.043327191160557,4.4225,4.4225,5.0425,5.0425,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,0,If,0.5224999785423279,0.6025000214576721,True,2.0082098740962498e-15,0.25,0.3015441331703468,0.6023878008153598,0.0625,0.5425,0.5824999999999999,0.6024999999999999,0.48,0.020000000000000018 --t217m2on-s/7,-t217m2on-s,7,1,you,0.6424999833106995,0.8025000095367432,True,8.301148532669292e-14,0.42261114716529846,0.6434799647001955,0.7975552797224235,0.6425,0.6425,0.7625,0.8025,0.0,0.040000000000000036 --t217m2on-s/7,-t217m2on-s,7,2,find,0.8424999713897705,1.122499942779541,True,2.140681512260989e-12,4.0,0.8412030234650724,1.1153169739376245,0.8424999999999999,0.8424999999999999,1.0625,1.1225,0.0,0.06000000000000005 --t217m2on-s/7,-t217m2on-s,7,3,that,1.1425000429153442,1.3424999713897705,True,1.610306723882004e-15,0.25,1.1383865714841004,1.3366361795938604,1.1025,1.1425,1.2825,1.3425,0.040000000000000036,0.06000000000000005 --t217m2on-s/7,-t217m2on-s,7,4,your,1.402500033378601,1.5425000190734863,True,4.7928254789421365e-14,0.25,1.4035147118997728,1.5474983663610131,1.4025,1.4025,1.5425,1.6025,0.0,0.06000000000000005 --t217m2on-s/7,-t217m2on-s,7,5,ears,1.7024999856948853,2.0225000381469727,True,1.2316479758821275e-13,0.6270315051078796,1.7035309636037015,2.0069588683882187,1.7025,1.7225,1.9425,2.0225,0.020000000000000018,0.08000000000000007 --t217m2on-s/7,-t217m2on-s,7,6,consistently,2.7225000858306885,4.002500057220459,True,5.272141772628969e-13,2.6840453147888184,2.6778890918337224,4.004975277533073,2.0225,2.7225,4.0025,4.0025,0.7000000000000002,0.0 --t217m2on-s/7,-t217m2on-s,7,7,do,4.28249979019165,4.402500152587891,True,2.9934043525164933e-12,4.0,4.279017335996583,4.399301163358553,4.282500000000001,4.282500000000001,4.402500000000001,4.402500000000001,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,8,this,4.522500038146973,4.702499866485596,True,1.2310472770565937e-14,0.25,4.4871016225096705,4.692839750002059,4.442500000000001,4.522500000000001,4.6625000000000005,4.702500000000001,0.08000000000000007,0.040000000000000036 --t217m2on-s/7,-t217m2on-s,7,9,and,4.78249979019165,4.882500171661377,True,2.4961423986807896e-14,0.25,4.771094354005105,4.876832336738395,4.702500000000001,4.782500000000001,4.8425,4.8825,0.08000000000000007,0.040000000000000036 --t217m2on-s/7,-t217m2on-s,7,10,you,4.902500152587891,5.002500057220459,True,2.269763060296537e-15,0.25,4.900825762575524,5.003465900093947,4.902500000000001,4.902500000000001,5.0025,5.022500000000001,0.0,0.020000000000000462 --t217m2on-s/7,-t217m2on-s,7,11,can't,5.042500019073486,5.34250020980835,True,1.9953183549858977e-10,4.0,5.0430587580198285,5.342191859761064,5.0425,5.0425,5.3425,5.3425,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,12,pop,5.5625,5.802499771118164,True,2.0839250464144143e-12,4.0,5.562475953090791,5.802493273965683,5.562500000000001,5.562500000000001,5.8025,5.8025,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,13,them,5.862500190734863,6.0625,True,3.170972870131933e-14,0.25,5.862493429115527,6.062460001350499,5.862500000000001,5.862500000000001,6.062500000000001,6.062500000000001,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,14,"often,",6.182499885559082,6.442500114440918,True,3.143114634123097e-14,0.25,6.167211454501574,6.46103084293285,6.102500000000001,6.1825,6.442500000000001,6.482500000000001,0.07999999999999918,0.040000000000000036 --t217m2on-s/7,-t217m2on-s,7,15,you'll,6.482500076293945,7.082499980926514,True,6.824945990302478e-12,4.0,6.574808668518842,7.082477879268368,6.482500000000001,6.742500000000001,7.0825000000000005,7.0825000000000005,0.2599999999999998,0.0 --t217m2on-s/7,-t217m2on-s,7,16,definitely,7.102499961853027,7.702499866485596,True,7.39595559275108e-13,3.7652781009674072,7.102586666901631,7.699765961681586,7.102500000000001,7.102500000000001,7.6825,7.702500000000001,0.0,0.020000000000000462 --t217m2on-s/7,-t217m2on-s,7,17,need,7.742499828338623,7.902500152587891,True,4.3668524742426773e-13,2.223162889480591,7.742483042947323,7.90343923658525,7.742500000000001,7.742500000000001,7.902500000000001,7.902500000000001,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,18,to,7.982500076293945,8.042499542236328,True,7.402548727805918e-15,0.25,7.967266830139866,8.02793425896618,7.942500000000001,7.982500000000001,8.0025,8.0425,0.040000000000000036,0.040000000000000924 --t217m2on-s/7,-t217m2on-s,7,19,see,8.0625,8.202500343322754,True,6.569796789923643e-13,3.3446807861328125,8.062499998585567,8.202499998830312,8.0625,8.0625,8.202499999999999,8.202499999999999,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,20,a,8.242500305175781,8.262499809265137,True,2.488813842516091e-11,4.0,8.242500408823773,8.262500408823774,8.2425,8.2425,8.2625,8.2625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,21,physician,8.322500228881836,8.882499694824219,True,4.652816760347678e-13,2.3687472343444824,8.318703380787824,8.881929128769334,8.282499999999999,8.3225,8.8825,8.8825,0.040000000000000924,0.0 --t217m2on-s/7,-t217m2on-s,7,22,regarding,8.90250015258789,9.922499656677246,True,1.5294997256568021e-13,0.778667688369751,8.904235783104783,9.922499327985719,8.9025,8.9025,9.9225,9.9225,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,23,those,9.942500114440918,10.162500381469727,True,1.9642521929732343e-13,1.0,9.942499955586987,10.162498402387016,9.942499999999999,9.942499999999999,10.1625,10.1625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,24,"things,",10.202500343322754,10.522500038146973,True,3.872858929171094e-14,0.25,10.202499406780868,10.522499422185247,10.2025,10.2025,10.522499999999999,10.522499999999999,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,25,it,10.90250015258789,10.982500076293945,True,1.0897798505618561e-13,0.5548064708709717,10.90176918863294,10.98161988705827,10.9025,10.9025,10.9825,10.9825,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,26,could,11.022500038146973,11.22249984741211,True,3.0060584013322234e-13,1.5303831100463867,11.022415515548126,11.222436263178336,11.022499999999999,11.022499999999999,11.2225,11.2225,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,27,be,11.242500305175781,11.302499771118164,True,3.1117084820300733e-13,1.5841695070266724,11.242499890968537,11.302500000932035,11.2425,11.2425,11.3025,11.3025,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,28,something,11.362500190734863,11.762499809265137,True,1.3482193775293545e-14,0.25,11.36250003427757,11.76250000280012,11.362499999999999,11.362499999999999,11.7625,11.7625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,29,more,11.782500267028809,11.982500076293945,True,2.739622764758032e-13,1.3947408199310303,11.782500172700903,11.982499985922653,11.782499999999999,11.782499999999999,11.9825,11.9825,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,30,of,12.0024995803833,12.0625,True,1.2251334495505528e-11,4.0,12.00250300781206,12.062503075601885,12.0025,12.0025,12.0625,12.0625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,31,a,12.1225004196167,12.142499923706055,True,1.9523441023572286e-12,4.0,12.122493511797718,12.142493511797717,12.1225,12.1225,12.1425,12.1425,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,32,nasal,12.202500343322754,12.482500076293945,True,1.2489926627667902e-12,4.0,12.202481052988533,12.482563637084958,12.2025,12.2025,12.4825,12.4825,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,33,infection,12.542499542236328,13.082500457763672,True,1.863801268268267e-13,0.9488604664802551,12.548488364093338,13.112190709215227,12.5425,12.5825,13.0825,13.2025,0.03999999999999915,0.120000000000001 --t217m2on-s/7,-t217m2on-s,7,34,or,13.202500343322754,13.362500190734863,True,1.096706384560997e-12,4.0,13.268630794746127,13.380613030877182,13.2025,13.3425,13.362499999999999,13.4025,0.1399999999999988,0.040000000000000924 --t217m2on-s/7,-t217m2on-s,7,35,a,13.482500076293945,13.5024995803833,True,5.468596062097042e-12,4.0,13.480302700842564,13.500302700842568,13.442499999999999,13.4825,13.4625,13.5025,0.040000000000000924,0.03999999999999915 --t217m2on-s/7,-t217m2on-s,7,36,sinus,13.542499542236328,13.802499771118164,True,6.39902573203871e-13,3.2577414512634277,13.55040940879016,13.803847280153091,13.5025,13.5825,13.8025,13.8025,0.08000000000000007,0.0 --t217m2on-s/7,-t217m2on-s,7,37,infection,13.842499732971191,14.5024995803833,True,1.1748278131579193e-12,4.0,13.844256664988658,14.507515066421155,13.8425,13.8425,14.5025,14.522499999999999,0.0,0.019999999999999574 --t217m2on-s/7,-t217m2on-s,7,38,that,14.6225004196167,15.322500228881836,True,1.0841008673450128e-14,0.25,14.62701468477748,15.32177485942577,14.6225,14.6225,15.3225,15.3225,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,39,he,15.382499694824219,15.462499618530273,True,1.5006362038411103e-12,4.0,15.382499899762017,15.462499926162874,15.3825,15.3825,15.4625,15.4625,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,40,would,15.522500038146973,15.742500305175781,True,7.458575722102054e-14,0.37971580028533936,15.523687096195976,15.743719806141243,15.522499999999999,15.522499999999999,15.7425,15.7425,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,41,need,15.782500267028809,15.982500076293945,True,6.329921260544644e-14,0.32225602865219116,15.783733230314803,15.975640595301629,15.7825,15.8025,15.9625,15.9825,0.019999999999999574,0.019999999999999574 --t217m2on-s/7,-t217m2on-s,7,42,to,16.022499084472656,16.082500457763672,True,2.435565990424951e-14,0.25,16.01099501177981,16.070999623759587,15.9825,16.0225,16.0425,16.082500000000003,0.040000000000000924,0.0400000000000027 --t217m2on-s/7,-t217m2on-s,7,43,deal,16.102500915527344,16.262500762939453,True,5.6791098482689064e-15,0.25,16.102225899317638,16.262741723317955,16.102500000000003,16.102500000000003,16.262500000000003,16.262500000000003,0.0,0.0 --t217m2on-s/7,-t217m2on-s,7,44,with.,16.342500686645508,16.522499084472656,True,4.8448539695074636e-14,0.25,16.342197762523412,16.52250670243474,16.3425,16.3425,16.5225,16.5225,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,0,The,0.5625,0.6625000238418579,True,6.435688570602394e-14,2.004267930984497,0.5356969890918912,0.658616661956421,0.3425,0.5625,0.6224999999999999,0.6625,0.21999999999999997,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,1,first,0.7024999856948853,0.9624999761581421,True,3.526284895593451e-14,1.098191738128662,0.7009661313991286,0.9602388802710622,0.7025,0.7025,0.9225,0.9624999999999999,0.0,0.039999999999999925 --tANM6ETl_M/3,-tANM6ETl_M,3,2,place,1.002500057220459,1.3224999904632568,True,1.331900779580732e-14,0.4147941470146179,0.999946689866764,1.308838714213955,1.0025,1.0025,1.2825,1.3225,0.0,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,3,is,1.3825000524520874,1.4424999952316284,True,4.107557105185273e-15,0.25,1.389821334669526,1.45349568534902,1.3625,1.4224999999999999,1.4425,1.4825,0.05999999999999983,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,4,either,1.5425000190734863,1.7825000286102295,True,2.895699020735547e-14,0.9018082022666931,1.5201676262785533,1.7605858681075623,1.4825,1.5425,1.7225,1.7825,0.06000000000000005,0.06000000000000005 --tANM6ETl_M/3,-tANM6ETl_M,3,5,you,1.8624999523162842,2.0225000381469727,True,3.5744268601835966e-14,1.1131845712661743,1.8553028266965272,2.0036440368509445,1.8225,1.8625,1.9224999999999999,2.0225,0.040000000000000036,0.10000000000000009 --tANM6ETl_M/3,-tANM6ETl_M,3,6,have,2.0625,2.202500104904175,True,7.299865464709121e-15,0.25,2.062385601587532,2.202536012487326,2.0625,2.0625,2.2025,2.2025,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,7,a,2.2825000286102295,2.302500009536743,True,1.1580021663792905e-11,4.0,2.282498533579872,2.302498533579872,2.2825,2.2825,2.3025,2.3025,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,8,"prospect,",2.322499990463257,2.882499933242798,True,4.4684812321421196e-15,0.25,2.3231238527656637,2.882916379552269,2.3225,2.3225,2.8825,2.8825,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,9,or,2.942500114440918,3.002500057220459,True,2.767639904147773e-13,4.0,2.942497276629371,3.0032399939030863,2.9425,2.9425,3.0025,3.0025,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,10,a,3.0625,3.0824999809265137,True,1.837679140803683e-11,4.0,3.0620961698677016,3.0820961698677016,3.0625,3.0625,3.0825,3.0825,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,11,customer,3.1024999618530273,3.4825000762939453,True,1.1064295367489013e-13,3.445755958557129,3.103188292319035,3.4840491250071417,3.1025,3.1025,3.4825,3.4825,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,12,who's,3.5225000381469727,3.682499885559082,True,3.082221630701376e-11,4.0,3.5274568778891453,3.6835674538224654,3.5225,3.5425,3.6825,3.6825,0.020000000000000018,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,13,never,3.882499933242798,4.182499885559082,True,8.976713720498884e-15,0.27956199645996094,3.8070924541804487,4.15445694471168,3.7225,3.8825,4.1225000000000005,4.1825,0.1599999999999997,0.05999999999999961 --tANM6ETl_M/3,-tANM6ETl_M,3,14,done,4.28249979019165,4.462500095367432,True,3.556252158937656e-15,0.25,4.255324390674593,4.449549096474829,4.202500000000001,4.282500000000001,4.4225,4.4625,0.08000000000000007,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,15,it,4.482500076293945,4.542500019073486,True,2.5008395351864936e-14,0.7788370251655579,4.485970581662388,4.545979863534526,4.482500000000001,4.522500000000001,4.5425,4.5825000000000005,0.040000000000000036,0.040000000000000036 --tANM6ETl_M/3,-tANM6ETl_M,3,16,"before,",4.602499961853027,4.982500076293945,True,2.1828892859954152e-14,0.6798176765441895,4.602479292145233,4.978115573980364,4.602500000000001,4.602500000000001,4.982500000000001,4.982500000000001,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,17,or,5.022500038146973,5.082499980926514,True,5.056437717533904e-14,1.5747275352478027,5.0206841105147095,5.0806852891637515,5.022500000000001,5.022500000000001,5.0825000000000005,5.0825000000000005,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,18,they've,5.102499961853027,5.34250020980835,True,3.8970549335291815e-13,4.0,5.101592897531236,5.337826895494357,5.102500000000001,5.102500000000001,5.322500000000001,5.3425,0.0,0.019999999999999574 --tANM6ETl_M/3,-tANM6ETl_M,3,19,never,5.422500133514404,5.662499904632568,True,2.0569947790450085e-14,0.6406103372573853,5.422381810653897,5.662099104183884,5.4225,5.4225,5.6625000000000005,5.6625000000000005,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,20,tried,5.722499847412109,6.002500057220459,True,1.0326153248988758e-14,0.32158762216567993,5.722183877149623,6.001503992763337,5.7225,5.7225,6.0025,6.0025,0.0,0.0 --tANM6ETl_M/3,-tANM6ETl_M,3,21,it.,6.0625,6.122499942779541,True,2.0461776804175935e-12,4.0,6.063300842860167,6.130318478523356,6.062500000000001,6.062500000000001,6.1225000000000005,6.1225000000000005,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,0,Make,0.0024999999441206455,0.14249999821186066,True,3.090905894946594e-14,0.25,0.02169566240224484,0.17425271469887066,0.0025000000000000005,0.0425,0.14250000000000002,0.1825,0.04,0.03999999999999998 --tPCytz4rww/11,-tPCytz4rww,11,1,sure,0.18250000476837158,0.6225000023841858,True,6.222975260095076e-12,4.0,0.346787166754646,0.6374821536776674,0.1825,0.5025,0.6224999999999999,0.6825,0.31999999999999995,0.06000000000000005 --tPCytz4rww/11,-tPCytz4rww,11,2,that,0.6625000238418579,0.8224999904632568,True,3.477579552449428e-13,1.9737460613250732,0.6725399070879395,0.8375782669395493,0.6625,0.7025,0.8225,0.8825,0.040000000000000036,0.05999999999999994 --tPCytz4rww/11,-tPCytz4rww,11,3,your,0.862500011920929,1.0225000381469727,True,3.126307340515556e-15,0.25,0.8784328640568163,1.0433921603265537,0.8624999999999999,0.9225,1.0225,1.1025,0.06000000000000005,0.08000000000000007 --tPCytz4rww/11,-tPCytz4rww,11,4,using,1.0425000190734863,1.222499966621399,True,1.8500610386110866e-13,1.050026535987854,1.0729127611360898,1.2830561203794688,1.0425,1.1624999999999999,1.2225,1.4625,0.11999999999999988,0.24 --tPCytz4rww/11,-tPCytz4rww,11,5,comps,1.3025000095367432,1.5425000190734863,True,6.064278853384897e-14,0.34418612718582153,1.3432746992012874,1.6062773987725414,1.2425,1.5625,1.5025,1.8825,0.32000000000000006,0.3800000000000001 --tPCytz4rww/11,-tPCytz4rww,11,6,that,1.5625,1.8224999904632568,True,6.134831141355046e-13,3.4819042682647705,1.6472699997478626,1.8896951897898389,1.5625,1.9025,1.8225,2.0625,0.3400000000000001,0.24 --tPCytz4rww/11,-tPCytz4rww,11,7,are,1.9225000143051147,2.0625,True,5.638180792741105e-15,0.25,1.971279454556468,2.097049019461683,1.9224999999999999,2.1025,2.0625,2.2025,0.18000000000000016,0.14000000000000012 --tPCytz4rww/11,-tPCytz4rww,11,8,not,2.1024999618530273,2.302500009536743,True,1.9874132721475757e-12,4.0,2.133150452735309,2.3222142523728997,2.1025,2.2225,2.3025,2.3825,0.1200000000000001,0.07999999999999963 --tPCytz4rww/11,-tPCytz4rww,11,9,"larger,",2.502500057220459,2.8424999713897705,True,2.330519674811904e-13,1.3227171897888184,2.4520214020764155,2.820602708781465,2.4025,2.5025,2.7625,2.8425,0.10000000000000009,0.07999999999999963 --tPCytz4rww/11,-tPCytz4rww,11,10,no,3.2225000858306885,3.322499990463257,True,7.816119171159819e-14,0.4436141550540924,3.2170783747909404,3.32249684334124,3.2225,3.2225,3.3225000000000002,3.3225000000000002,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,11,larger,3.382499933242798,3.682499885559082,True,9.446646752602622e-15,0.25,3.382499571327774,3.6829963136388377,3.3825,3.3825,3.6825,3.6825,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,12,than,3.702500104904175,3.9024999141693115,True,2.853919290018818e-14,0.25,3.703456733094279,3.884414196985789,3.7025,3.7025,3.8425,3.9025,0.0,0.06000000000000005 --tPCytz4rww/11,-tPCytz4rww,11,13,twelve,3.942500114440918,4.242499828338623,True,6.64411762912008e-14,0.37709563970565796,3.9459603741824063,4.243307639989169,3.9425,3.9625,4.242500000000001,4.242500000000001,0.020000000000000018,0.0 --tPCytz4rww/11,-tPCytz4rww,11,14,hundred,4.262499809265137,4.622499942779541,True,1.7619183835439894e-13,1.0,4.263793015967037,4.62249338252602,4.2625,4.2625,4.6225000000000005,4.6225000000000005,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,15,square,4.662499904632568,4.982500076293945,True,3.165683318782919e-13,1.7967252731323242,4.662500502666387,4.982478809793004,4.6625000000000005,4.6625000000000005,4.982500000000001,4.982500000000001,0.0,0.0 --tPCytz4rww/11,-tPCytz4rww,11,16,feet.,5.0625,5.28249979019165,True,3.689433470104725e-13,2.0939865112304688,5.062500417509267,5.282497153233201,5.062500000000001,5.062500000000001,5.282500000000001,5.282500000000001,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,0,Say,0.0024999999441206455,0.12250000238418579,True,3.2502712327131533e-11,4.0,0.002505057883538294,0.1225226855817078,0.0025000000000000005,0.0025000000000000005,0.1225,0.1225,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,1,twenty,0.20250000059604645,0.7225000262260437,True,6.181854507458784e-12,1.105215072631836,0.20256298455497526,0.7225242384992536,0.2025,0.2025,0.7224999999999999,0.7224999999999999,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,2,percent,0.8025000095367432,1.2024999856948853,True,4.0030847560379446e-13,0.25,0.8175673372248227,1.202531136762618,0.8025,0.9025,1.2025,1.2025,0.09999999999999998,0.0 --tPCytz4rww/10,-tPCytz4rww,10,3,so,1.2424999475479126,1.3825000524520874,True,1.546988042224265e-13,0.25,1.2430952066521186,1.3768309486444459,1.2425,1.2425,1.3625,1.3825,0.0,0.020000000000000018 --tPCytz4rww/10,-tPCytz4rww,10,4,if,1.5824999809265137,1.722499966621399,True,2.316052731543561e-12,0.41407257318496704,1.582535696275133,1.722513322878117,1.5825,1.5825,1.7225,1.7225,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,5,you've,1.9424999952316284,2.2825000286102295,True,1.4418195803944656e-10,4.0,1.9519629063531054,2.282409676113536,1.9425,1.9625,2.2825,2.2825,0.020000000000000018,0.0 --tPCytz4rww/10,-tPCytz4rww,10,6,got,2.4825000762939453,2.6424999237060547,True,1.1913363524851395e-11,2.129915714263916,2.4824364558586436,2.642480769217724,2.4825,2.4825,2.6425,2.6425,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,7,a,2.702500104904175,2.7225000858306885,True,8.198944127790764e-12,1.4658379554748535,2.702496664722302,2.7224966647239226,2.7025,2.7025,2.7225,2.7225,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,8,thousand,2.822499990463257,3.2825000286102295,True,6.314457962584841e-13,0.25,2.822534476359815,3.281519597807987,2.8225,2.8225,3.2825,3.2825,0.0,0.0 --tPCytz4rww/10,-tPCytz4rww,10,9,square,3.322499990463257,3.882499933242798,True,5.894128501243712e-13,0.25,3.4688986221058005,3.8957732430891063,3.3225000000000002,3.6825,3.8825,3.9225,0.3599999999999999,0.040000000000000036 --tPCytz4rww/10,-tPCytz4rww,10,10,foot,3.9024999141693115,4.122499942779541,True,2.942341509326596e-11,4.0,3.9231015848774184,4.122296003381612,3.9025,3.9625,4.1225000000000005,4.1225000000000005,0.06000000000000005,0.0 --tPCytz4rww/10,-tPCytz4rww,10,11,condo.,4.422500133514404,4.622499942779541,True,5.004845062689389e-12,0.8947849869728088,4.422276698912244,4.637538051382122,4.4225,4.4225,4.6225000000000005,4.6625000000000005,0.0,0.040000000000000036 --tPCytz4rww/12,-tPCytz4rww,12,0,No,0.08250000327825546,0.16249999403953552,True,1.4483042586402317e-12,2.761003017425537,0.08114481503602712,0.16217620079296532,0.0625,0.0825,0.1625,0.1625,0.020000000000000004,0.0 --tPCytz4rww/12,-tPCytz4rww,12,1,less,0.2224999964237213,0.4625000059604645,True,4.10721673685388e-12,4.0,0.22219314838082402,0.4616554631769186,0.2225,0.2225,0.4625,0.4625,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,2,than,0.5024999976158142,0.6825000047683716,True,5.063582474325312e-13,0.9653059244155884,0.5017505310429379,0.6818042885306567,0.5025,0.5025,0.6825,0.6825,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,3,eight,0.7024999856948853,1.0425000190734863,True,9.050614532997436e-13,1.7253814935684204,0.7020298754293801,1.0413866100393672,0.7025,0.7025,1.0425,1.0425,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,4,hundred,1.3624999523162842,1.7424999475479126,True,1.8136402481824104e-13,0.34574684500694275,1.3600963696569792,1.740637256117597,1.3625,1.3625,1.7425,1.7425,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,5,squire,1.7825000286102295,2.2225000858306885,True,1.852836894328247e-12,3.532191753387451,1.7936826033289333,2.203577332640142,1.7825,1.8225,2.0225,2.2225,0.040000000000000036,0.20000000000000018 --tPCytz4rww/12,-tPCytz4rww,12,6,feet,2.6024999618530273,2.7825000286102295,True,1.4244164658547276e-12,2.7154641151428223,2.562656419923969,2.737386374888778,2.0425,2.6225,2.2225,2.7825,0.5800000000000001,0.56 --tPCytz4rww/12,-tPCytz4rww,12,7,and,2.822499990463257,2.922499895095825,True,7.50539994737176e-12,4.0,2.8040084810806176,2.9083167912758485,2.6625,2.8225,2.8825,2.9225,0.1599999999999997,0.040000000000000036 --tPCytz4rww/12,-tPCytz4rww,12,8,going,2.9825000762939453,3.242500066757202,True,1.8712631270900726e-12,3.567318916320801,2.972907476639749,3.2485900463543764,2.9225,2.9825,3.1825,3.3425,0.06000000000000005,0.1599999999999997 --tPCytz4rww/12,-tPCytz4rww,12,9,to,3.322499990463257,3.4625000953674316,True,1.7221121718317273e-13,0.3282982110977173,3.3804194320253904,3.4819767729461604,3.2425,3.6425,3.3425,3.7225,0.3999999999999999,0.38000000000000034 --tPCytz4rww/12,-tPCytz4rww,12,10,similar,3.6424999237060547,4.022500038146973,True,8.151455313694378e-13,1.5539685487747192,3.6554667644090957,4.022687363378376,3.6025,3.7625,3.9625,4.062500000000001,0.16000000000000014,0.10000000000000098 --tPCytz4rww/12,-tPCytz4rww,12,11,"in,",4.042500019073486,4.122499942779541,True,6.854618001678192e-12,4.0,4.062473519113548,4.135646046857629,4.0425,4.1025,4.1225000000000005,4.1625000000000005,0.05999999999999961,0.040000000000000036 --tPCytz4rww/12,-tPCytz4rww,12,12,similar,4.942500114440918,5.322500228881836,True,3.7164002886398706e-13,0.7084832191467285,4.856473555076607,5.325159731998561,4.202500000000001,4.942500000000001,5.322500000000001,5.322500000000001,0.7400000000000002,0.0 --tPCytz4rww/12,-tPCytz4rww,12,13,with,5.582499980926514,5.78249979019165,True,5.430205913289736e-14,0.25,5.565606085452562,5.774287522003375,5.4625,5.5825000000000005,5.742500000000001,5.782500000000001,0.1200000000000001,0.040000000000000036 --tPCytz4rww/12,-tPCytz4rww,12,14,bedrooms,5.922500133514404,6.322500228881836,True,2.7115378627325104e-13,0.5169193148612976,5.91833513197217,6.321445991446334,5.9225,5.9225,6.322500000000001,6.322500000000001,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,15,and,6.422500133514404,6.582499980926514,True,1.5096515617457618e-12,2.8779537677764893,6.415225660149443,6.570339834181551,6.3825,6.4225,6.5025,6.5825000000000005,0.040000000000000036,0.08000000000000007 --tPCytz4rww/12,-tPCytz4rww,12,16,bathrooms,6.662499904632568,7.102499961853027,True,9.588660296539278e-14,0.25,6.649534180591883,7.103515746095183,6.5825000000000005,6.6625000000000005,7.102500000000001,7.102500000000001,0.08000000000000007,0.0 --tPCytz4rww/12,-tPCytz4rww,12,17,as,7.182499885559082,7.28249979019165,True,2.5051279952358063e-13,0.47756996750831604,7.182713277688271,7.281488350142335,7.1825,7.1825,7.282500000000001,7.282500000000001,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,18,well,7.882500171661377,8.082500457763672,True,1.5330628205714042e-13,0.2922584116458893,7.743203575778251,8.06287017488467,7.3025,7.8825,8.022499999999999,8.0825,0.5800000000000001,0.0600000000000005 --tPCytz4rww/12,-tPCytz4rww,12,19,and,8.242500305175781,8.342499732971191,True,1.736000666135995e-13,0.3309458792209625,8.195382128579398,8.336167495849628,8.1225,8.2425,8.3025,8.362499999999999,0.11999999999999922,0.05999999999999872 --tPCytz4rww/12,-tPCytz4rww,12,20,hopefully,8.362500190734863,8.762499809265137,True,5.427562696155852e-13,1.034693956375122,8.38126225674004,8.800728012660858,8.362499999999999,8.4225,8.7625,8.862499999999999,0.0600000000000005,0.09999999999999964 --tPCytz4rww/12,-tPCytz4rww,12,21,the,8.842499732971191,9.22249984741211,True,1.5777048450234948e-13,0.30076882243156433,8.939716417127721,9.233476473717799,8.8225,9.1425,9.2225,9.3025,0.3200000000000003,0.08000000000000007 --tPCytz4rww/12,-tPCytz4rww,12,22,complexes,9.40250015258789,9.862500190734863,True,4.707200883420637e-13,0.8973664045333862,9.400885044089927,9.865401598848464,9.4025,9.4025,9.862499999999999,9.9025,0.0,0.040000000000000924 --tPCytz4rww/12,-tPCytz4rww,12,23,have,9.942500114440918,10.102499961853027,True,2.9300275313642876e-12,4.0,9.941644931466874,10.10126370487901,9.942499999999999,9.942499999999999,10.1025,10.1025,0.0,0.0 --tPCytz4rww/12,-tPCytz4rww,12,24,similar,10.162500381469727,10.542499542236328,True,1.139832179379921e-13,0.25,10.163551445389935,10.604138254114487,10.1625,10.1625,10.5425,10.7025,0.0,0.16000000000000014 --tPCytz4rww/12,-tPCytz4rww,12,25,amenities.,10.6225004196167,11.602499961853027,True,3.5955082227506763e-12,4.0,10.709443501320298,11.61568048027064,10.6225,10.8425,11.5425,11.7225,0.21999999999999886,0.17999999999999972 --tPCytz4rww/16,-tPCytz4rww,16,0,So,0.5824999809265137,0.7225000262260437,True,1.0900147971726337e-11,2.107679605484009,0.5824988545142109,0.7225003068930176,0.5824999999999999,0.5824999999999999,0.7224999999999999,0.7224999999999999,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,1,make,0.8824999928474426,1.0824999809265137,True,8.273993469531948e-12,1.5998799800872803,0.8873053054868005,1.0895852394390597,0.8624999999999999,0.9624999999999999,1.0825,1.1425,0.09999999999999998,0.06000000000000005 --tPCytz4rww/16,-tPCytz4rww,16,2,sure,1.1825000047683716,1.3624999523162842,True,4.89173209317767e-13,0.25,1.1824972875537523,1.3625090418290506,1.1824999999999999,1.1824999999999999,1.3625,1.3625,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,3,to,1.4824999570846558,1.662500023841858,True,8.923322150633517e-13,0.25,1.4824959809329938,1.6625015961399936,1.4825,1.4825,1.6625,1.6625,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,4,get,1.9225000143051147,2.0425000190734863,True,2.0692742224576177e-12,0.4001200199127197,1.9224425845184057,2.0424873809086117,1.9224999999999999,1.9224999999999999,2.0425,2.0425,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,5,a,2.1024999618530273,2.122499942779541,True,1.6197307384224757e-11,3.1319515705108643,2.1024966862255567,2.122496686225557,2.1025,2.1025,2.1225,2.1225,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,6,through,2.202500104904175,2.5625,True,6.382161509346784e-13,0.25,2.194829264614116,2.557246651427563,2.1425,2.2025,2.5025,2.5625,0.06000000000000005,0.06000000000000005 --tPCytz4rww/16,-tPCytz4rww,16,7,or,3.1424999237060547,3.2225000858306885,True,2.622704137200671e-11,4.0,3.1424300748546017,3.2224545318159237,3.1425,3.1425,3.2225,3.2225,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,8,to,4.262499809265137,4.34250020980835,True,9.882743359562393e-14,0.25,4.210863172668486,4.341936445692636,3.9225,4.2625,4.3425,4.3425,0.3400000000000003,0.0 --tPCytz4rww/16,-tPCytz4rww,16,9,do,4.422500133514404,4.522500038146973,True,1.4996654959520406e-11,2.8997905254364014,4.422508837940596,4.5225076494860605,4.4225,4.4225,4.522500000000001,4.522500000000001,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,10,a,4.662499904632568,4.682499885559082,True,2.231034974820023e-11,4.0,4.6619831203681485,4.681983120368152,4.6625000000000005,4.6625000000000005,4.6825,4.6825,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,11,through,4.722499847412109,5.102499961853027,True,6.987023806748205e-13,0.25,4.722842072869845,5.102508213245117,4.7225,4.7225,5.102500000000001,5.102500000000001,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,12,cleaning,5.122499942779541,5.542500019073486,True,6.977411812388035e-13,0.25,5.123908908733183,5.544329216386563,5.1225000000000005,5.1225000000000005,5.5425,5.5425,0.0,0.0 --tPCytz4rww/16,-tPCytz4rww,16,13,of,5.582499980926514,5.862500190734863,True,1.382491083418147e-11,2.6732192039489746,5.616153867927133,5.8758597493577955,5.5825000000000005,5.8425,5.862500000000001,5.9625,0.2599999999999998,0.09999999999999964 --tPCytz4rww/16,-tPCytz4rww,16,14,the,6.262499809265137,6.502500057220459,True,1.5708401246428139e-10,4.0,6.266147318112281,6.501861147995156,6.2625,6.322500000000001,6.5025,6.5025,0.0600000000000005,0.0 --tPCytz4rww/16,-tPCytz4rww,16,15,property.,6.5625,7.122499942779541,True,1.007599602712017e-12,0.25,6.562252572276781,7.122499604680712,6.562500000000001,6.562500000000001,7.1225000000000005,7.1225000000000005,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,0,Do,0.5425000190734863,0.6424999833106995,True,6.279769673017688e-12,4.0,0.4577015282761664,0.5637259104571192,0.0425,0.5425,0.10250000000000001,0.6425,0.5,0.5399999999999999 --tPCytz4rww/18,-tPCytz4rww,18,1,so,0.762499988079071,0.9225000143051147,True,9.772651438089142e-13,4.0,0.7297637425083854,0.8808360734940381,0.5425,0.7625,0.6425,0.9225,0.21999999999999997,0.28 --tPCytz4rww/18,-tPCytz4rww,18,2,and,1.5225000381469727,1.622499942779541,True,3.8262928524386564e-13,2.566894769668579,1.3848902857859928,1.5188761214324742,0.7025,1.5225,0.9225,1.6225,0.82,0.7000000000000001 --tPCytz4rww/18,-tPCytz4rww,18,3,as,1.7024999856948853,1.7625000476837158,True,1.1480210228634324e-13,0.7701577544212341,1.6801337977048072,1.7422092862293954,1.5625,1.7025,1.6225,1.7625,0.1399999999999999,0.1399999999999999 --tPCytz4rww/18,-tPCytz4rww,18,4,well,1.8224999904632568,2.1024999618530273,True,4.317257136640315e-14,0.2896261513233185,1.8047449158844084,2.0739518481040995,1.7025,1.8225,1.9425,2.1025,0.1200000000000001,0.16000000000000014 --tPCytz4rww/18,-tPCytz4rww,18,5,you'll,2.5625,2.7825000286102295,True,4.398781686121289e-11,4.0,2.5010674825755013,2.7816743887812576,2.1025,2.5625,2.7825,2.7825,0.45999999999999996,0.0 --tPCytz4rww/18,-tPCytz4rww,18,6,also,2.822499990463257,3.0625,True,1.0343581460098283e-13,0.6939062476158142,2.8229435101245044,3.0581425038034475,2.8025,2.8425,3.0225,3.0825,0.03999999999999959,0.06000000000000005 --tPCytz4rww/18,-tPCytz4rww,18,7,want,3.1424999237060547,3.322499990463257,True,7.557796314510475e-15,0.25,3.14970832157837,3.3279337466346512,3.1425,3.2025,3.3225000000000002,3.3625,0.06000000000000005,0.03999999999999959 --tPCytz4rww/18,-tPCytz4rww,18,8,to,3.4024999141693115,3.5425000190734863,True,1.7010866456765306e-13,1.1411856412887573,3.4025001233819,3.5425000693102824,3.4025,3.4025,3.5425,3.5425,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,9,remove,3.622499942779541,3.942500114440918,True,5.3244467347482796e-14,0.3571941554546356,3.626808956541107,3.9453705442557396,3.6225,3.6825,3.9425,3.9825,0.06000000000000005,0.040000000000000036 --tPCytz4rww/18,-tPCytz4rww,18,10,any,3.9625000953674316,4.102499961853027,True,1.28017523881932e-13,0.8588143587112427,3.9746334399483496,4.104266931525665,3.9625,4.0025,4.1025,4.1025,0.04000000000000048,0.0 --tPCytz4rww/18,-tPCytz4rww,18,11,clutter,4.222499847412109,4.662499904632568,True,2.1503506847332307e-14,0.25,4.215421975132926,4.663287259592333,4.1425,4.2225,4.6625000000000005,4.6825,0.08000000000000007,0.019999999999999574 --tPCytz4rww/18,-tPCytz4rww,18,12,from,4.84250020980835,4.982500076293945,True,5.341139314683774e-13,3.5831398963928223,4.837114731944649,4.977657863914085,4.8425,4.8425,4.982500000000001,4.982500000000001,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,13,that,5.002500057220459,5.142499923706055,True,5.396275382785328e-16,0.25,4.999508925414773,5.139697183402384,5.0025,5.0025,5.1425,5.1425,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,14,you've,5.162499904632568,5.34250020980835,True,2.645488515751193e-11,4.0,5.16074853713978,5.342500661315979,5.1625000000000005,5.1625000000000005,5.3425,5.3425,0.0,0.0 --tPCytz4rww/18,-tPCytz4rww,18,15,accumulated.,5.402500152587891,6.522500038146973,True,8.827004340135336e-13,4.0,5.400102213934783,6.512170678859689,5.3825,5.4625,6.4625,6.522500000000001,0.08000000000000007,0.0600000000000005 --vxjVxOeScU/4,-vxjVxOeScU,4,0,You're,0.0024999999441206455,0.32249999046325684,True,1.5470193485619954e-12,3.368727445602417,0.0029563428740617594,0.29346729925949566,0.0025000000000000005,0.0025000000000000005,0.2025,0.4225,0.0,0.21999999999999997 --vxjVxOeScU/4,-vxjVxOeScU,4,1,going,0.5224999785423279,0.7024999856948853,True,9.600089820316349e-13,2.090477228164673,0.5220072174639193,0.7031866953765705,0.5225,0.5225,0.7025,0.7025,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,2,to,0.7225000262260437,0.7825000286102295,True,2.1261818531920912e-13,0.46298885345458984,0.7239908232403981,0.7843177483023872,0.7224999999999999,0.7224999999999999,0.7825,0.7825,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,3,talk,0.8424999713897705,1.122499942779541,True,3.087919934400933e-14,0.25,0.8273659506445371,1.0905327934600164,0.8025,0.8424999999999999,1.0425,1.1225,0.039999999999999925,0.08000000000000007 --vxjVxOeScU/4,-vxjVxOeScU,4,4,about,1.1825000047683716,1.6024999618530273,True,6.140968809853486e-13,1.3372328281402588,1.1852211848482213,1.6047478626253122,1.1824999999999999,1.1824999999999999,1.6025,1.6025,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,5,their,1.6425000429153442,2.0625,True,6.062020409437633e-15,0.25,1.6640391436621444,2.0694865731735606,1.6425,1.8825,2.0625,2.1025,0.24,0.040000000000000036 --vxjVxOeScU/4,-vxjVxOeScU,4,6,"life,",2.1024999618530273,2.5425000190734863,True,1.918054690769333e-12,4.0,2.140782386335001,2.579308133483268,2.1025,2.2025,2.5425,2.6425,0.10000000000000009,0.10000000000000009 --vxjVxOeScU/4,-vxjVxOeScU,4,7,their,2.622499942779541,3.182499885559082,True,1.0584406818343016e-14,0.25,2.7224969696046233,3.1752309276097845,2.6225,2.9625,3.1425,3.1825,0.33999999999999986,0.040000000000000036 --vxjVxOeScU/4,-vxjVxOeScU,4,8,marriage,3.242500066757202,3.822499990463257,True,9.971537823423061e-15,0.25,3.241210169650607,3.825199297988689,3.1825,3.2425,3.8225000000000002,3.8225000000000002,0.06000000000000005,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,9,and,4.462500095367432,4.5625,True,1.410027368302369e-12,3.0704190731048584,4.452967302117755,4.640251380407333,4.3825,4.5025,4.522500000000001,4.782500000000001,0.1200000000000001,0.2599999999999998 --vxjVxOeScU/4,-vxjVxOeScU,4,10,usually,4.762499809265137,5.482500076293945,True,1.6004079572001784e-12,3.4849843978881836,4.851114393665739,5.482486596984726,4.7625,5.0025,5.482500000000001,5.482500000000001,0.2400000000000002,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,11,how,5.522500038146973,5.722499847412109,True,4.592296052978451e-13,1.0,5.525640963404545,5.725972052153117,5.522500000000001,5.522500000000001,5.7225,5.7225,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,12,amazing,5.902500152587891,6.502500057220459,True,6.916904657545964e-13,1.506197452545166,5.9024997564652315,6.502508539881368,5.902500000000001,5.902500000000001,6.5025,6.5025,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,13,it,6.662499904632568,6.722499847412109,True,2.1100000002424735e-13,0.4594651460647583,6.662473520526038,6.722503456624384,6.6625000000000005,6.6625000000000005,6.7225,6.7225,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,14,is,6.84250020980835,6.902500152587891,True,6.980565807329772e-16,0.25,6.834745016706655,6.902340309975699,6.7625,6.8425,6.902500000000001,6.902500000000001,0.08000000000000007,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,15,that,6.982500076293945,7.162499904632568,True,1.3848431678741696e-13,0.30155789852142334,6.982535263961875,7.162875631455382,6.982500000000001,6.982500000000001,7.1625000000000005,7.1625000000000005,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,16,they've,7.182499885559082,7.502500057220459,True,1.4167369534467955e-12,3.0850296020507812,7.18830786025921,7.499636426891534,7.1825,7.202500000000001,7.4625,7.5025,0.020000000000000462,0.040000000000000036 --vxjVxOeScU/4,-vxjVxOeScU,4,17,stayed,7.522500038146973,7.862500190734863,True,2.276652174074134e-13,0.4957546591758728,7.5225191866663135,7.8628040961908825,7.522500000000001,7.522500000000001,7.862500000000001,7.862500000000001,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,18,together,7.922500133514404,8.482500076293945,True,7.508617208898394e-13,1.635046362876892,7.951205794831128,8.482846561288776,7.9225,7.982500000000001,8.4825,8.4825,0.0600000000000005,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,19,this,8.5625,8.782500267028809,True,1.077622973494341e-11,4.0,8.562488552433173,8.782505471531966,8.5625,8.5625,8.782499999999999,8.782499999999999,0.0,0.0 --vxjVxOeScU/4,-vxjVxOeScU,4,20,long.,8.862500190734863,9.022500038146973,True,1.3307234884873433e-14,0.25,8.84487426972022,9.02198370726463,8.8225,8.862499999999999,9.022499999999999,9.022499999999999,0.03999999999999915,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,0,So,0.6025000214576721,0.8224999904632568,True,4.930794271149064e-13,4.0,0.602499648212456,0.8224999940402619,0.6024999999999999,0.6024999999999999,0.8225,0.8225,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,1,if,0.9424999952316284,1.1425000429153442,True,3.520454937224089e-14,2.2842962741851807,0.9495859338563555,1.1379864720902402,0.9425,0.9425,1.1225,1.1425,0.0,0.020000000000000018 --wMB_hJL-3o/7,-wMB_hJL-3o,7,2,you,1.2024999856948853,1.3224999904632568,True,8.520083340831774e-15,0.5528374910354614,1.1899086153822664,1.3046461227483739,1.1624999999999999,1.2025,1.2625,1.3425,0.040000000000000036,0.08000000000000007 --wMB_hJL-3o/7,-wMB_hJL-3o,7,3,have,1.3825000524520874,1.5225000381469727,True,1.1133222165352064e-13,4.0,1.3555953310480724,1.5160767063261227,1.3225,1.3825,1.5025,1.5225,0.06000000000000005,0.020000000000000018 --wMB_hJL-3o/7,-wMB_hJL-3o,7,4,a,1.5425000190734863,1.5625,True,4.2741949302788074e-13,4.0,1.5406014206323078,1.5606014206323082,1.5225,1.5425,1.5425,1.5625,0.020000000000000018,0.020000000000000018 --wMB_hJL-3o/7,-wMB_hJL-3o,7,5,loved,1.6024999618530273,1.8224999904632568,True,2.6587540762762485e-12,4.0,1.6017163722664503,1.8216123303039624,1.6025,1.6025,1.8225,1.8225,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,6,one,1.902500033378601,2.002500057220459,True,6.939063734013153e-15,0.4502508342266083,1.896415540653591,2.000333519800293,1.8425,1.9025,1.9825,2.0025,0.06000000000000005,0.020000000000000018 --wMB_hJL-3o/7,-wMB_hJL-3o,7,7,who,2.0225000381469727,2.1624999046325684,True,2.149073570807008e-15,0.25,2.0224997179877056,2.149062987763053,2.0225,2.0225,2.1225,2.1625,0.0,0.040000000000000036 --wMB_hJL-3o/7,-wMB_hJL-3o,7,8,is,2.202500104904175,2.262500047683716,True,1.8062620152294213e-14,1.17201828956604,2.1850443997523645,2.2531201231317675,2.1425,2.2025,2.2225,2.2825,0.06000000000000005,0.06000000000000005 --wMB_hJL-3o/7,-wMB_hJL-3o,7,9,depressed,2.302500009536743,2.9024999141693115,True,8.335262128169425e-16,0.25,2.302498985661637,2.8908095885097147,2.3025,2.3025,2.8625,2.9025,0.0,0.040000000000000036 --wMB_hJL-3o/7,-wMB_hJL-3o,7,10,those,2.922499895095825,3.4024999141693115,True,2.3576904588807485e-14,1.5298203229904175,2.922649231893573,3.40250415475127,2.9225,2.9225,3.4025,3.4025,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,11,are,3.442500114440918,3.5625,True,4.111901791281625e-14,2.668064832687378,3.4426174034431227,3.5625004913768072,3.4425,3.4425,3.5625,3.5625,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,12,some,3.6024999618530273,3.762500047683716,True,6.959169839785423e-14,4.0,3.6024968346111734,3.762500000089275,3.6025,3.6025,3.7625,3.7625,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,13,of,3.7825000286102295,3.8424999713897705,True,8.037168681778416e-15,0.5215029120445251,3.782500000350712,3.8425000004085677,3.7825,3.7825,3.8425,3.8425,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,14,the,3.862499952316284,3.9625000953674316,True,9.505426603977334e-15,0.6167728900909424,3.8625000018810254,3.9625000861085344,3.8625,3.8625,3.9625,3.9625,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,15,things,4.022500038146973,4.28249979019165,True,2.8207193254314172e-14,1.8302631378173828,4.02228486995236,4.282719674458678,4.022500000000001,4.022500000000001,4.282500000000001,4.282500000000001,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,16,that,4.302499771118164,4.442500114440918,True,2.5476262894126795e-15,0.25,4.317388672273269,4.4650042095716405,4.3025,4.3425,4.442500000000001,4.5025,0.040000000000000036,0.05999999999999961 --wMB_hJL-3o/7,-wMB_hJL-3o,7,17,you,4.522500038146973,4.622499942779541,True,1.6509219537028477e-15,0.25,4.522330946791593,4.623047680043856,4.522500000000001,4.522500000000001,4.6225000000000005,4.6225000000000005,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,18,should,4.662499904632568,4.882500171661377,True,7.711374716913349e-16,0.25,4.662444498188476,4.892354900163382,4.6625000000000005,4.6625000000000005,4.8825,4.902500000000001,0.0,0.020000000000000462 --wMB_hJL-3o/7,-wMB_hJL-3o,7,19,think,4.922500133514404,5.162499904632568,True,6.412755661775252e-14,4.0,4.922610088256987,5.162437694966212,4.9225,4.9225,5.1625000000000005,5.1625000000000005,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,20,about,5.182499885559082,5.482500076293945,True,1.2760483587750254e-14,0.8279817700386047,5.182502383872996,5.482478605735311,5.1825,5.1825,5.482500000000001,5.482500000000001,0.0,0.0 --wMB_hJL-3o/7,-wMB_hJL-3o,7,21,doing.,5.502500057220459,5.942500114440918,True,5.4805903658440016e-17,0.25,5.502500038298437,5.9373994065594875,5.5025,5.5025,5.862500000000001,5.9625,0.0,0.09999999999999964 --wny0OAz3g8/1,-wny0OAz3g8,1,0,And,0.0625,0.16249999403953552,True,4.102848025515013e-14,0.25,0.06047791614036171,0.1625861933013224,0.0625,0.0625,0.1625,0.1625,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,1,because,0.2224999964237213,0.7225000262260437,True,1.2541806785823506e-12,4.0,0.22249961554408024,0.7191211883038924,0.2225,0.2225,0.6825,0.7224999999999999,0.0,0.039999999999999925 --wny0OAz3g8/1,-wny0OAz3g8,1,2,they,0.762499988079071,0.9225000143051147,True,5.174728917936577e-13,1.7670706510543823,0.7614318724074157,0.9217152166737397,0.7625,0.7625,0.9225,0.9225,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,3,couldn’t,0.9624999761581421,1.3224999904632568,True,1.1262601094796931e-12,3.8459622859954834,0.9657010687822808,1.3224324547627764,0.9624999999999999,1.0225,1.3225,1.3225,0.06000000000000005,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,4,join,1.3624999523162842,1.5824999809265137,True,9.85640280330713e-13,3.3657724857330322,1.3624999986916286,1.564069483231479,1.3625,1.3625,1.5225,1.5825,0.0,0.06000000000000005 --wny0OAz3g8/1,-wny0OAz3g8,1,5,"unions,",1.7024999856948853,2.002500057220459,True,9.332146871671859e-14,0.3186749219894409,1.7022222882961973,2.0023825938707542,1.7025,1.7025,2.0025,2.0025,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,6,many,2.1624999046325684,2.322499990463257,True,2.7132393554119005e-12,4.0,2.162479380379709,2.3224794737033037,2.1625,2.1625,2.3225,2.3225,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,7,Black,2.362499952316284,2.5824999809265137,True,1.329381618892303e-14,0.25,2.3624801795036876,2.58248096295503,2.3625,2.3625,2.5825,2.5825,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,8,workers,2.6024999618530273,2.9024999141693115,True,5.2922337329510197e-14,0.25,2.602482919026416,2.9021214065219594,2.6025,2.6025,2.9025,2.9025,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,9,couldn’t,2.922499895095825,3.202500104904175,True,6.70556167016445e-13,2.289820671081543,2.9221269767762283,3.2024805980770332,2.9225,2.9225,3.2025,3.2025,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,10,get,3.242500066757202,3.3424999713897705,True,2.4445779127639684e-14,0.25,3.2424999997519612,3.3425003277495025,3.2425,3.2425,3.3425,3.3425,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,11,manufacturing,3.362499952316284,4.002500057220459,True,1.1555361702220157e-13,0.39459341764450073,3.362711746755879,4.003943023298609,3.3625,3.3625,4.0025,4.022500000000001,0.0,0.020000000000000462 --wny0OAz3g8/1,-wny0OAz3g8,1,12,or,4.042500019073486,4.102499961853027,True,1.3667748573545357e-12,4.0,4.042501825771753,4.102502245324265,4.0425,4.0425,4.1025,4.1025,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,13,trade,4.162499904632568,4.362500190734863,True,2.0616414323870562e-14,0.25,4.157861064149656,4.35788689791687,4.1225000000000005,4.1625000000000005,4.322500000000001,4.362500000000001,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/1,-wny0OAz3g8,1,14,work,4.382500171661377,4.542500019073486,True,5.308752315378489e-15,0.25,4.3896416702266565,4.542659962096714,4.3425,4.402500000000001,4.5425,4.5425,0.0600000000000005,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,15,–,nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --wny0OAz3g8/1,-wny0OAz3g8,1,16,which,4.622499942779541,4.822500228881836,True,1.5271720282957705e-15,0.25,4.622532694722632,4.8226636212397045,4.6225000000000005,4.6225000000000005,4.822500000000001,4.822500000000001,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,17,paid,4.862500190734863,5.042500019073486,True,1.2096574826883644e-12,4.0,4.8626972941115385,5.042674934174243,4.862500000000001,4.862500000000001,5.0425,5.0425,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,18,much,5.082499980926514,5.28249979019165,True,3.8547794513690836e-13,1.3163331747055054,5.110245524980024,5.306652598313615,5.0825000000000005,5.1625000000000005,5.282500000000001,5.3425,0.08000000000000007,0.05999999999999961 --wny0OAz3g8/1,-wny0OAz3g8,1,19,better,5.382500171661377,5.702499866485596,True,1.6741794243795537e-14,0.25,5.3826235445812465,5.702633447017242,5.3825,5.3825,5.702500000000001,5.702500000000001,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,20,than,5.84250020980835,6.042500019073486,True,2.0020649634661103e-13,0.6836667656898499,5.8132744013990765,6.02222844530872,5.782500000000001,5.8425,6.0025,6.0425,0.05999999999999961,0.040000000000000036 --wny0OAz3g8/1,-wny0OAz3g8,1,21,service,6.102499961853027,6.482500076293945,True,4.1255077153946884e-13,1.4087817668914795,6.104189832791148,6.482308960960844,6.102500000000001,6.102500000000001,6.482500000000001,6.482500000000001,0.0,0.0 --wny0OAz3g8/1,-wny0OAz3g8,1,22,work,6.522500038146973,6.722499847412109,True,1.1393435023557275e-12,3.890639543533325,6.522533906902053,6.722653624861335,6.522500000000001,6.522500000000001,6.7225,6.7225,0.0,0.0 --wny0OAz3g8/0,-wny0OAz3g8,0,0,trade,0.4025000035762787,0.6825000047683716,True,6.750676840444614e-12,4.0,0.3219886038225564,0.6163855182678646,0.0425,0.4425,0.5025,0.7224999999999999,0.4,0.21999999999999997 --wny0OAz3g8/0,-wny0OAz3g8,0,1,labor,0.762499988079071,0.9825000166893005,True,2.737547818640329e-12,2.004483222961426,0.6723732232751013,0.8851323232237341,0.5225,0.7825,0.7224999999999999,0.9824999999999999,0.26,0.26 --wny0OAz3g8/0,-wny0OAz3g8,0,2,unions,1.1024999618530273,1.3424999713897705,True,1.97636991407929e-13,0.25,0.9698868422267436,1.2332032969798863,0.7625,1.1025,1.0625,1.3425,0.3400000000000001,0.28 --wny0OAz3g8/0,-wny0OAz3g8,0,3,didn’t,1.3825000524520874,1.6825000047683716,True,1.3657125560659344e-12,1.0,1.2886744936782788,1.5570604996248456,1.1425,1.3825,1.3625,1.6824999999999999,0.24,0.31999999999999984 --wny0OAz3g8/0,-wny0OAz3g8,0,4,allow,1.722499966621399,1.9424999952316284,True,7.948464342223335e-14,0.25,1.597575059821635,1.8485178438401406,1.3825,1.7625,1.6824999999999999,1.9825,0.3799999999999999,0.30000000000000004 --wny0OAz3g8/0,-wny0OAz3g8,0,5,Black,2.002500057220459,2.2825000286102295,True,1.8220999795787174e-12,1.3341753482818604,1.9280555178208747,2.2017594206098794,1.7225,2.0025,1.9425,2.2825,0.28,0.3400000000000003 --wny0OAz3g8/0,-wny0OAz3g8,0,6,workers,2.3424999713897705,2.762500047683716,True,2.0082564354973603e-13,0.25,2.278583747946665,2.7073076409000514,2.0025,2.4025,2.4225,2.8825,0.3999999999999999,0.45999999999999996 --wny0OAz3g8/0,-wny0OAz3g8,0,7,to,2.822499990463257,2.882499933242798,True,1.7081647240343306e-13,0.25,2.795627903779211,2.867191431881903,2.4825,2.9225,2.6225,2.9825,0.43999999999999995,0.3599999999999999 --wny0OAz3g8/0,-wny0OAz3g8,0,8,join,2.9625000953674316,3.202500104904175,True,2.8858948650928307e-12,2.113105535507202,2.9688315656033817,3.2127652271035108,2.9225,3.0625,3.2025,3.2425,0.14000000000000012,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,0,Which,0.0024999999441206455,0.30250000953674316,True,1.063455855494777e-12,4.0,0.002532140686589533,0.2758708790689764,0.0025000000000000005,0.0025000000000000005,0.2225,0.3025,0.0,0.07999999999999999 --wny0OAz3g8/3,-wny0OAz3g8,3,1,then,0.32249999046325684,0.48249998688697815,True,1.6761636837617916e-13,4.0,0.31656018451331996,0.4774565875920375,0.28250000000000003,0.3225,0.4425,0.4825,0.03999999999999998,0.03999999999999998 --wny0OAz3g8/3,-wny0OAz3g8,3,2,allowed,0.5425000190734863,0.8224999904632568,True,3.7471050296899316e-14,1.023621916770935,0.5441020695647343,0.8225031085039468,0.5425,0.5625,0.8225,0.8225,0.020000000000000018,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,3,the,0.9024999737739563,1.0225000381469727,True,2.521534498263695e-15,0.25,0.9020954097118318,1.022499565639348,0.9025,0.9025,1.0225,1.0225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,4,white,1.0625,1.2424999475479126,True,2.5187026130111395e-14,0.6880509853363037,1.0624995479965396,1.242500032360087,1.0625,1.0625,1.2425,1.2425,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,5,workers,1.2825000286102295,1.6825000047683716,True,9.733679113372792e-14,2.6590147018432617,1.2825008640728779,1.6824524891403205,1.2825,1.2825,1.6824999999999999,1.6824999999999999,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,6,and,1.722499966621399,1.8224999904632568,True,3.1632411533893956e-14,0.8641238808631897,1.7225790148273712,1.8226231419127565,1.7225,1.7225,1.8225,1.8225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,7,unions,1.962499976158142,2.2225000858306885,True,5.4908461878297975e-15,0.25,1.9136149388959556,2.1972650430525142,1.8825,1.9625,2.1825,2.2225,0.07999999999999985,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,8,to,2.242500066757202,2.322499990463257,True,2.5397617155523654e-14,0.6938038468360901,2.242499492552246,2.322500654512255,2.2425,2.2425,2.3225,2.3225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,9,justify,2.3424999713897705,2.702500104904175,True,2.7394013164643016e-13,4.0,2.3465313452716563,2.708943472591961,2.3425,2.4025,2.7025,2.8025,0.06000000000000005,0.10000000000000009 --wny0OAz3g8/3,-wny0OAz3g8,3,10,their,2.7825000286102295,3.002500057220459,True,3.7028682257998075e-14,1.0115374326705933,2.7863605350957643,3.0051172336331358,2.7825,2.8425,3.0025,3.0425,0.05999999999999961,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,11,decision,3.0625,3.442500114440918,True,1.0442468660338666e-14,0.28526395559310913,3.061928269798612,3.446658912342991,3.0625,3.0625,3.4425,3.4825,0.0,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,12,to,3.5225000381469727,3.622499942779541,True,1.8596247859745813e-13,4.0,3.522543175857031,3.6225387940545746,3.5225,3.5225,3.6225,3.6225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,13,not,3.762500047683716,3.922499895095825,True,6.781422211971089e-13,4.0,3.7626099906511024,3.922812910626179,3.7625,3.7625,3.9225,3.9225,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,14,allow,3.9825000762939453,4.262499809265137,True,3.618399728607356e-14,0.9884626269340515,3.9831660641663293,4.259794101175912,3.9825,3.9825,4.2225,4.2625,0.0,0.040000000000000036 --wny0OAz3g8/3,-wny0OAz3g8,3,15,black,4.302499771118164,4.622499942779541,True,5.304778426425127e-13,4.0,4.302467397441632,4.6238221223566285,4.3025,4.3025,4.6225000000000005,4.6425,0.0,0.019999999999999574 --wny0OAz3g8/3,-wny0OAz3g8,3,16,workers,4.822500228881836,5.182499885559082,True,3.043428004187096e-14,0.8313937187194824,4.7511172240657515,5.1626108171564065,4.6625000000000005,4.822500000000001,5.102500000000001,5.1825,0.16000000000000014,0.07999999999999918 --wny0OAz3g8/3,-wny0OAz3g8,3,17,into,5.202499866485596,5.382500171661377,True,1.884133819828656e-14,0.5147015452384949,5.19585098799925,5.381566976982173,5.1625000000000005,5.202500000000001,5.3825,5.3825,0.040000000000000036,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,18,their,5.422500133514404,5.762499809265137,True,4.205888736515551e-15,0.25,5.421675399709751,5.760589260048857,5.4225,5.4225,5.7625,5.7625,0.0,0.0 --wny0OAz3g8/3,-wny0OAz3g8,3,19,union,5.78249979019165,6.002500057220459,True,8.382809962674542e-13,4.0,5.782500067776348,6.002500253985618,5.782500000000001,5.782500000000001,6.0025,6.0025,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,0,And,0.0024999999441206455,0.10249999910593033,True,1.7852615273681455e-14,0.39666062593460083,0.009268933724016128,0.11272779056163183,0.0025000000000000005,0.0425,0.10250000000000001,0.1625,0.04,0.06 --wny0OAz3g8/2,-wny0OAz3g8,2,1,because,0.14249999821186066,0.48249998688697815,True,3.0093440760159407e-13,4.0,0.15613830486104457,0.4783389175803099,0.14250000000000002,0.2225,0.4425,0.4825,0.07999999999999999,0.03999999999999998 --wny0OAz3g8/2,-wny0OAz3g8,2,2,they,0.5024999976158142,0.6424999833106995,True,3.2948963314140788e-15,0.25,0.500228014631938,0.6411874151576354,0.4625,0.5025,0.6224999999999999,0.6425,0.039999999999999925,0.020000000000000018 --wny0OAz3g8/2,-wny0OAz3g8,2,3,couldn’t,0.6625000238418579,1.0425000190734863,True,1.0930697536340461e-12,4.0,0.6621778628090622,1.0338893709683967,0.6625,0.6625,0.9824999999999999,1.0425,0.0,0.06000000000000005 --wny0OAz3g8/2,-wny0OAz3g8,2,4,get,1.122499942779541,1.2424999475479126,True,9.124004449103523e-14,2.027228593826294,1.1044792090557718,1.2214760298378058,1.0025,1.1225,1.1025,1.2425,0.1200000000000001,0.1399999999999999 --wny0OAz3g8/2,-wny0OAz3g8,2,5,these,1.2625000476837158,1.4424999952316284,True,4.420967765998837e-15,0.25,1.2414761103051,1.4275101963469414,1.1225,1.2625,1.3425,1.4425,0.1399999999999999,0.09999999999999987 --wny0OAz3g8/2,-wny0OAz3g8,2,6,"jobs,",1.4824999570846558,1.622499942779541,True,9.117111257921076e-17,0.25,1.4671903258153054,1.6149842590168326,1.3825,1.4825,1.5625,1.6225,0.09999999999999987,0.06000000000000005 --wny0OAz3g8/2,-wny0OAz3g8,2,7,Black,1.6425000429153442,2.0225000381469727,True,4.183806079360863e-12,4.0,1.6425267594186002,2.0225037593548576,1.6425,1.6425,2.0225,2.0225,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,8,communities,2.2225000858306885,2.762500047683716,True,8.090853833263301e-13,4.0,2.2210278203653933,2.762521926399968,2.2225,2.2225,2.7625,2.7625,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,9,had,2.822499990463257,2.922499895095825,True,5.415088831576794e-15,0.25,2.8224974932022153,2.922509663136557,2.8225,2.8225,2.9225,2.9225,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,10,more,2.942500114440918,3.122499942779541,True,4.5007278611473855e-14,1.0,2.942523637142635,3.1224999999350835,2.9425,2.9425,3.1225,3.1225,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,11,men,3.1624999046325684,3.262500047683716,True,3.634124725866543e-14,0.8074526786804199,3.162499999504434,3.2625000000072166,3.1625,3.1625,3.2625,3.2625,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,12,out,3.2825000286102295,3.382499933242798,True,1.8249694102019344e-16,0.25,3.282708035980563,3.3827283868097444,3.2825,3.2825,3.3825,3.3825,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,13,of,3.422499895095825,3.4825000762939453,True,3.758835148519324e-13,4.0,3.4222075384617283,3.4822075403563395,3.4225,3.4225,3.4825,3.4825,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,14,"work,",3.5225000381469727,3.6624999046325684,True,3.3016277022459087e-14,0.7335764169692993,3.522500088158193,3.662500670791443,3.5225,3.5225,3.6625,3.6625,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,15,higher,3.7225000858306885,4.202499866485596,True,6.150453190229823e-15,0.25,3.724500860928217,4.153264174821034,3.7225,3.7225,4.1425,4.202500000000001,0.0,0.0600000000000005 --wny0OAz3g8/2,-wny0OAz3g8,2,16,rates,4.222499847412109,4.402500152587891,True,4.178439617420239e-14,0.9283919930458069,4.19143499158515,4.3718706695944265,4.1825,4.2225,4.362500000000001,4.402500000000001,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/2,-wny0OAz3g8,2,17,of,4.422500133514404,4.482500076293945,True,8.391063451712588e-14,1.8643791675567627,4.407059628080299,4.467172046816429,4.402500000000001,4.4225,4.4625,4.482500000000001,0.019999999999999574,0.020000000000000462 --wny0OAz3g8/2,-wny0OAz3g8,2,18,"poverty,",4.502500057220459,4.982500076293945,True,1.2409367424696255e-13,2.7571911811828613,4.502723343495389,4.982492269661619,4.5025,4.5025,4.982500000000001,4.982500000000001,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,19,and,5.082499980926514,5.182499885559082,True,4.206491823973996e-15,0.25,5.082532400809459,5.1832919282128875,5.0825000000000005,5.0825000000000005,5.1825,5.1825,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,20,more,5.28249979019165,5.522500038146973,True,7.057249478813893e-14,1.5680240392684937,5.286690563603926,5.522587796836602,5.282500000000001,5.362500000000001,5.522500000000001,5.522500000000001,0.08000000000000007,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,21,criminal,5.602499961853027,6.002500057220459,True,5.797682348313363e-14,1.2881654500961304,5.6024998811431255,6.004895968302497,5.602500000000001,5.602500000000001,6.0025,6.0025,0.0,0.0 --wny0OAz3g8/2,-wny0OAz3g8,2,22,behavior,6.042500019073486,6.502500057220459,True,8.128294044784656e-12,4.0,6.044907337430317,6.502260696408047,6.0425,6.0425,6.5025,6.5025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,0,This,0.0024999999441206455,0.26249998807907104,True,1.3640474383694312e-12,2.6988413333892822,0.003992614795843328,0.26402516901857465,0.0025000000000000005,0.0025000000000000005,0.2625,0.2625,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,1,is,0.3425000011920929,0.4025000035762787,True,5.003446875567752e-12,4.0,0.3455404024503229,0.4057867967217855,0.3425,0.3425,0.4025,0.4025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,2,what’s,0.4625000059604645,0.8224999904632568,True,1.3798032161282947e-11,4.0,0.4689256588302098,0.8257665121741922,0.4625,0.4625,0.8225,0.8225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,3,now,0.8824999928474426,1.002500057220459,True,7.477922361193157e-13,1.4795472621917725,0.884681124078631,1.005510603459454,0.8825,0.8825,1.0025,1.0025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,4,known,1.1024999618530273,1.402500033378601,True,6.791148996236271e-14,0.25,1.1045596670118656,1.4033642322333042,1.1025,1.1025,1.4025,1.4025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,5,as,1.5625,1.622499942779541,True,3.495196482499602e-13,0.691543459892273,1.5624999617181765,1.6224999909013247,1.5625,1.5625,1.6225,1.6225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,6,racial,1.7424999475479126,2.0225000381469727,True,2.5938493432124676e-14,0.25,1.7236696275904895,2.0135054081516173,1.7025,1.7425,1.9825,2.0225,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/5,-wny0OAz3g8,5,7,formation,2.0425000190734863,2.502500057220459,True,8.995870048227397e-14,0.25,2.042498883726085,2.502516094158499,2.0425,2.0425,2.5025,2.5025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,8,"theory,",2.5625,2.822499990463257,True,2.28589906335272e-13,0.45227745175361633,2.561166959632408,2.8224983990052683,2.5625,2.5625,2.8225,2.8225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,9,a,3.1024999618530273,3.122499942779541,True,1.5396341970438931e-12,3.0462491512298584,3.1025002400560107,3.122500240056011,3.1025,3.1025,3.1225,3.1225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,10,theory,3.182499885559082,3.4024999141693115,True,8.635281346317664e-14,0.25,3.182499996308189,3.4024999967473804,3.1825,3.1825,3.4025,3.4025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,11,formalized,3.422499895095825,3.922499895095825,True,3.7153637913629745e-13,0.7351047396659851,3.422499999704111,3.9223584723202434,3.4225,3.4225,3.9225,3.9225,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,12,by,3.942500114440918,4.002500057220459,True,2.2909525838887834e-12,4.0,3.942500007809117,4.002500007918179,3.9425,3.9425,4.0025,4.0025,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,13,modern,4.0625,4.362500190734863,True,4.389206148059169e-14,0.25,4.072012047116495,4.362462041170702,4.062500000000001,4.0825000000000005,4.362500000000001,4.362500000000001,0.019999999999999574,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,14,sociologists,4.382500171661377,5.022500038146973,True,7.646028992239207e-13,1.5128079652786255,4.3826825043211555,5.022640992110379,4.3825,4.3825,5.022500000000001,5.022500000000001,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,15,Michael,5.042500019073486,5.442500114440918,True,2.0837234119153863e-13,0.41227591037750244,5.047794963805033,5.442525017815331,5.0425,5.0825000000000005,5.442500000000001,5.442500000000001,0.040000000000000036,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,16,Omi,5.502500057220459,5.622499942779541,True,2.1487714765063698e-14,0.25,5.502505713472408,5.622502608037449,5.5025,5.5025,5.6225000000000005,5.6225000000000005,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,17,and,5.662499904632568,5.822500228881836,True,4.389145297212238e-12,4.0,5.662574457646359,5.822578829922969,5.6625000000000005,5.6625000000000005,5.822500000000001,5.822500000000001,0.0,0.0 --wny0OAz3g8/5,-wny0OAz3g8,5,18,Howard,5.922500133514404,6.222499847412109,True,6.393029009822693e-13,1.2648952007293701,5.921724323074964,6.218265066239246,5.9225,5.9225,6.1625000000000005,6.2225,0.0,0.05999999999999961 --wny0OAz3g8/5,-wny0OAz3g8,5,19,Winant,6.302499771118164,6.662499904632568,True,5.3509067836354784e-12,4.0,6.302641526631446,6.662501030109093,6.3025,6.3025,6.6625000000000005,6.6625000000000005,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,0,Omi,0.042500000447034836,0.18250000476837158,True,5.724248657544562e-13,2.633195638656616,0.04244340083416118,0.18338120669739688,0.0425,0.0425,0.1825,0.1825,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,1,and,0.2824999988079071,0.4424999952316284,True,2.6359350899923806e-13,1.212549090385437,0.28245764724904965,0.4467660931471465,0.28250000000000003,0.28250000000000003,0.4425,0.5025,0.0,0.05999999999999994 --wny0OAz3g8/7,-wny0OAz3g8,7,2,Winant,0.48249998688697815,0.8025000095367432,True,8.451231901795975e-12,4.0,0.49016837626293247,0.7853902575957773,0.4825,0.5225,0.7625,0.8025,0.03999999999999998,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,3,argue,0.8224999904632568,1.0625,True,7.892336957582291e-13,3.6305317878723145,0.8224757457922287,1.0676672843497057,0.8225,0.8225,1.0625,1.1025,0.0,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,4,that,1.1425000429153442,1.3224999904632568,True,6.123268938337117e-14,0.2816747725009918,1.1377341883765733,1.3195217703113347,1.0825,1.1425,1.2825,1.3225,0.06000000000000005,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,5,the,1.3424999713897705,1.4424999952316284,True,1.2730490998120927e-14,0.25,1.3440425032977281,1.4448918858978665,1.3425,1.3625,1.4425,1.4625,0.020000000000000018,0.020000000000000018 --wny0OAz3g8/7,-wny0OAz3g8,7,6,concept,1.5225000381469727,1.8624999523162842,True,2.460763797590415e-13,1.1319690942764282,1.5279296219329088,1.8634414995423771,1.5225,1.5425,1.8625,1.8625,0.020000000000000018,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,7,of,1.9225000143051147,1.9824999570846558,True,1.3178159735324768e-12,4.0,1.9226435499615355,1.9829324455007173,1.9224999999999999,1.9224999999999999,1.9825,1.9825,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,8,race,2.0824999809265137,2.242500066757202,True,3.6809178751914806e-14,0.25,2.0824184093373526,2.2424999998142665,2.0825,2.0825,2.2425,2.2425,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,9,came,2.2825000286102295,2.4625000953674316,True,1.1092636412277784e-13,0.510269284248352,2.282500009294918,2.46249946919691,2.2825,2.2825,2.4625,2.4625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,10,about,2.5225000381469727,2.702500104904175,True,1.5981041766614454e-14,0.25,2.50596479396657,2.688239210156162,2.4825,2.5225,2.6625,2.7025,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,11,as,2.742500066757202,2.822499990463257,True,1.3851884862661062e-13,0.6371966600418091,2.7414056060970298,2.820828518642153,2.7425,2.7425,2.8225,2.8225,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,12,a,2.8424999713897705,2.862499952316284,True,1.0622554051653577e-11,4.0,2.8424999992289264,2.862499999228926,2.8425,2.8425,2.8625,2.8625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,13,tool,2.922499895095825,3.122499942779541,True,2.4609784696205672e-12,4.0,2.922499514367321,3.122506056854534,2.9225,2.9225,3.1225,3.1225,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,14,to,3.202500104904175,3.262500047683716,True,2.1649702701149326e-13,0.995901882648468,3.2025010194804278,3.2625016442219503,3.2025,3.2025,3.2625,3.2625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,15,justify,3.322499990463257,3.762500047683716,True,6.311653131564621e-13,2.9034059047698975,3.3224999965992708,3.7625087559140353,3.3225000000000002,3.3225000000000002,3.7625,3.7625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,16,and,3.8424999713897705,3.9825000762939453,True,6.5197286385301235e-15,0.25,3.842662866801597,3.980611906887309,3.8425,3.8425,3.9625,3.9825,0.0,0.020000000000000018 --wny0OAz3g8/7,-wny0OAz3g8,7,17,maintain,4.002500057220459,4.462500095367432,True,2.042441863626585e-15,0.25,4.002481016298704,4.461250026333836,4.0025,4.0025,4.4625,4.4625,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,18,the,4.482500076293945,4.582499980926514,True,1.5537561471809513e-12,4.0,4.483034653975056,4.583079213530119,4.482500000000001,4.482500000000001,4.5825000000000005,4.5825000000000005,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,19,economic,4.642499923706055,5.042500019073486,True,2.8485436123597274e-13,1.3103505373001099,4.6367405754513396,5.044914535897253,4.6225000000000005,4.6425,5.0425,5.062500000000001,0.019999999999999574,0.020000000000000462 --wny0OAz3g8/7,-wny0OAz3g8,7,20,and,5.102499961853027,5.202499866485596,True,1.1146189900961348e-13,0.512732744216919,5.10250104206515,5.202626715315435,5.102500000000001,5.102500000000001,5.202500000000001,5.202500000000001,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,21,political,5.242499828338623,5.602499961853027,True,2.099547554381049e-15,0.25,5.244466340076222,5.62200941915277,5.242500000000001,5.242500000000001,5.602500000000001,5.6425,0.0,0.03999999999999915 --wny0OAz3g8/7,-wny0OAz3g8,7,22,power,5.622499942779541,5.982500076293945,True,2.1738790238409744e-13,1.0,5.653380236049285,5.989345211747652,5.6225000000000005,5.6625000000000005,5.942500000000001,5.982500000000001,0.040000000000000036,0.040000000000000036 --wny0OAz3g8/7,-wny0OAz3g8,7,23,held,6.042500019073486,6.222499847412109,True,2.3580451962790586e-14,0.25,6.05669488419283,6.2355620783067325,6.0425,6.0425,6.2225,6.2225,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,24,by,6.28249979019165,6.34250020980835,True,5.590170457070963e-14,0.25715187191963196,6.295711270231749,6.355844879545134,6.282500000000001,6.282500000000001,6.3425,6.3425,0.0,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,25,those,6.382500171661377,6.622499942779541,True,2.3303180132078216e-13,1.0719630718231201,6.397350767685814,6.637488449175456,6.3825,6.4225,6.6225000000000005,6.6225000000000005,0.040000000000000036,0.0 --wny0OAz3g8/7,-wny0OAz3g8,7,26,of,6.702499866485596,6.762499809265137,True,7.917183923701002e-15,0.25,6.714737727531676,6.779059354262793,6.6625000000000005,6.742500000000001,6.7625,6.8425,0.08000000000000007,0.08000000000000007 --wny0OAz3g8/7,-wny0OAz3g8,7,27,European,6.822500228881836,7.382500171661377,True,3.6068148251064414e-13,1.6591607332229614,6.839878020990127,7.40042919656395,6.822500000000001,7.0025,7.3825,7.442500000000001,0.17999999999999972,0.0600000000000005 --wny0OAz3g8/7,-wny0OAz3g8,7,28,descent,7.5625,7.902500152587891,True,5.926632448693958e-12,4.0,7.563703156515533,7.902497095601888,7.562500000000001,7.562500000000001,7.902500000000001,7.902500000000001,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,0,He,0.02250000089406967,0.10249999910593033,True,3.690228678188134e-12,2.9857406616210938,0.01570359411021808,0.08848826505862535,0.0025000000000000005,0.0225,0.0625,0.10250000000000001,0.019999999999999997,0.04000000000000001 --wny0OAz3g8/9,-wny0OAz3g8,9,1,explores,0.14249999821186066,0.6424999833106995,True,1.33896398742811e-12,1.0833473205566406,0.12249887573356905,0.6389535213723231,0.0825,0.1625,0.5824999999999999,0.6425,0.08,0.06000000000000005 --wny0OAz3g8/9,-wny0OAz3g8,9,2,why,0.6825000047683716,0.7825000286102295,True,9.870467290729046e-12,4.0,0.6788645232657202,0.784400961311651,0.6625,0.6825,0.7825,0.7825,0.020000000000000018,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,3,Black,0.862500011920929,1.1024999618530273,True,3.686124756124842e-12,2.9824202060699463,0.8631983314393331,1.1029731457106366,0.8624999999999999,0.8624999999999999,1.1025,1.1025,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,4,and,1.1825000047683716,1.3424999713897705,True,5.2772343786533504e-12,4.0,1.1825146996856872,1.342497699820921,1.1824999999999999,1.1824999999999999,1.3425,1.3425,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,5,White,1.402500033378601,1.5824999809265137,True,8.070731583043056e-13,0.6529977917671204,1.4024933386901064,1.5832357178381051,1.4025,1.4025,1.5825,1.5825,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,6,Americans,1.6425000429153442,2.0824999809265137,True,1.0394870999120426e-13,0.25,1.6424944033583473,2.0809849259710984,1.6425,1.6425,2.0825,2.0825,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,7,tend,2.1024999618530273,2.262500047683716,True,3.9480879909748953e-14,0.25,2.102222472329328,2.262513185028439,2.1025,2.1025,2.2625,2.2625,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,8,to,2.2825000286102295,2.3424999713897705,True,6.124948638552841e-13,0.4955657422542572,2.28261991037791,2.3426199111197468,2.2825,2.2825,2.3425,2.3425,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,9,have,2.362499952316284,2.502500057220459,True,1.3514602848277435e-12,1.0934580564498901,2.365759465357853,2.5093280317862643,2.3625,2.4025,2.5025,2.5625,0.040000000000000036,0.06000000000000005 --wny0OAz3g8/9,-wny0OAz3g8,9,10,such,2.5425000190734863,2.702500104904175,True,4.4576911606419856e-12,3.606689929962158,2.563265883639007,2.7336550703965714,2.5425,2.5825,2.7025,2.7625,0.040000000000000036,0.06000000000000005 --wny0OAz3g8/9,-wny0OAz3g8,9,11,different,2.802500009536743,3.322499990463257,True,2.1473345020520118e-13,0.25,2.8025875890661442,3.3226400145089827,2.8025,2.8025,3.3225000000000002,3.3225000000000002,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,12,outcomes,3.382499933242798,3.9024999141693115,True,8.532017323550911e-13,0.6903201341629028,3.382868179253032,3.9038235552458773,3.3825,3.3825,3.9025,3.9025,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,13,in,4.002500057220459,4.0625,True,3.568455752243904e-13,0.28872150182724,4.0023310891423645,4.062508621161237,4.0025,4.0025,4.062500000000001,4.062500000000001,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,14,terms,4.162499904632568,4.482500076293945,True,3.854301426631235e-12,3.1184909343719482,4.162879085237475,4.482499860398114,4.1625000000000005,4.1625000000000005,4.482500000000001,4.482500000000001,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,15,of,4.542500019073486,4.662499904632568,True,2.5795234749755556e-14,0.25,4.543504439499194,4.663010117094874,4.522500000000001,4.602500000000001,4.6625000000000005,4.6625000000000005,0.08000000000000007,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,16,"income,",4.722499847412109,5.082499980926514,True,1.1329377106600313e-12,0.9166527390480042,4.722783972994184,5.083036643699158,4.7225,4.7225,5.0825000000000005,5.0825000000000005,0.0,0.0 --wny0OAz3g8/9,-wny0OAz3g8,9,17,"education,",5.202499866485596,5.722499847412109,True,3.289460934544075e-12,2.661482095718384,5.20112438124031,5.617248567010778,5.202500000000001,5.202500000000001,5.5825000000000005,5.7225,0.0,0.13999999999999968 --wny0OAz3g8/9,-wny0OAz3g8,9,18,and,5.762499809265137,5.862500190734863,True,3.816548178837628e-14,0.25,5.647324687175936,5.747357974508286,5.6225000000000005,5.7625,5.7225,5.862500000000001,0.13999999999999968,0.14000000000000057 --wny0OAz3g8/9,-wny0OAz3g8,9,19,more,5.882500171661377,6.022500038146973,True,2.074348722305719e-12,1.6783424615859985,5.817082481580934,5.993166312381108,5.782500000000001,5.8825,5.982500000000001,6.022500000000001,0.09999999999999964,0.040000000000000036 --571d8cVauQ/0,-571d8cVauQ,0,0,This,0.42250001430511475,1.1425000429153442,True,1.6880530193742792e-15,0.25,0.36545353509037076,1.1497830274676015,0.0425,0.7625,1.1425,1.2025,0.72,0.05999999999999983 --571d8cVauQ/0,-571d8cVauQ,0,1,is,1.2024999856948853,1.2625000476837158,True,1.1562954370034073e-12,4.0,1.2072494672557035,1.2695928728483776,1.2025,1.2425,1.2625,1.3225,0.040000000000000036,0.06000000000000005 --571d8cVauQ/0,-571d8cVauQ,0,2,Rhett,1.3025000095367432,1.5425000190734863,True,1.0972020953467845e-13,0.5990563631057739,1.314310355709599,1.5400492489243816,1.3025,1.3425,1.5025,1.5425,0.040000000000000036,0.040000000000000036 --571d8cVauQ/0,-571d8cVauQ,0,3,"Reiger,",1.5625,1.8825000524520874,True,1.3116477305327723e-13,0.7161405682563782,1.5646701061124126,1.8860143148046669,1.5625,1.5825,1.8825,1.9025,0.020000000000000018,0.020000000000000018 --571d8cVauQ/0,-571d8cVauQ,0,4,White,1.9225000143051147,2.1424999237060547,True,7.403700862020773e-14,0.4042312800884247,1.9225171969481951,2.1425231516004852,1.9224999999999999,1.9224999999999999,2.1425,2.1425,0.0,0.0 --571d8cVauQ/0,-571d8cVauQ,0,5,Caspian,2.1624999046325684,2.5625,True,1.1581609152613859e-12,4.0,2.162561668852808,2.5634643731990616,2.1625,2.1625,2.5625,2.5625,0.0,0.0 --571d8cVauQ/0,-571d8cVauQ,0,6,Studios,2.622499942779541,3.0625,True,1.5272361825711955e-13,0.8338487148284912,2.622576837146804,3.0645311269113757,2.6225,2.6225,3.0625,3.1025,0.0,0.040000000000000036 --571d8cVauQ/0,-571d8cVauQ,0,7,on,3.1424999237060547,3.202500104904175,True,2.5477747082197633e-12,4.0,3.1424877070707216,3.2024918071032786,3.1425,3.1425,3.2025,3.2025,0.0,0.0 --571d8cVauQ/0,-571d8cVauQ,0,8,behalf,3.262500047683716,3.6024999618530273,True,7.087249352926567e-14,0.3869534730911255,3.262499999603474,3.5985239851668043,3.2625,3.2625,3.5825,3.6025,0.0,0.020000000000000018 --571d8cVauQ/0,-571d8cVauQ,0,9,of,3.622499942779541,3.682499885559082,True,2.463900936576502e-13,1.3452539443969727,3.622492865316998,3.6826183503531253,3.6225,3.6225,3.6825,3.6825,0.0,0.0 --571d8cVauQ/0,-571d8cVauQ,0,10,Expert,3.702500104904175,4.082499980926514,True,4.2966633940003107e-13,2.3459155559539795,3.718376212606498,4.069501795831513,3.7025,3.7825,4.0425,4.0825000000000005,0.08000000000000007,0.040000000000000036 --571d8cVauQ/0,-571d8cVauQ,0,11,Village.,4.102499961853027,4.982500076293945,True,2.135865269370374e-13,1.1661512851715088,4.102528781943396,4.942896755710078,4.1025,4.1025,4.902500000000001,4.982500000000001,0.0,0.08000000000000007 --571d8cVauQ/5,-571d8cVauQ,5,0,So,0.0625,0.14249999821186066,True,1.961745210721233e-10,4.0,0.061026267841565805,0.14039837035299824,0.0625,0.0625,0.14250000000000002,0.14250000000000002,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,1,very,0.5425000190734863,0.7225000262260437,True,5.542242012546161e-13,3.911118745803833,0.39099748158334563,0.7070536888457671,0.1825,0.5425,0.6825,0.7224999999999999,0.36,0.039999999999999925 --571d8cVauQ/5,-571d8cVauQ,5,2,important,0.762499988079071,1.3025000095367432,True,1.289873698302746e-13,0.9102542400360107,0.7504742650006566,1.2691000312282303,0.7224999999999999,0.7625,1.2025,1.3425,0.040000000000000036,0.14000000000000012 --571d8cVauQ/5,-571d8cVauQ,5,3,to,1.3424999713897705,1.402500033378601,True,6.078231137740422e-16,0.25,1.3274161608563455,1.3876820258440432,1.2825,1.4224999999999999,1.3425,1.4825,0.1399999999999999,0.1399999999999999 --571d8cVauQ/5,-571d8cVauQ,5,4,go,1.4225000143051147,1.4824999570846558,True,5.364711279376433e-12,4.0,1.4430355265690127,1.512146626666273,1.4224999999999999,1.6025,1.4825,1.7425,0.18000000000000016,0.26 --571d8cVauQ/5,-571d8cVauQ,5,5,out,1.6024999618530273,1.8025000095367432,True,9.859104635120963e-13,4.0,1.637724047396162,1.82970814525818,1.6025,1.9224999999999999,1.8025,2.0425,0.31999999999999984,0.24 --571d8cVauQ/5,-571d8cVauQ,5,6,to,1.9225000143051147,2.002500057220459,True,1.4170477671045928e-13,1.0,1.9843274093437095,2.066520692378383,1.9224999999999999,2.4625,2.0025,2.5625,0.54,0.56 --571d8cVauQ/5,-571d8cVauQ,5,7,the,2.4625000953674316,2.6424999237060547,True,4.532087053733813e-13,3.1982598304748535,2.464986666672805,2.648123686812614,2.4225,2.6225,2.5625,2.7225,0.20000000000000018,0.16000000000000014 --571d8cVauQ/5,-571d8cVauQ,5,8,"locations,",2.742500066757202,3.382499933242798,True,2.9229296392117854e-13,2.0626895427703857,2.7477243046229103,3.379244935643703,2.7425,2.8025,3.3625,3.3825,0.06000000000000005,0.020000000000000018 --571d8cVauQ/5,-571d8cVauQ,5,9,walk,3.4024999141693115,3.742500066757202,True,8.698149042223296e-16,0.25,3.405197916129465,3.7593203164399864,3.4025,3.4025,3.7225,4.022500000000001,0.0,0.3000000000000007 --571d8cVauQ/5,-571d8cVauQ,5,10,"around,",3.762500047683716,4.082499980926514,True,4.603956444055625e-14,0.32489776611328125,3.7894646126851903,4.113669860878586,3.7625,4.1425,4.0825000000000005,4.482500000000001,0.3799999999999999,0.40000000000000036 --571d8cVauQ/5,-571d8cVauQ,5,11,talk,4.142499923706055,4.442500114440918,True,1.35541764986237e-13,0.9565081000328064,4.190362867190777,4.484542398894459,4.1425,4.5025,4.442500000000001,4.9225,0.3600000000000003,0.47999999999999954 --571d8cVauQ/5,-571d8cVauQ,5,12,with,4.502500057220459,4.922500133514404,True,8.38819099358186e-14,0.5919483304023743,4.541720300661576,4.940244070441893,4.5025,4.9625,4.9225,5.102500000000001,0.45999999999999996,0.1800000000000006 --571d8cVauQ/5,-571d8cVauQ,5,13,the,5.002500057220459,5.102499961853027,True,2.0982386767193044e-14,0.25,5.018430928164901,5.1207677393025,5.0025,5.1625000000000005,5.102500000000001,5.2625,0.16000000000000014,0.15999999999999925 --571d8cVauQ/5,-571d8cVauQ,5,14,owners,5.162499904632568,5.482500076293945,True,3.260156359284616e-13,2.3006680011749268,5.183334041083281,5.504277901364218,5.1625000000000005,5.3425,5.482500000000001,5.602500000000001,0.17999999999999972,0.1200000000000001 --571d8cVauQ/5,-571d8cVauQ,5,15,if,5.502500057220459,5.5625,True,1.7678204942973808e-16,0.25,5.535479301056968,5.596502741603346,5.5025,5.6625000000000005,5.562500000000001,5.7225,0.16000000000000014,0.15999999999999925 --571d8cVauQ/5,-571d8cVauQ,5,16,you,5.582499980926514,5.722499847412109,True,4.4787060209583104e-14,0.3160589337348938,5.624735476439397,5.761850848637931,5.5825000000000005,5.742500000000001,5.7225,5.862500000000001,0.16000000000000014,0.14000000000000057 --571d8cVauQ/5,-571d8cVauQ,5,17,"can,",5.742499828338623,5.862500190734863,True,4.924350383299532e-14,0.3475077152252197,5.7829325715282085,5.904529793030309,5.742500000000001,5.8825,5.862500000000001,6.0825000000000005,0.13999999999999968,0.21999999999999975 --571d8cVauQ/5,-571d8cVauQ,5,18,tell,5.882500171661377,6.142499923706055,True,2.1348444930249788e-13,1.506543755531311,5.9288363923069145,6.188435906968508,5.8825,6.1625000000000005,6.142500000000001,6.5025,0.28000000000000025,0.35999999999999943 --571d8cVauQ/5,-571d8cVauQ,5,19,them,6.482500076293945,6.682499885559082,True,1.303422159700668e-13,0.9198152422904968,6.374882165415015,6.68920056196025,6.1625000000000005,6.562500000000001,6.642500000000001,6.742500000000001,0.40000000000000036,0.09999999999999964 --571d8cVauQ/5,-571d8cVauQ,5,20,what,6.722499847412109,6.882500171661377,True,4.340288640102517e-14,0.3062909245491028,6.74280020146315,6.90711834793619,6.6625000000000005,6.782500000000001,6.822500000000001,6.942500000000001,0.1200000000000001,0.1200000000000001 --571d8cVauQ/5,-571d8cVauQ,5,21,you're,6.922500133514404,7.162499904632568,True,6.8854756965497366e-12,4.0,6.949658840062754,7.201229129569963,6.862500000000001,6.982500000000001,7.1625000000000005,7.1625000000000005,0.1200000000000001,0.0 --571d8cVauQ/5,-571d8cVauQ,5,22,"doing,",7.182499885559082,7.422500133514404,True,2.756355798987087e-13,1.9451396465301514,7.2233293105612,7.466068313841497,7.1825,7.1825,7.4225,7.4225,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,23,build,7.682499885559082,7.902500152587891,True,4.2132330203880144e-14,0.29732468724250793,7.713083083513668,7.964945886320176,7.642500000000001,7.6825,7.902500000000001,7.9625,0.03999999999999915,0.05999999999999961 --571d8cVauQ/5,-571d8cVauQ,5,24,a,7.942500114440918,7.962500095367432,True,3.905624535263143e-14,0.27561700344085693,8.004457849233917,8.024457849233954,7.942500000000001,7.982500000000001,7.9625,8.0025,0.040000000000000036,0.03999999999999915 --571d8cVauQ/5,-571d8cVauQ,5,25,little,7.982500076293945,8.262499809265137,True,3.241799393489085e-14,0.25,8.047286338490512,8.319486249797016,7.982500000000001,8.0425,8.2625,8.3025,0.05999999999999961,0.040000000000000924 --571d8cVauQ/5,-571d8cVauQ,5,26,"excitement,",8.282500267028809,8.862500190734863,True,3.179512656306151e-14,0.25,8.340643589235283,8.907759359152092,8.282499999999999,8.3425,8.8225,8.862499999999999,0.0600000000000005,0.03999999999999915 --571d8cVauQ/5,-571d8cVauQ,5,27,put,9.422499656677246,9.522500038146973,True,7.793622653707102e-14,0.5499901175498962,9.448525366526367,9.558693789160388,9.4225,9.4225,9.522499999999999,9.522499999999999,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,28,a,9.5625,9.582500457763672,True,9.874617399804886e-13,4.0,9.599558257830385,9.619558257830356,9.5625,9.5825,9.5825,9.6025,0.019999999999999574,0.019999999999999574 --571d8cVauQ/5,-571d8cVauQ,5,29,little,9.6225004196167,9.882499694824219,True,8.713415053504189e-14,0.6148991584777832,9.657902881779448,9.933980702020806,9.6225,9.6225,9.8825,9.8825,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,30,glitz,9.982500076293945,10.182499885559082,True,2.0400179809310082e-11,4.0,9.989943619156207,10.19563550900826,9.9225,9.9825,10.1225,10.1825,0.0600000000000005,0.05999999999999872 --571d8cVauQ/5,-571d8cVauQ,5,31,on,10.282500267028809,10.382499694824219,True,1.639427634446411e-12,4.0,10.275149544087014,10.392708390362388,10.1625,10.282499999999999,10.3025,10.3825,0.11999999999999922,0.08000000000000007 --571d8cVauQ/5,-571d8cVauQ,5,32,there,10.422499656677246,10.6225004196167,True,4.004103296216359e-14,0.2825665771961212,10.454077564626562,10.673171811912887,10.362499999999999,10.4225,10.6225,10.6225,0.0600000000000005,0.0 --571d8cVauQ/5,-571d8cVauQ,5,33,and,10.642499923706055,11.322500228881836,True,8.731728447924914e-13,4.0,10.912128782316048,11.369185804372504,10.6425,11.2625,11.3225,11.362499999999999,0.6199999999999992,0.03999999999999915 --571d8cVauQ/5,-571d8cVauQ,5,34,let,11.542499542236328,11.662500381469727,True,4.009867524829014e-14,0.28297334909439087,11.569560975686585,11.698227520663279,11.5425,11.5425,11.6625,11.6625,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,35,everybody,11.702500343322754,12.082500457763672,True,9.238042188858264e-13,4.0,11.740536622390566,12.121806518011748,11.7025,11.7025,12.0825,12.0825,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,36,knows,12.102499961853027,12.302499771118164,True,9.206563734032863e-14,0.6497002840042114,12.144266740138423,12.349209984328715,12.1025,12.1025,12.3025,12.3225,0.0,0.019999999999999574 --571d8cVauQ/5,-571d8cVauQ,5,37,this,12.762499809265137,12.942500114440918,True,3.215953165661839e-13,2.2694740295410156,12.73313275369772,12.95957414691892,12.3225,12.7625,12.8825,12.942499999999999,0.4399999999999995,0.05999999999999872 --571d8cVauQ/5,-571d8cVauQ,5,38,is,12.982500076293945,13.042499542236328,True,4.4049882014576824e-13,3.108567237854004,13.019727126882461,13.080592199899773,12.9825,12.9825,13.0425,13.0425,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,39,a,13.162500381469727,13.182499885559082,True,3.718831232540909e-12,4.0,13.198762296608688,13.218762296608658,13.1625,13.1625,13.1825,13.1825,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,40,very,13.322500228881836,13.542499542236328,True,1.494705916113212e-13,1.0548027753829956,13.35522890003284,13.57665212442405,13.3225,13.3225,13.5425,13.5425,0.0,0.0 --571d8cVauQ/5,-571d8cVauQ,5,41,valuable,13.682499885559082,14.442500114440918,True,9.463182190985742e-13,4.0,13.714516252757837,14.474684869756821,13.6825,13.6825,14.442499999999999,14.5025,0.0,0.0600000000000005 --571d8cVauQ/5,-571d8cVauQ,5,42,spot.,14.662500381469727,14.962499618530273,True,1.3657936543884364e-12,4.0,14.673377195931495,14.96784942174552,14.6625,14.6625,14.9025,15.0425,0.0,0.14000000000000057 --I_e4mIh0yE/1,-I_e4mIh0yE,1,0,In,0.16249999403953552,0.24250000715255737,True,8.721677351684887e-13,4.0,0.1142069655201318,0.21499044195057157,0.0625,0.1625,0.1825,0.2425,0.1,0.06 --I_e4mIh0yE/1,-I_e4mIh0yE,1,1,the,0.8224999904632568,0.9624999761581421,True,4.6919015032767866e-14,0.4219171106815338,0.779575729691009,0.9513972402826147,0.28250000000000003,0.8424999999999999,0.8624999999999999,0.9624999999999999,0.5599999999999998,0.09999999999999998 --I_e4mIh0yE/1,-I_e4mIh0yE,1,2,United,1.0225000381469727,1.2825000286102295,True,1.241436966246956e-13,1.116356611251831,1.022454650069054,1.2824630944967872,1.0225,1.0225,1.2825,1.2825,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,3,States,1.3025000095367432,1.5824999809265137,True,2.2734993226268757e-15,0.25,1.3219094885294929,1.6019038732024349,1.3025,1.3425,1.5825,1.6225,0.040000000000000036,0.040000000000000036 --I_e4mIh0yE/1,-I_e4mIh0yE,1,4,we,1.6425000429153442,1.7424999475479126,True,5.195034398323972e-13,4.0,1.6425025929936214,1.7425022697843806,1.6425,1.6425,1.7425,1.7425,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,5,don't,1.8624999523162842,2.0824999809265137,True,3.625834965603758e-09,4.0,1.8625138297841282,2.0820620731977453,1.8625,1.8625,2.0825,2.0825,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,6,punish,2.202500104904175,2.502500057220459,True,9.826499016611992e-14,0.8836435079574585,2.2024907371313374,2.5025001246537304,2.2025,2.2025,2.5025,2.5025,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,7,failure,2.5625,3.0824999809265137,True,3.813583563522238e-14,0.342934787273407,2.57581736001806,3.080994991799556,2.5625,2.6425,3.0825,3.0825,0.08000000000000007,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,8,to,3.5225000381469727,3.622499942779541,True,3.0746702384641666e-13,2.764883279800415,3.52221470372663,3.622506415239828,3.5225,3.5225,3.6225,3.6225,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,9,the,3.682499885559082,3.7825000286102295,True,3.3432115989452324e-14,0.30063679814338684,3.6824927941693253,3.782492797896148,3.6825,3.6825,3.7825,3.7825,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,10,degree,3.802500009536743,4.162499904632568,True,6.262374109946081e-14,0.5631411671638489,3.802499999654366,4.162501191750939,3.8025,3.8025,4.1625000000000005,4.1625000000000005,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,11,to,4.302499771118164,4.362500190734863,True,8.0105626992788e-13,4.0,4.291511506534855,4.352642714953695,4.2625,4.3025,4.322500000000001,4.362500000000001,0.040000000000000036,0.040000000000000036 --I_e4mIh0yE/1,-I_e4mIh0yE,1,12,which,4.422500133514404,4.662499904632568,True,2.4741273347552925e-14,0.25,4.405226899791058,4.654286753863474,4.3425,4.4225,4.6225000000000005,4.6625000000000005,0.08000000000000007,0.040000000000000036 --I_e4mIh0yE/1,-I_e4mIh0yE,1,13,failure,4.742499828338623,5.28249979019165,True,2.8274669931771523e-13,2.5425868034362793,4.739944443800792,5.282521785832095,4.7225,4.742500000000001,5.282500000000001,5.282500000000001,0.020000000000000462,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,14,is,5.34250020980835,5.402500152587891,True,4.104637975539151e-14,0.369107723236084,5.34226851016023,5.4052279359374715,5.3425,5.3425,5.402500000000001,5.442500000000001,0.0,0.040000000000000036 --I_e4mIh0yE/1,-I_e4mIh0yE,1,15,punished,5.502500057220459,5.902500152587891,True,7.313151684706573e-14,0.6576318740844727,5.500985614179578,5.901454375232485,5.5025,5.5025,5.902500000000001,5.902500000000001,0.0,0.0 --I_e4mIh0yE/1,-I_e4mIh0yE,1,16,in,6.302499771118164,6.422500133514404,True,2.6584829715230185e-13,2.3906288146972656,6.284817034685479,6.406331516286906,6.1625000000000005,6.322500000000001,6.322500000000001,6.4225,0.16000000000000014,0.09999999999999964 --I_e4mIh0yE/1,-I_e4mIh0yE,1,17,Europe.,6.662499904632568,6.922500133514404,True,5.098422226935961e-13,4.0,6.656895370927335,6.922440434999503,6.642500000000001,6.6625000000000005,6.9225,6.9225,0.019999999999999574,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,0,It's,0.5824999809265137,0.7225000262260437,True,3.472087617417685e-10,4.0,0.5822805233935374,0.7211801408748947,0.5824999999999999,0.5824999999999999,0.7025,0.7224999999999999,0.0,0.019999999999999907 --I_e4mIh0yE/3,-I_e4mIh0yE,3,1,perceived,0.9225000143051147,1.4824999570846558,True,1.4726222086376695e-13,0.25,0.8894760335439114,1.4662801504889216,0.7224999999999999,0.9225,1.4425,1.4825,0.20000000000000007,0.040000000000000036 --I_e4mIh0yE/3,-I_e4mIh0yE,3,2,that,1.5425000190734863,1.7424999475479126,True,2.1773252959714912e-14,0.25,1.5118009805496575,1.7076001797166942,1.4625,1.5425,1.6625,1.7425,0.08000000000000007,0.07999999999999985 --I_e4mIh0yE/3,-I_e4mIh0yE,3,3,you,1.7625000476837158,1.8624999523162842,True,4.304102037343098e-14,0.25,1.7445763600944204,1.8591816436803994,1.7225,1.7625,1.8225,1.8825,0.040000000000000036,0.06000000000000005 --I_e4mIh0yE/3,-I_e4mIh0yE,3,4,have,1.9225000143051147,2.1424999237060547,True,3.9425008396820616e-12,4.0,1.923373173881217,2.165102454180477,1.9224999999999999,1.9224999999999999,2.1425,2.1825,0.0,0.040000000000000036 --I_e4mIh0yE/3,-I_e4mIh0yE,3,5,actually,2.1624999046325684,2.6424999237060547,True,4.010823791145146e-12,4.0,2.1990408804792785,2.68428186359062,2.1625,2.2225,2.6425,2.8025,0.06000000000000005,0.16000000000000014 --I_e4mIh0yE/3,-I_e4mIh0yE,3,6,learned,2.7825000286102295,3.122499942779541,True,4.510655087289206e-13,0.5370558500289917,2.781821592938269,3.1219551838308943,2.6625,2.8425,3.0025,3.1625,0.17999999999999972,0.16000000000000014 --I_e4mIh0yE/3,-I_e4mIh0yE,3,7,"something,",3.2225000858306885,3.702500104904175,True,1.2301406638118295e-12,1.4646525382995605,3.210776660611705,3.685039851830411,3.0625,3.2225,3.4425,3.7025,0.16000000000000014,0.26000000000000023 --I_e4mIh0yE/3,-I_e4mIh0yE,3,8,over,3.762500047683716,3.9625000953674316,True,6.062504827580167e-14,0.25,3.7497129407356837,3.9509308917543975,3.5025,3.8225000000000002,3.7025,4.0425,0.3200000000000003,0.3400000000000003 --I_e4mIh0yE/3,-I_e4mIh0yE,3,9,the,3.9825000762939453,4.082499980926514,True,7.674462736313725e-14,0.25,3.988517118629397,4.105443941306858,3.8225000000000002,4.1225000000000005,4.0425,4.362500000000001,0.30000000000000027,0.3200000000000003 --I_e4mIh0yE/3,-I_e4mIh0yE,3,10,course,4.262499809265137,4.542500019073486,True,1.2368673793505813e-12,1.4726616144180298,4.274087408439977,4.559334574473354,4.2625,4.3825,4.5425,4.6825,0.1200000000000001,0.13999999999999968 --I_e4mIh0yE/3,-I_e4mIh0yE,3,11,of,4.622499942779541,4.682499885559082,True,4.1304034303045467e-13,0.491781622171402,4.680345626946339,4.744915525012013,4.6225000000000005,5.1625000000000005,4.6825,5.2625,0.54,0.5800000000000001 --I_e4mIh0yE/3,-I_e4mIh0yE,3,12,your,5.242499828338623,5.5625,True,1.1803501752394308e-11,4.0,5.248953696227165,5.565865409986523,5.1625000000000005,5.4625,5.562500000000001,5.6225000000000005,0.2999999999999998,0.05999999999999961 --I_e4mIh0yE/3,-I_e4mIh0yE,3,13,"carreer,",5.682499885559082,6.022500038146973,True,5.404035292172482e-12,4.0,5.67524877133456,6.022628998062419,5.6625000000000005,5.6825,6.022500000000001,6.022500000000001,0.019999999999999574,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,14,and,6.042500019073486,6.162499904632568,True,1.5327623068342455e-12,1.824965476989746,6.051594198076872,6.163218343693183,6.0425,6.062500000000001,6.1625000000000005,6.1625000000000005,0.020000000000000462,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,15,that,6.222499847412109,6.362500190734863,True,8.04463049994264e-13,0.9578244686126709,6.223196086715864,6.366824761858561,6.2225,6.2225,6.362500000000001,6.362500000000001,0.0,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,16,you,6.482500076293945,6.742499828338623,True,8.753082893914188e-13,1.042175531387329,6.485121347497934,6.727387463759083,6.482500000000001,6.482500000000001,6.602500000000001,6.782500000000001,0.0,0.17999999999999972 --I_e4mIh0yE/3,-I_e4mIh0yE,3,17,won't,6.902500152587891,7.0625,True,6.68682470505999e-11,4.0,6.850043955382938,7.036261507491488,6.7225,6.902500000000001,7.0025,7.0825000000000005,0.1800000000000006,0.08000000000000007 --I_e4mIh0yE/3,-I_e4mIh0yE,3,18,make,7.082499980926514,7.462500095367432,True,4.19843760451899e-12,4.0,7.142215541761871,7.4625317001592855,7.0825000000000005,7.202500000000001,7.4625,7.4625,0.1200000000000001,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,19,the,7.702499866485596,7.942500114440918,True,2.8825946078898934e-13,0.34321272373199463,7.711799725224728,7.948721670810593,7.702500000000001,7.7225,7.942500000000001,7.9625,0.019999999999999574,0.019999999999999574 --I_e4mIh0yE/3,-I_e4mIh0yE,3,20,mistake,8.0024995803833,8.422499656677246,True,2.3740891522648633e-13,0.2826681435108185,8.001170899583583,8.421542106933641,8.0025,8.0025,8.4225,8.4225,0.0,0.0 --I_e4mIh0yE/3,-I_e4mIh0yE,3,21,twice.,8.662500381469727,8.962499618530273,True,1.2501067889192363e-14,0.25,8.587039530663365,8.962645923111332,8.4825,8.6825,8.9625,8.9625,0.1999999999999993,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,0,"Because,",0.02250000089406967,0.36250001192092896,True,1.475930685448327e-14,0.25,0.022500000004257795,0.3625472694174722,0.0225,0.0225,0.3625,0.3625,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,1,if,0.4025000035762787,0.4625000059604645,True,2.200777673164525e-13,1.5552595853805542,0.4026353313760795,0.46269966085687303,0.4025,0.4025,0.4625,0.4625,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,2,they,0.5224999785423279,0.7825000286102295,True,1.5841776675185049e-13,1.1195167303085327,0.5250717282237793,0.7824865774462136,0.5225,0.5625,0.7825,0.7825,0.040000000000000036,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,3,are,1.0225000381469727,1.162500023841858,True,2.8222001084298072e-14,0.25,0.9993191173234123,1.1572935816657954,0.9425,1.0225,1.1225,1.2425,0.07999999999999996,0.11999999999999988 --UacrmKiTn4/10,-UacrmKiTn4,10,4,not,1.1825000047683716,1.3025000095367432,True,1.917614504687304e-12,4.0,1.212195552950287,1.3411265228208296,1.1824999999999999,1.3825,1.3025,1.6225,0.20000000000000018,0.32000000000000006 --UacrmKiTn4/10,-UacrmKiTn4,10,5,in,1.5225000381469727,1.622499942779541,True,3.806191202059689e-13,2.6897835731506348,1.5472873413699313,1.6685377212106935,1.5225,1.7025,1.6225,2.2225,0.17999999999999994,0.6000000000000001 --UacrmKiTn4/10,-UacrmKiTn4,10,6,any,2.442500114440918,2.5625,True,4.2097759739984086e-13,2.9749913215637207,2.4295782593879096,2.562499437343288,2.2025,2.4425,2.5625,2.5625,0.23999999999999977,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,7,way,2.6424999237060547,2.7825000286102295,True,1.2459322039793524e-13,0.8804833292961121,2.642499960569166,2.782499959619364,2.6425,2.6425,2.7825,2.7825,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,8,held,2.882499933242798,3.0625,True,1.871778990483741e-12,4.0,2.882499663382942,3.06249970189482,2.8825,2.8825,3.0625,3.0625,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,9,accountable,3.1024999618530273,3.8424999713897705,True,2.4792566274706856e-14,0.25,3.1034985154384263,3.843995047957288,3.1025,3.1025,3.8425,3.8625,0.0,0.020000000000000018 --UacrmKiTn4/10,-UacrmKiTn4,10,10,for,3.9625000953674316,4.142499923706055,True,8.828910909655652e-13,4.0,3.9618912743665837,4.1422515787201855,3.9625,3.9625,4.1425,4.1425,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,11,reading,4.202499866485596,4.522500038146973,True,5.040660204231988e-14,0.35621657967567444,4.217612209195304,4.533819065539573,4.202500000000001,4.282500000000001,4.522500000000001,4.5825000000000005,0.08000000000000007,0.05999999999999961 --UacrmKiTn4/10,-UacrmKiTn4,10,12,the,4.602499961853027,4.702499866485596,True,4.6737817744691226e-14,0.3302897810935974,4.602499650444098,4.702499657454214,4.602500000000001,4.602500000000001,4.702500000000001,4.702500000000001,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,13,material,4.722499847412109,5.322500228881836,True,2.7899259508537555e-14,0.25,4.722500025503899,5.3163663313084415,4.7225,4.7225,5.2625,5.322500000000001,0.0,0.0600000000000005 --UacrmKiTn4/10,-UacrmKiTn4,10,14,they,5.34250020980835,6.122499942779541,True,1.6694450184241705e-14,0.25,5.346698826778056,6.105480164175808,5.3425,5.3425,6.062500000000001,6.1225000000000005,0.0,0.05999999999999961 --UacrmKiTn4/10,-UacrmKiTn4,10,15,won't,6.142499923706055,6.302499771118164,True,6.240683664282543e-11,4.0,6.1425008497064475,6.309788862779577,6.142500000000001,6.142500000000001,6.3025,6.3425,0.0,0.040000000000000036 --UacrmKiTn4/10,-UacrmKiTn4,10,16,do,6.402500152587891,6.502500057220459,True,8.948922874431331e-13,4.0,6.4025000340549125,6.502500042623035,6.402500000000001,6.402500000000001,6.5025,6.5025,0.0,0.0 --UacrmKiTn4/10,-UacrmKiTn4,10,17,it.,6.622499942779541,6.682499885559082,True,2.6122733262547854e-14,0.25,6.623106947941723,6.688042407993376,6.6225000000000005,6.6225000000000005,6.6825,6.702500000000001,0.0,0.020000000000000462 --UacrmKiTn4/4,-UacrmKiTn4,4,0,Just,0.0024999999441206455,0.6424999833106995,True,4.562072507957593e-12,4.0,0.004947655251735573,0.6504811942238563,0.0025000000000000005,0.0225,0.6425,0.7025,0.019999999999999997,0.06000000000000005 --UacrmKiTn4/4,-UacrmKiTn4,4,1,given,0.762499988079071,1.002500057220459,True,6.256041691632408e-14,1.8532153367996216,0.7364139253098347,0.9636047049601082,0.6825,0.7625,0.8825,1.0025,0.07999999999999996,0.12 --UacrmKiTn4/4,-UacrmKiTn4,4,2,to,1.0225000381469727,1.0824999809265137,True,1.5428832189179169e-15,0.25,1.0025937583992537,1.0639978885987913,0.9425,1.0225,1.0025,1.0825,0.07999999999999996,0.08000000000000007 --UacrmKiTn4/4,-UacrmKiTn4,4,3,their,1.1024999618530273,1.3224999904632568,True,2.0475887269816778e-16,0.25,1.0908284753739195,1.3135099105503243,1.0225,1.1025,1.2225,1.3225,0.08000000000000007,0.10000000000000009 --UacrmKiTn4/4,-UacrmKiTn4,4,4,own,1.3825000524520874,1.502500057220459,True,1.73095121474621e-15,0.25,1.37705306367968,1.4988073905995172,1.3225,1.3825,1.4625,1.5025,0.06000000000000005,0.040000000000000036 --UacrmKiTn4/4,-UacrmKiTn4,4,5,devices,1.5425000190734863,2.122499942779541,True,4.961841416797723e-14,1.4698368310928345,1.5408861101749405,2.117455888833504,1.5425,1.5425,2.0825,2.1225,0.0,0.040000000000000036 --UacrmKiTn4/4,-UacrmKiTn4,4,6,they,2.202500104904175,2.3424999713897705,True,6.740486261595097e-14,1.9967213869094849,2.2006234143728935,2.341308883214659,2.2025,2.2025,2.3425,2.3425,0.0,0.0 --UacrmKiTn4/4,-UacrmKiTn4,4,7,are,2.362499952316284,2.4825000762939453,True,3.205953569025005e-13,4.0,2.3615948075228883,2.481585319634036,2.3625,2.3625,2.4825,2.4825,0.0,0.0 --UacrmKiTn4/4,-UacrmKiTn4,4,8,not,2.5225000381469727,2.682499885559082,True,5.5410154410759013e-14,1.6414045095443726,2.52250550991515,2.6825054463052336,2.5225,2.5225,2.6825,2.6825,0.0,0.0 --UacrmKiTn4/4,-UacrmKiTn4,4,9,going,2.762500047683716,3.0425000190734863,True,1.7897125160993774e-14,0.5301631689071655,2.743302852447095,3.0226983210219336,2.7225,2.7625,3.0025,3.0425,0.040000000000000036,0.040000000000000036 --UacrmKiTn4/4,-UacrmKiTn4,4,10,to,3.0824999809265137,3.1424999237060547,True,4.2949559449852355e-15,0.25,3.0824999856606508,3.1425000044551568,3.0825,3.0825,3.1425,3.1425,0.0,0.0 --UacrmKiTn4/4,-UacrmKiTn4,4,11,read,3.2225000858306885,3.422499895095825,True,3.4303006890215463e-15,0.25,3.222702148003313,3.418441830412285,3.2225,3.2425,3.3825,3.4225,0.020000000000000018,0.040000000000000036 --UacrmKiTn4/4,-UacrmKiTn4,4,12,the,3.442500114440918,3.5425000190734863,True,1.6336893860283141e-15,0.25,3.4691372489699823,3.5816860218914264,3.4425,3.6625,3.5425,3.8225000000000002,0.2200000000000002,0.28000000000000025 --UacrmKiTn4/4,-UacrmKiTn4,4,13,textbook.,3.6624999046325684,4.142499923706055,True,1.503377094038208e-13,4.0,3.647318800103063,4.282380916193305,3.5625,3.9025,4.0825000000000005,4.6625000000000005,0.33999999999999986,0.5800000000000001 --hnBHBN8p5A/7,-hnBHBN8p5A,7,0,Picture,0.16249999403953552,0.6424999833106995,True,2.388505246451317e-13,1.0,0.16093215462874963,0.6492774175568524,0.1625,0.1625,0.6425,0.7025,0.0,0.06000000000000005 --hnBHBN8p5A/7,-hnBHBN8p5A,7,1,"this,",0.6825000047683716,1.002500057220459,True,1.7479208185011696e-12,4.0,0.6967693237113916,1.0043499540624952,0.6825,0.7424999999999999,1.0025,1.0025,0.05999999999999994,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,2,before,1.2625000476837158,1.8825000524520874,True,3.712301462326789e-14,0.25,1.2568305382584097,1.8793181771780305,1.1624999999999999,1.2625,1.8225,1.8825,0.10000000000000009,0.06000000000000005 --hnBHBN8p5A/7,-hnBHBN8p5A,7,3,we,1.9824999570846558,2.122499942779541,True,3.7951361346079404e-13,1.5889167785644531,1.9788165575745025,2.1177397110667036,1.9825,1.9825,2.1225,2.1225,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,4,actually,2.2225000858306885,3.682499885559082,True,2.084581151359094e-13,0.8727555274963379,2.217809149643507,3.6792332685589186,2.2225,2.2225,3.6225,3.6825,0.0,0.06000000000000005 --hnBHBN8p5A/7,-hnBHBN8p5A,7,5,start,3.742500066757202,4.082499980926514,True,1.9712341568084435e-12,4.0,3.7424998928545663,4.082502830606967,3.7425,3.7425,4.0825000000000005,4.0825000000000005,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,6,"hacking,",4.222499847412109,4.702499866485596,True,2.2997982980347588e-14,0.25,4.221812245247405,4.727831407783964,4.2225,4.242500000000001,4.702500000000001,4.782500000000001,0.020000000000000462,0.08000000000000007 --hnBHBN8p5A/7,-hnBHBN8p5A,7,7,we’re,4.922500133514404,5.142499923706055,True,1.77046884097809e-11,4.0,4.920433348583369,5.142277923757999,4.9225,4.9225,5.1425,5.1425,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,8,in,5.182499885559082,5.242499828338623,True,3.179557853984216e-13,1.3311915397644043,5.182457696430692,5.242471813790377,5.1825,5.1825,5.242500000000001,5.242500000000001,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,9,a,5.28249979019165,5.302499771118164,True,5.193478754553704e-15,0.25,5.284921857502635,5.3049218575026345,5.282500000000001,5.3025,5.3025,5.322500000000001,0.019999999999999574,0.020000000000000462 --hnBHBN8p5A/7,-hnBHBN8p5A,7,10,room,5.34250020980835,5.502500057220459,True,4.2035475374493404e-15,0.25,5.342924994112858,5.502498287036115,5.3425,5.3425,5.5025,5.5025,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,11,formal,5.522500038146973,5.902500152587891,True,1.3692799877892264e-13,0.5732790231704712,5.522499935669881,5.902497519672031,5.522500000000001,5.522500000000001,5.902500000000001,5.902500000000001,0.0,0.0 --hnBHBN8p5A/7,-hnBHBN8p5A,7,12,setting,6.882500171661377,7.242499828338623,True,5.154608294019591e-13,2.1580896377563477,6.876668294203695,7.242903312234083,6.8425,6.8825,7.242500000000001,7.242500000000001,0.040000000000000036,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,0,"Discussion,",0.20250000059604645,0.6025000214576721,True,7.413789807928048e-16,0.25,0.05734253021101946,0.6025331203999742,0.0025000000000000005,0.1825,0.6024999999999999,0.6024999999999999,0.18,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,1,how,0.6424999833106995,0.7825000286102295,True,1.488793626141377e-13,0.9633798003196716,0.6425435974958801,0.7825102245774119,0.6425,0.6425,0.7825,0.7825,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,2,to,0.862500011920929,0.9424999952316284,True,4.985656256329546e-14,0.3226155936717987,0.8625061466012319,0.9433688622350123,0.8624999999999999,0.8624999999999999,0.9425,0.9425,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,3,discuss,1.002500057220459,1.2825000286102295,True,5.747768390797561e-14,0.37193092703819275,1.002478560527885,1.282636894103437,1.0025,1.0025,1.2825,1.2825,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,4,ideas,1.3224999904632568,1.7424999475479126,True,7.848715709475595e-13,4.0,1.3870056407994262,1.7424735743902229,1.3225,1.4825,1.7425,1.7425,0.15999999999999992,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,5,and,1.7625000476837158,1.8624999523162842,True,1.503318303175405e-11,4.0,1.7624947007218714,1.8624953633132175,1.7625,1.7625,1.8625,1.8625,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,6,opinions,1.8825000524520874,2.2225000858306885,True,2.2395399337099553e-12,4.0,1.88249543276377,2.223362908527545,1.8825,1.8825,2.2225,2.2225,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,7,more,3.8424999713897705,3.9825000762939453,True,2.2274653909554187e-12,4.0,3.813089593624601,3.975339622828584,3.7625,3.8625,3.9225,4.0025,0.09999999999999964,0.08000000000000052 --hnBHBN8p5A/6,-hnBHBN8p5A,6,8,effectively,4.002500057220459,4.582499980926514,True,1.0292728990076924e-13,0.6660296320915222,4.001976607641939,4.579303253022034,3.9425,4.0425,4.5425,4.5825000000000005,0.10000000000000053,0.040000000000000036 --hnBHBN8p5A/6,-hnBHBN8p5A,6,9,than,4.602499961853027,4.942500114440918,True,1.545385997989146e-13,1.0,4.602805718995574,4.942454042242472,4.602500000000001,4.602500000000001,4.942500000000001,4.942500000000001,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,10,a,4.982500076293945,5.002500057220459,True,1.1543436345684555e-11,4.0,4.973083161483878,4.993083161483986,4.9625,4.982500000000001,4.982500000000001,5.0025,0.020000000000000462,0.019999999999999574 --hnBHBN8p5A/6,-hnBHBN8p5A,6,11,native,5.022500038146973,5.982500076293945,True,2.7570120123519837e-13,1.7840280532836914,5.023246868555434,5.982429087559479,5.022500000000001,5.022500000000001,5.982500000000001,5.982500000000001,0.0,0.0 --hnBHBN8p5A/6,-hnBHBN8p5A,6,12,speaker,6.002500057220459,6.322500228881836,True,1.1699105226248285e-13,0.7570344805717468,6.0024817013264,6.318882058754529,6.0025,6.0025,6.3025,6.322500000000001,0.0,0.020000000000000462 --qDkUB0GgYY/6,-qDkUB0GgYY,6,0,We,0.0024999999441206455,0.0625,True,9.337668442285785e-13,0.4947294592857361,0.004150950202978793,0.06513475794367299,0.0025000000000000005,0.0225,0.0625,0.0825,0.019999999999999997,0.020000000000000004 --qDkUB0GgYY/6,-qDkUB0GgYY,6,1,always,0.10249999910593033,0.4025000035762787,True,2.7101797368811464e-12,1.435910701751709,0.10268331893228248,0.4027179907882608,0.10250000000000001,0.10250000000000001,0.4025,0.4025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,2,love,0.48249998688697815,0.6625000238418579,True,1.558735508288417e-13,0.25,0.4821765173364106,0.6623160427138763,0.4825,0.4825,0.6625,0.6625,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,3,to,0.7225000262260437,0.8025000095367432,True,6.635134065444137e-13,0.35154345631599426,0.7222857214492022,0.8027704858444851,0.7224999999999999,0.7224999999999999,0.8025,0.8025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,4,hear,0.9825000166893005,1.1425000429153442,True,5.576355783382114e-12,2.9544713497161865,0.9824963006452994,1.1424985239327123,0.9824999999999999,0.9824999999999999,1.1425,1.1425,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,5,what,1.222499966621399,1.402500033378601,True,6.127329275473076e-14,0.25,1.2225943761411022,1.4025803637052279,1.2225,1.2225,1.4025,1.4025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,6,you,1.462499976158142,1.5625,True,1.2773379664372201e-14,0.25,1.4508682284819499,1.5565077680365997,1.4224999999999999,1.4625,1.5225,1.5625,0.040000000000000036,0.040000000000000036 --qDkUB0GgYY/6,-qDkUB0GgYY,6,7,"say,",1.5824999809265137,1.7424999475479126,True,2.74772760967823e-12,1.4558043479919434,1.5837011690674971,1.7213764295415506,1.5825,1.5825,1.6824999999999999,1.7625,0.0,0.08000000000000007 --qDkUB0GgYY/6,-qDkUB0GgYY,6,8,and,1.902500033378601,2.122499942779541,True,2.0415532112072476e-11,4.0,1.9027662090732207,2.123002766118228,1.9025,1.9025,2.1225,2.1225,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,9,like,2.202500104904175,2.702500104904175,True,1.0162235636324013e-12,0.5384168028831482,2.2032672035614334,2.67955362682512,2.2025,2.2025,2.6625,2.7025,0.0,0.040000000000000036 --qDkUB0GgYY/6,-qDkUB0GgYY,6,10,and,2.742500066757202,2.882499933242798,True,3.416096572284033e-11,4.0,2.7427614638556794,2.8827771318550117,2.7425,2.7425,2.8825,2.8825,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,11,share,2.942500114440918,3.242500066757202,True,1.1194105538347987e-11,4.0,2.9573490079626183,3.2448006952303574,2.9425,3.0225,3.2425,3.2625,0.08000000000000007,0.020000000000000018 --qDkUB0GgYY/6,-qDkUB0GgYY,6,12,this,3.322499990463257,3.5625,True,1.1903970864590718e-11,4.0,3.3267286352579317,3.5372195818299765,3.3225000000000002,3.3425,3.4825,3.5625,0.019999999999999574,0.08000000000000007 --qDkUB0GgYY/6,-qDkUB0GgYY,6,13,video,3.6024999618530273,3.9024999141693115,True,7.060332787162116e-12,3.7407138347625732,3.601699909740036,3.9012860766225366,3.6025,3.6025,3.9025,3.9025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,14,as,3.922499895095825,4.002500057220459,True,1.0646788355453407e-12,0.5640894174575806,3.9230020734809483,4.002898737678011,3.9225,3.9225,4.0025,4.0025,0.0,0.0 --qDkUB0GgYY/6,-qDkUB0GgYY,6,15,well.,4.042500019073486,4.522500038146973,True,7.8364842826667e-13,0.4151935279369354,4.1010353264747215,4.535698864584865,4.0425,4.4225,4.522500000000001,4.602500000000001,0.3799999999999999,0.08000000000000007 --uywlfIYOS8/4,-uywlfIYOS8,4,0,And,0.02250000089406967,0.16249999403953552,True,5.988297357983896e-11,4.0,0.022602156621392876,0.1630419189656043,0.0225,0.0225,0.1625,0.1625,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,1,then,0.42250001430511475,0.6625000238418579,True,1.9874759390331453e-12,4.0,0.4046817558066659,0.6583254135084216,0.3825,0.4225,0.6224999999999999,0.6625,0.03999999999999998,0.040000000000000036 --uywlfIYOS8/4,-uywlfIYOS8,4,2,from,0.8224999904632568,0.9825000166893005,True,1.0401202468757198e-12,4.0,0.8134819794270989,0.9735283961462557,0.7625,0.8225,0.9225,0.9824999999999999,0.06000000000000005,0.05999999999999994 --uywlfIYOS8/4,-uywlfIYOS8,4,3,a,1.0824999809265137,1.1024999618530273,True,6.67349883036808e-14,0.5558029413223267,1.0652153145723244,1.0852153145723227,0.9624999999999999,1.0825,0.9824999999999999,1.1025,0.1200000000000001,0.1200000000000001 --uywlfIYOS8/4,-uywlfIYOS8,4,4,brand,1.2024999856948853,1.4424999952316284,True,1.370544624920136e-14,0.25,1.1838669782022566,1.4434563674323948,1.0825,1.2025,1.4425,1.4425,0.11999999999999988,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,5,point,1.5824999809265137,1.9824999570846558,True,1.667973620550836e-13,1.3891733884811401,1.6264036729392484,2.021516023502526,1.5825,1.7625,1.9825,2.2025,0.17999999999999994,0.2200000000000002 --uywlfIYOS8/4,-uywlfIYOS8,4,6,of,2.122499942779541,2.202500104904175,True,3.508121744488779e-12,4.0,2.1405318506933226,2.2203001571442478,2.1225,2.2425,2.2025,2.3025,0.1200000000000001,0.10000000000000009 --uywlfIYOS8/4,-uywlfIYOS8,4,7,"view,",2.382499933242798,2.5425000190734863,True,3.820997066927151e-13,3.1823208332061768,2.382480809329932,2.54428426955582,2.3825,2.3825,2.5425,2.5625,0.0,0.020000000000000018 --uywlfIYOS8/4,-uywlfIYOS8,4,8,we,2.6424999237060547,2.7225000858306885,True,4.428764280761839e-14,0.3688500225543976,2.642474815240312,2.722478205982455,2.6425,2.6425,2.7225,2.7225,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,9,need,2.822499990463257,3.002500057220459,True,7.078356862233112e-14,0.589521586894989,2.8015348641925786,2.9936915393927097,2.7825,2.8225,2.9625,3.0025,0.03999999999999959,0.040000000000000036 --uywlfIYOS8/4,-uywlfIYOS8,4,10,to,3.0824999809265137,3.1424999237060547,True,1.7816755286826497e-14,0.25,3.082466476739016,3.142468401478628,3.0825,3.0825,3.1425,3.1425,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,11,make,3.202500104904175,3.362499952316284,True,7.334166233314773e-14,0.6108267307281494,3.182153732642052,3.342160432953954,3.1625,3.2025,3.3225000000000002,3.3625,0.040000000000000036,0.03999999999999959 --uywlfIYOS8/4,-uywlfIYOS8,4,12,sure,3.442500114440918,3.6024999618530273,True,5.5085622207348e-14,0.45878109335899353,3.43999834296272,3.5975051950710397,3.4025,3.4425,3.5425,3.6025,0.040000000000000036,0.06000000000000005 --uywlfIYOS8/4,-uywlfIYOS8,4,13,we're,3.682499885559082,3.862499952316284,True,3.684579377716268e-11,4.0,3.6833066425391294,3.8624815665334,3.6825,3.6825,3.8625,3.8625,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,14,driving,3.9024999141693115,4.222499847412109,True,4.1946619924260534e-14,0.34935280680656433,3.9024850739862837,4.222491770692106,3.9025,3.9025,4.2225,4.2225,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,15,the,4.242499828338623,4.34250020980835,True,3.531454168263955e-14,0.2941174805164337,4.242503793608108,4.342507015743014,4.242500000000001,4.242500000000001,4.3425,4.3425,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,16,relevant,4.362500190734863,4.722499847412109,True,3.1015102053451316e-13,2.5830955505371094,4.362528100683705,4.7228847343356,4.362500000000001,4.362500000000001,4.7225,4.7225,0.0,0.0 --uywlfIYOS8/4,-uywlfIYOS8,4,17,insight.,4.882500171661377,5.822500228881836,True,2.73377674664399e-12,4.0,4.881390463456283,5.819079782519511,4.862500000000001,4.8825,5.782500000000001,5.862500000000001,0.019999999999999574,0.08000000000000007 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,0,Many,0.08250000327825546,0.2824999988079071,True,7.850928566109638e-13,3.5132734775543213,0.10585923401689983,0.33275215154873433,0.0825,0.14250000000000002,0.28250000000000003,0.4425,0.06000000000000001,0.15999999999999998 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,1,people,0.4025000035762787,0.7024999856948853,True,9.156772562413096e-13,4.0,0.4426754338118783,0.7227427877341543,0.4025,0.4825,0.7025,0.7424999999999999,0.07999999999999996,0.039999999999999925 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,2,are,0.7825000286102295,0.9825000166893005,True,2.8262345263575794e-13,1.2647337913513184,0.7845461092295155,0.9620080068815149,0.7825,0.7825,0.9225,0.9824999999999999,0.0,0.05999999999999994 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,3,not,1.0225000381469727,1.1425000429153442,True,4.933300133420221e-14,0.25,1.0239882241362008,1.1437603517251953,1.0225,1.0225,1.1425,1.1425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,4,until,1.1825000047683716,1.5425000190734863,True,1.1237945253192438e-13,0.5028955936431885,1.1842781652958174,1.5435631922214206,1.1824999999999999,1.1824999999999999,1.5425,1.5425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,5,they,1.5824999809265137,1.9424999952316284,True,1.3072862562434062e-13,0.5850077271461487,1.5848135452386243,1.940893219335555,1.5825,1.5825,1.9425,1.9425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,6,get,2.0225000381469727,2.1624999046325684,True,1.3913614760384472e-14,0.25,2.0224595608245117,2.162566220706084,2.0225,2.0225,2.1625,2.1625,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,7,to,2.262500047683716,2.3424999713897705,True,2.638859826996614e-14,0.25,2.264419013866157,2.3477816786947727,2.2025,2.3225,2.3425,2.3825,0.11999999999999966,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,8,be,2.4024999141693115,2.502500057220459,True,4.1763540867788274e-13,1.8689093589782715,2.4025004305045847,2.502500872803157,2.4025,2.4025,2.5025,2.5025,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,9,a,2.6624999046325684,2.682499885559082,True,9.004533772262457e-13,4.0,2.6624925234542722,2.6824925234542722,2.6625,2.6625,2.6825,2.6825,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,10,older,3.322499990463257,3.5425000190734863,True,2.0256505620010884e-13,0.906474232673645,3.3198354757754682,3.5423462179745098,3.3225000000000002,3.3225000000000002,3.5425,3.5425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,11,age,3.762500047683716,3.922499895095825,True,3.217204334968887e-13,1.4396919012069702,3.7624739015695634,3.922669449686004,3.7625,3.7625,3.9225,3.9225,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,12,and,4.022500038146973,4.182499885559082,True,1.5305238223713505e-14,0.25,4.034930648788859,4.200137383139412,4.022500000000001,4.1025,4.1825,4.2225,0.07999999999999918,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,13,they,4.322500228881836,4.502500057220459,True,1.6199097185171096e-13,0.7249060869216919,4.321713048262524,4.501433219594749,4.322500000000001,4.322500000000001,4.5025,4.5025,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,14,realize,4.622499942779541,4.982500076293945,True,5.409635413233804e-13,2.420799970626831,4.619013484915951,4.951435893611253,4.6225000000000005,4.6225000000000005,4.9225,4.982500000000001,0.0,0.0600000000000005 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,15,that,5.0625,5.362500190734863,True,1.7663386031596673e-10,4.0,5.061109736838025,5.357915223735426,5.062500000000001,5.062500000000001,5.322500000000001,5.362500000000001,0.0,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,16,gosh,5.402500152587891,5.582499980926514,True,6.985038936146992e-11,4.0,5.412608374385401,5.583867731142024,5.402500000000001,5.442500000000001,5.5825000000000005,5.5825000000000005,0.040000000000000036,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,17,they,5.762499809265137,5.942500114440918,True,9.114082643972565e-14,0.40785321593284607,5.743872692230504,5.933506686984788,5.6225000000000005,5.7625,5.902500000000001,5.942500000000001,0.13999999999999968,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,18,wasted,5.962500095367432,6.28249979019165,True,6.252937400584016e-15,0.25,5.9755017709324125,6.318240025679866,5.9625,6.0025,6.282500000000001,6.3425,0.040000000000000036,0.05999999999999961 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,19,a,6.362500190734863,6.382500171661377,True,9.418957584370058e-13,4.0,6.362501464590465,6.3825014645904625,6.362500000000001,6.362500000000001,6.3825,6.3825,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,20,lot,6.442500114440918,6.5625,True,1.293143381004419e-13,0.5786788463592529,6.442499934859892,6.5625000135512535,6.442500000000001,6.442500000000001,6.562500000000001,6.562500000000001,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,21,of,6.622499942779541,6.702499866485596,True,2.4035458952992306e-12,4.0,6.615005116878734,6.6837688432472016,6.5825000000000005,6.6225000000000005,6.642500000000001,6.702500000000001,0.040000000000000036,0.05999999999999961 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,22,time,6.742499828338623,7.0625,True,3.271341869810242e-14,0.25,6.7425061991759785,7.062501833718776,6.742500000000001,6.742500000000001,7.062500000000001,7.062500000000001,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,23,and,7.142499923706055,7.242499828338623,True,7.342488162614957e-15,0.25,7.114865685343713,7.23577604907428,7.0825000000000005,7.142500000000001,7.202500000000001,7.242500000000001,0.0600000000000005,0.040000000000000036 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,24,money,7.34250020980835,7.5625,True,2.4677303386497207e-13,1.1043039560317993,7.342288899962515,7.563080577866272,7.3425,7.3425,7.562500000000001,7.562500000000001,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,25,in,8.602499961853027,8.982500076293945,True,1.5762575706484983e-13,0.7053718566894531,8.364611154997718,8.85810714822456,7.602500000000001,8.6225,8.6225,8.9825,1.0199999999999996,0.35999999999999943 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,26,placing,9.0625,9.6225004196167,True,1.828831275871648e-13,0.8183979988098145,9.066924678538173,9.62794870027181,8.9625,9.3025,9.6225,9.6625,0.33999999999999986,0.03999999999999915 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,27,bets,9.642499923706055,9.882499694824219,True,4.637042486099752e-12,4.0,9.682127934242377,9.909239237242446,9.6425,9.7025,9.8825,9.9225,0.0600000000000005,0.03999999999999915 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,28,on,9.942500114440918,10.042499542236328,True,3.03088699971088e-11,4.0,9.944177340580937,10.044079828969402,9.942499999999999,9.942499999999999,10.0425,10.0425,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,29,stock,10.1225004196167,10.40250015258789,True,1.0609971966757045e-14,0.25,10.13073084545628,10.425039706715383,10.1225,10.1625,10.4025,10.5025,0.03999999999999915,0.09999999999999964 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,30,that's,10.482500076293945,10.72249984741211,True,1.9271074319648918e-11,4.0,10.493521815416006,10.722486771164357,10.4825,10.5425,10.7225,10.7225,0.0600000000000005,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,31,ultimately,10.742500305175781,11.362500190734863,True,2.4436450584384983e-13,1.0935258865356445,10.744089634125213,11.362750520402031,10.7425,10.7425,11.362499999999999,11.362499999999999,0.0,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,32,lost,11.462499618530273,11.702500343322754,True,1.1303276904414163e-14,0.25,11.458537715767447,11.702573699169442,11.442499999999999,11.4625,11.7025,11.7025,0.02000000000000135,0.0 --6rXp3zJ3kc/8,-6rXp3zJ3kc,8,33,value.,11.842499732971191,12.242500305175781,True,1.0531544169731233e-12,4.0,11.844283755647352,12.241341598308948,11.8425,11.8425,12.2425,12.2425,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,0,"is,",0.0024999999441206455,0.0625,True,3.060766640139434e-10,4.0,0.0025112058701024778,0.0626767445741339,0.0025000000000000005,0.0025000000000000005,0.0625,0.0625,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,1,you,0.12250000238418579,0.2224999964237213,True,1.535432336829956e-10,4.0,0.12240190227637165,0.22247758827306965,0.1225,0.1225,0.2225,0.2225,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,2,can,0.26249998807907104,0.3824999928474426,True,2.600926939100301e-11,4.0,0.2624053486672871,0.3825267286438774,0.2625,0.2625,0.3825,0.3825,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,3,"say,",0.4625000059604645,0.6424999833106995,True,3.910752364305603e-12,1.8001569509506226,0.46261070574901725,0.6425522763266137,0.4625,0.4625,0.6425,0.6425,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,4,hey,0.7825000286102295,1.0425000190734863,True,3.1533831535562884e-12,1.4515326023101807,0.7823334202971419,1.04253429725383,0.7825,0.7825,1.0425,1.0425,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,5,I,1.1024999618530273,1.122499942779541,True,2.4848057639248466e-11,4.0,1.106823013333484,1.1268230133336934,1.0825,1.1225,1.1025,1.1425,0.040000000000000036,0.040000000000000036 --9y-fZ3swSY/0,-9y-fZ3swSY,0,6,really,1.462499976158142,1.7625000476837158,True,8.315149783999498e-12,3.8275434970855713,1.4586973624020396,1.760280018786308,1.4625,1.4625,1.7625,1.7625,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,7,like,1.7825000286102295,2.002500057220459,True,2.202190625764499e-14,0.25,1.8349168890968484,2.049505831918696,1.7825,1.9425,2.0025,2.1025,0.15999999999999992,0.10000000000000009 --9y-fZ3swSY/0,-9y-fZ3swSY,0,8,baby,2.0425000190734863,2.302500009536743,True,4.1377170786893736e-12,1.9046310186386108,2.0909214645105583,2.320850643943992,2.0425,2.1425,2.3025,2.3425,0.10000000000000009,0.03999999999999959 --9y-fZ3swSY/0,-9y-fZ3swSY,0,9,"skin,",2.4024999141693115,2.7225000858306885,True,1.806715150751148e-12,0.8316483497619629,2.3998643025533677,2.7200288568107656,2.4025,2.4025,2.7225,2.7225,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,10,they,2.9825000762939453,3.322499990463257,True,5.184020313714344e-12,2.38625431060791,2.982507862329635,3.3109432237495335,2.9825,2.9825,3.2625,3.3225000000000002,0.0,0.06000000000000005 --9y-fZ3swSY/0,-9y-fZ3swSY,0,11,are,3.442500114440918,3.6424999237060547,True,1.3909029093414627e-12,0.6402459740638733,3.442499981962662,3.6424997548724356,3.4425,3.4425,3.6425,3.6425,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,12,so,3.742500066757202,3.822499990463257,True,1.5805114378722451e-13,0.25,3.746053024158016,3.8230914471326036,3.7425,3.7625,3.8225000000000002,3.8225000000000002,0.020000000000000018,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,13,"soft,",3.882499933242798,4.102499961853027,True,2.7519693880477536e-13,0.25,3.8825012869595423,4.102466215181166,3.8825,3.8825,4.1025,4.1025,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,14,they,4.28249979019165,4.602499961853027,True,8.551512904915459e-13,0.3936343491077423,4.282513053129279,4.602513532707415,4.282500000000001,4.282500000000001,4.602500000000001,4.602500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,15,don’t,4.682499885559082,4.902500152587891,True,8.042919108497415e-11,4.0,4.683443564494826,4.903185399030555,4.6825,4.6825,4.902500000000001,4.902500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,16,have,4.962500095367432,5.222499847412109,True,1.4296327793469898e-13,0.25,4.967341030440776,5.226155811124558,4.9625,5.022500000000001,5.2225,5.2625,0.0600000000000005,0.040000000000000036 --9y-fZ3swSY/0,-9y-fZ3swSY,0,17,any,5.322500228881836,5.442500114440918,True,2.458334967883613e-12,1.131595253944397,5.322497337046365,5.442501103881926,5.322500000000001,5.322500000000001,5.442500000000001,5.442500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,18,hair,5.522500038146973,5.78249979019165,True,8.543707733475736e-13,0.39327508211135864,5.522549514283644,5.782686723091677,5.522500000000001,5.522500000000001,5.782500000000001,5.782500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,19,on,5.882500171661377,5.942500114440918,True,1.8865666407547055e-12,0.868404746055603,5.882448764418133,5.9425058227663135,5.8825,5.8825,5.942500000000001,5.942500000000001,0.0,0.0 --9y-fZ3swSY/0,-9y-fZ3swSY,0,20,their,6.022500038146973,6.34250020980835,True,4.095190170045476e-13,0.25,6.0212367814366665,6.362506654169406,6.022500000000001,6.022500000000001,6.3425,6.402500000000001,0.0,0.0600000000000005 --9y-fZ3swSY/0,-9y-fZ3swSY,0,21,face,6.462500095367432,6.722499847412109,True,3.871533356489959e-13,0.25,6.462437165544646,6.717253186710826,6.3825,6.522500000000001,6.6225000000000005,6.7225,0.14000000000000057,0.09999999999999964 --9y-fZ3swSY/4,-9y-fZ3swSY,4,0,I’ve,0.4025000035762787,0.5024999976158142,True,2.0237548525869897e-08,4.0,0.3604930640300776,0.5025259623963769,0.3025,0.4025,0.5025,0.5025,0.10000000000000003,0.0 --9y-fZ3swSY/4,-9y-fZ3swSY,4,1,seen,0.5625,0.7825000286102295,True,5.362225594107706e-11,1.505155086517334,0.5576079937154002,0.7374851585800298,0.5225,0.5625,0.7025,0.7825,0.040000000000000036,0.07999999999999996 --9y-fZ3swSY/4,-9y-fZ3swSY,4,2,this,0.8224999904632568,1.0425000190734863,True,3.008013326269432e-12,0.25,0.8081106426559449,1.0121021476836018,0.7625,0.8225,0.9225,1.0425,0.06000000000000005,0.12 --9y-fZ3swSY/4,-9y-fZ3swSY,4,3,in,1.122499942779541,1.2024999856948853,True,5.537627301155368e-11,1.5543895959854126,1.1072155754604693,1.192481658849235,1.0225,1.1225,1.1025,1.2225,0.10000000000000009,0.11999999999999988 --9y-fZ3swSY/4,-9y-fZ3swSY,4,4,US,1.2825000286102295,1.4824999570846558,True,5.11657140267463e-11,1.4362009763717651,1.2692331019302276,1.440947430874424,1.1225,1.2825,1.2025,1.4825,0.15999999999999992,0.28 --9y-fZ3swSY/4,-9y-fZ3swSY,4,5,or,1.5625,1.8424999713897705,True,1.1776651702433139e-12,0.25,1.5884781405612562,1.81895963877589,1.2825,1.8225,1.4825,1.9825,0.54,0.5 --9y-fZ3swSY/4,-9y-fZ3swSY,4,6,in,1.902500033378601,1.9824999570846558,True,4.608553664554871e-13,0.25,1.9176942178451357,2.0156957087418754,1.8225,2.0425,1.9825,2.1425,0.21999999999999997,0.16000000000000014 --9y-fZ3swSY/4,-9y-fZ3swSY,4,7,the,2.0425000190734863,2.322499990463257,True,3.562573500093258e-11,1.0,2.0775599959853452,2.2745366471050685,2.0425,2.2025,2.1825,2.3625,0.16000000000000014,0.17999999999999972 --9y-fZ3swSY/4,-9y-fZ3swSY,4,8,US,2.4024999141693115,2.5625,True,2.4587404594961226e-12,0.25,2.3624797829867665,2.563519061540398,2.3025,2.4625,2.5625,2.5625,0.1599999999999997,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,0,That’s,0.08250000327825546,0.42250001430511475,True,9.314120308356877e-11,4.0,0.0806608236315872,0.4421451291187421,0.0625,0.0825,0.4225,0.4825,0.020000000000000004,0.06 --9y-fZ3swSY/8,-9y-fZ3swSY,8,1,a,0.4625000059604645,0.48249998688697815,True,1.3536963433882776e-12,1.1048375368118286,0.5336691584869542,0.5536691584869534,0.4625,0.6024999999999999,0.4825,0.6224999999999999,0.1399999999999999,0.13999999999999996 --9y-fZ3swSY/8,-9y-fZ3swSY,8,2,more,0.9624999761581421,1.162500023841858,True,1.1315475872920519e-13,0.25,0.915860858527888,1.1624825207872158,0.6024999999999999,0.9624999999999999,1.1624999999999999,1.1624999999999999,0.36,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,3,decent,1.3624999523162842,1.8025000095367432,True,3.1133767981229854e-13,0.2541024386882782,1.361983429299652,1.8391916213136885,1.3625,1.3625,1.8025,2.1225,0.0,0.32000000000000006 --9y-fZ3swSY/8,-9y-fZ3swSY,8,4,and,2.1024999618530273,2.322499990463257,True,2.3098985831720986e-12,1.8852548599243164,2.114016786911137,2.3136987008669143,2.1025,2.2025,2.2625,2.3225,0.10000000000000009,0.05999999999999961 --9y-fZ3swSY/8,-9y-fZ3swSY,8,5,more,2.382499933242798,2.7825000286102295,True,1.0729073879334194e-12,0.8756678104400635,2.4241202403087847,2.781629417286628,2.3825,2.4825,2.7825,2.7825,0.10000000000000009,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,6,polite,2.922499895095825,3.2825000286102295,True,1.4056551062013867e-13,0.25,2.9215736524164218,3.2824992595126026,2.9225,2.9225,3.2825,3.2825,0.0,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,7,way,3.4825000762939453,3.6024999618530273,True,3.170587490469723e-12,2.5877177715301514,3.4208157907789016,3.5888738666999616,3.3425,3.4825,3.5425,3.6025,0.14000000000000012,0.06000000000000005 --9y-fZ3swSY/8,-9y-fZ3swSY,8,8,to,3.6624999046325684,4.022500038146973,True,6.365228981917992e-13,0.5195067524909973,3.6700739437613157,4.023422620318217,3.6625,3.6625,4.022500000000001,4.022500000000001,0.0,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,9,call,4.082499980926514,4.34250020980835,True,9.880774150261562e-12,4.0,4.082498071211092,4.342498071249503,4.0825000000000005,4.0825000000000005,4.3425,4.3425,0.0,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,10,a,4.382500171661377,4.402500152587891,True,1.398533979596328e-11,4.0,4.38238720408983,4.402387204089831,4.3825,4.3825,4.402500000000001,4.402500000000001,0.0,0.0 --9y-fZ3swSY/8,-9y-fZ3swSY,8,11,lady,4.722499847412109,4.902500152587891,True,1.096793229155013e-12,0.8951625823974609,4.725094218121915,4.90232210201576,4.7225,4.742500000000001,4.902500000000001,4.902500000000001,0.020000000000000462,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,0,The,0.14249999821186066,0.5824999809265137,True,1.1787321505419158e-14,0.25,0.27252131830482507,0.5876429988753463,0.1225,0.5225,0.5824999999999999,0.6224999999999999,0.39999999999999997,0.040000000000000036 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,1,perfect,0.6025000214576721,1.002500057220459,True,3.2728529088255076e-13,0.25,0.6092378774561676,1.0025942417269524,0.6024999999999999,0.6425,1.0025,1.0025,0.040000000000000036,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,2,soul,1.0425000190734863,1.2625000476837158,True,3.279520058296903e-11,4.0,1.042594908835863,1.262583948424393,1.0425,1.0425,1.2625,1.2625,0.0,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,3,mate,1.3025000095367432,1.5625,True,8.825176700533177e-12,1.7002969980239868,1.302545298794321,1.5265084161584652,1.3025,1.3025,1.4825,1.5625,0.0,0.08000000000000007 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,4,to,1.6024999618530273,1.662500023841858,True,1.4148528269114502e-12,0.2725917100906372,1.6056070545522665,1.6656434794159292,1.6025,1.6025,1.6625,1.6625,0.0,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,5,the,1.722499966621399,1.8224999904632568,True,5.101854268912964e-13,0.25,1.735751104704133,1.8401369240743506,1.7225,1.8625,1.8225,1.9825,0.14000000000000012,0.15999999999999992 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,6,Clarisonic,1.8624999523162842,2.5625,True,3.914031425356068e-12,0.7540943622589111,1.8851971012939985,2.6083585906340376,1.8625,2.0825,2.4825,3.4025,0.21999999999999997,0.9199999999999999 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,7,Spot,3.1024999618530273,3.4024999141693115,True,2.7890302914390652e-12,0.5373467206954956,3.1445458318034225,3.4526482322797505,3.1025,3.5625,3.4025,3.9625,0.45999999999999996,0.56 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,8,Therapy,3.5625,3.9625000953674316,True,1.1581469290533608e-11,2.2313363552093506,3.606693728375404,4.068442576095661,3.5625,4.1025,3.9625,5.0425,0.54,1.0800000000000005 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,9,"Brush,",4.042500019073486,4.322500228881836,True,4.2936924089971573e-13,0.25,4.152623283975696,4.44807120308332,4.0425,5.1825,4.322500000000001,5.562500000000001,1.1399999999999997,1.2400000000000002 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,10,this,4.942500114440918,5.122499942779541,True,2.7933718164084576e-13,0.25,4.98188533431721,5.221250227451901,4.562500000000001,5.742500000000001,5.0825000000000005,6.062500000000001,1.1799999999999997,0.9800000000000004 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,11,"gentle,",5.182499885559082,5.642499923706055,True,1.0272394046495492e-11,1.9791241884231567,5.298987408234679,5.714191899190055,5.1825,6.202500000000001,5.602500000000001,6.562500000000001,1.0200000000000005,0.96 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,12,Ph,5.742499828338623,5.822500228881836,True,4.917092080725105e-11,4.0,5.807498087279645,5.894975688983383,5.6625000000000005,6.6225000000000005,5.7225,6.7625,0.96,1.04 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,13,balanced,5.922500133514404,6.442500114440918,True,2.114638976077887e-12,0.4074155390262604,6.020752430532114,6.566108548962118,5.742500000000001,6.902500000000001,6.3025,7.4625,1.1600000000000001,1.1600000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,14,and,6.502500057220459,6.622499942779541,True,2.047474386215886e-12,0.3944753408432007,6.6474085491968244,6.8075910652999445,6.4225,7.602500000000001,6.562500000000001,7.702500000000001,1.1800000000000006,1.1399999999999997 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,15,hydrating,6.742499828338623,7.462500095367432,True,2.415321089833944e-13,0.25,6.931675287821492,7.679988215120003,6.742500000000001,7.822500000000001,7.4625,8.282499999999999,1.08,0.8199999999999985 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,16,exfoliant,7.682499885559082,9.022500038146973,True,9.834040699818747e-12,1.8946690559387207,7.832606323443743,8.842506718610109,7.5825000000000005,8.6025,8.1025,9.2225,1.0199999999999987,1.120000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,17,containing,9.382499694824219,10.322500228881836,True,7.338613224050494e-12,1.4138891696929932,9.053975217156005,9.999233994578377,8.1625,9.4025,9.1825,10.4825,1.2400000000000002,1.3000000000000007 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,18,jojoba,10.462499618530273,10.962499618530273,True,1.2090047712964846e-11,2.3293211460113525,10.153471942108839,10.632818750234803,9.3825,10.5425,9.862499999999999,10.9825,1.1600000000000001,1.120000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,19,and,11.142499923706055,11.362500190734863,True,6.746394415335644e-11,4.0,10.865587426362282,11.092469474696552,10.0025,11.1425,10.4825,11.362499999999999,1.1400000000000006,0.879999999999999 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,20,acai,11.442500114440918,11.582500457763672,True,3.173180121440744e-11,4.0,11.185082640911101,11.40354090768313,10.5425,11.4825,10.8825,11.6825,0.9399999999999995,0.7999999999999989 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,21,fruit,11.702500343322754,12.5625,True,3.936610586119382e-12,0.7584445476531982,11.542750165286042,12.199442610047605,10.9825,12.3225,11.362499999999999,12.5625,1.3399999999999999,1.200000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,22,oils,12.702500343322754,12.962499618530273,True,1.2418142035508506e-11,2.392533302307129,12.312457137645003,12.53612750534288,11.4225,12.7025,11.5825,12.9625,1.2800000000000011,1.3800000000000008 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,23,and,13.0024995803833,13.182499885559082,True,1.4112935646515279e-11,2.719059705734253,12.838743358082878,13.096751343161257,12.3825,13.1025,12.522499999999999,13.6025,0.7199999999999989,1.08 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,24,bamboo,13.5625,14.0625,True,6.703672773139546e-12,1.2915587425231934,13.33881562914513,13.769541054426064,12.7025,13.7425,13.1225,14.0625,1.0399999999999991,0.9399999999999995 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,25,and,14.142499923706055,14.362500190734863,True,8.946042691360123e-12,1.7235835790634155,13.864704428194672,14.118485920377335,13.2625,14.1425,13.5825,14.362499999999999,0.8800000000000008,0.7799999999999994 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,26,prickly,14.662500381469727,15.102499961853027,True,3.0223447442662144e-13,0.25,14.322593078254965,14.767185447932158,13.6625,14.6625,14.0625,15.1025,1.0,1.0399999999999991 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,27,bear,15.702500343322754,15.862500190734863,True,4.4243590995723947e-13,0.25,15.12995941247925,15.403516804053789,14.1425,15.7025,14.362499999999999,15.8625,1.5600000000000005,1.5000000000000018 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,28,extracts,15.942500114440918,16.322500228881836,True,1.6714140650946757e-13,0.25,15.53453527528529,16.17754300405277,14.6625,15.942499999999999,15.8225,16.4025,1.2799999999999994,0.5800000000000001 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,29,deliver,16.38249969482422,16.862499237060547,True,1.0248501672955809e-13,0.25,16.270850501054223,16.686664552019476,15.942499999999999,16.5025,16.282500000000002,16.8625,0.5600000000000023,0.5799999999999983 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,30,intense,16.90250015258789,17.322500228881836,True,7.725160029899147e-13,0.25,16.78021481124627,17.24047823295799,16.5025,16.962500000000002,17.0625,17.3225,0.46000000000000085,0.26000000000000156 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,31,"healing,",17.362499237060547,18.0625,True,4.7344672805685675e-14,0.25,17.313333485444595,18.013450491442516,17.122500000000002,17.622500000000002,17.942500000000003,18.0625,0.5,0.11999999999999744 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,32,while,18.122499465942383,18.38249969482422,True,1.305523180880619e-12,0.2515277862548828,18.121970016579592,18.382910922205134,18.122500000000002,18.122500000000002,18.3825,18.3825,0.0,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,33,smooth,18.462499618530273,18.922500610351562,True,6.875201363082395e-12,1.32460618019104,18.46245288047144,18.91253360874653,18.462500000000002,18.462500000000002,18.902500000000003,18.922500000000003,0.0,0.019999999999999574 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,34,granules,19.0625,19.622499465942383,True,6.236156729899633e-11,4.0,19.060714967031537,19.62165223075318,19.0625,19.0625,19.622500000000002,19.622500000000002,0.0,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,35,buff,19.702499389648438,20.342500686645508,True,5.486440295132677e-12,1.0570415258407593,19.89197269833418,20.35923287241106,19.7025,20.122500000000002,20.3425,20.402500000000003,0.4200000000000017,0.060000000000002274 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,36,away,20.6825008392334,20.962499618530273,True,1.5412348047111335e-12,0.29694101214408875,20.646430815099194,20.960356266677547,20.402500000000003,20.6825,20.962500000000002,20.962500000000002,0.2799999999999976,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,37,"dead,",20.982500076293945,21.142499923706055,True,7.757587107415365e-12,1.4946105480194092,20.986751725007654,21.14226403506907,20.9825,21.0025,21.142500000000002,21.142500000000002,0.019999999999999574,0.0 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,38,dry,21.202499389648438,21.38249969482422,True,5.190373738445109e-12,1.0,21.20398053353988,21.388662026269635,21.2025,21.2025,21.3825,21.442500000000003,0.0,0.060000000000002274 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,39,skin,21.482500076293945,21.622499465942383,True,6.363738702663824e-11,4.0,21.480180957830996,21.622991854845147,21.4825,21.4825,21.622500000000002,21.642500000000002,0.0,0.019999999999999574 --AUZQgSxyPQ/2,-AUZQgSxyPQ,2,40,cells.,21.642499923706055,21.842500686645508,True,2.2591633425106394e-11,4.0,21.64385152483771,21.84267698383198,21.642500000000002,21.6625,21.8425,21.8425,0.019999999999999574,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,0,The,0.24250000715255737,0.4025000035762787,True,1.1845117768582991e-11,4.0,0.1959311331958721,0.40235082912774206,0.0425,0.2425,0.4025,0.4025,0.19999999999999998,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,1,August,1.402500033378601,2.122499942779541,True,1.4341861032107772e-12,0.8177534341812134,1.4478832208215444,2.1272597779712865,1.4025,1.7625,2.1225,2.1625,0.3599999999999999,0.040000000000000036 --HeZS2-Prhc/2,-HeZS2-Prhc,2,2,jobs,2.322499990463257,2.6024999618530273,True,2.558615125325403e-11,4.0,2.320611337970802,2.6004673835909573,2.3225,2.3225,2.6025,2.6025,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,3,report,2.682499885559082,3.002500057220459,True,2.7453449669839758e-12,1.5653584003448486,2.68170298259558,3.009440710897623,2.6825,2.6825,3.0025,3.0825,0.0,0.08000000000000007 --HeZS2-Prhc/2,-HeZS2-Prhc,2,4,is,3.0625,3.1424999237060547,True,4.514987830221001e-13,0.2574384808540344,3.0812734603350624,3.1623839522789665,3.0625,3.3225000000000002,3.1425,3.4225,0.26000000000000023,0.2799999999999998 --HeZS2-Prhc/2,-HeZS2-Prhc,2,5,out,3.322499990463257,3.4625000953674316,True,2.090342601703682e-13,0.25,3.344295656509894,3.4967551728462754,3.1625,3.8625,3.4225,3.9825,0.6999999999999997,0.56 --HeZS2-Prhc/2,-HeZS2-Prhc,2,6,and,3.862499952316284,3.9825000762939453,True,8.034748539241521e-14,0.25,3.877242383787142,4.00469880646666,3.8625,4.062500000000001,3.9825,4.242500000000001,0.20000000000000107,0.2600000000000007 --HeZS2-Prhc/2,-HeZS2-Prhc,2,7,it,4.302499771118164,4.382500171661377,True,2.6032816310625484e-13,0.25,4.275837833615807,4.386597712761713,4.202500000000001,4.3025,4.3825,4.402500000000001,0.09999999999999964,0.020000000000000462 --HeZS2-Prhc/2,-HeZS2-Prhc,2,8,was,4.502500057220459,4.702499866485596,True,6.574726874042369e-12,3.7488200664520264,4.5026476132951725,4.702857723024613,4.5025,4.5025,4.702500000000001,4.702500000000001,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,9,seen,5.262499809265137,5.642499923706055,True,9.668883749203161e-12,4.0,5.26248580780262,5.643139038744664,5.2625,5.2625,5.6425,5.6625000000000005,0.0,0.020000000000000462 --HeZS2-Prhc/2,-HeZS2-Prhc,2,10,overall,6.202499866485596,6.722499847412109,True,2.0734386430021345e-12,1.1822465658187866,6.135176538397465,6.722530461705236,5.7625,6.202500000000001,6.7225,6.7225,0.4400000000000004,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,11,as,6.802499771118164,6.882500171661377,True,8.576729821144213e-13,0.4890334904193878,6.802730239021207,6.882715843694687,6.8025,6.8025,6.8825,6.8825,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,12,solid,6.942500114440918,7.202499866485596,True,8.672484930714874e-13,0.494493305683136,6.944034109898724,7.203577690574796,6.942500000000001,6.942500000000001,7.202500000000001,7.202500000000001,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,13,but,7.34250020980835,7.602499961853027,True,3.902187947768532e-11,4.0,7.342753371410023,7.602373466405716,7.3425,7.3425,7.602500000000001,7.602500000000001,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,14,not,7.662499904632568,7.802499771118164,True,2.746409328256705e-13,0.25,7.663526959768611,7.802856928638411,7.6625000000000005,7.6625000000000005,7.8025,7.8025,0.0,0.0 --HeZS2-Prhc/2,-HeZS2-Prhc,2,15,great.,7.862500190734863,8.082500457763672,True,2.701561847492928e-12,1.5403938293457031,7.8635557979644375,8.088359004012458,7.862500000000001,7.862500000000001,8.0825,8.1225,0.0,0.040000000000000924 --MeTTeMJBNc/0,-MeTTeMJBNc,0,0,"Okay,",0.9424999952316284,1.2424999475479126,True,3.710065674816798e-12,4.0,0.9432326596664109,1.2056759510520048,0.9225,1.0025,1.1025,1.2425,0.07999999999999996,0.1399999999999999 --MeTTeMJBNc/0,-MeTTeMJBNc,0,1,what,1.3025000095367432,1.502500057220459,True,1.2836673354322831e-14,1.0,1.2968934930611797,1.4994382940713709,1.3025,1.3025,1.5025,1.5025,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,2,happens,1.5625,2.122499942779541,True,1.206398787698083e-14,0.9398064017295837,1.558655575902781,2.121205916667169,1.5425,1.5625,2.1225,2.1225,0.020000000000000018,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,3,at,2.202500104904175,2.2825000286102295,True,2.9099730082044395e-15,0.25,2.1952647147514655,2.2751562874460314,2.1425,2.2025,2.2225,2.2825,0.06000000000000005,0.06000000000000005 --MeTTeMJBNc/0,-MeTTeMJBNc,0,4,this,2.322499990463257,2.4825000762939453,True,3.948112470227071e-15,0.3075650930404663,2.3262151924486174,2.488170818444105,2.3225,2.3625,2.4825,2.5225,0.040000000000000036,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,5,point,2.5225000381469727,2.882499933242798,True,3.802389345497915e-15,0.29621297121047974,2.5670758567670444,2.901260034406884,2.5225,2.6225,2.8825,2.9225,0.10000000000000009,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,6,after,3.202500104904175,3.422499895095825,True,4.455512734203183e-15,0.34709247946739197,3.1913281357894654,3.419919056829773,3.1825,3.2025,3.3825,3.4225,0.020000000000000018,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,7,we've,3.4625000953674316,3.682499885559082,True,1.9134102288709265e-11,4.0,3.4638915322588497,3.6825000001063266,3.4625,3.4625,3.6825,3.6825,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,8,taken,3.7225000858306885,4.002500057220459,True,3.697702849480861e-15,0.28805771470069885,3.7225000291431787,4.010663846188186,3.7225,3.7225,4.0025,4.0425,0.0,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,9,this,4.042500019073486,4.222499847412109,True,1.42208513980455e-14,1.1078299283981323,4.0553085178820885,4.231976591647393,4.0425,4.1025,4.2225,4.2625,0.05999999999999961,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,10,brief,4.462500095367432,4.702499866485596,True,1.0028261925834787e-14,0.7812196612358093,4.462443349973335,4.704718386141115,4.4625,4.4625,4.702500000000001,4.702500000000001,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,11,walk,4.78249979019165,5.142499923706055,True,6.560490405050842e-15,0.5110740065574646,4.813190037922622,5.151410892553026,4.7625,4.8825,5.1425,5.1825,0.1200000000000001,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,12,down,5.202499866485596,5.462500095367432,True,7.031535591414326e-14,4.0,5.203195360240823,5.463194049014288,5.202500000000001,5.202500000000001,5.4625,5.4625,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,13,memory,5.582499980926514,5.902500152587891,True,2.5533394842902624e-13,4.0,5.582483141326186,5.9028098326745795,5.5825000000000005,5.5825000000000005,5.902500000000001,5.902500000000001,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,14,"lane,",5.962500095367432,6.302499771118164,True,3.5930408496566424e-13,4.0,5.975316507304133,6.305343427476204,5.9625,6.142500000000001,6.3025,6.3425,0.1800000000000006,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,15,is,6.84250020980835,6.902500152587891,True,7.547921489214904e-12,4.0,6.8424998796201235,6.902499886452121,6.8425,6.8425,6.902500000000001,6.902500000000001,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,16,the,6.982500076293945,7.082499980926514,True,3.779567948558279e-16,0.25,6.9590939849105835,7.061940314452324,6.942500000000001,6.982500000000001,7.0425,7.0825000000000005,0.040000000000000036,0.040000000000000036 --MeTTeMJBNc/0,-MeTTeMJBNc,0,17,presentation,7.122499942779541,7.862500190734863,True,3.29521523931872e-15,0.2567031979560852,7.122435235689702,7.862573230447805,7.1225000000000005,7.1225000000000005,7.862500000000001,7.862500000000001,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,18,of,7.902500152587891,7.962500095367432,True,4.660046220433811e-13,4.0,7.903398437119373,7.963914730371576,7.902500000000001,7.902500000000001,7.9625,7.9625,0.0,0.0 --MeTTeMJBNc/0,-MeTTeMJBNc,0,19,the,8.042499542236328,8.162500381469727,True,4.581407410560179e-14,3.5689990520477295,8.064463282027926,8.1771366928654,8.0425,8.1025,8.1625,8.202499999999999,0.05999999999999872,0.03999999999999915 --MeTTeMJBNc/0,-MeTTeMJBNc,0,20,gift.,8.242500305175781,8.582500457763672,True,2.0623882376197278e-12,4.0,8.242500389132172,8.582544997263367,8.2425,8.2425,8.5825,8.5825,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,0,It,0.3425000011920929,0.4424999952316284,True,1.1344515604694294e-11,4.0,0.34125700409427956,0.4421278439000176,0.3425,0.3425,0.4425,0.4425,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,1,just,0.5024999976158142,1.3224999904632568,True,7.597669889058967e-12,4.0,0.6357895630564467,1.3217086751335387,0.5025,1.0625,1.3225,1.3225,0.56,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,2,needs,1.402500033378601,1.6425000429153442,True,1.3798399814239637e-13,2.5175373554229736,1.4024617994614186,1.6427802038920662,1.4025,1.4025,1.6425,1.6425,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,3,to,1.722499966621399,1.7825000286102295,True,1.4288968093597795e-14,0.2607042193412781,1.722500342761648,1.7825003763940888,1.7225,1.7225,1.7825,1.7825,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,4,be,1.8424999713897705,1.9225000143051147,True,4.578755452046279e-13,4.0,1.8425000061154377,1.922500028437251,1.8425,1.8425,1.9224999999999999,1.9224999999999999,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,5,a,2.0225000381469727,2.0425000190734863,True,4.161020486503908e-11,4.0,2.022500304881204,2.0425003048812034,2.0225,2.0225,2.0425,2.0425,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,6,simple,2.2225000858306885,2.5625,True,1.4861399057989472e-13,2.7114830017089844,2.2205922795367226,2.562499455730098,2.2225,2.2225,2.5625,2.5625,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,7,"statement,",2.5824999809265137,3.202500104904175,True,3.476265990695485e-15,0.25,2.5825113951826295,3.202756423531097,2.5825,2.5825,3.2025,3.2025,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,8,and,3.2225000858306885,3.6424999237060547,True,1.5177185476813494e-14,0.27690985798835754,3.230022374213786,3.639560341276429,3.2225,3.2225,3.6025,3.6425,0.0,0.040000000000000036 --MeTTeMJBNc/13,-MeTTeMJBNc,13,9,then,3.6624999046325684,3.802500009536743,True,8.128732731618369e-16,0.25,3.6595605480135602,3.799753359197992,3.6225,3.6625,3.7625,3.8025,0.040000000000000036,0.040000000000000036 --MeTTeMJBNc/13,-MeTTeMJBNc,13,10,you,3.822499990463257,3.922499895095825,True,3.6388318573610245e-14,0.6639099717140198,3.82250569880218,3.9263450272879767,3.8225000000000002,3.8225000000000002,3.9225,3.9425,0.0,0.020000000000000018 --MeTTeMJBNc/13,-MeTTeMJBNc,13,11,present,3.9825000762939453,4.34250020980835,True,5.4407035314182374e-15,0.25,3.982499819073152,4.342058126051071,3.9825,3.9825,4.3425,4.3425,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,12,the,4.362500190734863,4.482500076293945,True,4.4931062586879914e-14,0.8197734951972961,4.362503466738968,4.482502178652516,4.362500000000001,4.362500000000001,4.482500000000001,4.482500000000001,0.0,0.0 --MeTTeMJBNc/13,-MeTTeMJBNc,13,13,gift.,4.522500038146973,4.862500190734863,True,6.468716756908091e-14,1.180226445198059,4.5225058016335105,4.862473226976465,4.522500000000001,4.522500000000001,4.862500000000001,4.862500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,0,Maybe,0.042500000447034836,0.3824999928474426,True,3.770235466600373e-14,0.9178741574287415,0.04382005310267007,0.37623012683122026,0.0425,0.0625,0.3425,0.3825,0.019999999999999997,0.03999999999999998 --MeTTeMJBNc/7,-MeTTeMJBNc,7,1,you,0.6025000214576721,0.7425000071525574,True,2.4669209383598734e-11,4.0,0.6014484098137175,0.7393633434603328,0.5425,0.6224999999999999,0.7025,0.7424999999999999,0.07999999999999996,0.039999999999999925 --MeTTeMJBNc/7,-MeTTeMJBNc,7,2,could,0.8025000095367432,1.1425000429153442,True,6.561903256006862e-14,1.5975133180618286,0.8039196735936374,1.1445578036248207,0.8025,0.8025,1.1425,1.1824999999999999,0.0,0.039999999999999813 --MeTTeMJBNc/7,-MeTTeMJBNc,7,3,find,1.5625,1.8424999713897705,True,4.50442504476567e-12,4.0,1.561707044410993,1.8424368444910948,1.5625,1.5625,1.8425,1.8425,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,4,a,1.9424999952316284,1.962499976158142,True,3.019118375441332e-12,4.0,1.9424658593762911,1.962465859376291,1.9425,1.9425,1.9625,1.9625,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,5,picture,2.002500057220459,2.362499952316284,True,2.292061499094538e-14,0.5580086708068848,2.0026882078534736,2.377115832890999,2.0025,2.0025,2.3625,2.4025,0.0,0.040000000000000036 --MeTTeMJBNc/7,-MeTTeMJBNc,7,6,of,2.422499895095825,2.4825000762939453,True,5.609922791062483e-13,4.0,2.4277161082497907,2.498121486901269,2.4225,2.4625,2.4825,2.5625,0.040000000000000036,0.08000000000000007 --MeTTeMJBNc/7,-MeTTeMJBNc,7,7,the,2.5824999809265137,2.682499885559082,True,8.198723275808228e-15,0.25,2.579265356662479,2.6792832693436104,2.5425,2.5825,2.6425,2.6825,0.040000000000000036,0.040000000000000036 --MeTTeMJBNc/7,-MeTTeMJBNc,7,8,couple,2.702500104904175,3.0425000190734863,True,2.822629892947244e-14,0.6871770024299622,2.699416899972035,3.0720880659627245,2.6625,2.7025,3.0425,3.1025,0.040000000000000036,0.06000000000000005 --MeTTeMJBNc/7,-MeTTeMJBNc,7,9,from,3.0824999809265137,3.322499990463257,True,3.6664202286728655e-15,0.25,3.126486933752154,3.3590129371154913,3.0825,3.1625,3.3225000000000002,3.4225,0.08000000000000007,0.09999999999999964 --MeTTeMJBNc/7,-MeTTeMJBNc,7,10,their,3.4625000953674316,3.922499895095825,True,1.8800955055493264e-14,0.45771440863609314,3.462535896135531,3.9223684291480794,3.4625,3.4625,3.9225,3.9225,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,11,wedding,3.942500114440918,4.442500114440918,True,4.442729279081903e-13,4.0,3.942471261233754,4.44238517073405,3.9425,3.9425,4.442500000000001,4.442500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,12,or,4.642499923706055,4.702499866485596,True,2.026530222657471e-14,0.4933643341064453,4.642065602925446,4.702431704556223,4.6425,4.6425,4.702500000000001,4.702500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,13,sometime,4.902500152587891,5.362500190734863,True,3.989258874409138e-14,0.9711959958076477,4.86246421614156,5.34648215111066,4.8025,4.902500000000001,5.322500000000001,5.362500000000001,0.10000000000000053,0.040000000000000036 --MeTTeMJBNc/7,-MeTTeMJBNc,7,14,in,5.382500171661377,5.442500114440918,True,2.355478347635699e-15,0.25,5.382091465093613,5.442262434223061,5.3825,5.3825,5.442500000000001,5.442500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,15,their,5.462500095367432,5.682499885559082,True,6.219688816305443e-15,0.25,5.462726237673856,5.682500012758481,5.4625,5.4625,5.6825,5.6825,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,16,"life,",5.722499847412109,6.042500019073486,True,9.027134074968135e-12,4.0,5.722500033125576,6.042499810582332,5.7225,5.7225,6.0425,6.0425,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,17,or,6.082499980926514,6.182499885559082,True,3.5410132400055805e-13,4.0,6.0825677398853735,6.184103345429151,6.0825000000000005,6.0825000000000005,6.1825,6.202500000000001,0.0,0.020000000000000462 --MeTTeMJBNc/7,-MeTTeMJBNc,7,18,maybe,6.262499809265137,6.462500095367432,True,1.1368580772955217e-12,4.0,6.2625000261871655,6.462574470158711,6.2625,6.2625,6.4625,6.4625,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,19,even,6.582499980926514,6.762499809265137,True,1.2781007805925948e-12,4.0,6.549329203691951,6.727341946384188,6.5025,6.5825000000000005,6.702500000000001,6.7625,0.08000000000000007,0.05999999999999961 --MeTTeMJBNc/7,-MeTTeMJBNc,7,20,a,6.802499771118164,6.822500228881836,True,7.715516593675975e-12,4.0,6.802500479833359,6.822500479833361,6.8025,6.8025,6.822500000000001,6.822500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,21,current,6.922500133514404,7.322500228881836,True,4.107573452921155e-14,1.0,6.913379676245901,7.315998196321345,6.8425,6.9225,7.2625,7.322500000000001,0.08000000000000007,0.0600000000000005 --MeTTeMJBNc/7,-MeTTeMJBNc,7,22,picture,7.362500190734863,7.822500228881836,True,5.0320147083459527e-14,1.2250577211380005,7.362392721111548,7.822399324363143,7.362500000000001,7.362500000000001,7.822500000000001,7.822500000000001,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,23,and,7.84250020980835,8.262499809265137,True,2.99166141620906e-14,0.7283281683921814,7.899502149493908,8.268061882040197,7.8425,8.202499999999999,8.2625,8.3025,0.35999999999999854,0.040000000000000924 --MeTTeMJBNc/7,-MeTTeMJBNc,7,24,have,8.282500267028809,8.422499656677246,True,3.7114418932919155e-14,0.9035606980323792,8.288246235381468,8.42975344190582,8.282499999999999,8.3225,8.4225,8.4625,0.040000000000000924,0.040000000000000924 --MeTTeMJBNc/7,-MeTTeMJBNc,7,25,it,8.482500076293945,8.542499542236328,True,3.107029336504169e-13,4.0,8.482487466508353,8.542487472734381,8.4825,8.4825,8.5425,8.5425,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,26,put,8.582500457763672,8.72249984741211,True,1.557926015841385e-14,0.37928134202957153,8.582500416372213,8.722595724165645,8.5825,8.5825,8.7225,8.7225,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,27,in,8.782500267028809,8.842499732971191,True,1.3454399236162342e-14,0.3275510370731354,8.771773292426657,8.83177402315951,8.7425,8.782499999999999,8.8025,8.8425,0.03999999999999915,0.03999999999999915 --MeTTeMJBNc/7,-MeTTeMJBNc,7,28,a,8.862500190734863,8.882499694824219,True,1.2338593417381832e-13,3.003864288330078,8.862501392958345,8.882501392958348,8.862499999999999,8.862499999999999,8.8825,8.8825,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,29,beautiful,8.922499656677246,9.342499732971191,True,1.4680022876554372e-14,0.3573891818523407,8.922499999807949,9.342591336440261,8.9225,8.9225,9.3425,9.3425,0.0,0.0 --MeTTeMJBNc/7,-MeTTeMJBNc,7,30,frame.,9.40250015258789,9.842499732971191,True,2.176588553490233e-12,4.0,9.402788993999224,9.842498636710845,9.4025,9.4025,9.8425,9.8425,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,0,So,0.5625,0.7225000262260437,True,5.925417491739471e-13,1.2584871053695679,0.6104202949033318,0.7450530455063744,0.5625,0.7025,0.7224999999999999,0.8225,0.14,0.10000000000000009 --RfYyzHpjk4/11,-RfYyzHpjk4,11,1,get,1.0425000190734863,1.162500023841858,True,2.0000498653083287e-12,4.0,1.0352633548141328,1.157345829119535,1.0425,1.0425,1.1624999999999999,1.1624999999999999,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,2,your,1.222499966621399,1.462499976158142,True,3.061544344862971e-13,0.6502349972724915,1.2204229605923966,1.4590623617756209,1.2225,1.2225,1.4625,1.4625,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,3,friends,1.5425000190734863,1.8624999523162842,True,4.583915644523588e-14,0.25,1.5348954679823508,1.8560549491335907,1.5225,1.5425,1.8225,1.8625,0.020000000000000018,0.040000000000000036 --RfYyzHpjk4/11,-RfYyzHpjk4,11,4,lined,1.8825000524520874,2.0824999809265137,True,4.106438279694036e-12,4.0,1.8794013903302467,2.108598210328863,1.8825,1.8825,2.0825,2.1425,0.0,0.06000000000000005 --RfYyzHpjk4/11,-RfYyzHpjk4,11,5,"up,",2.1024999618530273,2.1624999046325684,True,2.6779603925011775e-14,0.25,2.132709439233833,2.1930046249082458,2.1025,2.1625,2.1625,2.2225,0.06000000000000005,0.06000000000000005 --RfYyzHpjk4/11,-RfYyzHpjk4,11,6,get,2.242500066757202,2.362499952316284,True,1.191502972675007e-11,4.0,2.236394349302283,2.3507283319084338,2.2025,2.2425,2.3025,2.3625,0.040000000000000036,0.05999999999999961 --RfYyzHpjk4/11,-RfYyzHpjk4,11,7,your,2.4024999141693115,2.5625,True,8.169106125062442e-13,1.7350194454193115,2.3922253609813726,2.5522580570477773,2.3625,2.4025,2.5225,2.5625,0.040000000000000036,0.040000000000000036 --RfYyzHpjk4/11,-RfYyzHpjk4,11,8,business,2.5824999809265137,2.9625000953674316,True,2.262260609961833e-13,0.4804767966270447,2.5807347769870606,2.959944910812453,2.5825,2.5825,2.9625,2.9625,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,9,colleagues,2.9825000762939453,3.502500057220459,True,1.0240140332906655e-11,4.0,2.980355311251425,3.5007947034242104,2.9825,2.9825,3.5025,3.5025,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,10,lined,3.5425000190734863,3.762500047683716,True,1.963730014101911e-13,0.4170725345611572,3.540848027479565,3.7591541239086355,3.5425,3.5425,3.7225,3.7625,0.0,0.040000000000000036 --RfYyzHpjk4/11,-RfYyzHpjk4,11,11,up,3.802500009536743,3.862499952316284,True,2.895594734039081e-13,0.6149893403053284,3.8011925535359468,3.8611960341643234,3.8025,3.8025,3.8625,3.8625,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,12,and,3.9024999141693115,4.042500019073486,True,4.708365858654973e-13,1.0,3.9015861885760605,4.035220914868527,3.9025,3.9025,4.022500000000001,4.0425,0.0,0.019999999999999574 --RfYyzHpjk4/11,-RfYyzHpjk4,11,13,have,4.122499942779541,4.262499809265137,True,1.2910945099489646e-13,0.27421286702156067,4.1214453840766305,4.266966117222006,4.1225000000000005,4.1225000000000005,4.2625,4.282500000000001,0.0,0.020000000000000462 --RfYyzHpjk4/11,-RfYyzHpjk4,11,14,a,4.302499771118164,4.322500228881836,True,1.385675343863529e-14,0.25,4.302061162786527,4.322061162786528,4.3025,4.3025,4.322500000000001,4.322500000000001,0.0,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,15,good,4.442500114440918,4.762499809265137,True,2.0156643281560305e-12,4.0,4.440412188277476,4.761898046857764,4.4225,4.442500000000001,4.7625,4.7625,0.020000000000000462,0.0 --RfYyzHpjk4/11,-RfYyzHpjk4,11,16,time.,4.84250020980835,5.042500019073486,True,3.757925221004044e-11,4.0,4.8422608498843465,5.0426273492741265,4.8425,4.8425,5.0425,5.0425,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,0,It's,0.48249998688697815,0.5824999809265137,True,1.4532529624133872e-09,4.0,0.29136950661161193,0.581764070401369,0.0025000000000000005,0.4825,0.5824999999999999,0.5824999999999999,0.48,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,1,"pretty,",0.6025000214576721,0.9624999761581421,True,1.087615783025575e-13,0.44241130352020264,0.602707872197142,0.9658396862173649,0.6024999999999999,0.6024999999999999,0.9624999999999999,1.0025,0.0,0.040000000000000036 --RfYyzHpjk4/8,-RfYyzHpjk4,8,2,pretty,0.9825000166893005,1.3025000095367432,True,1.235450950527392e-13,0.502546489238739,0.9901859507557427,1.3023208099968344,0.9824999999999999,1.0425,1.3025,1.3025,0.06000000000000005,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,3,easy,1.3424999713897705,1.5225000381469727,True,1.3342861429448127e-13,0.54274982213974,1.3425056143864518,1.5225050163964364,1.3425,1.3425,1.5225,1.5225,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,4,to,1.5625,1.6425000429153442,True,1.0378338101836148e-14,0.25,1.5678174351393568,1.645624593702227,1.5625,1.6225,1.6425,1.6824999999999999,0.06000000000000005,0.039999999999999813 --RfYyzHpjk4/8,-RfYyzHpjk4,8,5,do,1.7024999856948853,1.8025000095367432,True,5.799786649204886e-13,2.359189033508301,1.702500208385816,1.8025001764746025,1.7025,1.7025,1.8025,1.8025,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,6,but,1.8825000524520874,2.002500057220459,True,9.461694292910595e-15,0.25,1.8798579047304698,2.0026025026774623,1.8625,1.8825,2.0025,2.0025,0.020000000000000018,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,7,you,2.0425000190734863,2.1624999046325684,True,3.8030705971567325e-13,1.5469814538955688,2.0420726874845885,2.161792162433309,2.0425,2.0425,2.1625,2.1625,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,8,got,2.202500104904175,2.302500009536743,True,2.636872518295766e-14,0.25,2.2024992287053813,2.3025002711087743,2.2025,2.2025,2.3025,2.3025,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,9,to,2.3424999713897705,2.4024999141693115,True,5.37920436546966e-14,0.25,2.342231385558277,2.402500610322223,2.3425,2.3425,2.4025,2.4025,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,10,set,2.442500114440918,2.6024999618530273,True,8.723621326180153e-13,3.5485222339630127,2.4425000521424303,2.6025000862422343,2.4425,2.4425,2.6025,2.6025,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,11,up,2.682499885559082,2.762500047683716,True,1.8668987796649494e-12,4.0,2.6824947822719523,2.762491370232272,2.6825,2.6825,2.7625,2.7625,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,12,those,2.8424999713897705,3.0225000381469727,True,4.680378805875518e-13,1.9038455486297607,2.8410879425174627,3.021367438715059,2.8425,2.8425,3.0225,3.0225,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,13,numbers,3.0425000190734863,3.382499933242798,True,3.799781940917041e-13,1.54564368724823,3.0424927295428903,3.382497615488898,3.0425,3.0425,3.3825,3.3825,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,14,ahead,3.422499895095825,3.622499942779541,True,2.4583815343669213e-13,1.0,3.4224973293396848,3.623183535894426,3.4225,3.4225,3.6225,3.6225,0.0,0.0 --RfYyzHpjk4/8,-RfYyzHpjk4,8,15,of,3.862499952316284,3.942500114440918,True,4.153858246952469e-12,4.0,3.8543357537960174,3.9395657009074068,3.7025,3.8625,3.9225,3.9425,0.1599999999999997,0.020000000000000018 --RfYyzHpjk4/8,-RfYyzHpjk4,8,16,time.,3.9825000762939453,4.522500038146973,True,2.1563972124865466e-13,0.8771613240242004,3.987601471873267,4.522151300490132,3.9825,3.9825,4.522500000000001,4.522500000000001,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,0,You,0.5024999976158142,0.6025000214576721,True,3.148869782542557e-13,0.5932096838951111,0.4641556042763561,0.6001832615386288,0.2025,0.5025,0.5824999999999999,0.6024999999999999,0.29999999999999993,0.020000000000000018 --RfYyzHpjk4/2,-RfYyzHpjk4,2,1,can,0.6225000023841858,0.7225000262260437,True,5.3242779109174965e-12,4.0,0.6224966097439738,0.722793687589131,0.6224999999999999,0.6224999999999999,0.7224999999999999,0.7224999999999999,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,2,make,0.762499988079071,0.9024999737739563,True,9.325262784187771e-12,4.0,0.7624999520131719,0.902500457618032,0.7625,0.7625,0.9025,0.9025,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,3,a,0.9225000143051147,0.9424999952316284,True,5.164016322846063e-14,0.25,0.9225004608316872,0.9425004608316874,0.9225,0.9225,0.9425,0.9425,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,4,conference,0.9624999761581421,1.3624999523162842,True,2.2286452197595175e-12,4.0,0.9625004885635444,1.36250528925742,0.9624999999999999,0.9624999999999999,1.3625,1.3625,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,5,call,1.3825000524520874,1.5225000381469727,True,1.5288667684285051e-12,2.8802037239074707,1.3825053283407196,1.5225057774228026,1.3825,1.3825,1.5225,1.5225,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,6,but,1.5625,1.662500023841858,True,3.5439503436908437e-13,0.6676381826400757,1.5625044965401071,1.6625261687119126,1.5625,1.5625,1.6625,1.6625,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,7,it,1.6825000047683716,1.7424999475479126,True,1.332392182695763e-11,4.0,1.682547174299906,1.7425472119934313,1.6824999999999999,1.6824999999999999,1.7425,1.7425,0.0,0.0 --RfYyzHpjk4/2,-RfYyzHpjk4,2,8,takes,1.7625000476837158,1.9424999952316284,True,1.057303116586139e-11,4.0,1.762547757390463,1.9664725912063723,1.7625,1.7625,1.9425,2.0025,0.0,0.06000000000000005 --RfYyzHpjk4/2,-RfYyzHpjk4,2,9,quite,1.9824999570846558,2.182499885559082,True,8.99263163933739e-11,4.0,1.9998589771697997,2.2030919285306947,1.9825,2.0225,2.1825,2.2425,0.040000000000000036,0.06000000000000005 --RfYyzHpjk4/2,-RfYyzHpjk4,2,10,a,2.202500104904175,2.2225000858306885,True,6.754598508122711e-14,0.25,2.2377339721451066,2.257733972145132,2.2025,2.2825,2.2225,2.3025,0.08000000000000007,0.08000000000000007 --RfYyzHpjk4/2,-RfYyzHpjk4,2,11,bit,2.242500066757202,2.3424999713897705,True,5.308189868560853e-13,1.0,2.2777869381841898,2.3777903079199927,2.2425,2.3225,2.3425,2.4225,0.07999999999999963,0.08000000000000007 --RfYyzHpjk4/2,-RfYyzHpjk4,2,12,of,2.362499952316284,2.422499895095825,True,2.1314319666870807e-13,0.4015364944934845,2.402322583593929,2.4623288813321236,2.3625,2.4825,2.4225,2.5425,0.1200000000000001,0.1200000000000001 --RfYyzHpjk4/2,-RfYyzHpjk4,2,13,"planning,",2.442500114440918,2.802500009536743,True,8.171992948871956e-14,0.25,2.491071073576032,2.876893066167285,2.4425,2.5825,2.8025,2.9825,0.14000000000000012,0.17999999999999972 --RfYyzHpjk4/2,-RfYyzHpjk4,2,14,well,2.8424999713897705,2.9825000762939453,True,1.5282908402344808e-13,0.28791186213493347,2.9062972987384375,3.0472606332625687,2.8225,3.0025,2.9825,3.1425,0.18000000000000016,0.16000000000000014 --RfYyzHpjk4/2,-RfYyzHpjk4,2,15,not,3.002500057220459,3.1024999618530273,True,1.5203597658174778e-13,0.28641775250434875,3.0757937390130943,3.190636335135603,3.0025,3.1825,3.1025,3.3225000000000002,0.18000000000000016,0.2200000000000002 --RfYyzHpjk4/2,-RfYyzHpjk4,2,16,a,3.122499942779541,3.1424999237060547,True,5.646774280804179e-12,4.0,3.2204657819657214,3.2404657819657814,3.1225,3.3625,3.1425,3.3825,0.23999999999999977,0.23999999999999977 --RfYyzHpjk4/2,-RfYyzHpjk4,2,17,lot,3.182499885559082,3.322499990463257,True,2.893943290842478e-14,0.25,3.284991537547337,3.429295019753589,3.1825,3.4625,3.3025,3.6225,0.2799999999999998,0.31999999999999984 --RfYyzHpjk4/2,-RfYyzHpjk4,2,18,of,3.362499952316284,3.442500114440918,True,7.363686049810525e-12,4.0,3.5028824001740775,3.5721282708109663,3.3625,3.7225,3.4225,3.7825,0.3600000000000003,0.3600000000000003 --RfYyzHpjk4/2,-RfYyzHpjk4,2,19,planning,3.4625000953674316,3.882499933242798,True,4.804686379658585e-13,0.9051459431648254,3.6038900191011245,4.016084286438504,3.4625,3.8225000000000002,3.8825,4.242500000000001,0.3600000000000003,0.36000000000000076 --RfYyzHpjk4/2,-RfYyzHpjk4,2,20,it's,3.9825000762939453,4.162499904632568,True,4.394659983142368e-12,4.0,4.101904154902036,4.272254213499532,3.9825,4.3025,4.1625000000000005,4.442500000000001,0.3200000000000003,0.28000000000000025 --RfYyzHpjk4/2,-RfYyzHpjk4,2,21,pretty,4.28249979019165,4.582499980926514,True,3.0015099021681035e-13,0.5654488801956177,4.3755106093167,4.666803222682432,4.282500000000001,4.522500000000001,4.5825000000000005,4.822500000000001,0.2400000000000002,0.2400000000000002 --RfYyzHpjk4/2,-RfYyzHpjk4,2,22,easy,4.602499961853027,4.802499771118164,True,1.904853228276715e-13,0.35885176062583923,4.695244949059977,4.955538123762717,4.602500000000001,4.862500000000001,4.8025,5.202500000000001,0.2599999999999998,0.40000000000000036 --RfYyzHpjk4/2,-RfYyzHpjk4,2,23,to,5.182499885559082,5.262499809265137,True,6.027319702367473e-12,4.0,5.204784669311505,5.2778043991771115,5.1825,5.242500000000001,5.2625,5.3025,0.0600000000000005,0.040000000000000036 --RfYyzHpjk4/2,-RfYyzHpjk4,2,24,do.,5.322500228881836,5.382500171661377,True,6.078794152070133e-12,4.0,5.321774080990339,5.382084433603389,5.322500000000001,5.322500000000001,5.3825,5.3825,0.0,0.0 --UUCSKoHeMA/0,-UUCSKoHeMA,0,0,I,1.2625000476837158,1.2825000286102295,True,2.3367625542891624e-11,4.0,1.2625116119279252,1.282511611927958,1.2625,1.2625,1.2825,1.2825,0.0,0.0 --UUCSKoHeMA/0,-UUCSKoHeMA,0,1,mentioned,1.3424999713897705,1.9824999570846558,True,4.0802847212084714e-12,4.0,1.3425128709237222,1.9809538948415186,1.3425,1.3425,1.9025,2.0025,0.0,0.09999999999999987 --UUCSKoHeMA/0,-UUCSKoHeMA,0,2,a,2.0625,2.0824999809265137,True,1.506490071578881e-11,4.0,2.0622694400311663,2.082269440031173,2.0625,2.0625,2.0825,2.0825,0.0,0.0 --UUCSKoHeMA/0,-UUCSKoHeMA,0,3,word,2.122499942779541,2.442500114440918,True,2.1203724269013707e-13,0.25,2.122499953395551,2.4426974718634,2.1225,2.1225,2.4425,2.4425,0.0,0.0 --UUCSKoHeMA/0,-UUCSKoHeMA,0,4,in,2.5225000381469727,2.702500104904175,True,7.434052830788962e-13,0.8034750819206238,2.584625951615478,2.729305029738961,2.5225,2.6825,2.7025,2.8225,0.16000000000000014,0.11999999999999966 --UUCSKoHeMA/0,-UUCSKoHeMA,0,5,the,2.802500009536743,2.9024999141693115,True,3.7196781028578374e-13,0.40202412009239197,2.8131936626394123,2.9242575602781646,2.8025,2.9225,2.9025,3.1425,0.11999999999999966,0.2400000000000002 --UUCSKoHeMA/0,-UUCSKoHeMA,0,6,last,2.922499895095825,3.202500104904175,True,1.1070697310266997e-12,1.1965248584747314,2.9627315540783767,3.240944821647639,2.9225,3.3625,3.2025,3.6225,0.43999999999999995,0.41999999999999993 --UUCSKoHeMA/0,-UUCSKoHeMA,0,7,clip,3.362499952316284,3.622499942779541,True,4.579349486416584e-12,4.0,3.3990179872718147,3.655597836538976,3.3625,3.7625,3.6225,3.9825,0.40000000000000036,0.3599999999999999 --UUCSKoHeMA/0,-UUCSKoHeMA,0,8,called,3.682499885559082,4.042500019073486,True,3.3319351509623896e-13,0.36011672019958496,3.721438804024425,4.085925612332038,3.6825,4.1025,4.0425,4.522500000000001,0.41999999999999993,0.4800000000000004 --UUCSKoHeMA/0,-UUCSKoHeMA,0,9,"'monotone',",4.102499961853027,4.962500095367432,True,2.0431005498533494e-10,4.0,4.159192072906399,5.018715781241488,4.1025,4.562500000000001,4.9625,5.5025,0.46000000000000085,0.54 --UUCSKoHeMA/0,-UUCSKoHeMA,0,10,and,5.042500019073486,5.542500019073486,True,1.1826091050447934e-13,0.25,5.092525670092953,5.5518883043060585,5.0425,5.5825000000000005,5.522500000000001,5.702500000000001,0.54,0.17999999999999972 --UUCSKoHeMA/0,-UUCSKoHeMA,0,11,I,5.582499980926514,5.602499961853027,True,7.732506995816735e-11,4.0,5.606475013180348,5.626475013180339,5.5825000000000005,5.8425,5.602500000000001,5.862500000000001,0.2599999999999998,0.2599999999999998 --UUCSKoHeMA/0,-UUCSKoHeMA,0,12,want,5.622499942779541,5.862500190734863,True,5.911523983000155e-13,0.6389195919036865,5.658054633318278,5.887746639201687,5.6225000000000005,5.8825,5.862500000000001,6.102500000000001,0.2599999999999998,0.2400000000000002 --UUCSKoHeMA/0,-UUCSKoHeMA,0,13,to,6.022500038146973,6.102499961853027,True,3.9711322338126243e-13,0.4292013645172119,6.043176971260156,6.1243497078647895,6.022500000000001,6.2625,6.102500000000001,6.3425,0.23999999999999932,0.23999999999999932 --UUCSKoHeMA/0,-UUCSKoHeMA,0,14,talk,6.202499866485596,6.422500133514404,True,3.8754988259358247e-13,0.41886529326438904,6.231623776195076,6.445624482990393,6.202500000000001,6.522500000000001,6.4225,6.6825,0.3200000000000003,0.2599999999999998 --UUCSKoHeMA/0,-UUCSKoHeMA,0,15,about,6.462500095367432,6.682499885559082,True,1.5894668122917038e-13,0.25,6.486890400692468,6.713296690480922,6.442500000000001,6.742500000000001,6.6825,6.982500000000001,0.2999999999999998,0.3000000000000007 --UUCSKoHeMA/0,-UUCSKoHeMA,0,16,that,6.922500133514404,7.182499885559082,True,2.632421711168398e-11,4.0,6.914653107260793,7.186191805860277,6.8825,7.0425,7.1825,7.2225,0.16000000000000014,0.040000000000000036 --UUCSKoHeMA/0,-UUCSKoHeMA,0,17,briefly.,7.28249979019165,7.682499885559082,True,2.8430669277157428e-12,3.0727968215942383,7.266711655048036,7.682512766971292,7.2225,7.282500000000001,7.6825,7.6825,0.0600000000000005,0.0 --ri04Z7vwnc/0,-ri04Z7vwnc,0,0,going,0.0024999999441206455,0.18250000476837158,True,5.097316457813861e-10,1.124098539352417,0.024398001194100988,0.2924496876375644,0.0025000000000000005,0.0825,0.2025,0.4225,0.08,0.21999999999999997 --ri04Z7vwnc/0,-ri04Z7vwnc,0,1,to,0.20250000059604645,0.26249998807907104,True,4.4608275406865516e-10,0.983735203742981,0.3627568244570231,0.44950644808341267,0.2425,0.5025,0.3225,0.6024999999999999,0.25999999999999995,0.2799999999999999 --ri04Z7vwnc/0,-ri04Z7vwnc,0,2,get,0.32249999046325684,0.48249998688697815,True,4.529311370404798e-10,0.9988377690315247,0.5278888053411263,0.7010825325354665,0.3625,0.7025,0.5025,0.9824999999999999,0.34,0.48 --ri04Z7vwnc/0,-ri04Z7vwnc,0,3,married,0.6625000238418579,1.402500033378601,True,4.135821685125052e-10,0.9120624661445618,0.8307791916512809,1.3855115536618379,0.5625,1.1824999999999999,1.0425,1.5825,0.6199999999999999,0.54 --ri04Z7vwnc/0,-ri04Z7vwnc,0,4,or,1.4225000143051147,1.5225000381469727,True,4.414137388941697e-10,0.9734387397766113,1.4655582811151127,1.5522323688932067,1.2025,1.6824999999999999,1.3025,1.7625,0.48,0.45999999999999996 --ri04Z7vwnc/0,-ri04Z7vwnc,0,5,are,1.6825000047683716,1.8025000095367432,True,6.311645650569631e-10,1.3918914794921875,1.633247732983863,1.7776768200035915,1.3825,1.8225,1.5425,1.9625,0.43999999999999995,0.41999999999999993 --ri04Z7vwnc/0,-ri04Z7vwnc,0,6,already,1.8224999904632568,2.0824999809265137,True,4.5398515502448333e-10,1.0011621713638306,1.84121973750598,2.23857055338298,1.6425,2.0025,2.0425,2.3825,0.3599999999999999,0.33999999999999986 --ri04Z7vwnc/0,-ri04Z7vwnc,0,7,married,2.182499885559082,2.7225000858306885,True,4.677451204138094e-10,1.0315066576004028,2.3092338374060906,2.7215355637832084,2.1425,2.4425,2.6625,2.7425,0.2999999999999998,0.08000000000000007 --ri04Z7vwnc/2,-ri04Z7vwnc,2,0,most,0.042500000447034836,0.2824999988079071,True,3.2669008557389967e-12,0.9973236918449402,0.03533530632279461,0.2915191734222421,0.0025000000000000005,0.0825,0.1625,0.3425,0.08,0.18000000000000002 --ri04Z7vwnc/2,-ri04Z7vwnc,2,1,people,0.3824999928474426,0.8424999713897705,True,3.1715836554258026e-12,0.9682251214981079,0.37951785526938786,0.8040705105858642,0.3425,0.4225,0.6224999999999999,0.8825,0.07999999999999996,0.26 --ri04Z7vwnc/2,-ri04Z7vwnc,2,2,think,0.8824999928474426,1.3025000095367432,True,1.2376642679473582e-12,0.377835750579834,0.880992595479909,1.2249863793838607,0.6825,0.9225,0.8825,1.3425,0.24,0.4600000000000001 --ri04Z7vwnc/2,-ri04Z7vwnc,2,3,it'll,1.402500033378601,1.7825000286102295,True,2.4132093545681244e-10,4.0,1.3095751724347637,1.530903636824568,0.9225,1.4025,1.3025,1.7825,0.4800000000000001,0.48 --ri04Z7vwnc/2,-ri04Z7vwnc,2,4,sort,1.8624999523162842,2.302500009536743,True,4.662647004605169e-12,1.4234188795089722,1.606748818471774,1.8158763538890752,1.3825,1.8625,1.6824999999999999,2.0825,0.48,0.40000000000000013 --ri04Z7vwnc/2,-ri04Z7vwnc,2,5,itself,2.322499990463257,2.5425000190734863,True,2.164716756541951e-12,0.660847544670105,1.8871830849169948,2.3170325866349173,1.8425,2.1625,2.1625,2.4625,0.32000000000000006,0.2999999999999998 --ri04Z7vwnc/2,-ri04Z7vwnc,2,6,"out,",2.5625,2.702500104904175,True,1.7914156486345534e-12,0.5468856692314148,2.372672664661856,2.4993875621536525,2.2225,2.4825,2.3625,2.6025,0.2599999999999998,0.2400000000000002 --ri04Z7vwnc/2,-ri04Z7vwnc,2,7,"""we'll",2.7225000858306885,2.862499952316284,True,7.49354953089032e-11,4.0,2.5485761578230206,2.735453096562189,2.4025,2.6225,2.5825,2.8025,0.2200000000000002,0.2200000000000002 --ri04Z7vwnc/2,-ri04Z7vwnc,2,8,discuss,2.882499933242798,3.1424999237060547,True,1.244877790261556e-12,0.38003790378570557,2.769634619145668,3.0783780752313397,2.6025,2.8425,2.9625,3.1425,0.23999999999999977,0.18000000000000016 --ri04Z7vwnc/2,-ri04Z7vwnc,2,9,it,3.1624999046325684,3.2225000858306885,True,3.2844339227511288e-12,1.002676248550415,3.1168374269808057,3.182508050326935,3.0225,3.1625,3.1025,3.2425,0.14000000000000012,0.14000000000000012 --ri04Z7vwnc/2,-ri04Z7vwnc,2,10,"later,""",3.242500066757202,3.422499895095825,True,6.055528040810332e-12,1.8486393690109253,3.213576245227149,3.4151321092630402,3.1425,3.2825,3.3425,3.5025,0.14000000000000012,0.16000000000000014 --ri04Z7vwnc/2,-ri04Z7vwnc,2,11,it's,3.442500114440918,3.5425000190734863,True,1.0349616302862685e-10,4.0,3.451680714649028,3.576908987243269,3.3625,3.5425,3.4825,3.7225,0.18000000000000016,0.2400000000000002 --ri04Z7vwnc/2,-ri04Z7vwnc,2,12,not,3.5625,3.702500104904175,True,1.5995723625858438e-12,0.4883195161819458,3.6143920521257327,3.7361958558643753,3.5225,3.7825,3.6425,3.9025,0.26000000000000023,0.2599999999999998 --ri04Z7vwnc/2,-ri04Z7vwnc,2,13,something,3.742500066757202,4.422500133514404,True,2.2604188486263777e-12,0.6900635957717896,3.777755699176747,4.436873888478261,3.7025,3.9625,4.402500000000001,4.4625,0.2599999999999998,0.05999999999999961 --ri04Z7vwnc/2,-ri04Z7vwnc,2,14,too,4.84250020980835,5.022500038146973,True,2.015573125069281e-10,4.0,4.78410811013368,4.995321876065659,4.482500000000001,4.8425,4.942500000000001,5.022500000000001,0.35999999999999943,0.08000000000000007 --ri04Z7vwnc/2,-ri04Z7vwnc,2,15,important,5.042500019073486,5.542500019073486,True,5.276962460748491e-12,1.6109578609466553,5.042487498132745,5.543272605933794,5.0425,5.0425,5.5425,5.5425,0.0,0.0 --ri04Z7vwnc/5,-ri04Z7vwnc,5,0,"""",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --ri04Z7vwnc/5,-ri04Z7vwnc,5,1,So,0.02250000089406967,0.08250000327825546,True,5.531704955208383e-11,4.0,0.06342992056271926,0.14143729395540497,0.0225,0.2225,0.0825,0.28250000000000003,0.2,0.2 --ri04Z7vwnc/5,-ri04Z7vwnc,5,2,I,0.2224999964237213,0.24250000715255737,True,1.6104359599339313e-12,0.4176913797855377,0.303244394241129,0.3232443942411471,0.2225,0.5225,0.2425,0.5425,0.29999999999999993,0.3 --ri04Z7vwnc/5,-ri04Z7vwnc,5,3,think,0.5224999785423279,0.8025000095367432,True,8.501941013185077e-13,0.25,0.5468122658585075,0.7930804306754985,0.5225,0.6425,0.7424999999999999,0.8424999999999999,0.12,0.09999999999999998 --ri04Z7vwnc/5,-ri04Z7vwnc,5,4,it's,0.8824999928474426,1.0824999809265137,True,3.3593516857166605e-11,4.0,0.8827280561963028,1.0827702576220137,0.8825,0.8825,1.0825,1.0825,0.0,0.0 --ri04Z7vwnc/5,-ri04Z7vwnc,5,5,important,1.162500023841858,1.662500023841858,True,6.419234935273188e-12,1.6649274826049805,1.1539397855459228,1.6509180681704647,1.1225,1.2225,1.5425,1.7225,0.09999999999999987,0.17999999999999994 --ri04Z7vwnc/5,-ri04Z7vwnc,5,6,to,2.322499990463257,2.4625000953674316,True,3.383222955261056e-12,0.8774909973144531,2.098755755525236,2.258181340358846,1.6625,2.3225,1.7225,2.4625,0.6599999999999997,0.74 --ri04Z7vwnc/5,-ri04Z7vwnc,5,7,discuss,2.5225000381469727,2.862499952316284,True,2.7650663876610526e-12,0.717162549495697,2.4394224473580834,2.792790803676593,2.3025,2.5225,2.6625,2.8625,0.21999999999999975,0.19999999999999973 --ri04Z7vwnc/5,-ri04Z7vwnc,5,8,finances,2.9024999141693115,3.322499990463257,True,4.327905221701567e-12,1.1225088834762573,2.866859719198108,3.2956421389284056,2.8025,2.9025,3.2225,3.4225,0.09999999999999964,0.19999999999999973 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,0,But,0.5625,0.7024999856948853,True,2.1161275043455469e-13,1.0,0.5496646247778796,0.702164602009161,0.5425,0.5625,0.7025,0.7025,0.020000000000000018,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,1,the,0.8424999713897705,1.402500033378601,True,6.26251817330975e-13,2.959423780441284,0.8420559143387191,1.406478256178167,0.8225,0.9225,1.4025,1.4224999999999999,0.09999999999999998,0.019999999999999796 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,2,hardest,1.4824999570846558,1.8424999713897705,True,1.8739304270647128e-13,0.8855470418930054,1.4825189926134468,1.8518094365299989,1.4825,1.4825,1.8425,1.8825,0.0,0.040000000000000036 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,3,thing,2.0225000381469727,2.262500047683716,True,8.017903425612885e-14,0.378895103931427,2.020780201902659,2.2611176783830214,2.0225,2.0225,2.2625,2.2625,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,4,in,2.322499990463257,2.422499895095825,True,6.493226502269842e-14,0.3068447709083557,2.320865793192878,2.422328440790031,2.3225,2.3225,2.4225,2.4825,0.0,0.06000000000000005 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,5,life,2.6024999618530273,3.202500104904175,True,2.1193372848771902e-13,1.0015168190002441,2.600471167683731,3.1896348019912866,2.6025,2.6025,3.2025,3.2025,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,6,to,3.262500047683716,3.3424999713897705,True,6.471526873413902e-14,0.3058193325996399,3.2608217425607964,3.3421634910399116,3.2625,3.2625,3.3425,3.3425,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,7,"learn,",3.4024999141693115,3.6624999046325684,True,3.52563437428996e-13,1.6660784482955933,3.4027175839840345,3.66329101504063,3.4025,3.4025,3.6625,3.6625,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,8,is,3.802500009536743,3.882499933242798,True,1.9704459689341008e-13,0.9311565160751343,3.80444740581124,3.884674702639147,3.8025,3.8025,3.8825,3.8825,0.0,0.0 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,9,to,3.9825000762939453,4.042500019073486,True,4.005100016826052e-13,1.892655372619629,3.973553794154936,4.04829152917298,3.9425,4.0025,4.0025,4.1225000000000005,0.0600000000000005,0.1200000000000001 --s9qJ7ATP7w/1,-s9qJ7ATP7w,1,10,lose,4.222499847412109,4.502500057220459,True,8.517983193789824e-12,4.0,4.222786002066446,4.50249136136073,4.2225,4.2225,4.5025,4.5025,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,0,they,0.042500000447034836,0.24250000715255737,True,1.2452759092992927e-12,1.144152045249939,0.035296410608304025,0.24507960324400196,0.0225,0.0625,0.2225,0.3025,0.04,0.07999999999999999 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,1,have,0.2824999988079071,0.5425000190734863,True,9.242065663120358e-13,0.8491554260253906,0.30454331855208594,0.5638608046103386,0.28250000000000003,0.3425,0.5425,0.6024999999999999,0.06,0.05999999999999994 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,2,"doubt,",0.7024999856948853,0.9225000143051147,True,2.921006535608339e-13,0.2683803141117096,0.7016751768490946,0.9389234569792527,0.7025,0.7025,0.9225,0.9824999999999999,0.0,0.05999999999999994 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,3,they,1.0824999809265137,1.3025000095367432,True,2.414696914643244e-12,2.218608856201172,1.0824749234219044,1.3025575291805016,1.0825,1.0825,1.3025,1.3025,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,4,have,1.402500033378601,1.8424999713897705,True,1.4843991921059674e-12,1.3638570308685303,1.480454771743787,1.8441928116392656,1.4025,1.5825,1.8425,1.8625,0.17999999999999994,0.020000000000000018 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,5,"fear,",1.8624999523162842,2.0824999809265137,True,6.6919907115714494e-12,4.0,1.8672921296696843,2.08198887608499,1.8625,1.9025,2.0825,2.0825,0.040000000000000036,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,6,they,2.122499942779541,2.2825000286102295,True,7.773068213395851e-13,0.7141848206520081,2.1272049813809994,2.285785138186709,2.1225,2.1625,2.2625,2.3225,0.040000000000000036,0.05999999999999961 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,7,lose,2.302500009536743,2.4825000762939453,True,2.6457091725773374e-12,2.430861711502075,2.3155930936973776,2.470759112376862,2.3025,2.3425,2.4425,2.5025,0.03999999999999959,0.06000000000000005 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,8,in,2.502500057220459,2.5824999809265137,True,1.923453930852137e-11,4.0,2.5130796331063547,2.582583582398577,2.4825,2.5225,2.5825,2.5825,0.040000000000000036,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,9,a,2.622499942779541,2.6424999237060547,True,2.0894479722810555e-12,1.9197721481323242,2.622205727070115,2.6422057270701145,2.6225,2.6225,2.6425,2.6425,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,10,Ferrari,2.6624999046325684,3.0824999809265137,True,4.712646288831945e-13,0.43299511075019836,2.680606250238554,3.083650895889502,2.6625,2.7025,3.0825,3.0825,0.040000000000000036,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,11,"race,",3.1024999618530273,3.242500066757202,True,9.314907786078797e-13,0.8558481335639954,3.103672235105714,3.2438796035307136,3.1025,3.1025,3.2425,3.2425,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,12,or,3.262500047683716,3.3424999713897705,True,4.8605338504037476e-11,4.0,3.264086176296221,3.344207480546319,3.2625,3.2625,3.3425,3.3425,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,13,they,3.422499895095825,3.682499885559082,True,2.5745997347947913e-11,4.0,3.414911960971616,3.675032305819791,3.3625,3.4225,3.6425,3.6825,0.06000000000000005,0.040000000000000036 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,14,lose,3.7825000286102295,3.942500114440918,True,7.575693002602468e-13,0.6960501074790955,3.7616974887882315,3.920602352332232,3.7225,3.7825,3.8825,3.9425,0.06000000000000005,0.06000000000000005 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,15,in,4.102499961853027,4.162499904632568,True,3.397961165912955e-12,3.122026205062866,4.0719655131892,4.156779322130816,3.9225,4.1025,4.1225000000000005,4.1825,0.18000000000000016,0.05999999999999961 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,16,a,4.202499866485596,4.222499847412109,True,5.129190258287841e-13,0.47126689553260803,4.214525556613766,4.23452555661377,4.202500000000001,4.2225,4.2225,4.242500000000001,0.019999999999999574,0.020000000000000462 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,17,"race,",4.522500038146973,4.78249979019165,True,8.075219095834973e-13,0.741946280002594,4.523863360616268,4.783031253821107,4.522500000000001,4.522500000000001,4.782500000000001,4.782500000000001,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,18,and,5.182499885559082,5.322500228881836,True,2.4335565572235207e-13,0.25,5.142079904620651,5.314934950906367,4.8025,5.1825,5.242500000000001,5.3425,0.3799999999999999,0.09999999999999964 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,19,then,5.422500133514404,5.5625,True,1.1495937936398942e-13,0.25,5.406993962189282,5.55731323856257,5.3025,5.4225,5.522500000000001,5.562500000000001,0.1200000000000001,0.040000000000000036 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,20,they,5.582499980926514,5.762499809265137,True,1.4886081950438168e-11,4.0,5.582500046565337,5.7607591757751635,5.5825000000000005,5.5825000000000005,5.742500000000001,5.7625,0.0,0.019999999999999574 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,21,just,5.78249979019165,5.922500133514404,True,1.4744784169670733e-12,1.3547419309616089,5.782499967123763,5.922500201867736,5.782500000000001,5.782500000000001,5.9225,5.9225,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,22,give,5.962500095367432,6.162499904632568,True,2.479647346828595e-13,0.25,5.962426812392603,6.162335078542887,5.9625,5.9625,6.1625000000000005,6.1625000000000005,0.0,0.0 --s9qJ7ATP7w/0,-s9qJ7ATP7w,0,23,up,6.642499923706055,6.702499866485596,True,5.418672780442557e-13,0.49786439538002014,6.61413642460844,6.68734623928928,6.5825000000000005,6.642500000000001,6.6625000000000005,6.702500000000001,0.0600000000000005,0.040000000000000036 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,0,I,0.0024999999441206455,0.02250000089406967,True,8.043445250127679e-12,4.0,0.0032990285590143776,0.023299028559016173,0.0025000000000000005,0.0025000000000000005,0.0225,0.0225,0.0,0.0 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,1,have,0.08250000327825546,0.42250001430511475,True,3.238681241593544e-12,4.0,0.06717380191828314,0.32357469353523305,0.0425,0.1225,0.1825,0.4225,0.07999999999999999,0.24 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,2,been,0.5224999785423279,0.7425000071525574,True,1.3483621771198662e-12,2.748244285583496,0.3951901849667031,0.6275678443671301,0.2225,0.5225,0.4225,0.7424999999999999,0.29999999999999993,0.31999999999999995 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,3,beat,0.7825000286102295,1.3424999713897705,True,1.4721684700284843e-13,0.3000587522983551,0.6899705310710409,1.079888780943723,0.5225,1.1225,0.7424999999999999,1.3425,0.6000000000000001,0.6000000000000001 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,4,up,1.3624999523162842,1.4225000143051147,True,6.7126171158019e-13,1.3681719303131104,1.1659770402041265,1.3613713122992432,0.7825,1.3625,1.2425,1.4224999999999999,0.5800000000000001,0.17999999999999994 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,5,and,1.462499976158142,1.6024999618530273,True,2.956292166362423e-13,0.602554202079773,1.4205737927255522,1.5482913645398126,1.3225,1.4625,1.4224999999999999,1.6025,0.1399999999999999,0.18000000000000016 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,6,put,1.7424999475479126,1.9424999952316284,True,5.560912516057448e-13,1.1334303617477417,1.6589005375801091,1.842611262769312,1.4625,1.7425,1.6025,2.0025,0.28,0.3999999999999999 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,7,down,2.0425000190734863,2.442500114440918,True,8.62298581605532e-14,0.25,1.9559455563234505,2.310078948483982,1.7425,2.0425,2.0025,2.4425,0.30000000000000004,0.43999999999999995 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,8,and,2.5824999809265137,2.702500104904175,True,1.233855411505308e-13,0.25148555636405945,2.5125746937182574,2.693039630571944,2.3225,2.6025,2.6025,2.7025,0.28000000000000025,0.10000000000000009 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,9,everything,2.8424999713897705,3.9825000762939453,True,5.351290564099484e-14,0.25,2.8684364082492393,4.011810221786433,2.8425,2.8425,3.9825,3.9825,0.0,0.0 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,10,that,4.902500152587891,5.122499942779541,True,5.439115737665423e-12,4.0,4.89797082813463,5.13239484504563,4.902500000000001,4.9625,5.102500000000001,5.1625000000000005,0.05999999999999961,0.05999999999999961 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,11,can,5.162499904632568,5.34250020980835,True,4.8560754619926885e-14,0.25,5.188221176465659,5.361559915166783,5.1625000000000005,5.3825,5.2625,5.5025,0.21999999999999975,0.2400000000000002 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,12,go,5.422500133514404,5.502500057220459,True,1.665226523296301e-13,0.3394080102443695,5.454580933666803,5.539748928566693,5.4225,5.602500000000001,5.5025,5.702500000000001,0.1800000000000006,0.20000000000000018 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,13,"wrong,",5.602499961853027,6.202499866485596,True,4.906267449603097e-13,1.0,5.65194480101253,6.236338881255996,5.602500000000001,6.1225000000000005,6.142500000000001,6.8025,0.5199999999999996,0.6599999999999993 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,14,has,6.702499866485596,6.802499771118164,True,7.809464338225103e-13,1.5917322635650635,6.7180709524906215,6.819550494578619,6.702500000000001,6.942500000000001,6.8025,7.062500000000001,0.2400000000000002,0.2600000000000007 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,15,gone,6.862500190734863,7.0625,True,6.044744240742139e-14,0.25,6.884818614020353,7.0893755044610485,6.862500000000001,7.142500000000001,7.062500000000001,7.4225,0.28000000000000025,0.35999999999999943 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,16,wrong,7.28249979019165,7.582499980926514,True,6.222165794753098e-13,1.2682076692581177,7.27870407791671,7.598153728057217,7.142500000000001,7.482500000000001,7.5825000000000005,7.8025,0.33999999999999986,0.21999999999999975 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,17,for,7.662499904632568,7.802499771118164,True,2.2805403941152103e-13,0.46482187509536743,7.688982736794739,7.83358602588385,7.6625000000000005,8.022499999999999,7.8025,8.202499999999999,0.35999999999999854,0.3999999999999986 --s9qJ7ATP7w/5,-s9qJ7ATP7w,5,18,me,8.142499923706055,8.262499809265137,True,6.399445209859245e-12,4.0,8.149809329284016,8.26073298564643,8.1425,8.2425,8.2425,8.362499999999999,0.09999999999999964,0.11999999999999922 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,0,I,0.02250000089406967,0.042500000447034836,True,2.5145097115597537e-10,4.0,0.022581619903897353,0.04258161990389827,0.0225,0.0225,0.0425,0.0425,0.0,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,1,have,0.10249999910593033,0.32249999046325684,True,5.2369983349898064e-11,4.0,0.10275901230479557,0.3234319644678103,0.10250000000000001,0.10250000000000001,0.3225,0.3225,0.0,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,2,lost,0.4025000035762787,0.8025000095367432,True,4.580078720797798e-13,1.0,0.5590338658826747,0.8117400282480208,0.4025,0.6224999999999999,0.7825,0.8624999999999999,0.21999999999999992,0.07999999999999996 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,3,at,0.9024999737739563,1.0425000190734863,True,8.964015510773415e-13,1.9571750164031982,0.937330588114691,1.0553245531559798,0.9025,1.0225,1.0425,1.0825,0.12,0.040000000000000036 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,4,everything,1.3224999904632568,2.0824999809265137,True,2.723942219787917e-13,0.5947369933128357,1.323579559459154,2.0836404722492556,1.3225,1.3425,2.0825,2.0825,0.020000000000000018,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,5,you,2.182499885559082,2.362499952316284,True,1.1392326155785365e-13,0.25,2.166667845312565,2.364468039998308,2.1425,2.1825,2.3625,2.3625,0.040000000000000036,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,6,can,2.5225000381469727,2.702500104904175,True,1.9086615222888882e-14,0.25,2.5339093670209367,2.762821360795334,2.4825,2.6225,2.7025,2.8025,0.14000000000000012,0.10000000000000009 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,7,"imagine,",3.002500057220459,3.382499933242798,True,3.490432942676591e-11,4.0,2.9764326793531897,3.3827679885419752,2.8825,3.0025,3.3825,3.3825,0.1200000000000001,0.0 --s9qJ7ATP7w/4,-s9qJ7ATP7w,4,8,seriously,3.442500114440918,4.022500038146973,True,2.7026327681888007e-13,0.5900843739509583,3.4400904252806708,4.025070808901028,3.4225,3.4425,4.022500000000001,4.022500000000001,0.020000000000000018,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,0,I,0.02250000089406967,0.042500000447034836,True,6.969980148596733e-11,4.0,0.02678660345931201,0.04678660345934946,0.0225,0.10250000000000001,0.0425,0.1225,0.08000000000000002,0.07999999999999999 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,1,used,0.3824999928474426,0.5425000190734863,True,3.881826110552211e-11,4.0,0.35018109647684703,0.5409254689733906,0.10250000000000001,0.3825,0.5425,0.5425,0.28,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,2,to,0.5625,0.6225000023841858,True,5.419344834001194e-11,4.0,0.5617484066259408,0.6218305196632183,0.5625,0.5625,0.6224999999999999,0.6224999999999999,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,3,work,0.6424999833106995,0.7825000286102295,True,1.2994504560923104e-13,0.25,0.6439510150136057,0.7956763526640003,0.6425,0.6425,0.7825,0.8225,0.0,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,4,for,0.862500011920929,0.9825000166893005,True,5.919409927501729e-13,0.25,0.8703185545526655,0.9934280704856465,0.8624999999999999,0.9025,0.9824999999999999,1.0425,0.040000000000000036,0.06000000000000005 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,5,Warren,1.0425000190734863,1.3624999523162842,True,2.076905487868874e-12,0.55162113904953,1.0499859516799703,1.362546218062043,1.0425,1.0825,1.3625,1.3625,0.040000000000000036,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,6,"Avis,",1.4424999952316284,1.6825000047683716,True,5.145964557251581e-11,4.0,1.441602626222574,1.6811464831337093,1.4425,1.4425,1.6824999999999999,1.6824999999999999,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,7,the,1.7024999856948853,1.9824999570846558,True,7.724032351219545e-12,2.0514845848083496,1.7012855259737578,1.9803489043794023,1.7025,1.7025,1.9825,1.9825,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,8,guy,2.0225000381469727,2.1424999237060547,True,1.3621283788400884e-11,3.6177804470062256,2.0202241581787876,2.141641797729933,2.0225,2.0225,2.1425,2.1425,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,9,who,2.1624999046325684,2.262500047683716,True,1.5639521622201613e-10,4.0,2.1618504322195777,2.2658099313974582,2.1625,2.1625,2.2625,2.2825,0.0,0.020000000000000018 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,10,started,2.302500009536743,2.5625,True,3.76403162111183e-12,0.9997177720069885,2.301007069455703,2.562164512343847,2.3025,2.3025,2.5625,2.5625,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,11,Avis,2.6024999618530273,2.7825000286102295,True,1.6464942256821935e-11,4.0,2.6021628830951244,2.7711338037144446,2.6025,2.6025,2.7425,2.7825,0.0,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,12,Rent,2.802500009536743,3.002500057220459,True,3.706247982127042e-12,0.9843705892562866,2.799981994850341,2.994845622631275,2.7825,2.8225,2.9625,3.0025,0.03999999999999959,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,13,a,3.0225000381469727,3.0425000190734863,True,6.146569798276547e-13,0.25,3.014930168179437,3.034930168179444,2.9825,3.0225,3.0025,3.0425,0.040000000000000036,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,14,"Car,",3.0625,3.1624999046325684,True,6.6249657669492645e-12,1.7595750093460083,3.062430806341528,3.1624378407080567,3.0625,3.0625,3.1625,3.1625,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,15,and,3.182499885559082,3.302500009536743,True,1.0457030207022822e-12,0.27773621678352356,3.1825015875733373,3.2987676736960596,3.1825,3.1825,3.2825,3.3025,0.0,0.020000000000000018 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,16,he,3.3424999713897705,3.4024999141693115,True,1.69514088096262e-12,0.4502253532409668,3.3416142026375746,3.402494144256177,3.3425,3.3425,3.4025,3.4025,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,17,said,3.442500114440918,3.622499942779541,True,7.551881754490342e-13,0.25,3.442500007657619,3.6225138017818206,3.4425,3.4425,3.6225,3.6225,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,18,to,3.702500104904175,3.762500047683716,True,1.4711676497793265e-14,0.25,3.6993621761372792,3.7652132808598355,3.6625,3.7425,3.7625,3.8025,0.08000000000000007,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,19,me,3.882499933242798,3.9625000953674316,True,3.844036200462142e-12,1.0209667682647705,3.882508254724707,3.9624960997761027,3.8825,3.8825,3.9625,3.9625,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,20,one,4.022500038146973,4.182499885559082,True,4.8639130917349505e-12,1.2918436527252197,4.022505987413522,4.182510136166168,4.022500000000001,4.022500000000001,4.1825,4.1825,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,21,"day,",4.922500133514404,5.042500019073486,True,8.347099920980039e-12,2.2169697284698486,4.921261247051621,5.042292247022992,4.9225,4.9225,5.0425,5.0425,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,22,"""Robert,",5.162499904632568,5.442500114440918,True,3.313618097359333e-13,0.25,5.157729190413535,5.437921281142969,5.1225000000000005,5.1625000000000005,5.402500000000001,5.442500000000001,0.040000000000000036,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,23,"""what's",5.502500057220459,5.802499771118164,True,2.250095726061274e-12,0.5976200699806213,5.493928773068351,5.787613456780464,5.4225,5.5025,5.6625000000000005,5.8025,0.08000000000000007,0.13999999999999968 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,24,the,5.822500228881836,5.922500133514404,True,2.3816506492663203e-13,0.25,5.8117797173499195,5.916011727614231,5.7225,5.822500000000001,5.862500000000001,5.9225,0.10000000000000053,0.05999999999999961 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,25,purpose,5.942500114440918,6.28249979019165,True,1.1442309773290749e-11,3.0390501022338867,5.94251287266894,6.286029911061819,5.942500000000001,5.942500000000001,6.282500000000001,6.322500000000001,0.0,0.040000000000000036 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,26,of,6.362500190734863,6.442500114440918,True,3.76615709105077e-12,1.0002822875976562,6.3626166902528665,6.4423161972541,6.362500000000001,6.362500000000001,6.442500000000001,6.442500000000001,0.0,0.0 --s9qJ7ATP7w/7,-s9qJ7ATP7w,7,27,business,6.462500095367432,6.822500228881836,True,2.3635134648036793e-12,0.6277435421943665,6.462403766646239,6.821232931946638,6.4625,6.4625,6.8025,6.822500000000001,0.0,0.020000000000000462 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,0,But,0.042500000447034836,0.2224999964237213,True,6.265210247304032e-13,0.2663658559322357,0.04595421401102171,0.19960157519223576,0.0025000000000000005,0.10250000000000001,0.1225,0.28250000000000003,0.1,0.16000000000000003 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,1,I,0.26249998807907104,0.2824999988079071,True,4.512839754666764e-13,0.25,0.3053911111772659,0.3253911111772663,0.2625,0.6625,0.28250000000000003,0.6825,0.39999999999999997,0.39999999999999997 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,2,just,0.6625000238418579,1.402500033378601,True,9.192134033109145e-12,3.9080424308776855,0.6529190020049375,1.3804359304662248,0.5625,1.0025,1.2825,1.4025,0.43999999999999995,0.1200000000000001 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,3,kept,1.4824999570846558,1.6425000429153442,True,2.3521070074278283e-12,1.0,1.4731671348482018,1.634072464256062,1.3425,1.4825,1.5425,1.6425,0.1399999999999999,0.10000000000000009 --s9qJ7ATP7w/6,-s9qJ7ATP7w,6,4,going,1.7024999856948853,2.262500047683716,True,6.741932099402215e-12,2.866337299346924,1.7043089382694716,2.2765034438543106,1.7025,1.7025,2.2625,2.3025,0.0,0.040000000000000036 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,0,"""",nan,nan,False,0.0,nan,nan,nan,nan,nan,nan,nan,nan,nan --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,1,And,0.12250000238418579,0.24250000715255737,True,5.430787416993432e-11,2.974587917327881,0.13211225042789249,0.25671074886264156,0.1225,0.1625,0.2425,0.3425,0.04000000000000001,0.10000000000000003 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,2,I,0.32249999046325684,0.3425000011920929,True,2.400587367779039e-11,1.3148661851882935,0.3668057281520321,0.3868057281532408,0.3225,0.5225,0.3425,0.5425,0.19999999999999996,0.19999999999999996 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,3,"said,",0.5224999785423279,0.7225000262260437,True,2.0516449650287427e-11,1.1237410306930542,0.5449670600283814,0.7418337194054421,0.5225,0.6625,0.6825,0.9025,0.14,0.21999999999999997 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,4,"""Oh,",0.8224999904632568,0.9024999737739563,True,1.968307895144905e-12,0.25,0.8328528775409426,0.9142629965513558,0.7825,0.9824999999999999,0.9025,1.0425,0.19999999999999996,0.14 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,5,make,0.9825000166893005,1.2625000476837158,True,1.3614405436346289e-10,4.0,0.9900671560139473,1.2648433600608693,0.9225,1.0825,1.2625,1.2625,0.16000000000000003,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,6,a,1.3424999713897705,1.3624999523162842,True,1.5998102148584437e-11,0.8762589693069458,1.342202284908815,1.36220228490884,1.3425,1.3425,1.3625,1.3625,0.0,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,7,"profit,",1.3825000524520874,1.8624999523162842,True,1.0078236856170264e-11,0.5520120859146118,1.3882990201214693,1.8605397868103712,1.3825,1.4224999999999999,1.8625,1.8625,0.039999999999999813,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,8,and,1.902500033378601,2.002500057220459,True,2.559467611809585e-12,0.25,1.9024795079759782,2.002565711600564,1.9025,1.9025,2.0025,2.0025,0.0,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,9,all,2.0625,2.1624999046325684,True,1.0053620262684415e-11,0.5506637692451477,2.0632850578056883,2.1639649354818697,2.0625,2.0625,2.1625,2.1625,0.0,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,10,these,2.202500104904175,2.382499933242798,True,1.4980766193523065e-12,0.25,2.2033509669407514,2.3840730034618955,2.2025,2.2025,2.3825,2.3825,0.0,0.0 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,11,other,2.4825000762939453,2.742500066757202,True,4.946603543443118e-11,2.7093875408172607,2.4818356624233613,2.7605229751379516,2.4825,2.4825,2.7425,2.7825,0.0,0.040000000000000036 --s9qJ7ATP7w/8,-s9qJ7ATP7w,8,12,things,2.762500047683716,3.122499942779541,True,4.533611680512806e-11,2.4831807613372803,2.795081236142752,3.131064936710411,2.7625,2.8425,3.1225,3.1625,0.07999999999999963,0.040000000000000036 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,0,it,0.48249998688697815,0.5625,True,2.2540422653372083e-11,0.62372887134552,0.48282851572821867,0.5627058230843515,0.4825,0.4825,0.5625,0.5625,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,1,comes,0.5824999809265137,0.8224999904632568,True,4.899627231713666e-11,1.3558037281036377,0.6122922089193678,0.8890952566150813,0.5824999999999999,0.6224999999999999,0.8025,1.1025,0.040000000000000036,0.30000000000000004 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,2,with,0.862500011920929,1.1024999618530273,True,5.177407114143051e-11,1.432669758796692,1.0343328058467511,1.2714862000398628,0.8624999999999999,2.0425,1.1025,2.3825,1.1800000000000002,1.2799999999999998 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,3,the,1.162500023841858,2.1424999237060547,True,1.6085549559008427e-11,0.4451123774051666,1.7534948157635741,2.317185306234759,1.1624999999999999,2.5025,2.1425,2.8225,1.34,0.6799999999999997 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,4,assistance,2.242500066757202,3.4625000953674316,True,5.7605586967213185e-11,1.5940369367599487,2.4377709212653507,3.5095971681953815,2.2425,2.9625,3.4025,3.6225,0.7199999999999998,0.2200000000000002 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,5,and,3.4825000762939453,3.5824999809265137,True,1.789636147608853e-11,0.4952203929424286,4.0819872324546465,4.286099413915198,3.4425,4.5425,3.5625,4.8425,1.1000000000000005,1.2800000000000002 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,6,agitation,4.542500019073486,5.402500152587891,True,5.5601932780202645e-11,1.5385926961898804,4.775625557132102,5.481898357717567,4.5425,5.0825000000000005,5.402500000000001,5.6625000000000005,0.54,0.2599999999999998 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,7,of,5.482500076293945,5.602499961853027,True,9.260540945188467e-11,2.5625369548797607,5.806335671074142,5.92265445706831,5.482500000000001,6.482500000000001,5.602500000000001,6.6225000000000005,1.0,1.0199999999999996 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,8,states,6.482500076293945,6.982500076293945,True,5.0344433483173745e-11,1.3931094408035278,6.551695071292202,6.893363711888713,6.482500000000001,6.8025,6.7225,7.0825000000000005,0.3199999999999994,0.3600000000000003 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,9,and,7.102499961853027,7.822500228881836,True,1.104089805692432e-10,3.055189609527588,6.97998107934995,7.432471150361345,6.8025,7.102500000000001,6.982500000000001,7.822500000000001,0.3000000000000007,0.8399999999999999 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,10,"regimes,",7.882500171661377,8.262499809265137,True,5.572348485416434e-11,1.5419561862945557,7.716863671576262,8.18288138010086,7.102500000000001,7.8825,8.1025,8.2625,0.7799999999999994,0.16000000000000014 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,11,Yesterday,8.302499771118164,8.842499732971191,True,9.80091181823628e-12,0.2712065875530243,8.243045019532943,8.802590784018477,8.1825,8.3025,8.782499999999999,8.8425,0.120000000000001,0.0600000000000005 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,12,Iran,9.302499771118164,9.442500114440918,True,9.267232814469395e-12,0.25643885135650635,9.28248466227814,9.428928899971622,9.3025,9.3025,9.442499999999999,9.442499999999999,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,13,announced,9.462499618530273,9.922499656677246,True,4.382011420389631e-11,1.2125712633132935,9.470828595188868,9.948668430727986,9.4625,9.5025,9.9225,10.0625,0.03999999999999915,0.14000000000000057 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,14,that,10.082500457763672,10.22249984741211,True,6.737973894110905e-12,0.25,10.112171472976984,10.317274030543697,10.0825,10.0825,10.2225,10.3425,0.0,0.11999999999999922 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,15,it,11.022500038146973,11.082500457763672,True,1.3952581277842935e-11,0.3860898017883301,10.98394079716456,11.063832396543807,10.9025,11.022499999999999,11.0425,11.1625,0.11999999999999922,0.11999999999999922 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,16,will,11.162500381469727,11.362500190734863,True,4.104548298466959e-11,1.135792851448059,11.193551591725347,11.379601260949299,11.1425,11.2625,11.362499999999999,11.5025,0.11999999999999922,0.14000000000000057 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,17,finance,11.422499656677246,11.762499809265137,True,2.8811382099536154e-11,0.7972561120986938,11.4400876158457,11.820485161778358,11.4225,11.5625,11.7625,11.9225,0.14000000000000057,0.16000000000000014 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,18,the,11.802499771118164,11.922499656677246,True,3.0040948895138087e-11,0.8312801718711853,11.866457503420186,11.978970130729623,11.782499999999999,11.942499999999999,11.9025,12.0825,0.16000000000000014,0.17999999999999972 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,19,families,11.942500114440918,12.382499694824219,True,3.755114622028266e-11,1.039099097251892,12.034518519610577,12.432086350730916,11.942499999999999,12.1025,12.3825,12.6425,0.16000000000000014,0.2599999999999998 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,20,of,12.462499618530273,12.542499542236328,True,2.093970396382927e-12,0.25,12.519979089225814,12.600236932231414,12.4625,12.7225,12.5425,12.8225,0.2599999999999998,0.27999999999999936 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,21,the,12.6225004196167,12.822500228881836,True,1.0107663404174128e-12,0.25,12.683711846600158,12.879735837706036,12.6225,13.6025,12.8225,13.7625,0.9799999999999986,0.9399999999999995 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,22,terrorists,13.582500457763672,14.102499961853027,True,3.2193830934446055e-11,0.890853762626648,13.48057177307044,14.12193960941006,12.9225,13.8025,14.022499999999999,14.3225,0.8800000000000008,0.3000000000000007 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,23,and,14.242500305175781,14.442500114440918,True,6.540654823306014e-11,1.8099017143249512,14.243990294329773,14.439934456951706,14.1625,14.3825,14.3225,14.5025,0.22000000000000064,0.17999999999999972 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,24,murderers,14.602499961853027,15.202500343322754,True,5.3976757091733774e-11,1.4936214685440063,14.574493863698141,15.18575901971097,14.3825,14.6025,15.1625,15.2425,0.21999999999999886,0.08000000000000007 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,25,this,15.282500267028809,15.662500381469727,True,9.564676828333063e-11,2.646696090698242,15.271233298767404,15.507784351694662,15.2225,15.2825,15.4625,15.6625,0.0600000000000005,0.1999999999999993 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,26,shows,15.762499809265137,16.162500381469727,True,1.070308425221711e-10,2.9617111682891846,15.638094435743163,15.937968171367956,15.4825,15.7625,15.7425,16.1625,0.27999999999999936,0.4200000000000017 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,27,that,16.22249984741211,16.422500610351562,True,1.4821713301138573e-10,4.0,16.03565573699944,16.203248310703422,15.7625,16.2225,16.1625,16.4025,0.46000000000000085,0.23999999999999844 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,28,"Iran,",16.502500534057617,16.72249984741211,True,4.210410839422529e-11,1.1650867462158203,16.26544869858648,16.458665519694218,16.2225,16.5025,16.3625,16.7225,0.28000000000000114,0.35999999999999943 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,29,even,17.5625,17.762500762939453,True,9.684803653486895e-11,2.6799371242523193,16.61354563803406,16.827777791718276,16.5025,17.5625,16.7225,17.762500000000003,1.0599999999999987,1.0400000000000027 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,30,after,17.822500228881836,18.002500534057617,True,3.2281885498086638e-12,0.25,17.554003479065504,17.80643906509736,17.5225,17.8225,17.762500000000003,18.0025,0.3000000000000007,0.23999999999999844 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,31,the,18.0625,18.202499389648438,True,1.899481093248223e-11,0.5256162285804749,17.860950102720363,18.01428792989417,17.8225,18.0625,17.962500000000002,18.2025,0.23999999999999844,0.23999999999999844 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,32,nuclear,18.282499313354492,18.622499465942383,True,7.600799330209629e-12,0.25,18.086117188736683,18.544572324433087,18.0625,18.282500000000002,18.5225,18.622500000000002,0.22000000000000242,0.10000000000000142 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,33,"agreement,",18.6825008392334,19.502500534057617,True,1.3309903179603566e-10,3.683058977127075,18.689252254938246,19.452889571682917,18.622500000000002,18.6825,19.422500000000003,19.5025,0.05999999999999872,0.0799999999999983 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,34,is,19.6825008392334,19.762500762939453,True,6.084657951099803e-12,0.25,19.557316484633294,19.728965377547198,19.4825,19.6825,19.7025,19.762500000000003,0.1999999999999993,0.060000000000002274 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,35,continuing,20.202499389648438,20.662500381469727,True,5.529132013348814e-11,1.5299975872039795,20.186404995029186,20.684973994192696,20.2025,20.2025,20.6625,20.742500000000003,0.0,0.08000000000000185 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,36,to,21.502500534057617,21.582500457763672,True,9.673984946445557e-10,4.0,21.502510645887668,21.582509151446565,21.5025,21.5025,21.582500000000003,21.582500000000003,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,37,aid,21.662500381469727,21.782499313354492,True,4.237645789828548e-10,4.0,21.662507714897142,21.788616675529326,21.6625,21.6625,21.782500000000002,21.8225,0.0,0.03999999999999915 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,38,"terrorism,",21.842500686645508,22.342500686645508,True,9.071070283805938e-12,0.2510107457637787,21.838637629170766,22.378070868609647,21.802500000000002,21.8625,22.3425,22.442500000000003,0.05999999999999872,0.10000000000000142 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,39,including,22.6825008392334,23.162500381469727,True,6.269147527493413e-12,0.25,22.67344056762576,23.179853509455537,22.6825,22.6825,23.1625,23.262500000000003,0.0,0.10000000000000142 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,40,Palestinian,23.342500686645508,24.042499542236328,True,1.5516138721083372e-11,0.4293558895587921,23.341317527591546,24.04398988161638,23.3425,23.3425,24.0025,24.082500000000003,0.0,0.08000000000000185 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,41,"terrorism,",24.102500915527344,24.582500457763672,True,2.0350023749449164e-11,0.5631170868873596,24.10249792843023,24.583562825209636,24.102500000000003,24.102500000000003,24.582500000000003,24.582500000000003,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,42,Hezbollah,24.702499389648438,25.262500762939453,True,8.40829662840381e-12,0.25,24.68922904288737,25.2904581805427,24.642500000000002,24.7025,25.2025,25.3825,0.05999999999999872,0.17999999999999972 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,43,terrorism,25.322500228881836,26.002500534057617,True,7.838881627142413e-11,2.1691415309906006,25.346885974875732,26.004277391559278,25.3225,25.422500000000003,26.0025,26.0025,0.10000000000000142,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,44,and,26.842500686645508,26.982500076293945,True,3.270300696911477e-11,0.9049434661865234,26.818193885178502,26.98777704569297,26.802500000000002,26.8425,26.922500000000003,27.0225,0.03999999999999915,0.09999999999999787 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,45,its,27.082500457763672,27.202499389648438,True,3.613817578518308e-11,1.0,27.057459753673502,27.203179205761856,26.962500000000002,27.082500000000003,27.1625,27.242500000000003,0.120000000000001,0.08000000000000185 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,46,assistance,27.282499313354492,28.122499465942383,True,1.4203026255099616e-10,3.9302000999450684,27.289326195794334,28.100023559728623,27.282500000000002,27.282500000000002,28.062500000000004,28.142500000000002,0.0,0.0799999999999983 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,47,to,28.202499389648438,28.282499313354492,True,7.287294032098934e-12,0.25,28.202197475208703,28.282161719627336,28.2025,28.2025,28.282500000000002,28.282500000000002,0.0,0.0 --yRb-Jum7EQ/1,-yRb-Jum7EQ,1,48,Hamas.,28.342500686645508,28.782499313354492,True,2.1801163305190663e-11,0.6032723784446716,28.34016592506999,28.781192489043658,28.302500000000002,28.3425,28.782500000000002,28.782500000000002,0.03999999999999915,0.0 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,0,Yesterday,0.12250000238418579,0.862500011920929,True,2.1098019165055604e-11,0.4256690740585327,0.11640499422426298,0.8237373720723118,0.0625,0.1225,0.7825,0.8624999999999999,0.06,0.07999999999999996 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,1,Eliav,0.9024999737739563,1.162500023841858,True,6.203321190056954e-11,1.2515686750411987,0.8757917552775388,1.1610153192742962,0.8424999999999999,0.9025,1.1225,1.1624999999999999,0.06000000000000005,0.039999999999999813 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,2,Gelman,1.222499966621399,1.662500023841858,True,2.492321973801559e-11,0.5028454661369324,1.2232918082472444,1.6615993583479005,1.2225,1.2225,1.6025,1.6824999999999999,0.0,0.07999999999999985 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,3,lost,2.7225000858306885,2.862499952316284,True,5.466818109312044e-10,4.0,2.709146128467911,2.8661180187391095,2.7025,2.7625,2.8625,2.9225,0.06000000000000005,0.06000000000000005 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,4,his,2.9024999141693115,3.002500057220459,True,3.090692146656693e-10,4.0,2.9108267355157285,3.0414643223665143,2.9025,2.9625,3.0025,3.2425,0.06000000000000005,0.2400000000000002 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,5,life,3.1024999618530273,3.362499952316284,True,1.2600011312091652e-10,2.5421509742736816,3.1401606861304536,3.357841510464017,3.0225,3.3025,3.2425,3.5625,0.28000000000000025,0.31999999999999984 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,6,in,3.442500114440918,3.5625,True,2.6729063407060494e-10,4.0,3.428606886284111,3.532967025626201,3.3025,3.6625,3.3625,3.7225,0.3599999999999999,0.3600000000000003 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,7,another,3.6624999046325684,4.242499828338623,True,3.822804919839662e-11,0.771280825138092,3.6285882996720065,4.14030831047379,3.4425,3.8025,3.9425,4.242500000000001,0.3600000000000003,0.3000000000000007 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,8,terrorist,4.28249979019165,5.242499828338623,True,4.334416506268646e-11,0.8745024800300598,4.227954760286244,5.168522414951293,4.062500000000001,4.3425,4.9625,5.242500000000001,0.27999999999999936,0.28000000000000025 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,9,"attack,",5.34250020980835,5.682499885559082,True,6.46387596225928e-11,1.3041375875473022,5.276098853024842,5.630281070381624,5.1225000000000005,5.3425,5.442500000000001,5.7225,0.21999999999999975,0.27999999999999936 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,10,not,5.78249979019165,5.902500152587891,True,1.2150396140608866e-11,0.25,5.716276151729501,5.852571298442281,5.482500000000001,5.782500000000001,5.6825,5.9225,0.2999999999999998,0.2400000000000002 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,11,by,5.982500076293945,6.082499980926514,True,1.0236080212611132e-11,0.25,5.934587808080619,6.029627616167395,5.782500000000001,5.982500000000001,5.862500000000001,6.0825000000000005,0.20000000000000018,0.21999999999999975 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,12,the,6.122499942779541,6.302499771118164,True,4.95643699693904e-11,1.0,6.089065477376196,6.26458295760959,5.982500000000001,6.1225000000000005,6.2225,6.3025,0.13999999999999968,0.08000000000000007 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,13,students,6.322500228881836,6.802499771118164,True,1.086939913075291e-10,2.192986488342285,6.304983799467197,6.802649601015644,6.282500000000001,6.322500000000001,6.8025,6.8025,0.040000000000000036,0.0 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,14,of,7.422500133514404,7.522500038146973,True,3.4040623098086087e-10,4.0,7.421097526520383,7.5222877816487355,7.4225,7.4225,7.522500000000001,7.522500000000001,0.0,0.0 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,15,"terrorism,",7.662499904632568,8.362500190734863,True,1.7434940643989982e-11,0.3517635762691498,7.651950712497826,8.326166381941334,7.5825000000000005,7.6625000000000005,8.282499999999999,8.362499999999999,0.08000000000000007,0.08000000000000007 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,16,but,8.40250015258789,8.542499542236328,True,4.870383610300344e-12,0.25,8.40232606126986,8.544283059130962,8.4025,8.4025,8.5425,8.5425,0.0,0.0 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,17,by,8.662500381469727,8.72249984741211,True,3.2112475872869695e-11,0.6478943824768066,8.65890888774039,8.725163996226525,8.6425,8.7025,8.7225,8.7625,0.0600000000000005,0.03999999999999915 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,18,those,8.782500267028809,9.162500381469727,True,9.06100472430893e-11,1.828128695487976,8.782524958225315,9.20255260115738,8.782499999999999,8.782499999999999,9.1625,9.3225,0.0,0.16000000000000014 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,19,who,9.202500343322754,9.322500228881836,True,7.350461905808459e-11,1.4830132722854614,9.292884555691698,9.434678222093696,9.2025,9.4225,9.3225,9.5825,0.21999999999999886,0.2599999999999998 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,20,teach,9.422499656677246,9.72249984741211,True,4.777102324826643e-11,0.963817834854126,9.512342365613454,9.798499598139479,9.4025,9.6225,9.7225,9.8825,0.22000000000000064,0.16000000000000014 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,21,"terrorism,",9.822500228881836,10.6225004196167,True,2.364412277078287e-10,4.0,9.885742869369814,10.623797601314282,9.8225,9.9625,10.522499999999999,10.8025,0.14000000000000057,0.28000000000000114 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,22,a,10.842499732971191,10.862500190734863,True,7.060157580091042e-12,0.25,10.76912108115059,10.789121081150874,10.6025,10.8425,10.6225,10.862499999999999,0.2400000000000002,0.23999999999999844 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,23,Palestinian,10.962499618530273,12.522500038146973,True,4.3775850999683286e-11,0.8832120895385742,10.898607984569265,12.05718561330501,10.782499999999999,10.9625,11.6625,12.522499999999999,0.1800000000000015,0.8599999999999994 --yRb-Jum7EQ/5,-yRb-Jum7EQ,5,24,teacher.,12.5625,12.962499618530273,True,8.628150971468074e-11,1.7407970428466797,12.515745111313707,12.963414362425844,12.4625,12.6225,12.942499999999999,12.9825,0.16000000000000014,0.040000000000000924 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,0,"Therefore,",0.4025000035762787,0.9424999952316284,True,1.6840784322624813e-10,1.0,0.4024729803370939,0.9336746683054675,0.4025,0.4025,0.9025,0.9624999999999999,0.0,0.05999999999999994 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,1,the,1.0625,1.2024999856948853,True,9.747938567450376e-11,0.5788292288780212,1.0589909815193694,1.1984154783750278,1.0625,1.0625,1.1624999999999999,1.2025,0.0,0.040000000000000036 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,2,world,1.2625000476837158,1.5225000381469727,True,1.6847746808767994e-10,1.000413417816162,1.2558060204353718,1.5185764126331496,1.2225,1.2625,1.5225,1.5225,0.040000000000000036,0.0 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,3,needs,1.5625,1.902500033378601,True,1.0756048135496243e-10,0.6386904716491699,1.5614918157611801,1.8797170301478279,1.5625,1.5625,1.7825,1.9625,0.0,0.17999999999999994 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,4,to,1.9424999952316284,2.002500057220459,True,1.7243546868161985e-10,1.0239158868789673,1.9494528303504337,2.021352098064118,1.8825,2.2425,1.9625,2.3225,0.3600000000000001,0.3599999999999999 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,5,stand,2.0425000190734863,2.4625000953674316,True,1.421072287621783e-10,0.8438278436660767,2.1454380160719135,2.5239863439827293,2.0425,2.3825,2.4625,2.8225,0.33999999999999986,0.3599999999999999 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,6,with,2.5425000190734863,2.822499990463257,True,3.683832960899025e-10,2.1874473094940186,2.627782996043301,2.9132515079477193,2.5425,2.8625,2.8225,3.9025,0.31999999999999984,1.08 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,7,Israel,3.742500066757202,4.122499942779541,True,1.1312620978864985e-10,0.6717395782470703,3.7309032744213915,4.150365024998529,3.7425,3.9425,4.1225000000000005,4.2225,0.19999999999999973,0.09999999999999964 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,8,against,4.242499828338623,4.682499885559082,True,2.678313681947486e-10,1.5903735160827637,4.242938262935196,4.670751606173934,4.242500000000001,4.242500000000001,4.5825000000000005,4.7225,0.0,0.13999999999999968 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,9,this,4.742499828338623,4.902500152587891,True,9.41213912364347e-11,0.5588895678520203,4.724187153540902,4.903113320015298,4.6625000000000005,4.742500000000001,4.862500000000001,4.902500000000001,0.08000000000000007,0.040000000000000036 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,10,"incitement,",5.422500133514404,6.102499961853027,True,6.119732498532926e-11,0.3633876144886017,5.423858436959219,6.115242073345665,5.4225,5.4225,6.102500000000001,6.202500000000001,0.0,0.09999999999999964 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,11,which,6.502500057220459,6.802499771118164,True,2.0067764117115416e-10,1.1916170120239258,6.50248685522129,6.802489095420521,6.5025,6.5025,6.8025,6.8025,0.0,0.0 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,12,is,6.862500190734863,6.942500114440918,True,8.340554358277075e-10,4.0,6.8625014843098695,6.942410921639754,6.862500000000001,6.862500000000001,6.942500000000001,6.942500000000001,0.0,0.0 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,13,the,7.002500057220459,7.182499885559082,True,3.8694789039617206e-10,2.2976832389831543,7.002492295030229,7.182530934830729,7.0025,7.0025,7.1825,7.1825,0.0,0.0 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,14,number,7.402500152587891,7.762499809265137,True,2.0023324664997233e-10,1.1889781951904297,7.378536282253883,7.766816192156913,7.3025,7.402500000000001,7.6825,7.822500000000001,0.10000000000000053,0.14000000000000057 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,15,one,9.782500267028809,9.90250015258789,True,1.6587432816184133e-10,0.9849560856819153,9.77142133456187,9.889990542704302,9.7425,9.782499999999999,9.8425,9.9025,0.03999999999999915,0.0600000000000005 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,16,cause,9.962499618530273,10.162500381469727,True,1.8768611664832235e-10,1.1144737005233765,9.974359250912508,10.190149976930748,9.9625,10.0025,10.1625,10.2225,0.03999999999999915,0.0600000000000005 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,17,of,10.382499694824219,10.442500114440918,True,3.711768045699948e-11,0.25,10.352394778030892,10.418035924396685,10.2025,10.3825,10.2625,10.442499999999999,0.17999999999999972,0.17999999999999972 --yRb-Jum7EQ/6,-yRb-Jum7EQ,6,18,terrorism.,10.5024995803833,10.982500076293945,True,1.314886949321803e-10,0.7807753682136536,10.4911182530075,10.981002444124938,10.3825,10.5025,10.9825,10.9825,0.11999999999999922,0.0 diff --git a/final/Q1/问题一.pdf b/final/Q1/问题一.pdf deleted file mode 100644 index eda1efb..0000000 Binary files a/final/Q1/问题一.pdf and /dev/null differ diff --git a/final/README.md b/final/README.md new file mode 100644 index 0000000..2ea0362 --- /dev/null +++ b/final/README.md @@ -0,0 +1,185 @@ +# 复杂场景下多模态情感识别：独立运行项目 + +本目录包含题目 Q1、Q2、Q3 所需的模型、数据适配、训练、验证、推理、解释和实验记录代码。运行代码只依赖本目录；官方原始附件、预训练模型权重和 OpenFace 可执行程序需按下文准备，未复制进项目。 + +## 目录 + +| 路径 | 内容 | +|---|---| +| `adapter/` | 统一多模态对齐接口；区分真实时间对齐与未对齐序列的相对进程投影 | +| `model/` | 每个数学模型与保留的深度学习模型各自独立的定义文件 | +| `q1/` | Q1 原生特征提取、物理时间特征包构建、五折对照和可视化 | +| `q2/math/` | 数学方案 C0–C7、训练、缺失控制评估及附件 3 预测 | +| `q2/deep_learning/q2/` | EarlyConcat + BiGRU、MoFE-7 + MLP Router 及训练协议 | +| `q3/` | Q3 训练、验证、附件 4 全量预测和逐样本解释卡片 | +| `output/q1/` | 已整理的 Q1 100 个特征文件、对齐审计和已有结果 | +| `output/q2/` | 已整理的 Q2 未对齐数据模型对比结果表 | +| `output/q3/` | Q3 运行后生成的预测、解释和验证结果 | +| `experiments/q2/` | 本项目最新未对齐 Q2 对比运行、权重和审计结果 | +| `REPORTS.md` | Q1、Q2 实验结果和方法说明 | +| `data_paths.py` | 官方附件的默认位置与外部数据根目录设置 | + +## 准备官方附件 + +把附件按以下目录放入 `final/data/`。也可以把它们放在其他位置，再通过 `FINAL_DATA_DIR` 指向包含这些附件文件夹的根目录。 + +| 附件 | 相对 `FINAL_DATA_DIR` 的目录/文件 | +|---|---| +| 附件 1 | `附件1-数据集原始多模态样本/MOSEI数据集部分原始视频-100条/`；内含 `label-100.xlsx` 以及按 `video_id/clip_id.mp4` 组织的视频 | +| 附件 2 | `附件2-数据集特征文件/aligned_50.pkl` 与 `unaligned_50.pkl` | +| 附件 3 | `附件3-模态缺失特征样本/对齐版本/` 与 `未对齐版本/`；每个目录含 30 个样本文件 | +| 附件 4 | `附件4-可解释专项视频样本与特征文件/附件4-可解释专项视频样本与特征文件/未对齐版本/` 及同级 `videos/` | + +附件 2 的 pickle 文件约数 GB，Q2/Q3 训练需要较大内存；它们不会被复制进项目。附件 3 的未对齐样本缺少数值文本特征及可靠的音视频长度，本项目会从 `raw_text` 重建 BERT 文本表示，并从音视频非零行估计长度；对应限制会写入附件 3 审计 CSV。附件 4 未对齐样本具有数值文本特征和长度字段，可由统一 adapter 转换。 + +默认数据位置是 `final/data/`。Q2、Q3 会直接读取 `FINAL_DATA_DIR`。Q1 命令还需要把 `--data-dir` 指向附件 1 的视频目录。若使用其他数据根目录，在运行项目命令前设置： + +```powershell +$env:FINAL_DATA_DIR = "D:\task-data" +``` + +```bash +export FINAL_DATA_DIR="/data/task-data" +``` + +## 安装环境 + +需要 Python 3.11 或更新版本。在 `final/` 的上级目录执行下列命令。Q1 的 OpenFace 特征提取脚本调用 Windows PowerShell；Q2/Q3 可在 Windows、Linux 或 WSL 上运行。 + +```bash +python -m venv final/.venv +``` + +Windows PowerShell： + +```powershell +final\.venv\Scripts\Activate.ps1 +python -m pip install -r final/requirements.txt +``` + +Linux 或 WSL： + +```bash +source final/.venv/bin/activate +python -m pip install -r final/requirements.txt +``` + +也可使用 `uv sync --project final`。GPU 运行需安装与本机 CUDA 驱动匹配的 PyTorch 版本；CPU 可用于功能验证，但完整训练会更慢。首次运行 Q1/Q3 或附件 3 推理时，Transformers 会下载 `google-bert/bert-base-uncased`；Q1 还会用到 `facebook/wav2vec2-base-960h`。无网络环境需预先把权重放入 Hugging Face 缓存。 + +## Q1：构建物理时间对齐特征并比较方案 + +Q1 对附件 1 的 100 个视频生成原生时间特征和统一特征包。视频/音频时间戳来自媒体流与 OpenFace 帧；文本区间来自固定转写的 CTC 单调对齐。五折评估按 `video_id` 分组。分类探针衡量情感信息，不代表人工标注的边界准确率。 + +原生特征提取还需要 OpenFace 2.2.0 Windows 包。将其解压到 `final/q1/cache/openface/OpenFace_2.2.0_win_x64/`，其中应有 `FeatureExtraction.exe` 和 `model/main_clnf_general.txt`。项目提供 `q1/run_openface.ps1` 调用脚本，不附带 OpenFace 二进制文件。 + +```powershell +if (-not $env:FINAL_DATA_DIR) { $env:FINAL_DATA_DIR = "final/data" } +$a1 = Join-Path $env:FINAL_DATA_DIR "附件1-数据集原始多模态样本/MOSEI数据集部分原始视频-100条" +$run = "final/output/q1/rerun" + +python -m final.q1.compare_models ` + --data-dir $a1 ` + --cache-dir final/q1/cache/native ` + --output-dir "$run/model_comparison" ` + --bootstrap-repeats 2000 + +python -m final.q1.build_v2 ` + --data-dir $a1 ` + --cache-dir final/q1/cache/native ` + --run-manifest "$run/model_comparison/run_manifest.json" ` + --output-dir "$run/features_v2" + +python -m final.q1.compare_v2 ` + --data-dir $a1 ` + --feature-dir "$run/features_v2" ` + --output-dir "$run/model_comparison_v2" ` + --bootstrap-repeats 2000 +``` + +完整重跑使用独立的 `output/q1/rerun/`，保留随项目提供的现有结果包。 + +主要产物：`output/q1/features_v2/` 中有 100 个样本文件、100 行 `sample_summary.csv`（含样本 ID 和来源时长）、300 行 `modality_summary.csv`（含模态维度、有效时长、对齐粒度和状态）、`manifest_q1.jsonl`、`feature_manifest.json`；`output/q1/` 下的 `typical_sample_correspondence.csv` 和 `typical_sample_frames.jpg` 展示典型样本的转写、物理时间区间、音频来源行和视频帧。`alignment_query_example.png` 对照展示文本词区间到原始音频/视频位置的查询权重。`model_comparison/` 和 `model_comparison_v2/` 中有 OOF 预测、折内指标、组 Bootstrap 和运行清单。 + +已整理的 100 个样本可直接通过接口读取： + +```python +from pathlib import Path +from final.adapter import Q1AlignmentAdapter + +sample = Q1AlignmentAdapter().from_q1_sample( + "-iRBcNs9oI8/8", + feature_dir=Path("final/output/q1/features_v2"), +) +features, observed_mask = sample.q2_arrays() +``` + +## Q2：在题目未对齐数据上训练和比较模型 + +两个训练入口都默认使用附件 2 的 `unaligned_50.pkl`。训练、验证、测试均经 `adapter/` 投影到 50 个相对进程区间；这个坐标只表达模态内部的先后顺序，不是物理秒数。验证集用于模型选择，官方测试集用于最终评估。数学方案会比较 C0–C7 和 C6 单因素诊断；深度学习只保留题目要求的 EarlyConcat + BiGRU 与 MoFE-7 + MLP Router。 + +深度学习两模型共用交叉熵分类损失与 `0.5 × SmoothL1` 强度回归损失，AdamW 学习率 `3e-4`、权重衰减 `1e-3`、最多 12 轮、耐心值 3、梯度裁剪 1.0；训练遮蔽率为 0/10/30/50/70%，覆盖单模态、同步、部分重叠和异步连续缺失。EarlyConcat 使用 128 维模态投影和双向 GRU；MoFE 使用 7 个模态子集专家、MLP Router 和共享双向 GRU。以下命令显式设 batch size 128，以匹配报告中的对比实验。数学分支各方案的目标函数与内部留组选择设置记录在模型代码及 [数学运行清单](experiments/q2/unaligned_math_all_b128/run_manifest.json)。 + +从空的结果目录开始训练。若目标目录已存在非空结果，脚本会停止以免覆盖： + +```bash +python -m final.q2.math.train \ + --input-version unaligned_50 \ + --output-dir final/experiments/q2/math_rerun \ + --batch-size 128 \ + --device auto + +python -m final.q2.deep_learning.q2.train_math_protocol \ + --input-version unaligned_50 \ + --output-dir final/experiments/q2/deep_rerun \ + --batch-size 128 \ + --device auto +``` + +数学方案默认会用附件 3 未对齐样本生成 30 条 `attachment3_predictions.csv` 和 `attachment3_audit.csv`，预测文件包含极性、情感强度及类别概率。如只检查训练流程，可以增加 `--skip-attachment3`。深度学习运行会输出两模型的官方测试指标、验证缺失情景、AURC-MAE、Bootstrap 区间、权重和运行清单。为满足总附件大小限制，随项目提供的 Q2 归档保留逐情景指标和模型参数；逐行遮蔽/测试门控审计及官方测试单样本明细可由完整重训重新生成，未放入紧凑归档。 + +把两次运行的结果重新汇总到统一对比表： + +```bash +python final/compare_unaligned_q2.py \ + --math-dir final/experiments/q2/math_rerun \ + --deep-dir final/experiments/q2/deep_rerun \ + --output-dir final/output/q2 +``` + +结果文件包括 `comparison_validation.csv`（全模型验证对比）、`comparison_test.csv`（预先选出的数学模型及两种深度模型测试结果）、`comparison_aurc.csv`（四种缺失模式的 AURC-MAE）。指标定义、已有对比数值和边界说明见 [REPORTS.md](REPORTS.md)。 + +## Q3：可解释预测与附件 4 输出 + +Q3 使用附件 2 训练集训练 EarlyConcat + BiGRU；只用官方验证集选择 epoch。默认最多 12 轮、耐心值 3、batch size 64，AdamW 学习率 `3e-4`、权重衰减 `1e-3`，目标函数为交叉熵加 `0.5 × SmoothL1` 强度回归。验证选择使用自然缺失、30% 单模态/同步缺失和 50% 异步缺失情景。附件 4 的 20 个未对齐样本经同一 adapter 投影，生成类别、情感分数、三类概率、模态贡献、主导模态及逐样本解释卡片。 + +```bash +python -m final.q3.train_interpretable \ + --input-version unaligned_50 \ + --attachment4-version unaligned_50 \ + --output-dir final/output/q3 \ + --device auto +``` + +输出目录包含： + +- `attachment4_predictions.csv`：20 条完整预测及类别概率。 +- `attachment4_explanations.csv`：预测、各模态遮蔽影响、主导模态和原始转写。 +- `attachment4_local_evidence.csv`：文本 token、音频/视频来源行、归一化进程区间和单位置遮蔽影响。 +- `attachment4_input_audit.csv`：输入哈希、adapter 模式、长度和可见位置审计。 +- `explanation_cards/`：逐样本 Markdown 解释卡；`typical_explanation_card.md` 为置信度接近样本中位数的代表样例。 +- `validation_metrics.json`、`validation_predictions.csv`、`validation_errors.csv`、`validation_diagnostics.png`：验证指标、误差样本和图示。 + +附件 4 未对齐特征没有可靠物理时间戳，因此音频/视频证据位置以归一化进程和原始特征行表示，不伪造秒级时间。模态/位置分数由遮蔽特征前后的模型概率差计算，表示模型敏感性，不是因果效应或情感成因证明。输出中提供源视频相对路径，便于回到原片段查看。 + +## 模型与对齐约定 + +- `model/c0.py` 至 `model/c7_*.py` 分别保存数学方案；C6 的距离、重构和点遮蔽诊断也各有单独文件。 +- `model/early_concat.py` 和 `model/mofe.py` 是仅保留的两种深度学习方案。训练辅助模块从 `model/` 导入模型类，架构定义以此处为准。 +- `adapter/` 有两种坐标语义：Q1 原生数据需媒体时间戳、持续时长和来源哈希，才能物理时间对齐；附件 2 未对齐特征只支持相对进程投影。代码会检查输入证据，不从数组形状推断物理对齐。 +- 未对齐输入没有逐行质量分数时，按可见行质量权重为 1 处理；这一假设写入运行清单。 + +## 实验复现与文件约定 + +训练结果写入传入的 `--output-dir`；建议为每轮实验使用新的空目录。检查点、训练历史、数据/划分哈希、随机种子、特征版本、adapter 审计和验证/测试指标随运行结果保存。官方附件不进入版本库；`final/.gitignore` 已忽略 `data/`、Q1 中间缓存和环境目录。 + +当前已整理的 Q1/Q2 表格和结果摘要保存在 `output/`、`experiments/` 与 [REPORTS.md](REPORTS.md)。若要重建所有模型权重和预测，按上面的 Q1、Q2、Q3 顺序运行相应入口；Q1 原生视觉特征还需要单独准备 OpenFace 2.2.0。 diff --git a/final/REPORTS.md b/final/REPORTS.md new file mode 100644 index 0000000..5b177e9 --- /dev/null +++ b/final/REPORTS.md @@ -0,0 +1,97 @@ +# Q1/Q2 实验结果与复现摘要 + +本报告汇总 `final/` 内可复核的 Q1 物理时间对齐结果和 Q2 官方未对齐数据比较。模型代码、训练命令、数据目录和依赖说明见 [README.md](README.md)。数值结果链接均指向本项目内的 CSV、JSON 或实验审计文件。 + +## Q1：附件一物理时间特征 + +Q1 包含 100 条视频片段、37 个来源视频组。文本使用 BERT/CTC 词区间，音频为 74 维，视觉为 OpenFace 35 维；五折按 `video_id` 分组。表内分类指标表示情感探针表现，不是边界准确率。 + +| 分支 | OOF Accuracy | OOF Macro-F1 | OOF MAE | OOF Pearson | +|---|---:|---:|---:|---:| +| B0 | 0.5900 | 0.3373 | 0.5345 | 0.4255 | +| B1 | 0.5700 | 0.3084 | 0.5342 | 0.3981 | +| B2 | 0.5500 | 0.2769 | 0.5308 | 0.4232 | +| B3 | 0.5700 | 0.3084 | 0.5202 | 0.4114 | +| B4 | 0.6000 | 0.3882 | 0.5393 | 0.3413 | + +特征包保留每条样本的模态掩码、物理时间区间、来源哈希和配置哈希。96 条样本状态为 success，4 条为 partial；部分样本因 OpenFace 有效性门控未通过而保留视觉缺失状态。[Q1 完整 OOF 表](output/q1/model_comparison/comparison_summary.csv) · [100 样本特征清单](output/q1/features_v2/manifest_q1.jsonl) · [包体审计](output/q1/package_audit.json) + +Q1 adapter 使用带媒体时戳的原生特征进行物理时间聚合；Q2 未对齐特征只可按归一化进程投影，两个坐标语义不混用。 + +## Q2：附件二未对齐输入的全模型比较 + +### 统一 adapter：附件二未对齐数据全模型比较（本轮） + +本轮用题目附件二 unaligned_50.pkl 作为 Q2 输入，并由 [统一 adapter](adapter/) 的 adapt_official_split 接口统一投影。源文件 SHA-256 为 77eda14a06be9749a96c52ae45470c7cffcfa7219011eae391d231a0664c3762。比较覆盖数学分支 C0–C7 与三项 C6 单因素诊断，以及保留的 EarlyConcat + BiGRU、MoFE-7 + MLP Router；共 14 个模型版本。运行 seed 为 20260924；神经模型使用 batch size 128，C0 线性基线按各自实现训练。 + +附件二官方 train/valid/test 分别为 3,395/728/727 条，来源视频组数为 1,528/239/381，官方划分的视频组互不重叠。文本、音频、视觉原始特征形状分别为 (N,50,768)、(N,500,74)、(N,500,35)，适配器按归一化相对进程将各模态投影至 50 个目标位置。这是相对进程投影，不代表真实秒级时间同步。适配器排除了注意力掩码外的非零文本填充行；train/valid/test 的此类行数为 86,078/17,772/18,041。视觉长度冲突样本数为 618/141/131；数据没有可信的词/帧时间戳和逐行质量字段。 + +两条训练路线使用相同官方数据和 adapter 输出，但各自保留预处理、训练损失及检查点选择流程，因此跨分支差异不能单独解释为融合架构的因果效果。数学分支再从 train 内部划分可靠性选择与温度校准组；深度学习分支以官方 valid 的固定遮蔽情景选择轮次。验证集用于所有模型比较；正式测试只运行内部留组选择出的数学 C6 和两种指定深度学习方案，不用测试集挑选 12 个数学变体中的赢家。 + +#### 官方验证集自然缺失表现 + +下表为 728 条官方验证样本的自然缺失条件指标。CSV 保留完整精度。 + +| 模型 | Accuracy ↑ | Macro-F1 ↑ | MAE ↓ | RMSE ↓ | Pearson ↑ | +|---|---:|---:|---:|---:|---:| +| C0 | 0.5907 | 0.5573 | 0.7268 | 0.9494 | 0.5186 | +| C1 | 0.5879 | 0.4739 | 0.7446 | 0.9823 | 0.5362 | +| C2 | 0.5962 | 0.4503 | 0.6982 | 0.9260 | 0.5808 | +| C3 | 0.5907 | 0.4433 | 0.7318 | 0.9857 | 0.5457 | +| C4 | 0.5549 | 0.4536 | 0.7454 | 1.0156 | 0.5033 | +| C5 | 0.5824 | 0.4401 | 0.7788 | 1.0248 | 0.5123 | +| C6 | 0.6085 | 0.5188 | 0.6847 | 0.9208 | **0.6102** | +| C6 去距离/跨度惩罚 | 0.6071 | 0.4721 | 0.7118 | 0.9323 | 0.6075 | +| C6 去辅助重构 | 0.5838 | 0.4833 | 0.7092 | 0.9543 | 0.5635 | +| C6 点遮蔽 | 0.5810 | 0.5026 | 0.6792 | 0.9176 | 0.5959 | +| C7 蒸馏 | 0.5920 | 0.4871 | 0.7025 | 0.9442 | 0.5753 | +| C7 分组风险 | 0.6003 | 0.4509 | 0.7323 | 0.9356 | 0.6008 | +| EarlyConcat + BiGRU | **0.6236** | **0.5779** | 0.6614 | 0.8731 | 0.6092 | +| MoFE-7 + MLP Router | 0.6140 | 0.5661 | **0.6426** | **0.8499** | 0.6014 | + +自然缺失验证集上，EarlyConcat 的 Accuracy 和 Macro-F1 点估计最高，MoFE 的 MAE/RMSE 最低；C6 的 Pearson 在 14 个版本中最高。数学分支内部，C0 的 Macro-F1 最高，C6 的 Accuracy/Pearson 最高，C6 点遮蔽的 MAE/RMSE 最低。以上为单种子点估计，不据此声称统计显著。 + +#### 官方测试集结果 + +| 模型 | Accuracy ↑ | Macro-F1 ↑ | MAE ↓ | RMSE ↓ | Pearson ↑ | +|---|---:|---:|---:|---:|---:| +| C6（数学分支内部选择） | **0.6726** | 0.5485 | 0.7100 | 0.9693 | 0.6424 | +| EarlyConcat + BiGRU | 0.6699 | 0.5870 | 0.6851 | 0.9017 | **0.6613** | +| MoFE-7 + MLP Router | 0.6630 | **0.5879** | **0.6702** | **0.8895** | 0.6447 | + +数学 C6 在 727 条测试样本上的 300 次来源视频组 Bootstrap 95% 区间为 Accuracy [0.6337, 0.7084]、Macro-F1 [0.5108, 0.5872]、MAE [0.6579, 0.7620]、RMSE [0.8732, 1.0580]、Pearson [0.5640, 0.7087]。EarlyConcat 与 MoFE 的 1,000 次配对来源视频组 Bootstrap 比较如下，差值定义为 MoFE − EarlyConcat： + +| 指标 | 差值 | 95% Bootstrap 区间 | +|---|---:|---:| +| Accuracy | -0.0069 | [-0.0303, 0.0147] | +| Macro-F1 | +0.0009 | [-0.0312, 0.0295] | +| MAE | -0.0148 | [-0.0429, 0.0136] | +| RMSE | -0.0121 | [-0.0448, 0.0191] | +| Pearson | -0.0166 | [-0.0384, 0.0053] | + +这五个区间均跨零。Bootstrap 以来源视频组重采样，表示当前测试组上的抽样不确定性；本轮只有一个训练 seed，不能表示不同随机种子间的波动。 + +#### 连续缺失曲线表现 + +两条流水线均在官方验证集上评估 42 个固定遮蔽情景。下表把单模态、同步、部分重叠和异步四类 0%/10%/30%/50%/70% 遮蔽曲线压缩为归一化梯形 MAE 面积（AURC-MAE），按实际新增缺失率积分，越低越好。表中为点估计；各方案的训练目标与预处理仍有差别。 + +| 模型 | 单模态 | 同步 | 部分重叠 | 异步 | +|---|---:|---:|---:|---:| +| C0 | 0.7425 | 0.7643 | 0.7578 | 0.7696 | +| C1 | 0.7431 | 0.7849 | 0.7343 | 0.7406 | +| C2 | 0.7279 | 0.7444 | 0.7409 | 0.7512 | +| C3 | 0.7449 | 0.7573 | 0.7562 | 0.7577 | +| C4 | 0.7544 | 0.7610 | 0.7660 | 0.7770 | +| C5 | 0.7921 | 0.8176 | 0.8084 | 0.8118 | +| C6 | 0.7028 | 0.7237 | 0.7292 | 0.7283 | +| C6 去距离/跨度惩罚 | 0.7242 | 0.7387 | 0.7366 | 0.7333 | +| C6 去辅助重构 | 0.7128 | 0.7382 | 0.7271 | 0.7429 | +| C6 点遮蔽 | 0.6824 | 0.6988 | 0.6866 | 0.6951 | +| C7 蒸馏 | 0.7215 | 0.7468 | 0.7420 | 0.7359 | +| C7 分组风险 | 0.7437 | 0.7452 | 0.7453 | 0.7501 | +| EarlyConcat + BiGRU | 0.6613 | 0.6579 | 0.6493 | 0.6551 | +| MoFE-7 + MLP Router | **0.6509** | **0.6472** | **0.6417** | **0.6525** | + +在两种深度学习方案之间，MoFE − EarlyConcat 的 AURC-MAE 配对 95% 区间分别为：单模态 -0.0104 [-0.0347, 0.0111]、同步 -0.0107 [-0.0322, 0.0093]、部分重叠 -0.0075 [-0.0280, 0.0112]、异步 -0.0026 [-0.0219, 0.0149]；均跨零。数学分支 C6 的四种 AURC 点估计均低于 C0，但这是描述性点差；不把它解读为跨随机种子的稳健优势。 + +完整精度及审计文件：[14 个版本验证指标](output/q2/comparison_validation.csv) · [3 个正式测试结果](output/q2/comparison_test.csv) · [全部版本 AURC-MAE](output/q2/comparison_aurc.csv) · [数学运行清单](experiments/q2/unaligned_math_all_b128/run_manifest.json) · [深度学习运行清单](experiments/q2/unaligned_deep_two_b128/run_manifest.json) · [数学 42 情景明细（gzip CSV）](experiments/q2/unaligned_math_all_b128/controlled_missingness.csv.gz) · [深度学习 42 情景明细](experiments/q2/unaligned_deep_two_b128/controlled_metrics_by_scenario.csv) · [深度学习测试配对 Bootstrap](experiments/q2/unaligned_deep_two_b128/official_test_paired_bootstrap.csv)。 diff --git a/final/__init__.py b/final/__init__.py new file mode 100644 index 0000000..1f7fb9c --- /dev/null +++ b/final/__init__.py @@ -0,0 +1 @@ +"""Consolidated Q1 alignment and Q2 models.""" diff --git a/final/adapter/__init__.py b/final/adapter/__init__.py new file mode 100644 index 0000000..d3c1e77 --- /dev/null +++ b/final/adapter/__init__.py @@ -0,0 +1,17 @@ +"""Unified Q1 alignment interface for physical time and relative progress.""" + +from .core import ( + AlignedMultimodalSample, + AlignmentError, + ModalityProvenance, + Q1AlignmentAdapter, + adapt_official_split, +) + +__all__ = [ + "AlignedMultimodalSample", + "AlignmentError", + "ModalityProvenance", + "Q1AlignmentAdapter", + "adapt_official_split", +] diff --git a/final/adapter/coordinates.py b/final/adapter/coordinates.py new file mode 100644 index 0000000..13d98ec --- /dev/null +++ b/final/adapter/coordinates.py @@ -0,0 +1,26 @@ +"""Coordinate constructors; no feature aggregation happens here.""" +from __future__ import annotations + +import numpy as np + + +def physical_targets(duration_s: float, steps: int) -> np.ndarray: + """K fixed bins spanning verified real media duration, in seconds.""" + duration = float(duration_s) + if not np.isfinite(duration) or duration <= 0 or steps < 1: + raise ValueError("physical targets require positive finite duration and steps") + edges = np.linspace(0.0, duration, steps + 1, dtype=np.float64) + return np.column_stack((edges[:-1], edges[1:])) + + +def relative_cells(length: int) -> np.ndarray: + """Ordered source cells on a unit progress axis, with no time claim.""" + if length < 1: + raise ValueError("relative source length must be positive") + left = np.arange(length, dtype=np.float64) / length + return np.column_stack((left, left + 1.0 / length)) + + +def relative_targets(steps: int) -> np.ndarray: + """K fixed cells on the same unit progress axis.""" + return relative_cells(steps) diff --git a/final/adapter/core.py b/final/adapter/core.py new file mode 100644 index 0000000..717e83e --- /dev/null +++ b/final/adapter/core.py @@ -0,0 +1,238 @@ +"""Q1's common sample contract and evidence-based coordinate dispatch.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +import numpy as np +from scipy import sparse + +from .coordinates import physical_targets, relative_cells, relative_targets +from .projection import project_intervals + +MODALITIES = ("text", "audio", "vision") +DIMS = {"text": 768, "audio": 74, "vision": 35} +STEPS = {"text": 50, "audio": 500, "vision": 500} +K = 50 +VERSION = "q1-unified-1" + + +class AlignmentError(ValueError): + """The source cannot be assigned a defensible alignment coordinate.""" + + +@dataclass +class ModalityProvenance: + source_weights: sparse.csr_matrix + source_count: np.ndarray + first_source: np.ndarray + last_source: np.ndarray + source_span: int + original_source_length: int + reported_length: int | None + length_conflict: bool = False + tail_ambiguous: bool = False + observed_dimensions: np.ndarray | None = None + coverage_dimensions: np.ndarray | None = None + quality_available: np.ndarray | None = None + + +@dataclass +class AlignedMultimodalSample: + features: dict[str, np.ndarray] + observed: dict[str, np.ndarray] + coverage: dict[str, np.ndarray] + provenance: dict[str, ModalityProvenance] + metadata: dict[str, Any] + target_intervals: np.ndarray + quality_mean: dict[str, np.ndarray] = field(default_factory=dict) + quality_available_fraction: dict[str, np.ndarray] = field(default_factory=dict) + + def q2_arrays(self) -> tuple[dict[str, np.ndarray], np.ndarray]: + """The existing Q2 model input shapes, without changing that model.""" + return self.features, np.stack([self.observed[m] for m in MODALITIES], axis=-1) + + +@dataclass +class _Prepared: + values: np.ndarray + intervals: np.ndarray + observed: np.ndarray + quality: np.ndarray + reported_length: int | None + length_conflict: bool = False + tail_ambiguous: bool = False + quality_available: np.ndarray | None = None + + +def _relative_modality(record: dict[str, Any], name: str) -> _Prepared: + raw = np.asarray(record[name]) + if raw.shape != (STEPS[name], DIMS[name]) or not np.isfinite(raw).all(): + raise AlignmentError(f"{name}: expected finite {(STEPS[name], DIMS[name])}, got {raw.shape}") + if name == "text": + attention = np.asarray(record["attention_mask"], bool) + if attention.shape != (50,) or not np.array_equal(attention, np.arange(50) < int(attention.sum())): + raise AlignmentError("text attention mask must be a 50-position prefix") + length = int(attention.sum()) + if length < 1: + raise AlignmentError("empty text attention mask") + span = length + observed = attention[:span] & np.any(raw[:span] != 0, axis=1) + conflict = ambiguous = False + else: + length = int(record[f"{name}_length"]) + if length < 1 or length > STEPS[name]: + raise AlignmentError(f"{name}: invalid official length {length}") + nonzero = np.any(raw != 0, axis=1) + last = int(np.flatnonzero(nonzero)[-1]) + 1 if nonzero.any() else 0 + conflict = last > length + if name == "audio" and conflict: + raise AlignmentError("audio contains observed positions beyond audio_lengths") + span = max(length, last) + observed = nonzero[:span] + ambiguous = bool(conflict) + return _Prepared(raw[:span].astype(np.float32, copy=False), relative_cells(span), + observed, np.ones(span, np.float32), length, bool(conflict), bool(ambiguous), + np.zeros(span, bool)) + + +def _validate_physical(source: dict[str, Any]) -> tuple[float, dict[str, Any]]: + meta = source.get("_meta") + if not isinstance(meta, dict): + raise AlignmentError("physical mode requires stored Q1 metadata") + duration = float(meta.get("duration_s", float("nan"))) + if not np.isfinite(duration) or duration <= 0: + raise AlignmentError("physical mode requires a finite positive duration") + if meta.get("media", {}).get("status") != "ok" or not meta.get("source_video_sha256"): + raise AlignmentError("physical mode requires verified media status and source hash") + for name in MODALITIES: + if f"native_{name}_intervals" not in source: + raise AlignmentError(f"physical mode lacks {name} timestamps") + return duration, meta + + +class Q1AlignmentAdapter: + """Align either verified Q1 physical sources or official ordered sequences. + + `auto` uses evidence in the input contract only; tensor shape never decides + whether time is physical. A malformed physical source is an error rather + than a silent relative fallback. + """ + + def __init__(self, target_steps: int = K): + if target_steps < 1: + raise ValueError("target_steps must be positive") + self.target_steps = target_steps + + def align(self, source: dict[str, Any], mode: str = "auto") -> AlignedMultimodalSample: + if mode not in {"auto", "physical", "relative"}: + raise AlignmentError(f"unsupported coordinate mode: {mode}") + if mode == "auto": + if "_meta" in source or any(k.startswith("native_") for k in source): + mode = "physical" + elif source.get("sequence_order_verified") is True: + mode = "relative" + else: + raise AlignmentError("auto mode requires Q1 physical evidence or verified sequence order") + if mode == "physical": + from .source import native_arrays + + duration, meta = _validate_physical(source) + target = physical_targets(duration, self.target_steps) + prepared = {} + for name in MODALITIES: + values, observed, intervals, quality, available = native_arrays(source, name) + if np.asarray(intervals).shape != (len(values), 2) or np.any(np.asarray(intervals) < -1e-5) or np.any(np.asarray(intervals) > duration + 1e-5): + raise AlignmentError(f"{name}: physical timestamps outside media duration") + prepared[name] = _Prepared(values, intervals, observed, quality, len(values), + quality_available=available) + metadata = {"sample_id": meta.get("sample_id", f"{meta.get('video_id')}/{meta.get('clip_id')}"), + "coordinate_mode": "physical", "coordinate_unit": "seconds", + "physical_time_alignment": True, "duration_s": duration, + "source_video_sha256": meta["source_video_sha256"], + "dense_view": "views_sec_* (stored 0.1 s Q1 artifact)", + "quality_fields_available": {m: bool(np.asarray(prepared[m].quality_available).any()) for m in MODALITIES}} + else: + if source.get("sequence_order_verified") is not True: + raise AlignmentError("relative mode requires verified source order") + target = relative_targets(self.target_steps) + prepared = {name: _relative_modality(source, name) for name in MODALITIES} + metadata = {"sample_id": str(source.get("id", "")), "coordinate_mode": "relative", + "coordinate_unit": "normalized_progress", "physical_time_alignment": False, + "word_or_frame_timestamps_available": False, + "quality_fields_available": {m: False for m in MODALITIES}} + features = {} + observed = {} + coverage = {} + quality_mean = {} + quality_available_fraction = {} + provenance = {} + for name in MODALITIES: + item = prepared[name] + result = project_intervals(item.values, item.intervals, target, item.observed, item.quality, + item.quality_available) + features[name] = result.x + observed[name] = result.observed + coverage[name] = result.coverage + quality_mean[name] = result.quality_mean + quality_available_fraction[name] = result.quality_available_fraction + provenance[name] = ModalityProvenance(result.source_weights, result.source_count, + result.first_source, result.last_source, len(item.values), + len(source[name]) if mode == "relative" else len(item.values), item.reported_length, + item.length_conflict, item.tail_ambiguous, result.observed_dimensions, + result.coverage_dimensions, item.quality_available) + metadata.update({"target_steps": self.target_steps, "adapter_version": VERSION}) + return AlignedMultimodalSample(features, observed, coverage, provenance, metadata, target, + quality_mean, quality_available_fraction) + + def from_q1_sample(self, sample_id: str, feature_dir: Path | None = None) -> AlignedMultimodalSample: + from .source import FEATURE_DIR, load_sample + + return self.align(load_sample(sample_id, FEATURE_DIR if feature_dir is None else feature_dir), "auto") + + def from_unaligned_record(self, split: dict[str, Any], index: int) -> AlignedMultimodalSample: + """Build verified ordered input from one official unaligned pickle row.""" + attention = np.asarray(split["text_bert"][index, 1], bool) + raw_id = split["id"][index] + if isinstance(raw_id, bytes): + raw_id = raw_id.decode("utf-8", errors="replace") + record = {"id": str(raw_id), "sequence_order_verified": True, + "attention_mask": attention, + "text": split["text"][index], "audio": split["audio"][index], + "vision": split["vision"][index], + "audio_length": int(split["audio_lengths"][index]), + "vision_length": int(split["vision_lengths"][index])} + return self.align(record, "auto") + + +def adapt_official_split(split: dict[str, Any]) -> tuple[dict[str, np.ndarray], np.ndarray, dict[str, Any]]: + """Q2's batch bridge; all rows are produced through Q1AlignmentAdapter.""" + n = len(split["id"]) + output = {m: np.zeros((n, K, DIMS[m]), np.float32) for m in MODALITIES} + masks = np.zeros((n, K, len(MODALITIES)), bool) + conflicts = ambiguous = padding = 0 + coverage_sum = {m: 0.0 for m in MODALITIES} + observed_rows = {m: 0 for m in MODALITIES} + adapter = Q1AlignmentAdapter() + for i in range(n): + attention = np.asarray(split["text_bert"][i, 1], bool) + padding += int(np.count_nonzero(np.any(split["text"][i] != 0, axis=1) & ~attention)) + sample = adapter.from_unaligned_record(split, i) + for j, name in enumerate(MODALITIES): + output[name][i] = sample.features[name] + masks[i, :, j] = sample.observed[name] + coverage_sum[name] += float(sample.coverage[name].sum()) + observed_rows[name] += int(sample.observed[name].sum()) + conflicts += int(sample.provenance["vision"].length_conflict) + ambiguous += int(sample.provenance["vision"].tail_ambiguous) + audit = {"method": "shared_interval_overlap_on_normalized_progress", + "coordinate_mode": "relative", "physical_time_alignment": False, + "samples": n, "vision_length_conflict_samples": conflicts, + "vision_tail_ambiguous_samples": ambiguous, + "nonzero_text_rows_outside_attention": padding, + "observed_target_rows": observed_rows, + "mean_target_coverage": {m: coverage_sum[m] / (n * K) for m in MODALITIES}, + "quality_fields_available": False, + "word_or_frame_timestamps_available": False} + return output, masks, audit diff --git a/final/adapter/projection.py b/final/adapter/projection.py new file mode 100644 index 0000000..9b35e3d --- /dev/null +++ b/final/adapter/projection.py @@ -0,0 +1,108 @@ +"""The sole interval overlap projection kernel used by both coordinate modes.""" +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +from scipy import sparse + + +@dataclass +class Projection: + x: np.ndarray + observed: np.ndarray + coverage: np.ndarray + source_count: np.ndarray + first_source: np.ndarray + last_source: np.ndarray + source_weights: sparse.csr_matrix + observed_dimensions: np.ndarray + coverage_dimensions: np.ndarray + quality_mean: np.ndarray + quality_available_fraction: np.ndarray + + +def project_intervals( + values: np.ndarray, + source_intervals: np.ndarray, + target_intervals: np.ndarray, + observed: np.ndarray, + quality: np.ndarray | None = None, + quality_available: np.ndarray | None = None, +) -> Projection: + """Project source cells using overlap * quality * observed validity. + + Row provenance uses any valid dimension. Feature values use validity per + dimension, so partially observed physical features remain partially missing. + """ + source = np.asarray(values, dtype=np.float32) + src = np.asarray(source_intervals, dtype=np.float64) + dst = np.asarray(target_intervals, dtype=np.float64) + if source.ndim != 2 or src.shape != (len(source), 2) or dst.ndim != 2 or dst.shape[1] != 2: + raise ValueError("inconsistent source features or interval dimensions") + if not np.isfinite(source).all() or not np.isfinite(src).all() or not np.isfinite(dst).all(): + raise ValueError("non-finite source features or intervals") + if np.any(src[:, 1] < src[:, 0]) or np.any(dst[:, 1] <= dst[:, 0]): + raise ValueError("source widths must be nonnegative and target widths positive") + obs = np.asarray(observed, bool) + if obs.shape == (len(source),): + obs_dim = np.broadcast_to(obs[:, None], source.shape) + elif obs.shape == source.shape: + obs_dim = obs + obs = obs.any(axis=1) + else: + raise ValueError("observed must have source-row or source-feature shape") + q = np.ones(len(source), dtype=np.float64) if quality is None else np.asarray(quality, dtype=np.float64) + if q.shape != (len(source),) or not np.isfinite(q).all() or np.any(q < 0): + raise ValueError("quality must be finite and nonnegative per source row") + overlap = np.maximum(0.0, np.minimum(dst[:, None, 1], src[None, :, 1]) + - np.maximum(dst[:, None, 0], src[None, :, 0])) + available = np.zeros(len(source), bool) if quality_available is None else np.asarray(quality_available, bool) + if available.shape != (len(source),): + raise ValueError("quality availability must be per source row") + # Keep the same multiplication and accumulation order as the original + # official-unaligned projection when quality is uniformly one. + physical = overlap.copy() + physical *= obs[None, :] + row_weight = physical.copy() + row_weight *= q[None, :] + mass = row_weight.sum(axis=1) + row_valid = mass > 0 + normalized = np.zeros_like(row_weight, dtype=np.float32) + normalized[row_valid] = (row_weight[row_valid] / mass[row_valid, None]).astype(np.float32) + support = row_weight > 0 + count = support.sum(axis=1).astype(np.uint16) + first = np.full(len(dst), -1, dtype=np.int32) + last = np.full(len(dst), -1, dtype=np.int32) + if row_valid.any(): + first[row_valid] = support[row_valid].argmax(axis=1) + last[row_valid] = len(source) - 1 - support[row_valid, ::-1].argmax(axis=1) + width = dst[:, 1] - dst[:, 0] + physical_mass = physical.sum(axis=1) + coverage = np.clip(physical_mass / width, 0.0, 1.0).astype(np.float32) + qmean = np.ones(len(dst), np.float32) + qavailable = np.zeros(len(dst), np.float32) + physical_valid = physical_mass > 0 + qmean[physical_valid] = (mass[physical_valid] / physical_mass[physical_valid]).astype(np.float32) + qavailable[physical_valid] = ((physical[physical_valid] @ available.astype(np.float64)) + / physical_mass[physical_valid]).astype(np.float32) + + # The common full-dimension case follows the original matrix product + # exactly; this is also much faster for 500 x 768 input. + if np.array_equal(obs_dim, np.broadcast_to(obs[:, None], source.shape)): + x = np.zeros((len(dst), source.shape[1]), np.float32) + x[row_valid] = ((row_weight[row_valid] @ source) / mass[row_valid, None]).astype(np.float32) + observed_dimensions = np.broadcast_to(row_valid[:, None], x.shape).copy() + coverage_dimensions = np.broadcast_to(coverage[:, None], x.shape).copy() + else: + dim_physical = overlap[:, :, None] * obs_dim[None, :, :] + dim_weight = dim_physical * q[None, :, None] + dim_mass = dim_weight.sum(axis=1) + observed_dimensions = dim_mass > 0 + x = np.zeros((len(dst), source.shape[1]), np.float32) + numerator = np.einsum("ksd,sd->kd", dim_weight, source, optimize=True) + x[observed_dimensions] = (numerator[observed_dimensions] / dim_mass[observed_dimensions]).astype(np.float32) + coverage_dimensions = np.clip(dim_physical.sum(axis=1) / width[:, None], 0, 1).astype(np.float32) + return Projection(x, row_valid, coverage, count, first, last, + sparse.csr_matrix(normalized), observed_dimensions, coverage_dimensions, + qmean, qavailable) diff --git a/final/adapter/source.py b/final/adapter/source.py new file mode 100644 index 0000000..9e27ac9 --- /dev/null +++ b/final/adapter/source.py @@ -0,0 +1,51 @@ +"""Read the native Q1 artifacts needed by the unified alignment adapter.""" +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +import numpy as np + +FEATURE_DIR = Path(__file__).resolve().parents[1] / "output" / "q1" / "features_v2" + + +def load_sample(sample_id: str, feature_dir: Path = FEATURE_DIR) -> dict[str, Any]: + """Load a Q1 sample by its video/clip ID from a feature directory.""" + feature_dir = Path(feature_dir) + manifest = feature_dir / "manifest_q1.jsonl" + for line in manifest.read_text(encoding="utf-8").splitlines(): + row = json.loads(line) + if row["sample_id"] != sample_id: + continue + path = feature_dir / Path(row["feature_path"]).name + with np.load(path, allow_pickle=False) as archive: + result = {name: archive[name] for name in archive.files} + result["_path"] = path + result["_manifest"] = row + result["_meta"] = json.loads(str(result["meta_json"])) + return result + raise KeyError(f"sample_id not found: {sample_id}") + + +def native_arrays(sample: dict[str, Any], modality: str): + """Return values, observed dimensions, time intervals and quality evidence.""" + if modality == "text": + values = sample["native_text_features"].astype(np.float32) + return ( + values, + np.broadcast_to(sample["native_text_observed"][:, None], values.shape), + sample["native_text_intervals"].astype(np.float32), + sample["native_text_quality_effective"].astype(np.float32), + sample["native_text_quality_available"].astype(bool), + ) + if modality in ("audio", "vision"): + prefix = f"native_{modality}_" + return ( + sample[prefix + "features"].astype(np.float32), + sample[prefix + "mask"].astype(bool), + sample[prefix + "intervals"].astype(np.float32), + sample[prefix + "quality"].astype(np.float32), + sample[prefix + "quality_available"].astype(bool), + ) + raise ValueError(f"unknown modality: {modality}") diff --git a/final/compare_unaligned_q2.py b/final/compare_unaligned_q2.py new file mode 100644 index 0000000..af33611 --- /dev/null +++ b/final/compare_unaligned_q2.py @@ -0,0 +1,115 @@ +"""Consolidate Q2 validation and selected official-test scores on adapter data.""" +from __future__ import annotations + +import csv +import argparse +import gzip +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parent +OUT = ROOT / "output" / "q2" +EXPERIMENTS = ROOT / "experiments" / "q2" +MATH = EXPERIMENTS / "unaligned_math_all_b128" +DEEP = EXPERIMENTS / "unaligned_deep_two_b128" + + +def rows(path: Path) -> list[dict[str, str]]: + opener = gzip.open if path.suffix == ".gz" else open + with opener(path, "rt", newline="", encoding="utf-8-sig") as stream: + return list(csv.DictReader(stream)) + + +def write(path: Path, records: list[dict[str, str]]) -> None: + with path.open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter(stream, fieldnames=list(records[0])) + writer.writeheader() + writer.writerows(records) + + +def record(name: str, family: str, collection: str, scores: dict) -> dict[str, str]: + return { + "model": name, + "family": family, + "split": collection, + "n": str(scores.get("n", scores.get("n_valid", scores.get("n_test", "")))), + "accuracy": str(scores["accuracy"]), + "macro_f1": str(scores["macro_f1"]), + "mae": str(scores.get("regression_mae", scores.get("mae"))), + "rmse": str(scores.get("regression_rmse", scores.get("rmse"))), + "pearson": str(scores["pearson"]), + } + + +def normalized_auc(points: list[tuple[float, float]]) -> float: + points = sorted(points) + assert len(points) == 5, f"expected five missing-rate points, got {len(points)}" + width = points[-1][0] - points[0][0] + assert points[0][0] == 0.0 and width > 0 + area = sum((x1 - x0) * (y0 + y1) / 2 + for (x0, y0), (x1, y1) in zip(points, points[1:])) + return area / width + + +def main() -> None: + global OUT, MATH, DEEP + parser = argparse.ArgumentParser(description="Consolidate the unaligned Q2 comparison outputs.") + parser.add_argument("--math-dir", type=Path, default=MATH) + parser.add_argument("--deep-dir", type=Path, default=DEEP) + parser.add_argument("--output-dir", type=Path, default=OUT) + args = parser.parse_args() + MATH, DEEP, OUT = args.math_dir.resolve(), args.deep_dir.resolve(), args.output_dir.resolve() + OUT.mkdir(parents=True, exist_ok=True) + math_valid = rows(MATH / "ablation_validation.csv") + deep_valid = [r for r in rows(DEEP / "controlled_metrics_by_scenario.csv") + if r["scenario"] == "0.0/none"] + assert len(math_valid) == 12, f"expected 12 math variants, got {len(math_valid)}" + assert {r["method"] for r in deep_valid} == {"B0_early_concat", "B5_mofe_mlp"} + valid = [record(r["model"], "math", "official_valid", r) for r in math_valid] + valid += [record("EarlyConcat" if r["method"] == "B0_early_concat" else "MoFE-7", + "deep_learning", "official_valid", r) for r in deep_valid] + assert {r["n"] for r in valid} == {"728"} + write(OUT / "comparison_validation.csv", valid) + + math_test = json.loads((MATH / "test_metrics.json").read_text(encoding="utf-8")) + deep_test = rows(DEEP / "official_test_metrics_by_seed.csv") + selected = math_test.get("model") + if not selected: + manifest = json.loads((MATH / "run_manifest.json").read_text(encoding="utf-8")) + selected = manifest["selected_model"] + test = [record(selected, "math", "official_test", {"n": 727, **math_test})] + test += [record("EarlyConcat" if r["method"] == "B0_early_concat" else "MoFE-7", + "deep_learning", "official_test", r) for r in deep_test] + assert {r["n"] for r in test} == {"727"} + write(OUT / "comparison_test.csv", test) + + modes = ("single", "sync", "partial", "async") + math_sweep = rows(MATH / "controlled_missingness.csv.gz") + math_models = sorted({r["model"] for r in math_sweep}) + aurc: list[dict[str, str]] = [] + for model in math_models: + metric_row = {"model": model, "family": "math", "split": "official_valid", "n": "728"} + for mode in modes: + curve = [r for r in math_sweep + if r["model"] == model and r["mask_pattern"] in ("none", mode)] + assert len(curve) == 5, f"{model}/{mode}: expected five curve points, got {len(curve)}" + metric_row[mode] = f"{normalized_auc([(float(r['rate_realized_additional_global']), float(r['regression_mae'])) for r in curve]):.12g}" + aurc.append(metric_row) + + deep_map = {"B0_early_concat": "EarlyConcat", "B5_mofe_mlp": "MoFE-7"} + deep_auc = rows(DEEP / "aurc_mae_by_mode_seed.csv") + for method, model in deep_map.items(): + metric_row = {"model": model, "family": "deep_learning", "split": "official_valid", "n": "728"} + for mode in modes: + values = [r for r in deep_auc if r["method"] == method and r["mask_mode"] == mode] + assert len(values) == 1, f"{model}/{mode}: expected one AURC row, got {len(values)}" + metric_row[mode] = values[0]["aurc_mae"] + aurc.append(metric_row) + assert len(aurc) == 14, f"expected 14 AURC rows, got {len(aurc)}" + write(OUT / "comparison_aurc.csv", aurc) + print(f"wrote {len(valid)} validation, {len(test)} test and {len(aurc)} AURC rows; math test selection={selected}") + + +if __name__ == "__main__": + main() diff --git a/final/data/README.md b/final/data/README.md new file mode 100644 index 0000000..7671a87 --- /dev/null +++ b/final/data/README.md @@ -0,0 +1,3 @@ +# External task data + +Put the official attachment directories here using the names documented in `../README.md`, or set `FINAL_DATA_DIR` to another root. The large task data is intentionally not copied into this deliverable. diff --git a/final/data_paths.py b/final/data_paths.py new file mode 100644 index 0000000..7abbd70 --- /dev/null +++ b/final/data_paths.py @@ -0,0 +1,13 @@ +"""Shared input-data locations for the standalone deliverable.""" +from __future__ import annotations + +import os +from pathlib import Path + +PROJECT_ROOT = Path(__file__).resolve().parent +DATA_ROOT = Path(os.environ.get("FINAL_DATA_DIR", PROJECT_ROOT / "data")).expanduser().resolve() + +ATTACHMENT1 = DATA_ROOT / "附件1-数据集原始多模态样本" / "MOSEI数据集部分原始视频-100条" +ATTACHMENT2 = DATA_ROOT / "附件2-数据集特征文件" +ATTACHMENT3 = DATA_ROOT / "附件3-模态缺失特征样本" +ATTACHMENT4 = DATA_ROOT / "附件4-可解释专项视频样本与特征文件" diff --git a/final/experiments/q2/unaligned_deep_two_b128/aurc_mae_by_mode_seed.csv b/final/experiments/q2/unaligned_deep_two_b128/aurc_mae_by_mode_seed.csv new file mode 100644 index 0000000..3375af0 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/aurc_mae_by_mode_seed.csv @@ -0,0 +1,9 @@ +method,seed,mask_mode,aurc_mae,rates_realized +B0_early_concat,20260924,single,0.6613046329107035,"[0.0, 0.03337144161346991, 0.10001544588861884, 0.16664611113028518, 0.23332256502011975]" +B0_early_concat,20260924,sync,0.6578565844284859,"[0.0, 0.0824731117707892, 0.24870958809352056, 0.4126255406848969, 0.5789536263015227]" +B0_early_concat,20260924,partial,0.6492884830542397,"[0.0, 0.08271504489848806, 0.25229746282638565, 0.4133393742759596, 0.5839450723290429]" +B0_early_concat,20260924,async,0.655106630034653,"[0.0, 0.08259724236583968, 0.24879994634863523, 0.4163543647761491, 0.5781737204730611]" +B5_mofe_mlp,20260924,single,0.6509317685575335,"[0.0, 0.03337144161346991, 0.10001544588861884, 0.16664611113028518, 0.23332256502011975]" +B5_mofe_mlp,20260924,sync,0.6471657812189954,"[0.0, 0.0824731117707892, 0.24870958809352056, 0.4126255406848969, 0.5789536263015227]" +B5_mofe_mlp,20260924,partial,0.6417413313729995,"[0.0, 0.08271504489848806, 0.25229746282638565, 0.4133393742759596, 0.5839450723290429]" +B5_mofe_mlp,20260924,async,0.6524663015746559,"[0.0, 0.08259724236583968, 0.24879994634863523, 0.4163543647761491, 0.5781737204730611]" diff --git a/final/experiments/q2/unaligned_deep_two_b128/aurc_mae_paired_bootstrap.csv b/final/experiments/q2/unaligned_deep_two_b128/aurc_mae_paired_bootstrap.csv new file mode 100644 index 0000000..d3da4ba --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/aurc_mae_paired_bootstrap.csv @@ -0,0 +1,5 @@ +mask_mode,delta_aurc_mae_mofe_minus_earlyconcat,bootstrap_ci_2p5,bootstrap_ci_97p5,bootstrap_probability_delta_lt_0,replicates,resampling_unit,paired,seed +single,-0.010372864353169975,-0.034739033206989074,0.011121653228244181,0.832,1000,source video id,True,20260926 +sync,-0.010690803209490563,-0.03224668332673717,0.009269492203038398,0.852,1000,source video id,True,20260926 +partial,-0.007547151681240205,-0.028029454281346046,0.011218919524159786,0.799,1000,source video id,True,20260926 +async,-0.0026403284599970656,-0.021941700095468575,0.014894645229639519,0.624,1000,source video id,True,20260926 diff --git a/final/experiments/q2/unaligned_deep_two_b128/controlled_metrics_by_scenario.csv b/final/experiments/q2/unaligned_deep_two_b128/controlled_metrics_by_scenario.csv new file mode 100644 index 0000000..3b27ad5 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/controlled_metrics_by_scenario.csv @@ -0,0 +1,85 @@ +method,seed,scenario,realized_additional_global_rate,n_valid,accuracy,macro_f1,mae,rmse,pearson +B0_early_concat,20260924,0.0/none,0.0,728,0.6236263736263736,0.5779072819396367,0.6614056825637817,0.8730861646443621,0.6092134453424162 +B0_early_concat,20260924,0.1/single,0.03337144161346991,728,0.6222527472527473,0.5756295727842103,0.6565942764282227,0.867535461173715,0.6097236245001968 +B0_early_concat,20260924,0.1/sync,0.0824731117707892,728,0.6277472527472527,0.582082199138746,0.6544917821884155,0.863676231093789,0.6108662590466946 +B0_early_concat,20260924,0.1/partial,0.08271504489848806,728,0.6222527472527473,0.5729138628576042,0.6474129557609558,0.8571192178985961,0.6127499396646291 +B0_early_concat,20260924,0.1/async,0.08259724236583968,728,0.6263736263736264,0.5801957642716272,0.6535077095031738,0.863611977862628,0.6083479927990686 +B0_early_concat,20260924,0.3/single,0.10001544588861884,728,0.6167582417582418,0.5677215561929921,0.6565725803375244,0.8657024830868382,0.6025942746810006 +B0_early_concat,20260924,0.3/sync,0.24870958809352056,728,0.6277472527472527,0.5830444518546306,0.6476101279258728,0.8617082652252167,0.6011812144503749 +B0_early_concat,20260924,0.3/partial,0.25229746282638565,728,0.6098901098901099,0.5507039639053933,0.6338905692100525,0.8444783281512153,0.6071376228085325 +B0_early_concat,20260924,0.3/async,0.24879994634863523,728,0.6208791208791209,0.5727076447203662,0.6501115560531616,0.8573954932253441,0.5940509004212816 +B0_early_concat,20260924,0.5/single,0.16664611113028518,728,0.6181318681318682,0.5673158683926244,0.6670693755149841,0.8762100912085523,0.5850970970542624 +B0_early_concat,20260924,0.5/sync,0.4126255406848969,728,0.6085164835164835,0.5597533635039046,0.6538775563240051,0.8578028082517237,0.5921013502214066 +B0_early_concat,20260924,0.5/partial,0.4133393742759596,728,0.5975274725274725,0.5492312096994773,0.6596565842628479,0.8699468409531942,0.5653636443874772 +B0_early_concat,20260924,0.5/async,0.4163543647761491,728,0.6126373626373627,0.5579011692813914,0.6553950309753418,0.8714271899397752,0.5647483252398177 +B0_early_concat,20260924,0.7/single,0.23332256502011975,728,0.603021978021978,0.5476301273861442,0.6662526726722717,0.8841230404224142,0.5739000504542047 +B0_early_concat,20260924,0.7/sync,0.5789536263015227,728,0.6181318681318682,0.5794588999566664,0.6893671751022339,0.9076140759757262,0.5310735861778982 +B0_early_concat,20260924,0.7/partial,0.5839450723290429,728,0.6098901098901099,0.5570287286102937,0.6558725833892822,0.8691751880416998,0.5614266307858081 +B0_early_concat,20260924,0.7/async,0.5781737204730611,728,0.5906593406593407,0.540751646014804,0.6640652418136597,0.8917284704934869,0.5280100344793427 +B0_early_concat,20260924,0.3/modality_T,0.09999999999999999,728,0.6071428571428571,0.5467045242030458,0.6535701751708984,0.8695044623924548,0.5740677659010623 +B0_early_concat,20260924,0.3/modality_A,0.09999999999999999,728,0.6236263736263736,0.5802852067090076,0.6629742980003357,0.8729679490156239,0.6114311922109067 +B0_early_concat,20260924,0.3/modality_V,0.09761972825757124,728,0.6263736263736264,0.5850409435804974,0.6584386229515076,0.8707905742119356,0.6094125059522634 +B0_early_concat,20260924,0.3/modality_TA,0.19999999999999998,728,0.6043956043956044,0.5483122703376635,0.6477882266044617,0.8501547630449895,0.5941395419511633 +B0_early_concat,20260924,0.3/modality_TV,0.19753004834795773,728,0.6153846153846154,0.558287353675163,0.6419960856437683,0.8512022673882316,0.5874828176495971 +B0_early_concat,20260924,0.3/modality_AV,0.19775529608515166,728,0.625,0.5872757790508221,0.6611344814300537,0.8730712818941418,0.6102031129400097 +B0_early_concat,20260924,0.3/modality_TAV,0.297772943611562,728,0.6291208791208791,0.5864546518317283,0.6486479640007019,0.8647217965226256,0.610798832373964 +B0_early_concat,20260924,0.3/location_start_T,0.09999999999999999,728,0.5975274725274725,0.5351726533076114,0.6562638282775879,0.864667754283869,0.5817018346178215 +B0_early_concat,20260924,0.3/location_middle_T,0.09999999999999999,728,0.6195054945054945,0.5658978157744831,0.6375324726104736,0.8465725348051408,0.5972010628634644 +B0_early_concat,20260924,0.3/location_end_T,0.09999999999999999,728,0.6181318681318682,0.5567576890206859,0.6542375683784485,0.8580726117995574,0.5929550853656131 +B0_early_concat,20260924,0.3/span_long_T,0.09999999999999999,728,0.6208791208791209,0.562845463190525,0.6471637487411499,0.8538149869544863,0.5914544955286228 +B0_early_concat,20260924,0.3/span_multi_short_T,0.09999999999999999,728,0.6222527472527473,0.5623865187243154,0.6460644006729126,0.8516967737571002,0.5948229746582595 +B0_early_concat,20260924,0.3/location_start_A,0.09999999999999999,728,0.6291208791208791,0.588026021429827,0.6595423221588135,0.8698495438254136,0.6135346689640939 +B0_early_concat,20260924,0.3/location_middle_A,0.09999999999999999,728,0.6263736263736264,0.5853791183901843,0.6619890928268433,0.8723418527480369,0.6119717734281127 +B0_early_concat,20260924,0.3/location_end_A,0.09999999999999999,728,0.6222527472527473,0.5801699349967732,0.6609088182449341,0.8717659721431937,0.6110401312729596 +B0_early_concat,20260924,0.3/span_long_A,0.09999999999999999,728,0.6236263736263736,0.5797123207765644,0.6602560877799988,0.8699320415284744,0.613651629849919 +B0_early_concat,20260924,0.3/span_multi_short_A,0.09999999999999999,728,0.6222527472527473,0.5802057937364492,0.6612809300422668,0.8719530870478458,0.6122420111215663 +B0_early_concat,20260924,0.3/location_start_V,0.10012154787579122,728,0.6222527472527473,0.5814214462347244,0.6565123200416565,0.8683806107464582,0.6104583376065503 +B0_early_concat,20260924,0.3/location_middle_V,0.10012154787579122,728,0.625,0.582351483496762,0.6608109474182129,0.8735481888133111,0.6070971402195685 +B0_early_concat,20260924,0.3/location_end_V,0.10012154787579122,728,0.6291208791208791,0.5883168687676835,0.656943678855896,0.8694064644577811,0.6089477882879546 +B0_early_concat,20260924,0.3/span_long_V,0.10012154787579122,728,0.6263736263736264,0.5844995408631772,0.6554403305053711,0.868763531092371,0.61015901738798 +B0_early_concat,20260924,0.3/span_multi_short_V,0.10012154787579122,728,0.625,0.5829946286257456,0.6572867631912231,0.8695764027788331,0.6095111487857774 +B0_early_concat,20260924,0.3/synchrony_sync,0.29772828841123383,728,0.6085164835164835,0.5626589390552919,0.6693985462188721,0.8784603768120934,0.5968443695504774 +B0_early_concat,20260924,0.3/synchrony_partial,0.29809424120092387,728,0.6195054945054945,0.5687658572400007,0.6421869993209839,0.8532956885314406,0.5928230382571548 +B0_early_concat,20260924,0.3/synchrony_async,0.2979324078676833,728,0.6208791208791209,0.567557702567593,0.6289306282997131,0.8368734663706395,0.6108642541012679 +B5_mofe_mlp,20260924,0.0/none,0.0,728,0.614010989010989,0.5661110562085216,0.6426358222961426,0.8499269790699532,0.6013646920407029 +B5_mofe_mlp,20260924,0.1/single,0.03337144161346991,728,0.6112637362637363,0.5594129613928418,0.641840934753418,0.8502911167337094,0.5995157020375953 +B5_mofe_mlp,20260924,0.1/sync,0.0824731117707892,728,0.6071428571428571,0.5571711340828792,0.6430917382240295,0.8502062575347569,0.5986550324280812 +B5_mofe_mlp,20260924,0.1/partial,0.08271504489848806,728,0.6126373626373627,0.5525536207262688,0.6412531733512878,0.8473718403375929,0.6011943924366795 +B5_mofe_mlp,20260924,0.1/async,0.08259724236583968,728,0.6071428571428571,0.5516910274191827,0.6438398957252502,0.8492486648414319,0.598803034002672 +B5_mofe_mlp,20260924,0.3/single,0.10001544588861884,728,0.6208791208791209,0.5573394889229436,0.6467976570129395,0.8498497984513651,0.5965841685920008 +B5_mofe_mlp,20260924,0.3/sync,0.24870958809352056,728,0.5989010989010989,0.5385459804114902,0.6412105560302734,0.8482154372931673,0.5958513975498313 +B5_mofe_mlp,20260924,0.3/partial,0.25229746282638565,728,0.6112637362637363,0.5283507174639464,0.6258935332298279,0.8289982492085595,0.6127149075284516 +B5_mofe_mlp,20260924,0.3/async,0.24879994634863523,728,0.6016483516483516,0.5197151900845509,0.6410508751869202,0.8450358094860905,0.5961687643306721 +B5_mofe_mlp,20260924,0.5/single,0.16664611113028518,728,0.6153846153846154,0.5464158181876891,0.6513766646385193,0.8600636237473732,0.5846925766750563 +B5_mofe_mlp,20260924,0.5/sync,0.4126255406848969,728,0.6085164835164835,0.5470597996610734,0.6449754238128662,0.8444687995793463,0.5925573038909221 +B5_mofe_mlp,20260924,0.5/partial,0.4133393742759596,728,0.5906593406593407,0.49613066579938897,0.6517915725708008,0.8600206550623896,0.5714346439568058 +B5_mofe_mlp,20260924,0.5/async,0.4163543647761491,728,0.592032967032967,0.5006927483440172,0.6684890389442444,0.8769546882598469,0.5487937593111066 +B5_mofe_mlp,20260924,0.7/single,0.23332256502011975,728,0.5906593406593407,0.5188176725643628,0.676094114780426,0.8832226703409539,0.5615064753038177 +B5_mofe_mlp,20260924,0.7/sync,0.5789536263015227,728,0.5989010989010989,0.5402081732547881,0.671673595905304,0.8967949156943728,0.5324896897010112 +B5_mofe_mlp,20260924,0.7/partial,0.5839450723290429,728,0.5947802197802198,0.4998097006038016,0.653204619884491,0.8656261237512448,0.5638949756152036 +B5_mofe_mlp,20260924,0.7/async,0.5781737204730611,728,0.5892857142857143,0.5042495984868866,0.6616786122322083,0.8815221555633493,0.5423866288771326 +B5_mofe_mlp,20260924,0.3/modality_T,0.09999999999999999,728,0.5947802197802198,0.5063814849809531,0.6539718508720398,0.8704605538196851,0.5723805318852029 +B5_mofe_mlp,20260924,0.3/modality_A,0.09999999999999999,728,0.614010989010989,0.5598818746617674,0.6425811648368835,0.850795015025813,0.6039956657547587 +B5_mofe_mlp,20260924,0.3/modality_V,0.09761972825757124,728,0.6153846153846154,0.5690694861127272,0.6356155276298523,0.8384938591702088,0.6043727083237042 +B5_mofe_mlp,20260924,0.3/modality_TA,0.19999999999999998,728,0.5837912087912088,0.48780687902979797,0.6420891880989075,0.8471604756400429,0.5924223715639282 +B5_mofe_mlp,20260924,0.3/modality_TV,0.19753004834795773,728,0.6043956043956044,0.5255932011370116,0.64052414894104,0.8469339272806086,0.5874898383721209 +B5_mofe_mlp,20260924,0.3/modality_AV,0.19775529608515166,728,0.614010989010989,0.5668160786603322,0.6386415362358093,0.8418676076676349,0.6040556699846994 +B5_mofe_mlp,20260924,0.3/modality_TAV,0.297772943611562,728,0.6167582417582418,0.5715181632974516,0.6396267414093018,0.8512298914345021,0.5979056912272757 +B5_mofe_mlp,20260924,0.3/location_start_T,0.09999999999999999,728,0.6057692307692307,0.5154200999774394,0.6563181281089783,0.8651397860040234,0.5788243336192032 +B5_mofe_mlp,20260924,0.3/location_middle_T,0.09999999999999999,728,0.6002747252747253,0.5155996862820228,0.6410806775093079,0.8556772265437145,0.5880992195657162 +B5_mofe_mlp,20260924,0.3/location_end_T,0.09999999999999999,728,0.6043956043956044,0.5132960652960653,0.6567513346672058,0.8618302038887535,0.5900327107371653 +B5_mofe_mlp,20260924,0.3/span_long_T,0.09999999999999999,728,0.6002747252747253,0.5184665608001056,0.6476861238479614,0.8577126463981695,0.5876898168366306 +B5_mofe_mlp,20260924,0.3/span_multi_short_T,0.09999999999999999,728,0.6057692307692307,0.5198857243995252,0.6483989953994751,0.8542659438210808,0.590794095340954 +B5_mofe_mlp,20260924,0.3/location_start_A,0.09999999999999999,728,0.614010989010989,0.5586252945683551,0.6418027877807617,0.8500467511952089,0.60447489707413 +B5_mofe_mlp,20260924,0.3/location_middle_A,0.09999999999999999,728,0.6167582417582418,0.5615279110341475,0.6426709890365601,0.8512982999792055,0.603457838057345 +B5_mofe_mlp,20260924,0.3/location_end_A,0.09999999999999999,728,0.6085164835164835,0.5525221615769731,0.644776463508606,0.8538535210889053,0.6002579946736821 +B5_mofe_mlp,20260924,0.3/span_long_A,0.09999999999999999,728,0.6167582417582418,0.5615891998583441,0.6422436237335205,0.8508989741317513,0.6034505225052593 +B5_mofe_mlp,20260924,0.3/span_multi_short_A,0.09999999999999999,728,0.614010989010989,0.5606369387366029,0.6426882743835449,0.8524206894258077,0.6024598958557875 +B5_mofe_mlp,20260924,0.3/location_start_V,0.10012154787579122,728,0.614010989010989,0.5670474705493806,0.6374409198760986,0.8386711984106638,0.6039920835912731 +B5_mofe_mlp,20260924,0.3/location_middle_V,0.10012154787579122,728,0.6195054945054945,0.5734933438258247,0.6365443468093872,0.8400289996568183,0.603012598786289 +B5_mofe_mlp,20260924,0.3/location_end_V,0.10012154787579122,728,0.6112637362637363,0.5652012668666679,0.6374439597129822,0.8398283136412973,0.6023922863626555 +B5_mofe_mlp,20260924,0.3/span_long_V,0.10012154787579122,728,0.6222527472527473,0.5773678980636182,0.6366006135940552,0.8389656977160165,0.6035623465356755 +B5_mofe_mlp,20260924,0.3/span_multi_short_V,0.10012154787579122,728,0.6126373626373627,0.5649179859132109,0.6358081102371216,0.8380371179098933,0.6047162305666233 +B5_mofe_mlp,20260924,0.3/synchrony_sync,0.29772828841123383,728,0.5961538461538461,0.544815501958359,0.6474998593330383,0.8597538199821015,0.5882829626997734 +B5_mofe_mlp,20260924,0.3/synchrony_partial,0.29809424120092387,728,0.5975274725274725,0.5154921681321046,0.6415172219276428,0.8522963205597589,0.5844467918138794 +B5_mofe_mlp,20260924,0.3/synchrony_async,0.2979324078676833,728,0.6153846153846154,0.5320488406321139,0.638544499874115,0.8461633711725599,0.5940998868035174 diff --git a/final/experiments/q2/unaligned_deep_two_b128/hypothesis.md b/final/experiments/q2/unaligned_deep_two_b128/hypothesis.md new file mode 100644 index 0000000..857d823 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/hypothesis.md @@ -0,0 +1,13 @@ +# R03: 按 math/Q2 V2 口径重训两种保留模型 + +## 假设 + +在保持 EarlyConcat + BiGRU 与 MoFE-7 + MLP Router 结构及共同监督目标不变的情况下，使用数学方案中的官方划分、连续块缺失训练和 42 个固定验证情景，可以公平比较两种模型的干净测试表现与缺失鲁棒性。 + +## 唯一实验改动 + +相对现有检查点，本轮重新训练时将缺失训练改为 0/10/30/50/70% 与 single/sync/partial/async，每个被选模态至少保留 20% 观测；训练和批次顺序在两个模型间配对。数学方案中的 C5 概率损失不适用于现有确定性分类/回归头，因此保留项目既有的 CE + 0.5 SmoothL1 联合目标。 + +## 数据使用 + +标准化器只在官方训练集观测行上拟合；官方验证集只用于早停与缺失评估；官方测试集在全部检查点确定后做一次干净评估。 diff --git a/final/experiments/q2/unaligned_deep_two_b128/models/B0_early_concat/seed_20260924/model_best.pt b/final/experiments/q2/unaligned_deep_two_b128/models/B0_early_concat/seed_20260924/model_best.pt new file mode 100644 index 0000000..41dd282 Binary files /dev/null and b/final/experiments/q2/unaligned_deep_two_b128/models/B0_early_concat/seed_20260924/model_best.pt differ diff --git a/final/experiments/q2/unaligned_deep_two_b128/models/B0_early_concat/seed_20260924/training_history.csv b/final/experiments/q2/unaligned_deep_two_b128/models/B0_early_concat/seed_20260924/training_history.csv new file mode 100644 index 0000000..e9ac483 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/models/B0_early_concat/seed_20260924/training_history.csv @@ -0,0 +1,7 @@ +method,seed,epoch,train_loss,valid_selection_loss,valid_clean_loss +B0_early_concat,20260924,1,1.0491621979960688,0.9790831139454474,0.9719539965902056 +B0_early_concat,20260924,2,0.8702195706190886,0.8858414890019448,0.8805038850386064 +B0_early_concat,20260924,3,0.7755516767501831,0.8766277267055197,0.8737426747332563 +B0_early_concat,20260924,4,0.7278096808327569,0.8919816681971916,0.8898496392009022 +B0_early_concat,20260924,5,0.6790786606294138,0.8997945186201033,0.9034162877680181 +B0_early_concat,20260924,6,0.6248726513650682,0.930847568662612,0.9370587906994663 diff --git a/final/experiments/q2/unaligned_deep_two_b128/models/B5_mofe_mlp/seed_20260924/model_best.pt b/final/experiments/q2/unaligned_deep_two_b128/models/B5_mofe_mlp/seed_20260924/model_best.pt new file mode 100644 index 0000000..90f8e0e Binary files /dev/null and b/final/experiments/q2/unaligned_deep_two_b128/models/B5_mofe_mlp/seed_20260924/model_best.pt differ diff --git a/final/experiments/q2/unaligned_deep_two_b128/models/B5_mofe_mlp/seed_20260924/training_history.csv b/final/experiments/q2/unaligned_deep_two_b128/models/B5_mofe_mlp/seed_20260924/training_history.csv new file mode 100644 index 0000000..17a2269 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/models/B5_mofe_mlp/seed_20260924/training_history.csv @@ -0,0 +1,8 @@ +method,seed,epoch,train_loss,valid_selection_loss,valid_clean_loss +B5_mofe_mlp,20260924,1,1.057857162422604,1.001513828779315,0.9967762952322489 +B5_mofe_mlp,20260924,2,0.907955339661351,0.9335334502406173,0.9310275303138481 +B5_mofe_mlp,20260924,3,0.8342712808538366,0.9082694209211475,0.9078151596771492 +B5_mofe_mlp,20260924,4,0.7950075798564487,0.8872917859436391,0.8826478844160562 +B5_mofe_mlp,20260924,5,0.7324514389038086,0.9029257117391942,0.9091550825716375 +B5_mofe_mlp,20260924,6,0.695859447673515,0.9317116825790196,0.9370182791909019 +B5_mofe_mlp,20260924,7,0.6615819312908031,0.9626517438299054,0.9812416643886775 diff --git a/final/experiments/q2/unaligned_deep_two_b128/official_test_metrics_by_seed.csv b/final/experiments/q2/unaligned_deep_two_b128/official_test_metrics_by_seed.csv new file mode 100644 index 0000000..17be99f --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/official_test_metrics_by_seed.csv @@ -0,0 +1,3 @@ +method,seed,best_epoch,n_test,accuracy,macro_f1,mae,rmse,pearson +B0_early_concat,20260924,3,727,0.6698762035763411,0.5869825623561269,0.6850546002388,0.9016550912366708,0.6612713411018789 +B5_mofe_mlp,20260924,4,727,0.6629986244841816,0.5878736806271413,0.6702061891555786,0.8895326574633714,0.6446983125605155 diff --git a/final/experiments/q2/unaligned_deep_two_b128/official_test_paired_bootstrap.csv b/final/experiments/q2/unaligned_deep_two_b128/official_test_paired_bootstrap.csv new file mode 100644 index 0000000..218ae8d --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/official_test_paired_bootstrap.csv @@ -0,0 +1,6 @@ +comparison,metric,delta,bootstrap_ci_2p5,bootstrap_ci_97p5,bootstrap_probability_delta_gt_0,replicates,resampling_unit,paired,seed +B5_mofe_mlp minus B0_early_concat,accuracy,-0.00687757909215958,-0.030277504057500616,0.014748800095774674,0.218,1000,source video id,True,20260925 +B5_mofe_mlp minus B0_early_concat,macro_f1,0.0008911182710144017,-0.031212396866488787,0.029535171595866133,0.498,1000,source video id,True,20260925 +B5_mofe_mlp minus B0_early_concat,mae,-0.014848411083221436,-0.0428707018494606,0.013604016602039337,0.156,1000,source video id,True,20260925 +B5_mofe_mlp minus B0_early_concat,rmse,-0.012122433773299357,-0.04483613634341237,0.019086285550621508,0.239,1000,source video id,True,20260925 +B5_mofe_mlp minus B0_early_concat,pearson,-0.016573028541363444,-0.03844840453360015,0.005332473039635121,0.07,1000,source video id,True,20260925 diff --git a/final/experiments/q2/unaligned_deep_two_b128/official_test_summary.csv b/final/experiments/q2/unaligned_deep_two_b128/official_test_summary.csv new file mode 100644 index 0000000..72cac48 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/official_test_summary.csv @@ -0,0 +1,11 @@ +method,metric,mean,sd_across_seeds,n_seeds +B0_early_concat,accuracy,0.6698762035763411,0.0,1 +B0_early_concat,macro_f1,0.5869825623561269,0.0,1 +B0_early_concat,mae,0.6850546002388,0.0,1 +B0_early_concat,rmse,0.9016550912366708,0.0,1 +B0_early_concat,pearson,0.6612713411018789,0.0,1 +B5_mofe_mlp,accuracy,0.6629986244841816,0.0,1 +B5_mofe_mlp,macro_f1,0.5878736806271413,0.0,1 +B5_mofe_mlp,mae,0.6702061891555786,0.0,1 +B5_mofe_mlp,rmse,0.8895326574633714,0.0,1 +B5_mofe_mlp,pearson,0.6446983125605155,0.0,1 diff --git a/final/experiments/q2/unaligned_deep_two_b128/parameter_count.csv b/final/experiments/q2/unaligned_deep_two_b128/parameter_count.csv new file mode 100644 index 0000000..11d5fd3 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/parameter_count.csv @@ -0,0 +1,3 @@ +method,parameters_total,parameters_trainable,best_epoch +B0_early_concat,253124,253124,3 +B5_mofe_mlp,306523,306523,4 diff --git a/final/experiments/q2/unaligned_deep_two_b128/run_manifest.json b/final/experiments/q2/unaligned_deep_two_b128/run_manifest.json new file mode 100644 index 0000000..b339adc --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/run_manifest.json @@ -0,0 +1,153 @@ +{ + "experiment": "Retrained EarlyConcat and MoFE-7 + MLP Router using math/Q2 V2-compatible protocol", + "created_unix": 1790329152.2777789, + "device": "cuda", + "cuda_device": "NVIDIA GeForce RTX 5070 Ti", + "feature_file": "/home/gloamxun/modeling_zhaocui/E\u9898\u6570\u636e/\u9644\u4ef62-\u6570\u636e\u96c6\u7279\u5f81\u6587\u4ef6/unaligned_50.pkl", + "feature_sha256": "77eda14a06be9749a96c52ae45470c7cffcfa7219011eae391d231a0664c3762", + "representation": "final.adapter relative-progress projection of official unaligned_50; not physical-time alignment", + "adapter": "final.adapter.adapt_official_split", + "adapter_audit": { + "train": { + "method": "shared_interval_overlap_on_normalized_progress", + "coordinate_mode": "relative", + "physical_time_alignment": false, + "samples": 3395, + "vision_length_conflict_samples": 618, + "vision_tail_ambiguous_samples": 618, + "nonzero_text_rows_outside_attention": 86078, + "observed_target_rows": { + "text": 169750, + "audio": 169750, + "vision": 163302 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.9540337701314328 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + }, + "valid": { + "method": "shared_interval_overlap_on_normalized_progress", + "coordinate_mode": "relative", + "physical_time_alignment": false, + "samples": 728, + "vision_length_conflict_samples": 141, + "vision_tail_ambiguous_samples": 141, + "nonzero_text_rows_outside_attention": 17772, + "observed_target_rows": { + "text": 36400, + "audio": 36400, + "vision": 35315 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.96090314748523 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + }, + "test": { + "method": "shared_interval_overlap_on_normalized_progress", + "coordinate_mode": "relative", + "physical_time_alignment": false, + "samples": 727, + "vision_length_conflict_samples": 131, + "vision_tail_ambiguous_samples": 131, + "nonzero_text_rows_outside_attention": 18041, + "observed_target_rows": { + "text": 36350, + "audio": 36350, + "vision": 35034 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.9549938172651288 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + } + }, + "train_valid_test_counts": { + "train": 3395, + "valid": 728, + "test": 727 + }, + "source_video_groups": { + "train": 1528, + "valid": 239, + "test": 381 + }, + "official_group_splits_disjoint": true, + "train_only_scaler": "/home/gloamxun/modeling_zhaocui/final/output/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz", + "scaler_fit": "median and 1.4826*MAD on observed training rows only; zero-MAD fallback to std then 1", + "seed": 20260924, + "model_seeds": [ + 20260924 + ], + "training_configuration": { + "epoch_limit": 12, + "early_stopping_patience": 3, + "batch_size": 128, + "optimizer": "AdamW", + "learning_rate": 0.0003, + "weight_decay": 0.001, + "gradient_clip_norm": 1.0, + "early_stopping_metric": "mean validation joint CE + 0.5*SmoothL1 over 0.0/none, 0.3/single, 0.3/sync, 0.5/async", + "architecture_preserved": { + "B0_early_concat": "EarlyConcat + BiGRU", + "B5_mofe_mlp": "MoFE-7 + MLP Router" + }, + "objective": "cross entropy + 0.5 * SmoothL1(intensity/3, label/3); same objective for both methods", + "training_corruption": { + "rates": [ + 0.0, + 0.1, + 0.3, + 0.5, + 0.7 + ], + "patterns": [ + "single", + "sync", + "partial", + "async" + ], + "preserve_at_least_fraction_per_selected_modality": 0.2, + "generator_seed": 20261227, + "same_sample_masks_and_batch_orders_across_models": true + } + }, + "validation_protocol": { + "scenario_seed": 20261833, + "scenario_count": 42, + "same_fixed_masks_for_both_models": true, + "scenario_design": "math/Q2 42 controlled continuous-mask scenarios regenerated on each sample's original observation mask", + "selection_scenarios": [ + "0.0/none", + "0.3/single", + "0.3/sync", + "0.5/async" + ], + "selection_note": "Deterministic-model adaptation; uses joint supervised loss instead of C5's probabilistic selection NLL.", + "aurc": "normalized trapezoidal MAE area over realized equal-modality-weighted additional missing rate for single/sync/partial/async at 0/.1/.3/.5/.7" + }, + "test_protocol": { + "official_test_final_clean_passes": 1, + "test_used_for_training_or_checkpoint_selection": false, + "metrics": [ + "accuracy", + "macro_f1", + "mae", + "rmse", + "pearson" + ], + "paired_group_bootstrap_replicates": 1000, + "bootstrap_unit": "source video id", + "bootstrap_seed": 20260925 + } +} \ No newline at end of file diff --git a/final/experiments/q2/unaligned_deep_two_b128/training_history.csv b/final/experiments/q2/unaligned_deep_two_b128/training_history.csv new file mode 100644 index 0000000..249dea6 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/training_history.csv @@ -0,0 +1,14 @@ +method,seed,epoch,train_loss,valid_selection_loss,valid_clean_loss +B0_early_concat,20260924,1,1.0491621979960688,0.9790831139454474,0.9719539965902056 +B0_early_concat,20260924,2,0.8702195706190886,0.8858414890019448,0.8805038850386064 +B0_early_concat,20260924,3,0.7755516767501831,0.8766277267055197,0.8737426747332563 +B0_early_concat,20260924,4,0.7278096808327569,0.8919816681971916,0.8898496392009022 +B0_early_concat,20260924,5,0.6790786606294138,0.8997945186201033,0.9034162877680181 +B0_early_concat,20260924,6,0.6248726513650682,0.930847568662612,0.9370587906994663 +B5_mofe_mlp,20260924,1,1.057857162422604,1.001513828779315,0.9967762952322489 +B5_mofe_mlp,20260924,2,0.907955339661351,0.9335334502406173,0.9310275303138481 +B5_mofe_mlp,20260924,3,0.8342712808538366,0.9082694209211475,0.9078151596771492 +B5_mofe_mlp,20260924,4,0.7950075798564487,0.8872917859436391,0.8826478844160562 +B5_mofe_mlp,20260924,5,0.7324514389038086,0.9029257117391942,0.9091550825716375 +B5_mofe_mlp,20260924,6,0.695859447673515,0.9317116825790196,0.9370182791909019 +B5_mofe_mlp,20260924,7,0.6615819312908031,0.9626517438299054,0.9812416643886775 diff --git a/final/experiments/q2/unaligned_deep_two_b128/training_mask_distribution.csv b/final/experiments/q2/unaligned_deep_two_b128/training_mask_distribution.csv new file mode 100644 index 0000000..086a783 --- /dev/null +++ b/final/experiments/q2/unaligned_deep_two_b128/training_mask_distribution.csv @@ -0,0 +1,41 @@ +method,seed,rate_mode,sample_epoch_assignments +B0_early_concat,20260924,0.0/async,1034 +B0_early_concat,20260924,0.0/partial,1074 +B0_early_concat,20260924,0.0/single,1084 +B0_early_concat,20260924,0.0/sync,1065 +B0_early_concat,20260924,0.1/async,1059 +B0_early_concat,20260924,0.1/partial,1029 +B0_early_concat,20260924,0.1/single,1034 +B0_early_concat,20260924,0.1/sync,969 +B0_early_concat,20260924,0.3/async,1047 +B0_early_concat,20260924,0.3/partial,989 +B0_early_concat,20260924,0.3/single,978 +B0_early_concat,20260924,0.3/sync,1014 +B0_early_concat,20260924,0.5/async,1004 +B0_early_concat,20260924,0.5/partial,1017 +B0_early_concat,20260924,0.5/single,1013 +B0_early_concat,20260924,0.5/sync,986 +B0_early_concat,20260924,0.7/async,975 +B0_early_concat,20260924,0.7/partial,1006 +B0_early_concat,20260924,0.7/single,982 +B0_early_concat,20260924,0.7/sync,1011 +B5_mofe_mlp,20260924,0.0/async,1215 +B5_mofe_mlp,20260924,0.0/partial,1242 +B5_mofe_mlp,20260924,0.0/single,1246 +B5_mofe_mlp,20260924,0.0/sync,1246 +B5_mofe_mlp,20260924,0.1/async,1219 +B5_mofe_mlp,20260924,0.1/partial,1205 +B5_mofe_mlp,20260924,0.1/single,1210 +B5_mofe_mlp,20260924,0.1/sync,1141 +B5_mofe_mlp,20260924,0.3/async,1210 +B5_mofe_mlp,20260924,0.3/partial,1146 +B5_mofe_mlp,20260924,0.3/single,1148 +B5_mofe_mlp,20260924,0.3/sync,1169 +B5_mofe_mlp,20260924,0.5/async,1173 +B5_mofe_mlp,20260924,0.5/partial,1184 +B5_mofe_mlp,20260924,0.5/single,1192 +B5_mofe_mlp,20260924,0.5/sync,1159 +B5_mofe_mlp,20260924,0.7/async,1157 +B5_mofe_mlp,20260924,0.7/partial,1153 +B5_mofe_mlp,20260924,0.7/single,1172 +B5_mofe_mlp,20260924,0.7/sync,1178 diff --git a/final/experiments/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz b/final/experiments/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz new file mode 100644 index 0000000..5ba5556 Binary files /dev/null and b/final/experiments/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz differ diff --git a/final/experiments/q2/unaligned_math_all_b128/ablation_validation.csv b/final/experiments/q2/unaligned_math_all_b128/ablation_validation.csv new file mode 100644 index 0000000..8bfe468 --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/ablation_validation.csv @@ -0,0 +1,13 @@ +model,n,accuracy,macro_f1,negative_support,neutral_support,positive_support,negative_recall,middle_recall,positive_recall,regression_mae,regression_rmse,pearson,brier,classification_nll,ece_15,interval_90_coverage,interval_90_mean_width,selection_nll,temperature,description,predictive_variance_mean_uncalibrated,within_trajectory_variance_mean,between_trajectory_variance_mean,predictive_mean_mean_calibrated,predictive_variance_mean_calibrated,validation_selection_loss,rho_imp,lambda_u,lambda_gap,lambda_span,lambda_group,group_temperature +C0,728,0.5906593406593407,0.5573246854806906,206,184,338,0.5970873786407767,0.34782608695652173,0.7189349112426036,0.7267872095108032,0.9493674330569902,0.5186371207237244,0.5240320990297133,0.8831461776473413,0.04826531354954992,0.9354395604395604,3.5201075077056885,0.9197877716698537,2.320690790447983,observed mean/std + masks + maximum gap; logistic/ridge,,,,,,,,,,,, +C1,728,0.5879120879120879,0.47389937106918234,206,184,338,0.6262135922330098,0.06521739130434782,0.849112426035503,0.7446202039718628,0.9822674244990333,0.5362412333488464,0.527031031283926,0.8880350589752197,0.060032890352246515,0.9148351648351648,2.7002923488616943,2.818410634994507,0.7559785314090598,"masked BiGRU; no posterior imputation, explicit reliability or source gate",0.767420768737793,0.767420768737793,0.0,0.2549660801887512,0.705826461315155,2.818410634994507,0.5,0.05,0.05,0.05,nan,nan +C2,728,0.5961538461538461,0.450273379924267,206,184,338,0.6699029126213593,0.005434782608695652,0.8727810650887574,0.698158860206604,0.9260472748411018,0.5808078050613403,0.5106602366936009,0.8622351288795471,0.06248928942195664,0.9065934065934066,2.5688624382019043,2.7997119426727295,0.8488490623779881,exact Gaussian posterior mean; no joint trajectory integral,0.6898056864738464,0.6898056864738464,0.0,0.21209104359149933,0.6582686305046082,2.7997119426727295,0.5,0.05,0.05,0.05,nan,nan +C3,728,0.5906593406593407,0.443346825657295,206,184,338,0.6699029126213593,0.0,0.863905325443787,0.7317898869514465,0.985695966827773,0.5456575155258179,0.5164441425522118,0.8702284693717957,0.06148201186250855,0.9065934065934066,2.4972548484802246,2.811898708343506,1.0120878694650972,joint trajectory integral plus final reliability/content fusion gate,0.633382260799408,0.6333822011947632,7.366534049424445e-08,0.23270586133003235,0.6362141370773315,2.811903953552246,0.5,0.0,0.0,0.0,nan,nan +C4,728,0.554945054945055,0.4536351679548698,206,184,338,0.46601941747572817,0.09782608695652174,0.8579881656804734,0.7454220056533813,1.0156130863371253,0.503315806388855,0.5250710164495501,0.8871082663536072,0.07315124743259872,0.8791208791208791,2.4709346294403076,2.848031759262085,1.1013056928889957,C3 plus bounded cross-time source attention and null source,0.6384363770484924,0.6384357810020447,5.842408654643805e-07,0.273845911026001,0.6572040319442749,2.8480465412139893,0.7,0.05,0.05,0.05,nan,nan +C5,728,0.5824175824175825,0.4401015447285379,206,184,338,0.6601941747572816,0.005434782608695652,0.849112426035503,0.7787972092628479,1.024799634376836,0.5123178958892822,0.5304346310715194,0.8944377899169922,0.030563883729033414,0.8997252747252747,2.767879009246826,2.851557731628418,1.209048884859704,C4 plus reliability-modulated BiGRU update,0.7191231846809387,0.7191231846809387,3.2177736031258064e-09,0.14806683361530304,0.7649285197257996,2.851555109024048,0.3,0.05,0.05,0.05,nan,nan +C6,728,0.6085164835164835,0.518781225964892,206,184,338,0.6796116504854369,0.125,0.8284023668639053,0.6846672296524048,0.9208069125796768,0.6101511716842651,0.4977339610947482,0.8427405953407288,0.039509518706536545,0.8873626373626373,2.391038656234741,2.7836689949035645,1.122980387832455,C5 plus optional rank-4 CP residual,0.5537752509117126,0.5537748336791992,4.292722337595478e-07,0.20950497686862946,0.5816965699195862,2.78372859954834,0.3,0.05,0.05,0.05,nan,nan +C6_no_distance,728,0.6071428571428571,0.4720788453840183,206,184,338,0.7038834951456311,0.02717391304347826,0.863905325443787,0.7117781043052673,0.9322639795095579,0.6074535250663757,0.5082462727230644,0.8651163578033447,0.05943080136081674,0.8983516483516484,2.3641908168792725,2.8279078006744385,1.3842246499425326,C6 with uncertainty retained but both distance/span reliability penalties fixed to zero,0.5044943690299988,0.5044927000999451,1.6798628621472744e-06,0.1626720279455185,0.5792984366416931,2.8279669284820557,0.5,0.0,0.0,0.0,nan,nan +C6_no_reconstruction,728,0.5837912087912088,0.48325805552048157,206,184,338,0.6359223300970874,0.09239130434782608,0.8195266272189349,0.709170937538147,0.9543444540035635,0.5635251402854919,0.5161844703592794,0.8740492463111877,0.06073655040709527,0.8928571428571429,2.3883748054504395,2.825748920440674,1.141447589690795,C6 trained without the auxiliary hidden-feature reconstruction loss,0.5810816287994385,0.5810815691947937,4.87382330049968e-08,0.20374375581741333,0.6135826706886292,2.8257386684417725,0.3,0.05,0.05,0.05,nan,nan +C6_pointmask,728,0.5810439560439561,0.5025832785659426,206,184,338,0.5436893203883495,0.15760869565217392,0.834319526627219,0.679211437702179,0.9175777170595291,0.5958787798881531,0.5092818643196679,0.8619159460067749,0.05020872241520621,0.8901098901098901,2.4665889739990234,2.818068265914917,1.1026513503354962,C6 trained with independent point masking instead of contiguous spans,0.6072730422019958,0.6072730422019958,3.120251079735681e-08,0.24521614611148834,0.633151650428772,2.8180501461029053,0.3,0.05,0.05,0.05,nan,nan +C7_distill,728,0.592032967032967,0.4870960207460408,206,184,338,0.6650485436893204,0.07608695652173914,0.8284023668639053,0.7024686336517334,0.9442218762916826,0.5752816796302795,0.5166531638152239,0.8771014213562012,0.03309895266052133,0.8818681318681318,2.485671043395996,2.828686475753784,1.2328404661886574,C6 plus entropy/retention-weighted teacher distillation only,0.5969328880310059,0.5969326496124268,2.2941341626392386e-07,0.1737147569656372,0.6470145583152771,2.828679084777832,0.5,0.05,0.05,0.05,nan,nan +C7_group,728,0.6002747252747253,0.4509009833805456,206,184,338,0.7038834951456311,0.0,0.863905325443787,0.7323286533355713,0.9355743977553851,0.6008110046386719,0.5087103578214832,0.8661918044090271,0.06044913705561188,0.9024725274725275,2.5981743335723877,2.8037712574005127,1.0417083718358262,C6 plus smooth worst-group risk only,0.6530460119247437,0.6530460119247437,5.441940942318979e-08,0.14995495975017548,0.6626315116882324,2.8037784099578857,0.5,0.0,0.0,0.0,0.1,0.1 diff --git a/final/experiments/q2/unaligned_math_all_b128/controlled_missingness.csv.gz b/final/experiments/q2/unaligned_math_all_b128/controlled_missingness.csv.gz new file mode 100644 index 0000000..9874d9e Binary files /dev/null and b/final/experiments/q2/unaligned_math_all_b128/controlled_missingness.csv.gz differ diff --git a/final/experiments/q2/unaligned_math_all_b128/crg_student.pt b/final/experiments/q2/unaligned_math_all_b128/crg_student.pt new file mode 100644 index 0000000..4faa4b2 Binary files /dev/null and b/final/experiments/q2/unaligned_math_all_b128/crg_student.pt differ diff --git a/final/experiments/q2/unaligned_math_all_b128/group_bootstrap_ci.csv b/final/experiments/q2/unaligned_math_all_b128/group_bootstrap_ci.csv new file mode 100644 index 0000000..024c345 --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/group_bootstrap_ci.csv @@ -0,0 +1,8 @@ +metric,estimate,ci_2_5,ci_97_5,replicates,unit +accuracy,0.6717585232291114,0.6337437582438289,0.7083999622974622,300,source video group +macro_f1,0.5478055347952107,0.5108259770753341,0.5872370160716736,300,source video group +mae,0.7108274400234222,0.6579044133424758,0.7620256423950195,300,source video group +rmse,0.9714674680679505,0.8731931045635853,1.057953415758872,300,source video group +pearson,0.6378498673439026,0.5640437543392182,0.7087083235383034,300,source video group +interval_90_coverage,0.8956505821036529,0.8708135115106022,0.9182254293066758,300,source video group +interval_90_mean_width,2.486721992492676,2.4194449901580812,2.5640856266021728,300,source video group diff --git a/final/experiments/q2/unaligned_math_all_b128/group_risk_tuning.csv b/final/experiments/q2/unaligned_math_all_b128/group_risk_tuning.csv new file mode 100644 index 0000000..fc92c69 --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/group_risk_tuning.csv @@ -0,0 +1,6 @@ +model,candidate,lambda_group,group_temperature,selected_reliability,inner_selection_nll,selected,selection_split +C7_group,C7_group_lambda0.05_tau0.10,0.05,0.1,"(0.3, 0.05, 0.05, 0.05)",2.9074912071228027,False,reliability_validation +C7_group,C7_group_lambda0.10_tau0.05,0.1,0.05,"(0.5, 0.1, 0.0, 0.0)",2.937703788280487,False,reliability_validation +C7_group,C7_group_lambda0.10_tau0.10,0.1,0.1,"(0.5, 0.0, 0.0, 0.0)",2.887659251689911,True,reliability_validation +C7_group,C7_group_lambda0.10_tau0.20,0.1,0.2,"(0.3, 0.05, 0.05, 0.05)",2.903527617454529,False,reliability_validation +C7_group,C7_group_lambda0.20_tau0.10,0.2,0.1,"(0.3, 0.05, 0.05, 0.05)",2.934680759906769,False,reliability_validation diff --git a/final/experiments/q2/unaligned_math_all_b128/preprocessor.npz b/final/experiments/q2/unaligned_math_all_b128/preprocessor.npz new file mode 100644 index 0000000..7fd83ae Binary files /dev/null and b/final/experiments/q2/unaligned_math_all_b128/preprocessor.npz differ diff --git a/final/experiments/q2/unaligned_math_all_b128/reliability_hparam_tuning.csv b/final/experiments/q2/unaligned_math_all_b128/reliability_hparam_tuning.csv new file mode 100644 index 0000000..bb90699 --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/reliability_hparam_tuning.csv @@ -0,0 +1,70 @@ +model,rho_imp,lambda_u,lambda_gap,lambda_span,inner_selection_nll,scenario_selection_nll,selected,selection_split,note,group_lambda,group_temperature,risk_candidate_selected,candidate +teacher,0.5,0.0,0.0,0.0,2.8551357984542847,"{""0.0/natural"": 2.8325021266937256, ""0.3/single"": 2.82944655418396, ""0.3/sync"": 2.859112024307251, ""0.5/async"": 2.899482488632202}",False,reliability_validation,,,,, +teacher,0.5,0.05,0.05,0.05,2.854786992073059,"{""0.0/natural"": 2.832460403442383, ""0.3/single"": 2.829251289367676, ""0.3/sync"": 2.8586926460266113, ""0.5/async"": 2.8987436294555664}",False,reliability_validation,,,,, +teacher,0.5,0.1,0.0,0.0,2.8547691702842712,"{""0.0/natural"": 2.83245849609375, ""0.3/single"": 2.8292293548583984, ""0.3/sync"": 2.858625650405884, ""0.5/async"": 2.8987631797790527}",False,reliability_validation,,,,, +teacher,0.3,0.05,0.05,0.05,2.8533658385276794,"{""0.0/natural"": 2.8321738243103027, ""0.3/single"": 2.8287193775177, ""0.3/sync"": 2.8565878868103027, ""0.5/async"": 2.895982265472412}",True,reliability_validation,,,,, +teacher,0.7,0.05,0.05,0.05,2.8578426241874695,"{""0.0/natural"": 2.8327653408050537, ""0.3/single"": 2.8310093879699707, ""0.3/sync"": 2.862823486328125, ""0.5/async"": 2.9047722816467285}",False,reliability_validation,,,,, +C1,0.5,0.05,0.05,0.05,nan,,True,,not used by this ablation,,,, +C2,0.5,0.05,0.05,0.05,nan,,True,,not used by this ablation,,,, +C3,0.5,0.0,0.0,0.0,2.868969738483429,"{""0.0/natural"": 2.8552894592285156, ""0.3/single"": 2.855238199234009, ""0.3/sync"": 2.898164987564087, ""0.5/async"": 2.8671863079071045}",True,reliability_validation,,,,, +C3,0.5,0.05,0.05,0.05,2.8690268993377686,"{""0.0/natural"": 2.8552863597869873, ""0.3/single"": 2.8554182052612305, ""0.3/sync"": 2.8979132175445557, ""0.5/async"": 2.867489814758301}",False,reliability_validation,,,,, +C3,0.5,0.1,0.0,0.0,2.8690126538276672,"{""0.0/natural"": 2.85528302192688, ""0.3/single"": 2.855442762374878, ""0.3/sync"": 2.8978819847106934, ""0.5/async"": 2.8674428462982178}",False,reliability_validation,,,,, +C3,0.3,0.05,0.05,0.05,2.870371162891388,"{""0.0/natural"": 2.855177640914917, ""0.3/single"": 2.8578999042510986, ""0.3/sync"": 2.896186351776123, ""0.5/async"": 2.872220754623413}",False,reliability_validation,,,,, +C3,0.7,0.05,0.05,0.05,2.869048058986664,"{""0.0/natural"": 2.8553781509399414, ""0.3/single"": 2.8541696071624756, ""0.3/sync"": 2.900033950805664, ""0.5/async"": 2.866610527038574}",False,reliability_validation,,,,, +C4,0.5,0.0,0.0,0.0,2.856395184993744,"{""0.0/natural"": 2.844621181488037, ""0.3/single"": 2.8557333946228027, ""0.3/sync"": 2.865926742553711, ""0.5/async"": 2.859299421310425}",False,reliability_validation,,,,, +C4,0.5,0.05,0.05,0.05,2.8565751910209656,"{""0.0/natural"": 2.8446505069732666, ""0.3/single"": 2.8562142848968506, ""0.3/sync"": 2.865856409072876, ""0.5/async"": 2.859579563140869}",False,reliability_validation,,,,, +C4,0.5,0.1,0.0,0.0,2.8565635085105896,"{""0.0/natural"": 2.8446319103240967, ""0.3/single"": 2.8562064170837402, ""0.3/sync"": 2.8658132553100586, ""0.5/async"": 2.859602451324463}",False,reliability_validation,,,,, +C4,0.3,0.05,0.05,0.05,2.857943594455719,"{""0.0/natural"": 2.8447465896606445, ""0.3/single"": 2.859677314758301, ""0.3/sync"": 2.864553928375244, ""0.5/async"": 2.8627965450286865}",False,reliability_validation,,,,, +C4,0.7,0.05,0.05,0.05,2.8553494215011597,"{""0.0/natural"": 2.844590663909912, ""0.3/single"": 2.852660894393921, ""0.3/sync"": 2.8664493560791016, ""0.5/async"": 2.857696771621704}",True,reliability_validation,,,,, +C5,0.5,0.0,0.0,0.0,2.858191132545471,"{""0.0/natural"": 2.837888717651367, ""0.3/single"": 2.8408918380737305, ""0.3/sync"": 2.865405321121216, ""0.5/async"": 2.8885786533355713}",False,reliability_validation,,,,, +C5,0.5,0.05,0.05,0.05,2.858053147792816,"{""0.0/natural"": 2.83787202835083, ""0.3/single"": 2.8407843112945557, ""0.3/sync"": 2.8655388355255127, ""0.5/async"": 2.888017416000366}",False,reliability_validation,,,,, +C5,0.5,0.1,0.0,0.0,2.858084976673126,"{""0.0/natural"": 2.8378803730010986, ""0.3/single"": 2.8407742977142334, ""0.3/sync"": 2.8655664920806885, ""0.5/async"": 2.8881187438964844}",False,reliability_validation,,,,, +C5,0.3,0.05,0.05,0.05,2.857841432094574,"{""0.0/natural"": 2.8378095626831055, ""0.3/single"": 2.839801549911499, ""0.3/sync"": 2.8676390647888184, ""0.5/async"": 2.886115550994873}",True,reliability_validation,,,,, +C5,0.7,0.05,0.05,0.05,2.8592984676361084,"{""0.0/natural"": 2.8379809856414795, ""0.3/single"": 2.8421387672424316, ""0.3/sync"": 2.865103244781494, ""0.5/async"": 2.8919708728790283}",False,reliability_validation,,,,, +C6,0.5,0.0,0.0,0.0,2.8277538418769836,"{""0.0/natural"": 2.8142545223236084, ""0.3/single"": 2.816837787628174, ""0.3/sync"": 2.844792127609253, ""0.5/async"": 2.8351309299468994}",False,reliability_validation,,,,, +C6,0.5,0.05,0.05,0.05,2.827627658843994,"{""0.0/natural"": 2.8141915798187256, ""0.3/single"": 2.816967725753784, ""0.3/sync"": 2.8447818756103516, ""0.5/async"": 2.8345694541931152}",False,reliability_validation,,,,, +C6,0.5,0.1,0.0,0.0,2.8276638984680176,"{""0.0/natural"": 2.814206123352051, ""0.3/single"": 2.817033052444458, ""0.3/sync"": 2.8448281288146973, ""0.5/async"": 2.8345882892608643}",False,reliability_validation,,,,, +C6,0.3,0.05,0.05,0.05,2.827383041381836,"{""0.0/natural"": 2.8139631748199463, ""0.3/single"": 2.818815231323242, ""0.3/sync"": 2.8443305492401123, ""0.5/async"": 2.832423210144043}",True,reliability_validation,,,,, +C6,0.7,0.05,0.05,0.05,2.8284868597984314,"{""0.0/natural"": 2.8144543170928955, ""0.3/single"": 2.815439224243164, ""0.3/sync"": 2.8442962169647217, ""0.5/async"": 2.8397576808929443}",False,reliability_validation,,,,, +C6_no_distance,0.5,0.0,0.0,0.0,2.8592775464057922,"{""0.0/natural"": 2.846463441848755, ""0.3/single"": 2.839015483856201, ""0.3/sync"": 2.851423501968384, ""0.5/async"": 2.900207757949829}",True,reliability_validation,,,,, +C6_no_distance,0.5,0.1,0.0,0.0,2.859361946582794,"{""0.0/natural"": 2.8463997840881348, ""0.3/single"": 2.8394994735717773, ""0.3/sync"": 2.8509953022003174, ""0.5/async"": 2.9005532264709473}",False,reliability_validation,,,,, +C6_no_reconstruction,0.5,0.0,0.0,0.0,2.814443826675415,"{""0.0/natural"": 2.797691583633423, ""0.3/single"": 2.7918920516967773, ""0.3/sync"": 2.818723678588867, ""0.5/async"": 2.8494679927825928}",False,reliability_validation,,,,, +C6_no_reconstruction,0.5,0.05,0.05,0.05,2.8139390349388123,"{""0.0/natural"": 2.7977092266082764, ""0.3/single"": 2.7918813228607178, ""0.3/sync"": 2.8184382915496826, ""0.5/async"": 2.8477272987365723}",False,reliability_validation,,,,, +C6_no_reconstruction,0.5,0.1,0.0,0.0,2.813948094844818,"{""0.0/natural"": 2.7976696491241455, ""0.3/single"": 2.791857957839966, ""0.3/sync"": 2.8183655738830566, ""0.5/async"": 2.8478991985321045}",False,reliability_validation,,,,, +C6_no_reconstruction,0.3,0.05,0.05,0.05,2.811177134513855,"{""0.0/natural"": 2.79757022857666, ""0.3/single"": 2.7916157245635986, ""0.3/sync"": 2.816753625869751, ""0.5/async"": 2.83876895904541}",True,reliability_validation,,,,, +C6_no_reconstruction,0.7,0.05,0.05,0.05,2.8178954124450684,"{""0.0/natural"": 2.7978579998016357, ""0.3/single"": 2.792189121246338, ""0.3/sync"": 2.8217227458953857, ""0.5/async"": 2.859811782836914}",False,reliability_validation,,,,, +C6_pointmask,0.5,0.0,0.0,0.0,2.8172501921653748,"{""0.0/natural"": 2.7916030883789062, ""0.3/single"": 2.7908267974853516, ""0.3/sync"": 2.8246986865997314, ""0.5/async"": 2.8618721961975098}",False,reliability_validation,,,,, +C6_pointmask,0.5,0.05,0.05,0.05,2.8165746927261353,"{""0.0/natural"": 2.791574001312256, ""0.3/single"": 2.790780544281006, ""0.3/sync"": 2.823692560195923, ""0.5/async"": 2.8602516651153564}",False,reliability_validation,,,,, +C6_pointmask,0.5,0.1,0.0,0.0,2.8165820837020874,"{""0.0/natural"": 2.791569232940674, ""0.3/single"": 2.7907233238220215, ""0.3/sync"": 2.82354998588562, ""0.5/async"": 2.860485792160034}",False,reliability_validation,,,,, +C6_pointmask,0.3,0.05,0.05,0.05,2.8119084239006042,"{""0.0/natural"": 2.7913870811462402, ""0.3/single"": 2.790656089782715, ""0.3/sync"": 2.814664602279663, ""0.5/async"": 2.850925922393799}",True,reliability_validation,,,,, +C6_pointmask,0.7,0.05,0.05,0.05,2.821407377719879,"{""0.0/natural"": 2.791775703430176, ""0.3/single"": 2.791663885116577, ""0.3/sync"": 2.8317697048187256, ""0.5/async"": 2.870420217514038}",False,reliability_validation,,,,, +C7_distill,0.5,0.0,0.0,0.0,2.8871994614601135,"{""0.0/natural"": 2.871961832046509, ""0.3/single"": 2.8787894248962402, ""0.3/sync"": 2.902831792831421, ""0.5/async"": 2.895214796066284}",False,reliability_validation,,,,, +C7_distill,0.5,0.05,0.05,0.05,2.887099504470825,"{""0.0/natural"": 2.8719308376312256, ""0.3/single"": 2.8785901069641113, ""0.3/sync"": 2.902597427368164, ""0.5/async"": 2.8952796459198}",True,reliability_validation,,,,, +C7_distill,0.5,0.1,0.0,0.0,2.8871177434921265,"{""0.0/natural"": 2.871976137161255, ""0.3/single"": 2.878659248352051, ""0.3/sync"": 2.902616262435913, ""0.5/async"": 2.895219326019287}",False,reliability_validation,,,,, +C7_distill,0.3,0.05,0.05,0.05,2.8883190751075745,"{""0.0/natural"": 2.8715980052948, ""0.3/single"": 2.8790061473846436, ""0.3/sync"": 2.9035727977752686, ""0.5/async"": 2.899099349975586}",False,reliability_validation,,,,, +C7_distill,0.7,0.05,0.05,0.05,2.888582468032837,"{""0.0/natural"": 2.8725264072418213, ""0.3/single"": 2.87967848777771, ""0.3/sync"": 2.905454635620117, ""0.5/async"": 2.896670341491699}",False,reliability_validation,,,,, +C7_group,0.5,0.0,0.0,0.0,2.9084319472312927,"{""0.0/natural"": 2.888453960418701, ""0.3/single"": 2.8795268535614014, ""0.3/sync"": 2.932856798171997, ""0.5/async"": 2.9328901767730713}",False,reliability_validation,,0.05,0.1,False,C7_group_lambda0.05_tau0.10 +C7_group,0.5,0.05,0.05,0.05,2.9081557989120483,"{""0.0/natural"": 2.8884048461914062, ""0.3/single"": 2.879855155944824, ""0.3/sync"": 2.932080030441284, ""0.5/async"": 2.9322831630706787}",False,reliability_validation,,0.05,0.1,False,C7_group_lambda0.05_tau0.10 +C7_group,0.5,0.1,0.0,0.0,2.908179998397827,"{""0.0/natural"": 2.888439893722534, ""0.3/single"": 2.8799004554748535, ""0.3/sync"": 2.9319827556610107, ""0.5/async"": 2.93239688873291}",False,reliability_validation,,0.05,0.1,False,C7_group_lambda0.05_tau0.10 +C7_group,0.3,0.05,0.05,0.05,2.9074912071228027,"{""0.0/natural"": 2.8882105350494385, ""0.3/single"": 2.8837697505950928, ""0.3/sync"": 2.92631459236145, ""0.5/async"": 2.9316699504852295}",False,reliability_validation,,0.05,0.1,False,C7_group_lambda0.05_tau0.10 +C7_group,0.7,0.05,0.05,0.05,2.9103721976280212,"{""0.0/natural"": 2.888576030731201, ""0.3/single"": 2.8777272701263428, ""0.3/sync"": 2.9382715225219727, ""0.5/async"": 2.9369139671325684}",False,reliability_validation,,0.05,0.1,False,C7_group_lambda0.05_tau0.10 +C7_group,0.5,0.0,0.0,0.0,2.937848687171936,"{""0.0/natural"": 2.90403151512146, ""0.3/single"": 2.9019672870635986, ""0.3/sync"": 2.9718685150146484, ""0.5/async"": 2.973527431488037}",False,reliability_validation,,0.1,0.05,False,C7_group_lambda0.10_tau0.05 +C7_group,0.5,0.05,0.05,0.05,2.937710404396057,"{""0.0/natural"": 2.9039723873138428, ""0.3/single"": 2.9019112586975098, ""0.3/sync"": 2.9715962409973145, ""0.5/async"": 2.9733617305755615}",False,reliability_validation,,0.1,0.05,False,C7_group_lambda0.10_tau0.05 +C7_group,0.5,0.1,0.0,0.0,2.937703788280487,"{""0.0/natural"": 2.9039785861968994, ""0.3/single"": 2.901944160461426, ""0.3/sync"": 2.971575975418091, ""0.5/async"": 2.9733164310455322}",False,reliability_validation,,0.1,0.05,False,C7_group_lambda0.10_tau0.05 +C7_group,0.3,0.05,0.05,0.05,2.93798565864563,"{""0.0/natural"": 2.9036080837249756, ""0.3/single"": 2.9026758670806885, ""0.3/sync"": 2.970571994781494, ""0.5/async"": 2.9750866889953613}",False,reliability_validation,,0.1,0.05,False,C7_group_lambda0.10_tau0.05 +C7_group,0.7,0.05,0.05,0.05,2.939401626586914,"{""0.0/natural"": 2.9043807983398438, ""0.3/single"": 2.90256404876709, ""0.3/sync"": 2.9743330478668213, ""0.5/async"": 2.9763286113739014}",False,reliability_validation,,0.1,0.05,False,C7_group_lambda0.10_tau0.05 +C7_group,0.5,0.0,0.0,0.0,2.887659251689911,"{""0.0/natural"": 2.8679378032684326, ""0.3/single"": 2.866154193878174, ""0.3/sync"": 2.889091968536377, ""0.5/async"": 2.92745304107666}",True,reliability_validation,,0.1,0.1,True,C7_group_lambda0.10_tau0.10 +C7_group,0.5,0.05,0.05,0.05,2.8877416253089905,"{""0.0/natural"": 2.8679301738739014, ""0.3/single"": 2.8663411140441895, ""0.3/sync"": 2.8891632556915283, ""0.5/async"": 2.9275319576263428}",False,reliability_validation,,0.1,0.1,True,C7_group_lambda0.10_tau0.10 +C7_group,0.5,0.1,0.0,0.0,2.88775235414505,"{""0.0/natural"": 2.867924213409424, ""0.3/single"": 2.8663671016693115, ""0.3/sync"": 2.889188051223755, ""0.5/async"": 2.92753005027771}",False,reliability_validation,,0.1,0.1,True,C7_group_lambda0.10_tau0.10 +C7_group,0.3,0.05,0.05,0.05,2.8894190192222595,"{""0.0/natural"": 2.867828130722046, ""0.3/single"": 2.868374824523926, ""0.3/sync"": 2.8905928134918213, ""0.5/async"": 2.930880308151245}",False,reliability_validation,,0.1,0.1,True,C7_group_lambda0.10_tau0.10 +C7_group,0.7,0.05,0.05,0.05,2.887662351131439,"{""0.0/natural"": 2.8680334091186523, ""0.3/single"": 2.8651366233825684, ""0.3/sync"": 2.889106273651123, ""0.5/async"": 2.928373098373413}",False,reliability_validation,,0.1,0.1,True,C7_group_lambda0.10_tau0.10 +C7_group,0.5,0.0,0.0,0.0,2.9051475524902344,"{""0.0/natural"": 2.882009983062744, ""0.3/single"": 2.8719751834869385, ""0.3/sync"": 2.9277143478393555, ""0.5/async"": 2.9388906955718994}",False,reliability_validation,,0.1,0.2,False,C7_group_lambda0.10_tau0.20 +C7_group,0.5,0.05,0.05,0.05,2.9047818183898926,"{""0.0/natural"": 2.8819169998168945, ""0.3/single"": 2.8721556663513184, ""0.3/sync"": 2.9272356033325195, ""0.5/async"": 2.937819004058838}",False,reliability_validation,,0.1,0.2,False,C7_group_lambda0.10_tau0.20 +C7_group,0.5,0.1,0.0,0.0,2.904840886592865,"{""0.0/natural"": 2.8819549083709717, ""0.3/single"": 2.8721954822540283, ""0.3/sync"": 2.9272100925445557, ""0.5/async"": 2.9380030632019043}",False,reliability_validation,,0.1,0.2,False,C7_group_lambda0.10_tau0.20 +C7_group,0.3,0.05,0.05,0.05,2.903527617454529,"{""0.0/natural"": 2.8815088272094727, ""0.3/single"": 2.8748013973236084, ""0.3/sync"": 2.9242103099823, ""0.5/async"": 2.9335899353027344}",False,reliability_validation,,0.1,0.2,False,C7_group_lambda0.10_tau0.20 +C7_group,0.7,0.05,0.05,0.05,2.907408356666565,"{""0.0/natural"": 2.8823306560516357, ""0.3/single"": 2.8710365295410156, ""0.3/sync"": 2.9312281608581543, ""0.5/async"": 2.945038080215454}",False,reliability_validation,,0.1,0.2,False,C7_group_lambda0.10_tau0.20 +C7_group,0.5,0.0,0.0,0.0,2.9365825057029724,"{""0.0/natural"": 2.9062917232513428, ""0.3/single"": 2.898517370223999, ""0.3/sync"": 2.957914352416992, ""0.5/async"": 2.9836065769195557}",False,reliability_validation,,0.2,0.1,False,C7_group_lambda0.20_tau0.10 +C7_group,0.5,0.05,0.05,0.05,2.9361995458602905,"{""0.0/natural"": 2.9062345027923584, ""0.3/single"": 2.8984618186950684, ""0.3/sync"": 2.957249641418457, ""0.5/async"": 2.9828522205352783}",False,reliability_validation,,0.2,0.1,False,C7_group_lambda0.20_tau0.10 +C7_group,0.5,0.1,0.0,0.0,2.936208724975586,"{""0.0/natural"": 2.906248092651367, ""0.3/single"": 2.898481845855713, ""0.3/sync"": 2.9571707248687744, ""0.5/async"": 2.9829342365264893}",False,reliability_validation,,0.2,0.1,False,C7_group_lambda0.20_tau0.10 +C7_group,0.3,0.05,0.05,0.05,2.934680759906769,"{""0.0/natural"": 2.9059057235717773, ""0.3/single"": 2.8992602825164795, ""0.3/sync"": 2.95295786857605, ""0.5/async"": 2.9805991649627686}",False,reliability_validation,,0.2,0.1,False,C7_group_lambda0.20_tau0.10 +C7_group,0.7,0.05,0.05,0.05,2.93935626745224,"{""0.0/natural"": 2.9065608978271484, ""0.3/single"": 2.899205207824707, ""0.3/sync"": 2.9629967212677, ""0.5/async"": 2.9886622428894043}",False,reliability_validation,,0.2,0.1,False,C7_group_lambda0.20_tau0.10 diff --git a/final/experiments/q2/unaligned_math_all_b128/run_manifest.json b/final/experiments/q2/unaligned_math_all_b128/run_manifest.json new file mode 100644 index 0000000..95d7b1f --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/run_manifest.json @@ -0,0 +1,422 @@ +{ + "seed": 20260924, + "text_encoder": "official precomputed text field; encoder revision not supplied", + "training_configuration": { + "student_epoch_limit": 12, + "imputer_epochs": 8, + "batch_size": 128, + "early_stopping_patience": 3, + "device": "cuda", + "device_name": "NVIDIA GeForce RTX 5070 Ti", + "optimizer": "AdamW", + "student_learning_rate": 0.0003, + "student_weight_decay": 0.001, + "imputer_learning_rate": 0.0003, + "imputer_weight_decay": 0.0001, + "early_stopping_metric": "mean untempered selection_nll over fixed group-disjoint internal training scenarios", + "inner_selection_scenarios": [ + "0.0/natural", + "0.3/single", + "0.3/sync", + "0.5/async" + ], + "inner_selection_source_video_groups": 76 + }, + "training_input": "E题数据/附件2-数据集特征文件/unaligned_50.pkl", + "input_version": "unaligned_50", + "q1_alignment_adapter": { + "train": { + "method": "shared_interval_overlap_on_normalized_progress", + "coordinate_mode": "relative", + "physical_time_alignment": false, + "samples": 3395, + "vision_length_conflict_samples": 618, + "vision_tail_ambiguous_samples": 618, + "nonzero_text_rows_outside_attention": 86078, + "observed_target_rows": { + "text": 169750, + "audio": 169750, + "vision": 163302 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.9540337701314328 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + }, + "valid": { + "method": "shared_interval_overlap_on_normalized_progress", + "coordinate_mode": "relative", + "physical_time_alignment": false, + "samples": 728, + "vision_length_conflict_samples": 141, + "vision_tail_ambiguous_samples": 141, + "nonzero_text_rows_outside_attention": 17772, + "observed_target_rows": { + "text": 36400, + "audio": 36400, + "vision": 35315 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.96090314748523 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + }, + "test": { + "method": "shared_interval_overlap_on_normalized_progress", + "coordinate_mode": "relative", + "physical_time_alignment": false, + "samples": 727, + "vision_length_conflict_samples": 131, + "vision_tail_ambiguous_samples": 131, + "nonzero_text_rows_outside_attention": 18041, + "observed_target_rows": { + "text": 36350, + "audio": 36350, + "vision": 35034 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.9549938172651288 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + } + }, + "training_sha256": "77eda14a06be9749a96c52ae45470c7cffcfa7219011eae391d231a0664c3762", + "official_group_overlap": { + "train_valid": 0, + "train_test": 0, + "valid_test": 0 + }, + "official_splits": { + "train": { + "n": 3395, + "source_video_groups": 1528 + }, + "valid": { + "n": 728, + "source_video_groups": 239 + }, + "test": { + "n": 727, + "source_video_groups": 381 + } + }, + "internal_train_holdouts": { + "fit": { + "n": 3030, + "video_groups": 1375 + }, + "reliability_selection": { + "n": 190, + "video_groups": 76 + }, + "temperature_calibration": { + "n": 175, + "video_groups": 77 + }, + "all_group_disjoint": true + }, + "feature_standardization": "fit-only observed rows, per-dimension; fixed for valid/test/attachment3", + "missing_mask": "official text attention and source lengths plus row observation; normalized-progress overlap preserves empty bins; q*=1 only where visible, J_Q=0", + "observation_quality": { + "quality_score_fields_present": false, + "quality_available_flag_present": false, + "fallback": "q*=1 and J_Q=0 for visible rows; R_eff=R", + "quality_noise_mapping_ablation": "not identifiable on unaligned_50 because no row quality score varies" + }, + "imputer": { + "type": "structured linear Gaussian shared-private state space", + "state_dims": { + "shared": 8, + "private_each": 4 + }, + "posterior": "block-tridiagonal equivalent Kalman information filter + RTS smoother", + "sampling": "joint latent trajectories and missing emissions; observed features copied exactly", + "fit_objective": "train-only observed Gaussian marginal likelihood including log determinants", + "epochs": 8, + "frozen_before_teacher_student": true + }, + "architecture": { + "projection": 32, + "bigru_hidden_each_direction": 16, + "cross_source_layers": 1, + "cross_time_read": true, + "rank": 4, + "reliability_gru": "directional hidden decay; reset applied before candidate map; update gate multiplied by rho", + "final_gate": "rho times bounded content score plus positive null prior", + "output": "neutral point mass plus sign-specific Beta magnitudes; K-path probabilities mixed before decoding" + }, + "reliability_hyperparameters": { + "selected_per_model_on": "group-disjoint internal training reliability-validation slice", + "candidate_values": [ + [ + 0.5, + 0.0, + 0.0, + 0.0 + ], + [ + 0.5, + 0.05, + 0.05, + 0.05 + ], + [ + 0.5, + 0.1, + 0.0, + 0.0 + ], + [ + 0.3, + 0.05, + 0.05, + 0.05 + ], + [ + 0.7, + 0.05, + 0.05, + 0.05 + ] + ], + "validation_scenarios": [ + "0.0/natural", + "0.3/single", + "0.3/sync", + "0.5/async" + ], + "selected_by_model": { + "teacher": [ + 0.3, + 0.05, + 0.05, + 0.05 + ], + "C1": [ + 0.5, + 0.05, + 0.05, + 0.05 + ], + "C2": [ + 0.5, + 0.05, + 0.05, + 0.05 + ], + "C3": [ + 0.5, + 0.0, + 0.0, + 0.0 + ], + "C4": [ + 0.7, + 0.05, + 0.05, + 0.05 + ], + "C5": [ + 0.3, + 0.05, + 0.05, + 0.05 + ], + "C6": [ + 0.3, + 0.05, + 0.05, + 0.05 + ], + "C6_no_distance": [ + 0.5, + 0.0, + 0.0, + 0.0 + ], + "C6_no_reconstruction": [ + 0.3, + 0.05, + 0.05, + 0.05 + ], + "C6_pointmask": [ + 0.3, + 0.05, + 0.05, + 0.05 + ], + "C7_distill": [ + 0.5, + 0.05, + 0.05, + 0.05 + ], + "C7_group": [ + 0.5, + 0.0, + 0.0, + 0.0 + ] + } + }, + "group_risk_hyperparameters": { + "selection": "lambda_group and group_temperature jointly selected with reliability hyperparameters on fixed group-disjoint internal training scenarios", + "candidate_values": [ + [ + 0.05, + 0.1 + ], + [ + 0.1, + 0.05 + ], + [ + 0.1, + 0.1 + ], + [ + 0.1, + 0.2 + ], + [ + 0.2, + 0.1 + ] + ], + "selected": [ + 0.1, + 0.1 + ], + "selection_split": "reliability_validation" + }, + "loss": { + "supervision": "negative log mixture of Beta interval masses plus scaled Huber mean term", + "delta_u": 0.027777499999999997, + "delta_u_source": "half the minimum positive spacing of nonzero absolute labels in fit only", + "lambda_y": 1.0, + "lambda_distill": 0.1, + "lambda_reconstruction": 0.05, + "lambda_group_default": 0.1, + "group_temperature_default": 0.1, + "selected_group_risk": [ + 0.1, + 0.1 + ], + "distill_temperature": 2.0, + "distill_retention_exponent": 1.0, + "imputer_regularization": { + "emission_l2": 0.0001, + "transition_l2": 0.0001 + }, + "group_and_distill_separate": true + }, + "calibration": { + "method": "temperature scaling on a group-disjoint internal official-train holdout, separated from reliability selection", + "temperature": 1.122980387832455, + "valid_used_for_selection": true, + "test_used_for_selection_or_calibration": false + }, + "selected_model": "C6", + "attachment3_low_information_priors": { + "class_probability_method": "fit counts + one pseudocount per class", + "class_probability_values": [ + 0.2786020441806792, + 0.2205736894164194, + 0.5008242664029015 + ], + "negative_beta": [ + 1.1441766023635864, + 1.8200817108154297 + ], + "positive_beta": [ + 1.271026611328125, + 2.4681642055511475 + ] + }, + "ablation_definitions": { + "C1": "masked BiGRU; no posterior imputation, explicit reliability or source gate", + "C2": "exact Gaussian posterior mean; no joint trajectory integral", + "C3": "joint trajectory integral plus final reliability/content fusion gate", + "C4": "C3 plus bounded cross-time source attention and null source", + "C5": "C4 plus reliability-modulated BiGRU update", + "C6": "C5 plus optional rank-4 CP residual", + "C6_no_distance": "C6 with uncertainty retained but both distance/span reliability penalties fixed to zero", + "C6_no_reconstruction": "C6 trained without the auxiliary hidden-feature reconstruction loss", + "C6_pointmask": "C6 trained with independent point masking instead of contiguous spans", + "C7_distill": "C6 plus entropy/retention-weighted teacher distillation only", + "C7_group": "C6 plus smooth worst-group risk only" + }, + "masking": { + "rates": [ + 0.0, + 0.1, + 0.3, + 0.5, + 0.7 + ], + "patterns": [ + "single", + "sync", + "partial", + "async" + ], + "preserve_at_least_fraction_per_selected_modality": 0.2, + "controlled_sweep_split": "official validation", + "identical_masks_across_models": true, + "scenario_count": 42, + "scenario_seed": 20261833, + "training_mask_rng_seed": 20261227, + "reliability_scenario_seed": 20261830, + "controlled_torch_sampling_seed": 20261476, + "paired_control_bootstrap_seed": 20261477, + "mask_audit_file": null, + "mask_audit_omitted_reason": "Row-level audit omitted from the size-limited deliverable; regenerated by rerunning final.q2.math.train.", + "additional_one_factor_controls": [ + "modality T/A/V and combinations", + "start/middle/end", + "one-long/multiple-short", + "sync/partial/async" + ], + "semantic_position_control": "not run: unaligned_50 does not provide audited semantic boundary indices; raw text is prohibited in student inputs" + }, + "final_test_metrics": { + "n": 727, + "accuracy": 0.672627235213205, + "macro_f1": 0.5484581573154621, + "negative_support": 207, + "neutral_support": 158, + "positive_support": 362, + "negative_recall": 0.7439613526570048, + "middle_recall": 0.10126582278481013, + "positive_recall": 0.8812154696132597, + "regression_mae": 0.7100059986114502, + "regression_rmse": 0.969292458045841, + "pearson": 0.6424147486686707, + "brier": 0.4367243729993746, + "classification_nll": 0.7538501024246216, + "ece_15": 0.047142177615237854, + "selection_nll": 2.8449835777282715, + "interval_90_coverage": 0.8954607977991746, + "interval_90_mean_width": 2.48697829246521, + "predictive_variance_mean_uncalibrated": 0.6001424193382263, + "within_trajectory_variance_mean": 0.6001414060592651, + "between_trajectory_variance_mean": 1.021712705551181e-06, + "predictive_mean_mean_calibrated": 0.18709982931613922, + "predictive_variance_mean_calibrated": 0.6344733238220215 + }, + "test_gate_diagnostics_file": null, + "test_gate_diagnostics_omitted_reason": "Row-level audit omitted from the size-limited deliverable; regenerated by rerunning final.q2.math.train.", + "attachment3_cases": 0, + "attachment3_labeled_metrics": null, + "completed_utc": "2026-09-25T12:10:16Z" +} diff --git a/final/experiments/q2/unaligned_math_all_b128/structured_imputer.pt b/final/experiments/q2/unaligned_math_all_b128/structured_imputer.pt new file mode 100644 index 0000000..062e5e6 Binary files /dev/null and b/final/experiments/q2/unaligned_math_all_b128/structured_imputer.pt differ diff --git a/final/experiments/q2/unaligned_math_all_b128/teacher.pt b/final/experiments/q2/unaligned_math_all_b128/teacher.pt new file mode 100644 index 0000000..81452ab Binary files /dev/null and b/final/experiments/q2/unaligned_math_all_b128/teacher.pt differ diff --git a/final/experiments/q2/unaligned_math_all_b128/test_metrics.json b/final/experiments/q2/unaligned_math_all_b128/test_metrics.json new file mode 100644 index 0000000..76f2936 --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/test_metrics.json @@ -0,0 +1,27 @@ +{ + "n": 727, + "accuracy": 0.672627235213205, + "macro_f1": 0.5484581573154621, + "negative_support": 207, + "neutral_support": 158, + "positive_support": 362, + "negative_recall": 0.7439613526570048, + "middle_recall": 0.10126582278481013, + "positive_recall": 0.8812154696132597, + "regression_mae": 0.7100059986114502, + "regression_rmse": 0.969292458045841, + "pearson": 0.6424147486686707, + "brier": 0.4367243729993746, + "classification_nll": 0.7538501024246216, + "ece_15": 0.047142177615237854, + "selection_nll": 2.8449835777282715, + "interval_90_coverage": 0.8954607977991746, + "interval_90_mean_width": 2.48697829246521, + "predictive_variance_mean_uncalibrated": 0.6001424193382263, + "within_trajectory_variance_mean": 0.6001414060592651, + "between_trajectory_variance_mean": 1.021712705551181e-06, + "predictive_mean_mean_calibrated": 0.18709982931613922, + "predictive_variance_mean_calibrated": 0.6344733238220215, + "selected_model": "C6", + "temperature": 1.122980387832455 +} \ No newline at end of file diff --git a/final/experiments/q2/unaligned_math_all_b128/training_history.csv b/final/experiments/q2/unaligned_math_all_b128/training_history.csv new file mode 100644 index 0000000..901ef64 --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/training_history.csv @@ -0,0 +1,201 @@ +stage,epoch,train_observed_nll_per_scalar,train_loss,inner_selection_nll,inner_natural_accuracy,inner_natural_macro_f1,inner_natural_mae,selection_source +structured_imputer,1,1.5532822509606679,,,,,, +structured_imputer,2,1.528148040175438,,,,,, +structured_imputer,3,1.4966334303220112,,,,,, +structured_imputer,4,1.4702217976252239,,,,,, +structured_imputer,5,1.4513782262802124,,,,,, +structured_imputer,6,1.4369136691093445,,,,,, +structured_imputer,7,1.4246017287174861,,,,,, +structured_imputer,8,1.4141698280970256,,,,,, +teacher,1,,3.462034652630488,3.3566014766693115,0.4052631578947368,0.19225967540574282,1.5161516666412354,group_disjoint_internal_scenarios +teacher,2,,3.328680286804835,3.29034423828125,0.4052631578947368,0.19225967540574282,1.324126124382019,group_disjoint_internal_scenarios +teacher,3,,3.246292660633723,3.3052875995635986,0.4052631578947368,0.19225967540574282,1.2483724355697632,group_disjoint_internal_scenarios +teacher,4,,3.2161220411459603,3.2765040397644043,0.4052631578947368,0.19225967540574282,1.2843283414840698,group_disjoint_internal_scenarios +teacher,5,,3.1693802575270333,3.212984323501587,0.4052631578947368,0.19225967540574282,1.287352204322815,group_disjoint_internal_scenarios +teacher,6,,3.1070047418276467,3.1152713298797607,0.4631578947368421,0.3023726851851852,1.155389428138733,group_disjoint_internal_scenarios +teacher,7,,3.022074888149897,3.018214225769043,0.5526315789473685,0.42452164919373797,0.909277081489563,group_disjoint_internal_scenarios +teacher,8,,2.9289448956648507,2.9519646167755127,0.5894736842105263,0.46448873962286097,0.8066921234130859,group_disjoint_internal_scenarios +teacher,9,,2.8224541743596396,2.891688346862793,0.631578947368421,0.4972827549098735,0.7159743309020996,group_disjoint_internal_scenarios +teacher,10,,2.72388419508934,2.8486835956573486,0.6578947368421053,0.5555971188335681,0.69667649269104,group_disjoint_internal_scenarios +teacher,11,,2.6484831869602203,2.832411527633667,0.6684210526315789,0.5861105609087014,0.6799808740615845,group_disjoint_internal_scenarios +teacher,12,,2.58272918065389,2.8332951068878174,0.6473684210526316,0.5827807874521221,0.6745083332061768,group_disjoint_internal_scenarios +C1,1,,3.625088413556417,3.4457728266716003,0.4052631578947368,0.19225967540574282,1.6337891817092896,group_disjoint_internal_scenarios +C1,2,,3.446867972612381,3.3080549240112305,0.4052631578947368,0.19225967540574282,1.4519895315170288,group_disjoint_internal_scenarios +C1,3,,3.298306554555893,3.2529711723327637,0.4052631578947368,0.19225967540574282,1.2774684429168701,group_disjoint_internal_scenarios +C1,4,,3.2426380018393197,3.245816707611084,0.4052631578947368,0.19225967540574282,1.2816030979156494,group_disjoint_internal_scenarios +C1,5,,3.2038341959317527,3.210524797439575,0.4052631578947368,0.19225967540574282,1.3113044500350952,group_disjoint_internal_scenarios +C1,6,,3.1642958422501883,3.1716975569725037,0.43157894736842106,0.24643380368571208,1.2311570644378662,group_disjoint_internal_scenarios +C1,7,,3.115392714738846,3.1206790804862976,0.5526315789473685,0.4230936819172113,0.9385260939598083,group_disjoint_internal_scenarios +C1,8,,3.064149796962738,3.0661433339118958,0.5842105263157895,0.4583850931677018,0.8695384860038757,group_disjoint_internal_scenarios +C1,9,,3.003529200951258,3.0254677534103394,0.6052631578947368,0.4791029561671764,0.810080349445343,group_disjoint_internal_scenarios +C1,10,,2.9559323290983834,2.9842147827148438,0.6157894736842106,0.4852838310465429,0.7587557435035706,group_disjoint_internal_scenarios +C1,11,,2.902411013841629,2.9415759444236755,0.6157894736842106,0.5008969964989866,0.7657654881477356,group_disjoint_internal_scenarios +C1,12,,2.855019211769104,2.901727080345154,0.6263157894736842,0.5366694231835751,0.7315037250518799,group_disjoint_internal_scenarios +C2,1,,3.537787844737371,3.3620360493659973,0.4052631578947368,0.19225967540574282,1.5527148246765137,group_disjoint_internal_scenarios +C2,2,,3.3904086450735726,3.266030967235565,0.4052631578947368,0.19225967540574282,1.3979792594909668,group_disjoint_internal_scenarios +C2,3,,3.2736210326353707,3.2449108958244324,0.4052631578947368,0.19225967540574282,1.2796080112457275,group_disjoint_internal_scenarios +C2,4,,3.229355643192927,3.224508762359619,0.4052631578947368,0.19225967540574282,1.293556571006775,group_disjoint_internal_scenarios +C2,5,,3.1855053901672363,3.1826480627059937,0.4052631578947368,0.19225967540574282,1.2995216846466064,group_disjoint_internal_scenarios +C2,6,,3.1340996424357095,3.1313324570655823,0.48947368421052634,0.34304341077249445,1.1094801425933838,group_disjoint_internal_scenarios +C2,7,,3.080450693766276,3.0894970893859863,0.5421052631578948,0.4115856014590192,0.9663518071174622,group_disjoint_internal_scenarios +C2,8,,3.033092975616455,3.0433709025382996,0.5789473684210527,0.4515823720792665,0.8674063682556152,group_disjoint_internal_scenarios +C2,9,,2.973719706137975,2.991317570209503,0.6052631578947368,0.4753863267795156,0.7790361046791077,group_disjoint_internal_scenarios +C2,10,,2.9355501532554626,2.9423431754112244,0.5947368421052631,0.46559829059829055,0.7608610391616821,group_disjoint_internal_scenarios +C2,11,,2.8770320216814675,2.9117713570594788,0.6,0.46507619383397963,0.7450714707374573,group_disjoint_internal_scenarios +C2,12,,2.823687126239141,2.9001985788345337,0.6,0.47456646492674553,0.72516268491745,group_disjoint_internal_scenarios +C3,1,,3.6558842261632285,3.384518265724182,0.4052631578947368,0.19225967540574282,1.559212327003479,group_disjoint_internal_scenarios +C3,2,,3.4921026726563773,3.268451452255249,0.4052631578947368,0.19225967540574282,1.3527249097824097,group_disjoint_internal_scenarios +C3,3,,3.384129673242569,3.2711148262023926,0.4052631578947368,0.19225967540574282,1.2468868494033813,group_disjoint_internal_scenarios +C3,4,,3.351481109857559,3.2908926010131836,0.4052631578947368,0.19225967540574282,1.2705941200256348,group_disjoint_internal_scenarios +C3,5,,3.3176944851875305,3.2589112520217896,0.4052631578947368,0.19225967540574282,1.282788872718811,group_disjoint_internal_scenarios +C3,6,,3.2691671550273895,3.2086825370788574,0.4052631578947368,0.19225967540574282,1.3017680644989014,group_disjoint_internal_scenarios +C3,7,,3.202888548374176,3.14694482088089,0.47368421052631576,0.3192168546499255,1.115231990814209,group_disjoint_internal_scenarios +C3,8,,3.1294551491737366,3.0830236077308655,0.5736842105263158,0.44487613884496646,0.8796601891517639,group_disjoint_internal_scenarios +C3,9,,3.0485441088676453,3.0048487782478333,0.6105263157894737,0.4795948021754473,0.7953904271125793,group_disjoint_internal_scenarios +C3,10,,2.973482757806778,2.938908636569977,0.6157894736842106,0.4797003509874797,0.7715549468994141,group_disjoint_internal_scenarios +C3,11,,2.903808911641439,2.9034862518310547,0.631578947368421,0.48848207475209765,0.7345849871635437,group_disjoint_internal_scenarios +C3,12,,2.844705899556478,2.8694076538085938,0.631578947368421,0.4899428057322794,0.6989896893501282,group_disjoint_internal_scenarios +C4,1,,3.6013375520706177,3.332814633846283,0.2736842105263158,0.17082621082621083,0.9378437399864197,group_disjoint_internal_scenarios +C4,2,,3.479383409023285,3.271034359931946,0.4052631578947368,0.19225967540574282,1.3177491426467896,group_disjoint_internal_scenarios +C4,3,,3.3795010447502136,3.285194993019104,0.4052631578947368,0.19225967540574282,1.2431234121322632,group_disjoint_internal_scenarios +C4,4,,3.3379427591959634,3.259019613265991,0.4052631578947368,0.19225967540574282,1.286502718925476,group_disjoint_internal_scenarios +C4,5,,3.29158349831899,3.1893283128738403,0.41578947368421054,0.21487471298792052,1.2786555290222168,group_disjoint_internal_scenarios +C4,6,,3.2405782540639243,3.128665268421173,0.5789473684210527,0.4518936827046156,0.9166836142539978,group_disjoint_internal_scenarios +C4,7,,3.1832628746827445,3.073520600795746,0.5736842105263158,0.4461403430475596,0.884451150894165,group_disjoint_internal_scenarios +C4,8,,3.136505603790283,3.003846764564514,0.5894736842105263,0.46306900341257445,0.8538134098052979,group_disjoint_internal_scenarios +C4,9,,3.0712250769138336,2.957157254219055,0.6263157894736842,0.4971737077289579,0.7650657296180725,group_disjoint_internal_scenarios +C4,10,,3.000265379746755,2.894549548625946,0.6263157894736842,0.4957351290684624,0.7408508062362671,group_disjoint_internal_scenarios +C4,11,,2.9216533501942954,2.8611021637916565,0.6263157894736842,0.5147248652604963,0.7227466702461243,group_disjoint_internal_scenarios +C4,12,,2.855973720550537,2.854860305786133,0.6421052631578947,0.5766158315177923,0.6649736166000366,group_disjoint_internal_scenarios +C5,1,,3.648116648197174,3.391767203807831,0.4052631578947368,0.19225967540574282,1.5741225481033325,group_disjoint_internal_scenarios +C5,2,,3.492997576793035,3.279097855091095,0.4052631578947368,0.19225967540574282,1.3879157304763794,group_disjoint_internal_scenarios +C5,3,,3.371471087137858,3.2802674770355225,0.4052631578947368,0.19225967540574282,1.2592568397521973,group_disjoint_internal_scenarios +C5,4,,3.334533045689265,3.250081419944763,0.4052631578947368,0.19225967540574282,1.313844084739685,group_disjoint_internal_scenarios +C5,5,,3.2844751278559365,3.161647081375122,0.5631578947368421,0.4363765629588414,0.9632841348648071,group_disjoint_internal_scenarios +C5,6,,3.223735253016154,3.067867398262024,0.6157894736842106,0.4870478245235527,0.8223584890365601,group_disjoint_internal_scenarios +C5,7,,3.1524856885274253,2.982391655445099,0.631578947368421,0.4990825688073395,0.7661831378936768,group_disjoint_internal_scenarios +C5,8,,3.0939479370911918,2.939557671546936,0.631578947368421,0.4997578692493947,0.7375786304473877,group_disjoint_internal_scenarios +C5,9,,3.0242554247379303,2.8982234597206116,0.6263157894736842,0.4912183908045977,0.7326452136039734,group_disjoint_internal_scenarios +C5,10,,2.963417728741964,2.873548924922943,0.6263157894736842,0.48854166666666665,0.724014937877655,group_disjoint_internal_scenarios +C5,11,,2.890074610710144,2.8580190539360046,0.6105263157894737,0.47402164435554184,0.7357648611068726,group_disjoint_internal_scenarios +C5,12,,2.8306974867979684,2.864997088909149,0.6,0.4757099720980011,0.7161284685134888,group_disjoint_internal_scenarios +C6,1,,3.5560630758603415,3.3430938720703125,0.4052631578947368,0.19225967540574282,1.4975899457931519,group_disjoint_internal_scenarios +C6,2,,3.453421543041865,3.2893688678741455,0.4052631578947368,0.19225967540574282,1.3801339864730835,group_disjoint_internal_scenarios +C6,3,,3.3749252458413443,3.283313035964966,0.4052631578947368,0.19225967540574282,1.2556867599487305,group_disjoint_internal_scenarios +C6,4,,3.3421215315659842,3.2622945308685303,0.4052631578947368,0.19225967540574282,1.2634124755859375,group_disjoint_internal_scenarios +C6,5,,3.293413112560908,3.1872403621673584,0.4052631578947368,0.19225967540574282,1.2729334831237793,group_disjoint_internal_scenarios +C6,6,,3.2178768316904702,3.0630359053611755,0.5947368421052631,0.4702380952380952,0.8375385403633118,group_disjoint_internal_scenarios +C6,7,,3.106997847557068,2.9654109477996826,0.6263157894736842,0.49602446483180423,0.7280592918395996,group_disjoint_internal_scenarios +C6,8,,2.9996988773345947,2.8977559208869934,0.6368421052631579,0.4975609756097561,0.7092863321304321,group_disjoint_internal_scenarios +C6,9,,2.899491469065348,2.8658425211906433,0.6263157894736842,0.486972286972287,0.7103310227394104,group_disjoint_internal_scenarios +C6,10,,2.836098611354828,2.8470621705055237,0.6368421052631579,0.5472151240756029,0.6935749053955078,group_disjoint_internal_scenarios +C6,11,,2.775729159514109,2.826770067214966,0.6473684210526316,0.5701847903340441,0.6837493777275085,group_disjoint_internal_scenarios +C6,12,,2.729355792204539,2.8364994525909424,0.6631578947368421,0.6057268651783106,0.6609827280044556,group_disjoint_internal_scenarios +C6_no_distance,1,,3.6177941958109536,3.4013763666152954,0.32105263157894737,0.16201859229747675,1.5172733068466187,group_disjoint_internal_scenarios +C6_no_distance,2,,3.5209619303544364,3.3226051926612854,0.5421052631578948,0.41686377768852,1.1565769910812378,group_disjoint_internal_scenarios +C6_no_distance,3,,3.412694732348124,3.2563387751579285,0.4052631578947368,0.19225967540574282,1.2960847616195679,group_disjoint_internal_scenarios +C6_no_distance,4,,3.341610232988993,3.2529987692832947,0.4052631578947368,0.19225967540574282,1.2612007856369019,group_disjoint_internal_scenarios +C6_no_distance,5,,3.278877317905426,3.161101818084717,0.5894736842105263,0.4579296277409484,0.91334068775177,group_disjoint_internal_scenarios +C6_no_distance,6,,3.1850637594858804,3.042643129825592,0.631578947368421,0.49,0.8398722410202026,group_disjoint_internal_scenarios +C6_no_distance,7,,3.065849353869756,2.9447609186172485,0.6263157894736842,0.4852110852110852,0.8017374277114868,group_disjoint_internal_scenarios +C6_no_distance,8,,2.9769005378087363,2.9019368290901184,0.631578947368421,0.4889713461142033,0.7686651945114136,group_disjoint_internal_scenarios +C6_no_distance,9,,2.90603373448054,2.880134403705597,0.6210526315789474,0.4805194805194805,0.7406255006790161,group_disjoint_internal_scenarios +C6_no_distance,10,,2.8530720472335815,2.8841158151626587,0.6210526315789474,0.4788202390033054,0.7437321543693542,group_disjoint_internal_scenarios +C6_no_distance,11,,2.796961178382238,2.8580946922302246,0.6263157894736842,0.49312670189452423,0.7221003770828247,group_disjoint_internal_scenarios +C6_no_distance,12,,2.765232632557551,2.8783057928085327,0.6368421052631579,0.5416640263323352,0.6775975227355957,group_disjoint_internal_scenarios +C6_no_reconstruction,1,,3.470627357562383,3.3347200751304626,0.4052631578947368,0.19225967540574282,1.476837158203125,group_disjoint_internal_scenarios +C6_no_reconstruction,2,,3.3288468619187674,3.2538111209869385,0.4052631578947368,0.19225967540574282,1.2974903583526611,group_disjoint_internal_scenarios +C6_no_reconstruction,3,,3.2453169425328574,3.2878774404525757,0.4052631578947368,0.19225967540574282,1.2463195323944092,group_disjoint_internal_scenarios +C6_no_reconstruction,4,,3.2214003006617227,3.2748806476593018,0.4052631578947368,0.19225967540574282,1.2813801765441895,group_disjoint_internal_scenarios +C6_no_reconstruction,5,,3.189190844694773,3.2269041538238525,0.4052631578947368,0.19225967540574282,1.2981008291244507,group_disjoint_internal_scenarios +C6_no_reconstruction,6,,3.1484953264395394,3.166981816291809,0.4473684210526316,0.2754928841885364,1.2332595586776733,group_disjoint_internal_scenarios +C6_no_reconstruction,7,,3.089041749636332,3.0948572158813477,0.5789473684210527,0.4504310344827586,0.902295708656311,group_disjoint_internal_scenarios +C6_no_reconstruction,8,,3.025175084670385,3.0188884139060974,0.6210526315789474,0.489092866451357,0.7917438745498657,group_disjoint_internal_scenarios +C6_no_reconstruction,9,,2.930894603331884,2.948912024497986,0.6210526315789474,0.48499135627848494,0.7593851089477539,group_disjoint_internal_scenarios +C6_no_reconstruction,10,,2.843903740247091,2.877197504043579,0.631578947368421,0.49062049062049057,0.700749933719635,group_disjoint_internal_scenarios +C6_no_reconstruction,11,,2.754317512114843,2.8311679363250732,0.6263157894736842,0.4962017839210822,0.7137499451637268,group_disjoint_internal_scenarios +C6_no_reconstruction,12,,2.699484477440516,2.8148857951164246,0.6473684210526316,0.5708362197925766,0.6621017456054688,group_disjoint_internal_scenarios +C6_pointmask,1,,3.675270746151606,3.418432056903839,0.4052631578947368,0.1929824561403509,1.6332911252975464,group_disjoint_internal_scenarios +C6_pointmask,2,,3.5329595804214478,3.3148856163024902,0.4052631578947368,0.19225967540574282,1.4748961925506592,group_disjoint_internal_scenarios +C6_pointmask,3,,3.3884001771608987,3.2753511667251587,0.4052631578947368,0.19225967540574282,1.2785439491271973,group_disjoint_internal_scenarios +C6_pointmask,4,,3.323949694633484,3.233676314353943,0.4052631578947368,0.19225967540574282,1.295992374420166,group_disjoint_internal_scenarios +C6_pointmask,5,,3.2646474639574685,3.1521087288856506,0.4631578947368421,0.3023726851851852,1.1706576347351074,group_disjoint_internal_scenarios +C6_pointmask,6,,3.1913488705952964,3.0605063438415527,0.5473684210526316,0.41843434343434344,0.940229058265686,group_disjoint_internal_scenarios +C6_pointmask,7,,3.1177627940972648,2.9455742835998535,0.6157894736842106,0.4875222435760553,0.7932655811309814,group_disjoint_internal_scenarios +C6_pointmask,8,,3.046340892712275,2.9041759371757507,0.6105263157894737,0.4840848615605897,0.774253785610199,group_disjoint_internal_scenarios +C6_pointmask,9,,2.961234758297602,2.8403647541999817,0.6526315789473685,0.561970651995049,0.6979454159736633,group_disjoint_internal_scenarios +C6_pointmask,10,,2.8737920025984445,2.830514669418335,0.6736842105263158,0.6283095854088634,0.6451393365859985,group_disjoint_internal_scenarios +C6_pointmask,11,,2.8031854728857675,2.8172165155410767,0.6947368421052632,0.64877769289534,0.6230531930923462,group_disjoint_internal_scenarios +C6_pointmask,12,,2.7474692165851593,2.8274717926979065,0.6842105263157895,0.6469497580608691,0.6270145773887634,group_disjoint_internal_scenarios +C7_distill,1,,3.544741004705429,3.3426119089126587,0.4052631578947368,0.19225967540574282,1.4921711683273315,group_disjoint_internal_scenarios +C7_distill,2,,3.421057472626368,3.2900941371917725,0.4052631578947368,0.19225967540574282,1.3196529150009155,group_disjoint_internal_scenarios +C7_distill,3,,3.358182022968928,3.2872865796089172,0.4052631578947368,0.19225967540574282,1.2709370851516724,group_disjoint_internal_scenarios +C7_distill,4,,3.3243547777334848,3.2372600436210632,0.4052631578947368,0.19225967540574282,1.299700140953064,group_disjoint_internal_scenarios +C7_distill,5,,3.264988531668981,3.132521629333496,0.5526315789473685,0.42452164919373797,0.9660311341285706,group_disjoint_internal_scenarios +C7_distill,6,,3.19478115439415,3.0291983485221863,0.6105263157894737,0.48260073260073266,0.8188617825508118,group_disjoint_internal_scenarios +C7_distill,7,,3.1164951622486115,2.9537498354911804,0.6368421052631579,0.5064208285758588,0.727095901966095,group_disjoint_internal_scenarios +C7_distill,8,,3.051979055007299,2.91681307554245,0.6210526315789474,0.4931657848324515,0.7496492266654968,group_disjoint_internal_scenarios +C7_distill,9,,2.9685777525107064,2.896312952041626,0.6105263157894737,0.4775074757446364,0.7416189312934875,group_disjoint_internal_scenarios +C7_distill,10,,2.9016930758953094,2.8869298100471497,0.6210526315789474,0.5304137409147704,0.7004271149635315,group_disjoint_internal_scenarios +C7_distill,11,,2.830535590648651,2.886876702308655,0.6263157894736842,0.5409901565507972,0.7052735686302185,group_disjoint_internal_scenarios +C7_distill,12,,2.788292189439138,2.8916757702827454,0.631578947368421,0.5709561765741541,0.6583392024040222,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,1,,3.6699901123841605,3.3892515301704407,0.4052631578947368,0.19225967540574282,1.5651878118515015,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,2,,3.522734651962916,3.285323143005371,0.4052631578947368,0.19225967540574282,1.3842427730560303,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,3,,3.4277438720067344,3.2828831672668457,0.4052631578947368,0.19225967540574282,1.3060847520828247,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,4,,3.364328056573868,3.2473665475845337,0.4052631578947368,0.19225967540574282,1.342193365097046,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,5,,3.3265909254550934,3.1884942054748535,0.4631578947368421,0.3023726851851852,1.1814532279968262,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,6,,3.286705712477366,3.0972980856895447,0.6105263157894737,0.48356377487969066,0.8808850646018982,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,7,,3.2267816066741943,3.0294854640960693,0.5947368421052631,0.46817691477885653,0.886831521987915,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,8,,3.189849923054377,2.9762118458747864,0.6157894736842106,0.48937616862145167,0.8175166249275208,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,9,,3.146577626466751,2.944009244441986,0.6105263157894737,0.4843416953598951,0.8442237377166748,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,10,,3.0875621835390725,2.920766055583954,0.6,0.4726287262872629,0.836346447467804,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,11,,3.0492197473843894,2.911943554878235,0.6157894736842106,0.4852838310465429,0.7720956802368164,group_disjoint_internal_scenarios +C7_group_lambda0.05_tau0.10,12,,3.01773864030838,2.9069700241088867,0.6052631578947368,0.47309969092147314,0.786773681640625,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,1,,3.7917567988236747,3.431395471096039,0.4052631578947368,0.19225967540574282,1.6089566946029663,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,2,,3.6467959682146707,3.30967777967453,0.4052631578947368,0.19225967540574282,1.4272804260253906,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,3,,3.5376113255818686,3.306707441806793,0.4052631578947368,0.19225967540574282,1.2905869483947754,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,4,,3.456825832525889,3.2857162952423096,0.4052631578947368,0.19225967540574282,1.3240010738372803,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,5,,3.4495691061019897,3.240543782711029,0.4052631578947368,0.19225967540574282,1.3067970275878906,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,6,,3.443036218484243,3.1910694241523743,0.43157894736842106,0.24643380368571208,1.2924253940582275,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,7,,3.3698772887388864,3.144593596458435,0.5526315789473685,0.42452164919373797,0.9700701832771301,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,8,,3.331037163734436,3.073027789592743,0.5894736842105263,0.463676061929337,0.858289361000061,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,9,,3.298823058605194,3.0421711802482605,0.5947368421052631,0.4696449026345933,0.827397882938385,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,10,,3.2250280479590097,2.980533242225647,0.6210526315789474,0.4931657848324515,0.7491006851196289,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,11,,3.178482929865519,2.9425472021102905,0.6210526315789474,0.4921348618031082,0.7286807298660278,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.05,12,,3.122002293666204,2.936999499797821,0.6,0.47473416643377114,0.7731947898864746,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,1,,3.7982660233974457,3.4424321055412292,0.41578947368421054,0.33451043338683784,0.970120370388031,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,2,,3.6560287376244864,3.3182848691940308,0.4052631578947368,0.19225967540574282,1.4864188432693481,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,3,,3.5502622425556183,3.271094262599945,0.4052631578947368,0.19225967540574282,1.3396120071411133,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,4,,3.4411672353744507,3.282557487487793,0.4052631578947368,0.19225967540574282,1.31191086769104,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,5,,3.420692811409632,3.2186851501464844,0.43157894736842106,0.24643380368571208,1.2519593238830566,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,6,,3.384774992863337,3.120095133781433,0.5947368421052631,0.4600684047324364,0.9273067116737366,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,7,,3.2720022002855935,3.0318233966827393,0.6105263157894737,0.4775641025641026,0.8281511664390564,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,8,,3.1874267061551413,2.959826111793518,0.6263157894736842,0.48854166666666665,0.7915177345275879,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,9,,3.1204189161459603,2.8964684009552,0.5894736842105263,0.4558302122347066,0.8305084109306335,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,10,,3.0327975749969482,2.9093905091285706,0.6052631578947368,0.46880931453102503,0.7937615513801575,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,11,,2.9967327415943146,2.8864620327949524,0.6,0.46428888635123644,0.7574557662010193,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.10,12,,2.9405342042446136,2.8889219164848328,0.6052631578947368,0.4669717772692601,0.7580579519271851,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,1,,3.7016623417536416,3.388068735599518,0.4052631578947368,0.19225967540574282,1.5812524557113647,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,2,,3.5898077686627707,3.2949065566062927,0.4052631578947368,0.19225967540574282,1.4478111267089844,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,3,,3.4932630161444345,3.2835686802864075,0.4052631578947368,0.19225967540574282,1.3127483129501343,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,4,,3.408191959063212,3.281365990638733,0.4052631578947368,0.19225967540574282,1.3370603322982788,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,5,,3.387180229028066,3.231750249862671,0.4052631578947368,0.19225967540574282,1.3287807703018188,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,6,,3.367981960376104,3.1600507497787476,0.5736842105263158,0.44487613884496646,0.9737974405288696,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,7,,3.2893512845039368,3.100860357284546,0.5894736842105263,0.46128364389233956,0.8847028017044067,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,8,,3.2452140053113303,3.030231535434723,0.6,0.4697802197802197,0.8622543215751648,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,9,,3.1978043218453727,2.9798112511634827,0.6105263157894737,0.4803591470258137,0.8322786688804626,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,10,,3.117221931616465,2.945197343826294,0.6105263157894737,0.47991452991453,0.8018269538879395,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,11,,3.060431480407715,2.933944344520569,0.6368421052631579,0.49956747980463395,0.7462747693061829,group_disjoint_internal_scenarios +C7_group_lambda0.10_tau0.20,12,,3.0112144152323403,2.9046136140823364,0.6210526315789474,0.48499135627848494,0.7529937624931335,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,1,,3.8340122203032174,3.400073766708374,0.4052631578947368,0.19225967540574282,1.5749945640563965,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,2,,3.702662537495295,3.3013370633125305,0.4052631578947368,0.19225967540574282,1.4297661781311035,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,3,,3.64327542980512,3.2696509957313538,0.4052631578947368,0.19225967540574282,1.3239336013793945,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,4,,3.511779417594274,3.261994481086731,0.4052631578947368,0.19225967540574282,1.3278520107269287,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,5,,3.510362615187963,3.237016201019287,0.4052631578947368,0.19225967540574282,1.3181118965148926,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,6,,3.5117766757806144,3.1881971955299377,0.4052631578947368,0.19225967540574282,1.3670039176940918,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,7,,3.414328763882319,3.1437845826148987,0.4631578947368421,0.3023726851851852,1.165671944618225,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,8,,3.363941212495168,3.085771083831787,0.5526315789473685,0.4230936819172113,0.9231602549552917,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,9,,3.312815030415853,3.058401048183441,0.5894736842105263,0.4643199233716475,0.8149368166923523,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,10,,3.2131250699361167,3.021866261959076,0.6,0.476140898837411,0.7716059684753418,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,11,,3.154651701450348,2.950363576412201,0.6263157894736842,0.4949440900048021,0.7079406976699829,group_disjoint_internal_scenarios +C7_group_lambda0.20_tau0.10,12,,3.109651426474253,2.93612539768219,0.631578947368421,0.49446974625894596,0.7153028845787048,group_disjoint_internal_scenarios diff --git a/final/experiments/q2/unaligned_math_all_b128/validation_group_bootstrap_deltas.csv b/final/experiments/q2/unaligned_math_all_b128/validation_group_bootstrap_deltas.csv new file mode 100644 index 0000000..761c798 --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/validation_group_bootstrap_deltas.csv @@ -0,0 +1,78 @@ +model,baseline,metric,point_delta,bootstrap_median_delta,ci_2_5,ci_97_5,replicates,unit +C1,C0,accuracy,-0.0027472527472527375,-0.001340239501606888,-0.0415489514789989,0.03692233632862645,300,paired source-video group resample +C1,C0,macro_f1,-0.08342531441150824,-0.0839257600579568,-0.12562870345607602,-0.04242823989116619,300,paired source-video group resample +C1,C0,mae,0.01783299446105957,0.016138792037963867,-0.02564724236726761,0.0629775285720825,300,paired source-video group resample +C1,C0,rmse,0.03289999144204303,0.031675785828170544,-0.020259642051488887,0.09688892746372023,300,paired source-video group resample +C1,C0,pearson,0.01760411262512207,0.01890239119529724,-0.03369738236069679,0.06402220875024796,300,paired source-video group resample +C1,C0,interval_90_coverage,-0.020604395604395642,-0.020156060783624363,-0.04706951966973708,0.0014244097679602268,300,paired source-video group resample +C1,C0,interval_90_mean_width,-0.8198151588439941,-0.8159161806106567,-0.8973418056964875,-0.7244095444679262,300,paired source-video group resample +C2,C0,accuracy,0.005494505494505475,0.006734034217056317,-0.03492168144435322,0.039263388113621865,300,paired source-video group resample +C2,C0,macro_f1,-0.10705130555642356,-0.10647497871020942,-0.14529034521411816,-0.07227670367224108,300,paired source-video group resample +C2,C0,mae,-0.02862834930419922,-0.02886521816253662,-0.0708615779876709,0.017617069184780107,300,paired source-video group resample +C2,C0,rmse,-0.023320158215888398,-0.024536035329587313,-0.07390452049272797,0.03574524043224036,300,paired source-video group resample +C2,C0,pearson,0.06217068433761597,0.062325745820999146,0.019706763327121735,0.10440436601638789,300,paired source-video group resample +C2,C0,interval_90_coverage,-0.028846153846153855,-0.029139374090247472,-0.05483842226931229,-0.007421393766663628,300,paired source-video group resample +C2,C0,interval_90_mean_width,-0.9512450695037842,-0.9483258724212646,-1.040887188911438,-0.8597516894340516,300,paired source-video group resample +C3,C0,accuracy,0.0,-0.0014105382526434784,-0.037480075163610405,0.03973893273401969,300,paired source-video group resample +C3,C0,macro_f1,-0.11397785982339559,-0.11550118636787543,-0.15025330407933285,-0.07573850765342569,300,paired source-video group resample +C3,C0,mae,0.0050026774406433105,0.00607532262802124,-0.038323822617530826,0.05409729927778244,300,paired source-video group resample +C3,C0,rmse,0.03632853377078282,0.034883465886420384,-0.02374622453678723,0.10238240376277774,300,paired source-video group resample +C3,C0,pearson,0.027020394802093506,0.028322190046310425,-0.024162814021110535,0.07840722724795342,300,paired source-video group resample +C3,C0,interval_90_coverage,-0.028846153846153855,-0.028296790655086446,-0.05640123255954723,-0.0027437057991513618,300,paired source-video group resample +C3,C0,interval_90_mean_width,-1.0228526592254639,-1.0194878578186035,-1.1037156105041503,-0.9390599608421326,300,paired source-video group resample +C4,C0,accuracy,-0.0357142857142857,-0.034815800464262414,-0.0772917370598698,-0.0013288600915486893,300,paired source-video group resample +C4,C0,macro_f1,-0.10368951752582078,-0.10223723410343993,-0.14512141397513129,-0.06291875743935396,300,paired source-video group resample +C4,C0,mae,0.018634796142578125,0.018803030252456665,-0.03187550157308579,0.07048249393701546,300,paired source-video group resample +C4,C0,rmse,0.0662456532801351,0.06606137343205698,-0.000771750973311937,0.13834455080325986,300,paired source-video group resample +C4,C0,pearson,-0.015321314334869385,-0.014323621988296509,-0.06995467767119408,0.045190202444791765,300,paired source-video group resample +C4,C0,interval_90_coverage,-0.05631868131868134,-0.05692091288405615,-0.08358959916190373,-0.028953700403650265,300,paired source-video group resample +C4,C0,interval_90_mean_width,-1.0491728782653809,-1.044885516166687,-1.1438844561576844,-0.9337571382522584,300,paired source-video group resample +C5,C0,accuracy,-0.008241758241758212,-0.008629674665607934,-0.044991426516953356,0.026282724151226042,300,paired source-video group resample +C5,C0,macro_f1,-0.11722314075215268,-0.11822550052537656,-0.1499375115712949,-0.08097724629441232,300,paired source-video group resample +C5,C0,mae,0.05200999975204468,0.05211746692657471,-0.0040017873048782335,0.11093356907367702,300,paired source-video group resample +C5,C0,rmse,0.07543220131984574,0.0784406160029566,0.003570230707261707,0.1612439999757444,300,paired source-video group resample +C5,C0,pearson,-0.006319224834442139,-0.008950561285018921,-0.0744188591837883,0.05456443652510638,300,paired source-video group resample +C5,C0,interval_90_coverage,-0.0357142857142857,-0.036052426258835424,-0.0611382067095792,-0.010121494473371696,300,paired source-video group resample +C5,C0,interval_90_mean_width,-0.7522284984588623,-0.7501976490020752,-0.851237690448761,-0.651238238811493,300,paired source-video group resample +C6,C0,accuracy,0.017857142857142794,0.018242022657626977,-0.015221933420851212,0.04816562201934867,300,paired source-video group resample +C6,C0,macro_f1,-0.03854345951579863,-0.03931690543571159,-0.0728723243438216,-0.001288528649975503,300,paired source-video group resample +C6,C0,mae,-0.04211997985839844,-0.041862428188323975,-0.08747663199901581,-0.0026129513978958374,300,paired source-video group resample +C6,C0,rmse,-0.02856052047731339,-0.028529750312167423,-0.07851328647353324,0.023412958789233378,300,paired source-video group resample +C6,C0,pearson,0.09151405096054077,0.09196549654006958,0.053130829334259035,0.13536421805620186,300,paired source-video group resample +C6,C0,interval_90_coverage,-0.04807692307692313,-0.048597053531264034,-0.07352282979641461,-0.02437113702623907,300,paired source-video group resample +C6,C0,interval_90_mean_width,-1.1290688514709473,-1.1257045269012451,-1.2072301983833313,-1.0356801092624666,300,paired source-video group resample +C6_no_distance,C0,accuracy,0.016483516483516425,0.015602962450278701,-0.025158176948386863,0.05368575542755869,300,paired source-video group resample +C6_no_distance,C0,macro_f1,-0.0852458400966723,-0.08818768263991761,-0.12609670666918588,-0.04349884073514668,300,paired source-video group resample +C6_no_distance,C0,mae,-0.015009105205535889,-0.014875531196594238,-0.05908146798610687,0.02703451812267302,300,paired source-video group resample +C6_no_distance,C0,rmse,-0.017103453547432368,-0.01823479256332139,-0.06720366401839234,0.03965089682585672,300,paired source-video group resample +C6_no_distance,C0,pearson,0.08881640434265137,0.08928048610687256,0.049032670259475705,0.12815435305237768,300,paired source-video group resample +C6_no_distance,C0,interval_90_coverage,-0.03708791208791207,-0.036860222829389055,-0.06295045068130059,-0.016341489525701837,300,paired source-video group resample +C6_no_distance,C0,interval_90_mean_width,-1.155916690826416,-1.1544511318206787,-1.2067680954933167,-1.0983048617839812,300,paired source-video group resample +C6_no_reconstruction,C0,accuracy,-0.006868131868131844,-0.008153393092879657,-0.04159180129850518,0.030601993085214534,300,paired source-video group resample +C6_no_reconstruction,C0,macro_f1,-0.07406662996020902,-0.07427164155320246,-0.11354473287065868,-0.028505535727230268,300,paired source-video group resample +C6_no_reconstruction,C0,mae,-0.01761627197265625,-0.018726110458374023,-0.0649495080113411,0.03427876979112624,300,paired source-video group resample +C6_no_reconstruction,C0,rmse,0.004977020946573263,0.0027477694204465775,-0.05508550586289301,0.07941780287834657,300,paired source-video group resample +C6_no_reconstruction,C0,pearson,0.04488801956176758,0.045243918895721436,-0.00925953760743141,0.09828210026025767,300,paired source-video group resample +C6_no_reconstruction,C0,interval_90_coverage,-0.04258241758241754,-0.04290758131319522,-0.06940336171756782,-0.018095951160513678,300,paired source-video group resample +C6_no_reconstruction,C0,interval_90_mean_width,-1.131732702255249,-1.1248958110809326,-1.2080435454845428,-1.0486360669136048,300,paired source-video group resample +C6_pointmask,C0,accuracy,-0.009615384615384581,-0.009738001914636085,-0.04920340824308543,0.022331564635391116,300,paired source-video group resample +C6_pointmask,C0,macro_f1,-0.054741406914747937,-0.05509936281932051,-0.09933300769039154,-0.014947960975273141,300,paired source-video group resample +C6_pointmask,C0,mae,-0.04757577180862427,-0.047405123710632324,-0.08711361587047577,-0.010291239619255097,300,paired source-video group resample +C6_pointmask,C0,rmse,-0.031789715997461165,-0.03308342959875521,-0.08183316816372967,0.02130973142654214,300,paired source-video group resample +C6_pointmask,C0,pearson,0.07724165916442871,0.07814452052116394,0.030774897336959837,0.12344256937503814,300,paired source-video group resample +C6_pointmask,C0,interval_90_coverage,-0.04532967032967039,-0.046236097755335825,-0.06903910849453329,-0.02037191114069394,300,paired source-video group resample +C6_pointmask,C0,interval_90_mean_width,-1.053518533706665,-1.050612449645996,-1.1356862008571624,-0.9624875009059907,300,paired source-video group resample +C7_distill,C0,accuracy,0.0013736263736263687,0.0,-0.03564026436677138,0.03808366113053612,300,paired source-video group resample +C7_distill,C0,macro_f1,-0.07022866473464978,-0.07150025845602764,-0.11333475377843333,-0.02820262605190197,300,paired source-video group resample +C7_distill,C0,mae,-0.024318575859069824,-0.023236483335494995,-0.06809726357460022,0.022609198093414166,300,paired source-video group resample +C7_distill,C0,rmse,-0.005145556765307591,-0.003754520280238871,-0.06475408824403364,0.06043374588794306,300,paired source-video group resample +C7_distill,C0,pearson,0.056644558906555176,0.05477696657180786,0.0036024510860443114,0.10223198831081384,300,paired source-video group resample +C7_distill,C0,interval_90_coverage,-0.0535714285714286,-0.05340785546264998,-0.08114066852367696,-0.029819319822607264,300,paired source-video group resample +C7_distill,C0,interval_90_mean_width,-1.0344364643096924,-1.0316048860549927,-1.1263263642787933,-0.9360203683376312,300,paired source-video group resample +C7_group,C0,accuracy,0.009615384615384581,0.00991596668164968,-0.026735576237341265,0.04448888522064489,300,paired source-video group resample +C7_group,C0,macro_f1,-0.10642370210014501,-0.1059487945832571,-0.14284757254188665,-0.06908091423711933,300,paired source-video group resample +C7_group,C0,mae,0.005541443824768066,0.005858421325683594,-0.03842498958110809,0.04856431335210795,300,paired source-video group resample +C7_group,C0,rmse,-0.013793035301605139,-0.013648813546030314,-0.06215944631884653,0.033087321259013286,300,paired source-video group resample +C7_group,C0,pearson,0.08217388391494751,0.08152049779891968,0.04730007201433182,0.1242825150489807,300,paired source-video group resample +C7_group,C0,interval_90_coverage,-0.03296703296703296,-0.03199443671766339,-0.05872082981670811,-0.011221050556814867,300,paired source-video group resample +C7_group,C0,interval_90_mean_width,-0.9219331741333008,-0.918962836265564,-0.9758361577987671,-0.8550595462322236,300,paired source-video group resample diff --git a/final/experiments/q2/unaligned_math_all_b128/validation_metrics.json b/final/experiments/q2/unaligned_math_all_b128/validation_metrics.json new file mode 100644 index 0000000..8cec5eb --- /dev/null +++ b/final/experiments/q2/unaligned_math_all_b128/validation_metrics.json @@ -0,0 +1,27 @@ +{ + "n": 728, + "accuracy": 0.6085164835164835, + "macro_f1": 0.518781225964892, + "negative_support": 206, + "neutral_support": 184, + "positive_support": 338, + "negative_recall": 0.6796116504854369, + "middle_recall": 0.125, + "positive_recall": 0.8284023668639053, + "regression_mae": 0.6846789717674255, + "regression_rmse": 0.9208215740080159, + "pearson": 0.6101368069648743, + "brier": 0.4977383080922297, + "classification_nll": 0.8427478075027466, + "ece_15": 0.03950894476620705, + "selection_nll": 2.783693552017212, + "interval_90_coverage": 0.8873626373626373, + "interval_90_mean_width": 2.3910491466522217, + "predictive_variance_mean_uncalibrated": 0.5537729859352112, + "within_trajectory_variance_mean": 0.5537727475166321, + "between_trajectory_variance_mean": 2.9140662149984564e-07, + "predictive_mean_mean_calibrated": 0.2094990462064743, + "predictive_variance_mean_calibrated": 0.5816943049430847, + "selected_model": "C6", + "temperature": 1.122980387832455 +} \ No newline at end of file diff --git a/final/model/__init__.py b/final/model/__init__.py new file mode 100644 index 0000000..65bb1a4 --- /dev/null +++ b/final/model/__init__.py @@ -0,0 +1,23 @@ +"""Q2 model entry points, one file per paper scheme.""" + +from .crg import CRG, StructuredGaussianImputer +from .c0 import C0 +from .c1 import C1 +from .c2 import C2 +from .c3 import C3 +from .c4 import C4 +from .c5 import C5 +from .c6 import C6 +from .c6_no_distance import C6NoDistance +from .c6_no_reconstruction import C6NoReconstruction +from .c6_pointmask import C6PointMask +from .c7_distill import C7Distill +from .c7_group import C7Group +from .early_concat import AlignedFusionModel +from .mofe import MixtureOfFusionExperts + +__all__ = [ + "CRG", "StructuredGaussianImputer", "C0", "C1", "C2", "C3", "C4", "C5", "C6", + "C6NoDistance", "C6NoReconstruction", "C6PointMask", "C7Distill", "C7Group", + "AlignedFusionModel", "MixtureOfFusionExperts", +] diff --git a/final/model/c0.py b/final/model/c0.py new file mode 100644 index 0000000..a276a9d --- /dev/null +++ b/final/model/c0.py @@ -0,0 +1,57 @@ +"""C0: observed statistics and mask baseline from E题V2, table 5.9.""" +from __future__ import annotations + +import numpy as np +from sklearn.linear_model import LogisticRegression, Ridge + +MODALITIES = ("text", "audio", "vision") + + +def sample_statistics(arrays: dict[str, np.ndarray], mask: np.ndarray) -> np.ndarray: + """Mean, standard deviation, missing fraction and longest gap per modality.""" + mask = np.asarray(mask, dtype=bool) + if mask.ndim != 3 or mask.shape[-1] != 3: + raise ValueError("mask must have shape (N, T, 3)") + parts = [] + for index, name in enumerate(MODALITIES): + x = np.asarray(arrays[name], dtype=np.float32) + if x.shape[:2] != mask.shape[:2]: + raise ValueError(f"{name}: feature and mask shapes disagree") + visible = mask[:, :, index] + count = visible.sum(axis=1, keepdims=True) + mean = (x * visible[:, :, None]).sum(axis=1) / np.maximum(count, 1) + variance = (((x - mean[:, None, :]) ** 2) * visible[:, :, None]).sum(axis=1) / np.maximum(count, 1) + missing = 1.0 - visible.mean(axis=1, keepdims=True) + max_gap = [] + for row in visible: + longest = current = 0 + for observed in row: + current = 0 if observed else current + 1 + longest = max(longest, current) + max_gap.append(longest / max(len(row), 1)) + parts.extend((mean, np.sqrt(variance), missing, np.asarray(max_gap, np.float32)[:, None])) + return np.concatenate(parts, axis=1).astype(np.float32) + + +class C0: + """Logistic polarity classifier and Ridge intensity regressor.""" + + def __init__(self) -> None: + self.classifier = LogisticRegression(C=0.05, max_iter=2500, random_state=20260924) + self.regressor = Ridge(alpha=25.0) + + def fit(self, arrays: dict[str, np.ndarray], mask: np.ndarray, + polarity: np.ndarray, intensity: np.ndarray) -> "C0": + features = sample_statistics(arrays, mask) + self.classifier.fit(features, polarity) + self.regressor.fit(features, intensity) + return self + + def predict(self, arrays: dict[str, np.ndarray], mask: np.ndarray) -> dict[str, np.ndarray]: + features = sample_statistics(arrays, mask) + probabilities = np.zeros((len(features), 3), np.float64) + probabilities[:, self.classifier.classes_] = self.classifier.predict_proba(features) + return { + "probabilities": probabilities, + "intensity": np.clip(self.regressor.predict(features), -3.0, 3.0), + } diff --git a/final/model/c1.py b/final/model/c1.py new file mode 100644 index 0000000..ee40481 --- /dev/null +++ b/final/model/c1.py @@ -0,0 +1,9 @@ +"""C1: masked BiGRU, without probabilistic completion or explicit gates.""" +from .crg import CRG + + +class C1(CRG): + def __init__(self, **kwargs): + super().__init__(use_imputer=False, use_joint_draws=False, + use_final_gate=False, use_source_attention=False, + reliability_update=False, use_low_rank=False, **kwargs) diff --git a/final/model/c2.py b/final/model/c2.py new file mode 100644 index 0000000..022ed8d --- /dev/null +++ b/final/model/c2.py @@ -0,0 +1,9 @@ +"""C2: Gaussian posterior mean completion, without trajectory integration.""" +from .crg import CRG + + +class C2(CRG): + def __init__(self, **kwargs): + super().__init__(use_imputer=True, use_joint_draws=False, + use_final_gate=False, use_source_attention=False, + reliability_update=False, use_low_rank=False, **kwargs) diff --git a/final/model/c3.py b/final/model/c3.py new file mode 100644 index 0000000..9d8e0d5 --- /dev/null +++ b/final/model/c3.py @@ -0,0 +1,9 @@ +"""C3: joint trajectory integration and final reliability/content fusion.""" +from .crg import CRG + + +class C3(CRG): + def __init__(self, **kwargs): + super().__init__(use_imputer=True, use_joint_draws=True, + use_final_gate=True, use_source_attention=False, + reliability_update=False, use_low_rank=False, **kwargs) diff --git a/final/model/c4.py b/final/model/c4.py new file mode 100644 index 0000000..6f576e9 --- /dev/null +++ b/final/model/c4.py @@ -0,0 +1,9 @@ +"""C4: C3 with bounded source attention and a null source.""" +from .crg import CRG + + +class C4(CRG): + def __init__(self, **kwargs): + super().__init__(use_imputer=True, use_joint_draws=True, + use_final_gate=True, use_source_attention=True, + reliability_update=False, use_low_rank=False, **kwargs) diff --git a/final/model/c5.py b/final/model/c5.py new file mode 100644 index 0000000..568be97 --- /dev/null +++ b/final/model/c5.py @@ -0,0 +1,9 @@ +"""C5: C4 with reliability-modulated recurrent updates.""" +from .crg import CRG + + +class C5(CRG): + def __init__(self, **kwargs): + super().__init__(use_imputer=True, use_joint_draws=True, + use_final_gate=True, use_source_attention=True, + reliability_update=True, use_low_rank=False, **kwargs) diff --git a/final/model/c6.py b/final/model/c6.py new file mode 100644 index 0000000..cf905a4 --- /dev/null +++ b/final/model/c6.py @@ -0,0 +1,9 @@ +"""C6: C5 with the optional rank-four CP interaction residual enabled.""" +from .crg import CRG + + +class C6(CRG): + def __init__(self, **kwargs): + super().__init__(use_imputer=True, use_joint_draws=True, + use_final_gate=True, use_source_attention=True, + reliability_update=True, use_low_rank=True, **kwargs) diff --git a/final/model/c6_no_distance.py b/final/model/c6_no_distance.py new file mode 100644 index 0000000..2bd44d5 --- /dev/null +++ b/final/model/c6_no_distance.py @@ -0,0 +1,7 @@ +"""C6 diagnostic: disable uncertainty distance and span penalties.""" +from .c6 import C6 + + +class C6NoDistance(C6): + def __init__(self, **kwargs): + super().__init__(reliability_hparams=(0.5, 0.05, 0.0, 0.0), **kwargs) diff --git a/final/model/c6_no_reconstruction.py b/final/model/c6_no_reconstruction.py new file mode 100644 index 0000000..7801634 --- /dev/null +++ b/final/model/c6_no_reconstruction.py @@ -0,0 +1,8 @@ +"""C6 diagnostic: omit auxiliary hidden-feature reconstruction while fitting.""" +from .c6 import C6 + + +class C6NoReconstruction(C6): + """Use the C6 forward pass and set reconstruction loss weight to zero.""" + + reconstruction_loss_weight = 0.0 diff --git a/final/model/c6_pointmask.py b/final/model/c6_pointmask.py new file mode 100644 index 0000000..4259c82 --- /dev/null +++ b/final/model/c6_pointmask.py @@ -0,0 +1,8 @@ +"""C6 diagnostic: train with independent point masks instead of spans.""" +from .c6 import C6 + + +class C6PointMask(C6): + """Use the C6 forward pass with mask_kind='point' during fitting.""" + + training_mask_kind = "point" diff --git a/final/model/c7_distill.py b/final/model/c7_distill.py new file mode 100644 index 0000000..219fb40 --- /dev/null +++ b/final/model/c7_distill.py @@ -0,0 +1,41 @@ +"""C7 distillation-only branch: C6 architecture plus teacher loss.""" +from __future__ import annotations + +import math + +import numpy as np +import torch +from torch.nn import functional as F + +from .c6 import C6 + +DISTILL_TEMPERATURE = 2.0 +DISTILL_WEIGHT = 0.1 + + +class C7Distill(C6): + """Inference uses C6; training adds weighted teacher distillation.""" + + +def distillation_per_sample(student: dict, teacher: dict, + original: np.ndarray, current: np.ndarray) -> torch.Tensor: + """Entropy/retention-weighted KL and score term for Q2 distillation.""" + temp = DISTILL_TEMPERATURE + p_teacher = teacher["tempered_probs_by_path"].mean(dim=0).detach().clamp_min(1e-8) + p_student = student["tempered_probs_by_path"].mean(dim=0).clamp_min(1e-8) + entropy = -(p_teacher * p_teacher.log()).sum(dim=-1) + confidence_weight = (1.0 - entropy / math.log(3.0)).clamp(0.0, 1.0) + orig_t = torch.as_tensor(original, device=p_teacher.device, dtype=torch.float32) + curr_t = torch.as_tensor(current, device=p_teacher.device, dtype=torch.float32) + retained = [] + for modality in range(3): + denominator = orig_t[:, :, modality].sum(dim=1) + ratio = (orig_t[:, :, modality] * curr_t[:, :, modality]).sum(dim=1) / denominator.clamp_min(1.0) + retained.append(torch.where(denominator > 0, ratio, torch.ones_like(ratio))) + weight = confidence_weight * torch.stack(retained, dim=-1).mean(dim=-1) + kl = (p_teacher * (p_teacher.log() - p_student.log())).sum(dim=-1) * temp * temp + teacher_score = teacher["mixed_score"].detach() + student_score = student["mixed_score"] + regression = F.huber_loss((teacher_score - student_score) / 3.0, + torch.zeros_like(teacher_score), reduction="none", delta=0.25) + return weight * (kl + regression) diff --git a/final/model/c7_group.py b/final/model/c7_group.py new file mode 100644 index 0000000..d6590c2 --- /dev/null +++ b/final/model/c7_group.py @@ -0,0 +1,32 @@ +"""C7 group-risk-only branch: C6 architecture plus smooth worst-group loss.""" +from __future__ import annotations + +import numpy as np +import torch + +from .c6 import C6 + + +class C7Group(C6): + """Inference uses C6; training adds smooth worst-group risk.""" + + +def smooth_group_risk(losses: torch.Tensor, group_ids: np.ndarray, + lambda_group: float = 0.1, + group_temperature: float = 0.05) -> torch.Tensor: + """Match the selected group penalty from the Q2 training protocol.""" + if group_temperature <= 0 or not 0 <= lambda_group <= 1: + raise ValueError("invalid group risk parameters") + groups = torch.as_tensor(group_ids, device=losses.device, dtype=torch.long) + if groups.shape != losses.shape: + raise ValueError("group_ids must match per-sample losses") + group_losses, priors = [], [] + for group in torch.unique(groups): + selected = groups == group + group_losses.append(losses[selected].mean()) + priors.append(selected.float().mean()) + values = torch.stack(group_losses) + prior = torch.stack(priors).clamp_min(1e-8) + expected = (prior * values).sum() + worst = group_temperature * torch.logsumexp(torch.log(prior) + values / group_temperature, dim=0) + return (1.0 - lambda_group) * expected + lambda_group * worst diff --git a/final/model/crg.py b/final/model/crg.py new file mode 100644 index 0000000..bb6ae36 --- /dev/null +++ b/final/model/crg.py @@ -0,0 +1,556 @@ +"""Structured Gaussian imputation and reliability-aware CRG sequence model.""" +from __future__ import annotations + +import math +from typing import Sequence + +import torch +from torch import nn +from torch.nn import functional as F + +MODALITIES = ("text", "audio", "vision") +INPUT_DIMS = (768, 74, 35) +HIDDEN = 32 +SHARED_STATE = 8 +PRIVATE_STATE = 4 +STATE_DIM = SHARED_STATE + len(MODALITIES) * PRIVATE_STATE + + +def _inv_softplus(value: float) -> float: + return math.log(math.expm1(value)) + + +class StructuredGaussianImputer(nn.Module): + """Linear-Gaussian shared/private state model with exact block-Gaussian inference. + + The state is [shared(8), text-private(4), audio-private(4), vision-private(4)]. + Each modality emits from the shared state and its own private state only. The + filtering likelihood uses the matrix determinant lemma, retaining its log-det + normalization without forming a covariance matrix in observation space. + """ + + def __init__(self, input_dims: Sequence[int] = INPUT_DIMS) -> None: + super().__init__() + self.input_dims = tuple(int(x) for x in input_dims) + self.state_dim = STATE_DIM + transition_mask = torch.zeros(STATE_DIM, STATE_DIM) + blocks = [slice(0, SHARED_STATE)] + [ + slice(SHARED_STATE + i * PRIVATE_STATE, SHARED_STATE + (i + 1) * PRIVATE_STATE) + for i in range(len(MODALITIES)) + ] + for block in blocks: + transition_mask[block, block] = 1.0 + self.register_buffer("transition_mask", transition_mask) + self.transition_raw = nn.Parameter(0.8 * torch.eye(STATE_DIM)) + self.mu0 = nn.Parameter(torch.zeros(STATE_DIM)) + self.pi0_raw = nn.Parameter(torch.full((STATE_DIM,), _inv_softplus(1.0))) + self.q_raw = nn.Parameter(torch.full((STATE_DIM,), _inv_softplus(0.08))) + self.emission_raw = nn.ParameterList() + self.biases = nn.ParameterList() + self.r_raw = nn.ParameterList() + for index, dim in enumerate(self.input_dims): + mask = torch.zeros(dim, STATE_DIM) + mask[:, :SHARED_STATE] = 1.0 + private_start = SHARED_STATE + index * PRIVATE_STATE + mask[:, private_start:private_start + PRIVATE_STATE] = 1.0 + self.register_buffer(f"emission_mask_{index}", mask) + self.emission_raw.append(nn.Parameter(torch.randn(dim, STATE_DIM) * 0.025)) + self.biases.append(nn.Parameter(torch.zeros(dim))) + self.r_raw.append(nn.Parameter(torch.full((dim,), _inv_softplus(0.5)))) + + def _transition(self) -> torch.Tensor: + matrix = self.transition_raw * self.transition_mask + norm = torch.linalg.matrix_norm(matrix, ord=2).clamp_min(1e-8) + return matrix * torch.clamp(0.98 / norm, max=1.0) + + def _covariances(self) -> tuple[torch.Tensor, torch.Tensor]: + eye = torch.eye(self.state_dim, device=self.mu0.device, dtype=self.mu0.dtype) + p0 = torch.diag(F.softplus(self.pi0_raw) + 1e-4) + 1e-5 * eye + q = torch.diag(F.softplus(self.q_raw) + 1e-4) + 1e-5 * eye + return p0, q + + def emissions(self) -> list[torch.Tensor]: + return [raw * getattr(self, f"emission_mask_{i}") for i, raw in enumerate(self.emission_raw)] + + def _filter( + self, + xs: Sequence[torch.Tensor], + observed: torch.Tensor, + *, + calculate_log_likelihood: bool, + retain_states: bool, + ) -> tuple[torch.Tensor | None, dict[str, list[torch.Tensor]] | None]: + # xs[m]: [B,T,Dm], observed: [B,T,3] + batch, steps, _ = observed.shape + transition = self._transition() + p0, process_noise = self._covariances() + emissions = self.emissions() + noise = [F.softplus(x) + 1e-4 for x in self.r_raw] + mu_prior = self.mu0.expand(batch, -1) + p_prior = p0.expand(batch, -1, -1) + total_nll = torch.zeros(batch, device=observed.device, dtype=mu_prior.dtype) + prior_means: list[torch.Tensor] = [] + prior_covs: list[torch.Tensor] = [] + filtered_means: list[torch.Tensor] = [] + filtered_covs: list[torch.Tensor] = [] + + for t in range(steps): + if retain_states: + prior_means.append(mu_prior) + prior_covs.append(p_prior) + p_chol = torch.linalg.cholesky(p_prior + 1e-6 * torch.eye(self.state_dim, device=p_prior.device)) + p_inv = torch.cholesky_inverse(p_chol) + information_parts: list[torch.Tensor] = [] + vector_parts: list[torch.Tensor] = [] + quadratic_parts: list[torch.Tensor] = [] + logdet_r = torch.zeros(batch, device=p_prior.device, dtype=p_prior.dtype) + n_observed = torch.zeros_like(logdet_r) + for m, (x, emission, variance) in enumerate(zip(xs, emissions, noise)): + active = observed[:, t, m].to(dtype=mu_prior.dtype) + weights = active[:, None] / variance[None, :] + centered = x[:, t] - self.biases[m] + residual = centered - mu_prior @ emission.T + information_parts.append(torch.einsum("di,bd,dj->bij", emission, weights, emission)) + vector_parts.append((residual * weights) @ emission) + quadratic_parts.append((residual.square() * weights).sum(dim=-1)) + logdet_r = logdet_r + active * torch.log(variance).sum() + n_observed = n_observed + active * x.shape[-1] + information = torch.stack(information_parts).sum(dim=0) + innovation = torch.stack(vector_parts).sum(dim=0) + precision = p_inv + information + precision_chol = torch.linalg.cholesky(precision + 1e-6 * torch.eye(self.state_dim, device=precision.device)) + p_filtered = torch.cholesky_inverse(precision_chol) + mu_filtered = mu_prior + torch.einsum("bij,bj->bi", p_filtered, innovation) + if calculate_log_likelihood: + logdet_p = 2.0 * torch.log(torch.diagonal(p_chol, dim1=-2, dim2=-1)).sum(dim=-1) + logdet_precision = 2.0 * torch.log(torch.diagonal(precision_chol, dim1=-2, dim2=-1)).sum(dim=-1) + quad = torch.stack(quadratic_parts).sum(dim=0) + correction = torch.einsum("bi,bij,bj->b", innovation, p_filtered, innovation) + log_likelihood = logdet_r + logdet_p + logdet_precision + (quad - correction).clamp_min(0.0) + log_likelihood = log_likelihood + n_observed * math.log(2.0 * math.pi) + total_nll = total_nll + 0.5 * log_likelihood + if retain_states: + filtered_means.append(mu_filtered) + filtered_covs.append(p_filtered) + mu_prior = mu_filtered @ transition.T + p_prior = transition @ p_filtered @ transition.T + process_noise + + states = None + if retain_states: + states = { + "prior_mean": prior_means, + "prior_cov": prior_covs, + "filtered_mean": filtered_means, + "filtered_cov": filtered_covs, + "transition": [transition], + } + return (total_nll if calculate_log_likelihood else None), states + + def observed_nll(self, xs: Sequence[torch.Tensor], observed: torch.Tensor) -> torch.Tensor: + """Exact observed-data Gaussian NLL, including covariance log determinants.""" + nll, _ = self._filter(xs, observed, calculate_log_likelihood=True, retain_states=False) + assert nll is not None + return nll + + @staticmethod + def _draw(mean: torch.Tensor, covariance: torch.Tensor, paths: int) -> torch.Tensor: + chol = torch.linalg.cholesky(covariance + 1e-5 * torch.eye(covariance.shape[-1], device=covariance.device)) + noise = torch.randn((paths, *mean.shape), dtype=mean.dtype, device=mean.device) + return mean.unsqueeze(0) + torch.einsum("bij,kbj->kbi", chol, noise) + + @torch.no_grad() + def complete( + self, + xs: Sequence[torch.Tensor], + observed: torch.Tensor, + paths: int, + *, + joint_draws: bool, + ) -> tuple[list[torch.Tensor], list[torch.Tensor]]: + """RTS smooth, draw joint latent trajectories, then draw missing emissions.""" + _, stored = self._filter(xs, observed, calculate_log_likelihood=False, retain_states=True) + assert stored is not None + fm, fc = stored["filtered_mean"], stored["filtered_cov"] + pm, pc = stored["prior_mean"], stored["prior_cov"] + transition = stored["transition"][0] + steps = len(fm) + smoother_gains: list[torch.Tensor] = [torch.empty(0, device=observed.device)] * max(0, steps - 1) + smooth_cov: list[torch.Tensor] = [torch.empty(0, device=observed.device)] * steps + smooth_cov[-1] = fc[-1] + for t in range(steps - 2, -1, -1): + next_chol = torch.linalg.cholesky(pc[t + 1] + 1e-6 * torch.eye(self.state_dim, device=observed.device)) + gain = torch.cholesky_solve((fc[t] @ transition.T).transpose(-1, -2), next_chol).transpose(-1, -2) + smoother_gains[t] = gain + smooth_cov[t] = fc[t] + gain @ (smooth_cov[t + 1] - pc[t + 1]) @ gain.transpose(-1, -2) + smooth_cov[t] = 0.5 * (smooth_cov[t] + smooth_cov[t].transpose(-1, -2)) + + if joint_draws: + state = torch.empty((paths, observed.shape[0], steps, self.state_dim), device=observed.device, dtype=fm[0].dtype) + state[:, :, -1] = self._draw(fm[-1], fc[-1], paths) + for t in range(steps - 2, -1, -1): + gain = smoother_gains[t] + conditional_mean = fm[t].unsqueeze(0) + torch.einsum( + "bij,kbj->kbi", gain, state[:, :, t + 1] - pm[t + 1].unsqueeze(0) + ) + conditional_cov = fc[t] - gain @ pc[t + 1] @ gain.transpose(-1, -2) + conditional_cov = 0.5 * (conditional_cov + conditional_cov.transpose(-1, -2)) + chol = torch.linalg.cholesky(conditional_cov + 1e-5 * torch.eye(self.state_dim, device=observed.device)) + eps = torch.randn_like(conditional_mean) + state[:, :, t] = conditional_mean + torch.einsum("bij,kbj->kbi", chol, eps) + else: + means = torch.stack(fm, dim=1) + covs = torch.stack(smooth_cov, dim=1) + smoothed_means = [fm[-1]] * steps + smoothed_means[-1] = fm[-1] + for t in range(steps - 2, -1, -1): + smoothed_means[t] = fm[t] + torch.einsum( + "bij,bj->bi", smoother_gains[t], smoothed_means[t + 1] - pm[t + 1] + ) + state = torch.stack(smoothed_means, dim=1).unsqueeze(0).expand(paths, -1, -1, -1) + + completed: list[torch.Tensor] = [] + variances: list[torch.Tensor] = [] + for m, (x, emission) in enumerate(zip(xs, self.emissions())): + mean = torch.einsum("kbti,di->kbtd", state, emission) + self.biases[m] + if joint_draws: + noise = torch.randn_like(mean) * torch.sqrt(F.softplus(self.r_raw[m]) + 1e-4) + draws = mean + noise + else: + draws = mean + visible = observed[:, :, m].unsqueeze(0).unsqueeze(-1) + completed.append(torch.where(visible, x.unsqueeze(0), draws)) + projected_cov = torch.einsum("di,btij,dj->btd", emission, torch.stack(smooth_cov, dim=1), emission) + variance = projected_cov + (F.softplus(self.r_raw[m]) + 1e-4) + variances.append(torch.where(observed[:, :, m, None], torch.zeros_like(variance), variance.clamp_min(1e-6))) + return completed, variances + + +class ReliabilityGRU(nn.Module): + """One-layer BiGRU with directional time decay and rho-scaled updates.""" + + def __init__(self, input_dim: int, hidden: int = 16) -> None: + super().__init__() + self.hidden = hidden + self.x_proj = nn.Linear(input_dim, 3 * hidden) + self.h_proj = nn.Linear(hidden, 2 * hidden, bias=False) + self.candidate_h = nn.Linear(hidden, hidden, bias=False) + self.decay_raw = nn.Parameter(torch.full((hidden,), -3.0)) + + def _one_direction( + self, + x: torch.Tensor, + rho: torch.Tensor, + distance: torch.Tensor, + reverse: bool, + reliability_update: bool, + ) -> torch.Tensor: + batch, steps, _ = x.shape + state = torch.zeros(batch, self.hidden, dtype=x.dtype, device=x.device) + x_parts = self.x_proj(x).chunk(3, dim=-1) + output: list[torch.Tensor | None] = [None] * steps + indices = range(steps - 1, -1, -1) if reverse else range(steps) + for t in indices: + if reliability_update: + decay = torch.exp(-F.softplus(self.decay_raw)[None, :] * distance[:, t:t + 1]) + decayed_state = decay * state + else: + decayed_state = state + hz, hr = self.h_proj(decayed_state).chunk(2, dim=-1) + z = torch.sigmoid(x_parts[0][:, t] + hz) + r = torch.sigmoid(x_parts[1][:, t] + hr) + candidate = torch.tanh(x_parts[2][:, t] + self.candidate_h(r * decayed_state)) + effective_z = rho[:, t:t + 1] * z if reliability_update else z + state = (1.0 - effective_z) * decayed_state + effective_z * candidate + output[t] = state + return torch.stack([v for v in output if v is not None], dim=1) + + def forward( + self, + x: torch.Tensor, + rho: torch.Tensor, + dminus: torch.Tensor, + dplus: torch.Tensor, + reliability_update: bool, + ) -> torch.Tensor: + if not reliability_update: + rho = torch.ones_like(rho) + return torch.cat(( + self._one_direction(x, rho, dminus, False, reliability_update), + self._one_direction(x, rho, dplus, True, reliability_update), + ), dim=-1) + + +class CRG(nn.Module): + """Quality-aware multimodal sequence predictor for a configured ablation.""" + + def __init__( + self, + imputer: StructuredGaussianImputer | None = None, + input_dims: Sequence[int] = INPUT_DIMS, + *, + use_imputer: bool = True, + use_joint_draws: bool = True, + use_final_gate: bool = True, + use_source_attention: bool = True, + reliability_update: bool = True, + use_low_rank: bool = True, + reliability_hparams: tuple[float, float, float, float] = (0.5, 0.05, 0.05, 0.05), + ) -> None: + super().__init__() + self.use_imputer = use_imputer + self.use_joint_draws = use_joint_draws + self.use_final_gate = use_final_gate + self.use_source_attention = use_source_attention + self.reliability_update = reliability_update + self.use_low_rank = use_low_rank + self.imputer = imputer if imputer is not None else StructuredGaussianImputer(input_dims) + self.projections = nn.ModuleList( + nn.Sequential(nn.Linear(d, HIDDEN), nn.LayerNorm(HIDDEN), nn.GELU()) for d in input_dims + ) + recurrent_input = HIDDEN + 13 + self.temporal = nn.ModuleList(ReliabilityGRU(recurrent_input, 16) for _ in MODALITIES) + rho_imp, lambda_u, lambda_gap, lambda_span = reliability_hparams + if not 0.0 < rho_imp < 1.0 or min(lambda_u, lambda_gap, lambda_span) < 0.0: + raise ValueError("reliability requires 0 tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + # Time positions are valid sequence locations even when all three sources are missing. + batch, steps, modalities = observed.shape + device = observed.device + positions = torch.arange(steps, device=device).view(1, steps).expand(batch, -1) + previous = torch.full((batch, modalities), -1, device=device, dtype=torch.long) + before, before_edge = [], [] + for t in range(steps): + before_edge.append(previous < 0) + before.append(torch.where(previous < 0, torch.ones_like(previous, dtype=torch.float32), (t - previous).float() / max(1, steps - 1))) + previous = torch.where(observed[:, t], torch.full_like(previous, t), previous) + following = torch.full((batch, modalities), steps, device=device, dtype=torch.long) + after, after_edge = [None] * steps, [None] * steps + for t in range(steps - 1, -1, -1): + after_edge[t] = following >= steps + after[t] = torch.where(following >= steps, torch.ones_like(following, dtype=torch.float32), (following - t).float() / max(1, steps - 1)) + following = torch.where(observed[:, t], torch.full_like(following, t), following) + dminus = torch.stack(before, dim=1) + dplus = torch.stack([x for x in after if x is not None], dim=1) + edge_minus = torch.stack(before_edge, dim=1) + edge_plus = torch.stack([x for x in after_edge if x is not None], dim=1) + dminus = torch.where(observed, torch.zeros_like(dminus), dminus) + dplus = torch.where(observed, torch.zeros_like(dplus), dplus) + edge_minus = edge_minus & ~observed + edge_plus = edge_plus & ~observed + missing = ~observed + left_run = torch.zeros((batch, steps, modalities), device=device, dtype=torch.float32) + run = torch.zeros((batch, modalities), device=device, dtype=torch.float32) + for t in range(steps): + run = torch.where(missing[:, t], run + 1.0, torch.zeros_like(run)) + left_run[:, t] = run + right_run = torch.zeros_like(left_run) + run.zero_() + for t in range(steps - 1, -1, -1): + run = torch.where(missing[:, t], run + 1.0, torch.zeros_like(run)) + right_run[:, t] = run + span = torch.where(missing, (left_run + right_run - 1.0) / max(1, steps), torch.zeros_like(left_run)) + return dminus, dplus, span, torch.stack((edge_minus, edge_plus), dim=-1).float() + + def _reliability( + self, observed: torch.Tensor, uncertainty: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + dminus, dplus, span, edges = self._gap_features(observed) + gap = torch.minimum(dminus, dplus) + gap = torch.where(observed, torch.zeros_like(gap), gap) + u = torch.where(observed, torch.zeros_like(uncertainty), uncertainty).clamp_min(0.0) + qstar = observed.float() # External Q2 quality is unavailable: q*=1 only for visible rows; J=0. + rho_missing = self.rho_imp.clamp(1e-4, 0.999) * torch.exp( + -self.rel_u[None, None, :] * u + -self.rel_gap[None, None, :] * gap + -self.rel_span[None, None, :] * span + ) + rho = torch.where(observed, qstar, rho_missing).clamp(1e-4, 1.0) + return rho, u, gap, span, dminus, dplus, torch.cat((qstar.unsqueeze(-1), torch.zeros_like(qstar).unsqueeze(-1), edges), dim=-1) + + def _cross_source(self, hidden: torch.Tensor, rho: torch.Tensor) -> torch.Tensor: + # hidden [B,T,M,H]; each query reads every legal time in each other source. + batch, steps, modalities, width = hidden.shape + outputs = [] + q = self.query(hidden) + k = self.key(hidden) + v = torch.tanh(self.value(hidden)) + loc = torch.arange(steps, device=hidden.device) + relative_index = (loc[None, :] - loc[:, None] + 49).clamp(0, 98) + relative = self.relative_bias(relative_index).squeeze(-1) + for target in range(modalities): + numerator = torch.zeros((batch, steps, width), device=hidden.device, dtype=hidden.dtype) + denominator = torch.ones((batch, steps, 1), device=hidden.device, dtype=hidden.dtype) + for source in range(modalities): + if source == target: + continue + raw = torch.matmul(q[:, :, target], k[:, :, source].transpose(-1, -2)) / math.sqrt(width) + scores = 2.0 * torch.tanh(raw + relative) + base = 1.0 / (max(1, modalities - 1) * steps) + weights = base * rho[:, None, :, source] * torch.exp(scores.clamp(-2.0, 2.0)) + numerator = numerator + torch.matmul(weights, v[:, :, source]) + denominator = denominator + weights.sum(dim=-1, keepdim=True) + context = numerator / denominator + eta = torch.sigmoid(self.cross_eta_logit) + outputs.append(torch.tanh(self.cross_base(hidden[:, :, target]) + eta * self.cross_out(context))) + return torch.stack(outputs, dim=2) + + def _low_rank_residual(self, gated: torch.Tensor) -> torch.Tensor: + # Linear CP factors use [1; z_m] and subtract their constant all-zero term. + batch, steps, modalities, width = gated.shape + one = torch.ones((batch, steps, 1), device=gated.device, dtype=gated.dtype) + products = torch.ones((batch, steps, 4), device=gated.device, dtype=gated.dtype) + constant = torch.ones(4, device=gated.device, dtype=gated.dtype) + for m in range(modalities): + factor = self.cp_factors[m](torch.cat((one, gated[:, :, m]), dim=-1)) + products = products * factor + zero_input = torch.zeros((1, 1, width + 1), device=gated.device, dtype=gated.dtype) + zero_input[..., 0] = 1.0 + constant = constant * self.cp_factors[m](zero_input)[0, 0] + residual = (products - constant) @ self.cp_output + return torch.sigmoid(self.low_rank_eta_logit) * self.low_rank_output(torch.tanh(residual)) + + def forward( + self, + xs: Sequence[torch.Tensor], + observed_mask: torch.Tensor, + *, + paths: int = 4, + joint_draws: bool | None = None, + ) -> dict[str, torch.Tensor | list[torch.Tensor]]: + batch, steps, modalities = observed_mask.shape + if joint_draws is None: + joint_draws = self.use_joint_draws + if self.use_imputer: + completed, variance = self.imputer.complete(xs, observed_mask, paths, joint_draws=joint_draws) + else: + completed = [torch.where(observed_mask[:, :, m, None], x, torch.zeros_like(x)).unsqueeze(0) for m, x in enumerate(xs)] + variance = [torch.zeros_like(x) for x in xs] + paths = completed[0].shape[0] + uncertainty_parts = [v.mean(dim=-1) for v in variance] + uncertainty = torch.stack(uncertainty_parts, dim=-1) + rho, u, gap, span, dminus, dplus, quality_fields = self._reliability(observed_mask, uncertainty) + position = torch.linspace(0.0, 1.0, steps, device=observed_mask.device, dtype=xs[0].dtype) + pe = torch.stack((torch.sin(2 * math.pi * position), torch.cos(2 * math.pi * position), + torch.sin(4 * math.pi * position), torch.cos(4 * math.pi * position)), dim=-1) + encoded_paths: list[torch.Tensor] = [] + reconstructed_paths: list[list[torch.Tensor]] = [] + logits_paths: list[torch.Tensor] = [] + beta_paths: list[torch.Tensor] = [] + fusion_weight_paths: list[torch.Tensor] = [] + null_weight_paths: list[torch.Tensor] = [] + time_pool_weight_paths: list[torch.Tensor] = [] + for path_index in range(paths): + enc = [projection(completed[m][path_index]) for m, projection in enumerate(self.projections)] + hmods, reconstruction = [], [] + for m, encoder in enumerate(self.temporal): + if self.use_final_gate or self.use_source_attention or self.reliability_update: + scalar = torch.cat((observed_mask[:, :, m:m + 1].float(), quality_fields[:, :, m], + torch.log1p(u[:, :, m:m + 1]), dminus[:, :, m:m + 1], + dplus[:, :, m:m + 1], span[:, :, m:m + 1], + pe.unsqueeze(0).expand(batch, -1, -1)), dim=-1) + else: + # C1/C2 receive only the visibility mask and legal position code. + scalar = torch.zeros((batch, steps, 13), dtype=pe.dtype, device=pe.device) + scalar[:, :, 0] = observed_mask[:, :, m].float() + scalar[:, :, -4:] = pe.unsqueeze(0) + # q*, J_Q, edge flags, directional gaps, uncertainty and span are explicit. + seq = torch.cat((enc[m], scalar), dim=-1) + h = encoder(seq, rho[:, :, m], dminus[:, :, m], dplus[:, :, m], self.reliability_update) + hmods.append(h) + reconstruction.append(self.reconstruction_heads[m](h)) + hidden = torch.stack(hmods, dim=2) + if self.use_source_attention: + enhanced = self._cross_source(hidden, rho) + else: + enhanced = hidden + if self.use_final_gate: + content = 2.0 * torch.tanh(self.content_score(enhanced).squeeze(-1)) + weights_unnorm = rho * torch.exp(content.clamp(-2.0, 2.0)) + denom = 1.0 + weights_unnorm.sum(dim=-1, keepdim=True) + alpha = weights_unnorm / denom + null_alpha = 1.0 / denom.squeeze(-1) + gated = enhanced * alpha.unsqueeze(-1) + fused = gated.sum(dim=2) + null_alpha.unsqueeze(-1) * self.null_expert + else: + if self.use_imputer: + alpha = torch.full_like(observed_mask.float(), 1.0 / modalities) + else: + alpha = observed_mask.float() / observed_mask.float().sum(dim=-1, keepdim=True).clamp_min(1.0) + null_alpha = torch.zeros((batch, steps), device=observed_mask.device, dtype=alpha.dtype) + fused = (enhanced * alpha.unsqueeze(-1)).sum(dim=2) + gated = enhanced * alpha.unsqueeze(-1) + if self.use_low_rank: + fused = fused + self._low_rank_residual(gated) + pool_logits = 2.0 * torch.tanh(self.pool_score(torch.tanh(self.pool_hidden(fused))).squeeze(-1)) + pool_weight = torch.softmax(pool_logits, dim=1) + pooled = (pool_weight.unsqueeze(-1) * fused).sum(dim=1) + missing_rate = 1.0 - observed_mask.float().mean(dim=1) + mean_rho = rho.mean(dim=1) if (self.use_final_gate or self.use_source_attention or self.reliability_update) else observed_mask.float().mean(dim=1) + max_gap = gap.max(dim=1).values + max_span = span.max(dim=1).values + edge_rate = self._gap_features(observed_mask)[3].mean(dim=1).reshape(batch, -1) + stats = torch.cat((missing_rate, mean_rho, max_gap, max_span, edge_rate), dim=-1) + representation = torch.cat((pooled, stats), dim=-1) + feature = self.head(representation) + logits_paths.append(self.classifier(feature)) + mean_fraction = torch.sigmoid(self.magnitude_mean(feature)).clamp(1e-4, 1.0 - 1e-4) + concentration = F.softplus(self.concentration_raw).clamp_min(1e-3) + alpha_beta = torch.stack((mean_fraction * concentration, (1.0 - mean_fraction) * concentration), dim=-1) + beta_paths.append(alpha_beta) + reconstructed_paths.append(reconstruction) + encoded_paths.append(hidden) + fusion_weight_paths.append(alpha) + null_weight_paths.append(null_alpha) + time_pool_weight_paths.append(pool_weight) + class_logits = torch.stack(logits_paths, dim=0) + beta_params = torch.stack(beta_paths, dim=0) + class_probs_by_path = torch.softmax(class_logits, dim=-1) + beta_mean = beta_params[..., 0] / beta_params.sum(dim=-1) + conditional_mean = 3.0 * (class_probs_by_path[..., 2] * beta_mean[..., 1] - class_probs_by_path[..., 0] * beta_mean[..., 0]) + return { + "class_logits": class_logits, + "class_probs_by_path": class_probs_by_path, + "class_probs": class_probs_by_path.mean(dim=0), + "tempered_probs_by_path": torch.softmax(class_logits / 2.0, dim=-1), + "beta_params": beta_params, + "beta_mean": beta_mean, + "mixed_score": conditional_mean.mean(dim=0), + "reconstructions": [torch.stack([reconstructed_paths[k][m] for k in range(paths)], dim=0) for m in range(modalities)], + "reliability": rho, + "imputation_uncertainty": uncertainty, + "gap": gap, + "span": span, + "distance_before": dminus, + "distance_after": dplus, + "fusion_weights_by_path": torch.stack(fusion_weight_paths, dim=0), + "null_weights_by_path": torch.stack(null_weight_paths, dim=0), + "time_pool_weights_by_path": torch.stack(time_pool_weight_paths, dim=0), + "low_rank_scale": torch.sigmoid(self.low_rank_eta_logit), + } diff --git a/final/model/early_concat.py b/final/model/early_concat.py new file mode 100644 index 0000000..debe0b4 --- /dev/null +++ b/final/model/early_concat.py @@ -0,0 +1,67 @@ +from __future__ import annotations + +import torch +from torch import nn + + +class AlignedFusionModel(nn.Module): + """Early concatenation + BiGRU model for the supplied aligned sequence.""" + + def __init__( + self, + kind: str, + dims: tuple[int, int, int], + steps: int = 50, + hidden: int = 128, + dropout: float = 0.15, + ) -> None: + super().__init__() + if kind != "concat": + raise ValueError(f"only the selected EarlyConcat model is maintained; got: {kind}") + self.kind = kind + self.hidden = hidden + self.projections = nn.ModuleList( + nn.Sequential(nn.Linear(size, hidden), nn.GELU(), nn.LayerNorm(hidden)) + for size in dims + ) + self.position = nn.Parameter(torch.randn(1, steps, hidden) * 0.02) + self.modality = nn.Parameter(torch.randn(1, 1, 3, hidden) * 0.02) + self.dropout = nn.Dropout(dropout) + self.fusion = nn.Sequential( + nn.Linear(hidden * 3 + 3, hidden), nn.GELU(), nn.LayerNorm(hidden), nn.Dropout(dropout) + ) + self.temporal = nn.GRU( + input_size=hidden, + hidden_size=hidden // 2, + num_layers=1, + batch_first=True, + bidirectional=True, + ) + self.head = nn.Sequential(nn.Linear(hidden, hidden // 2), nn.GELU(), nn.Dropout(dropout)) + self.classifier = nn.Linear(hidden // 2, 3) + self.regressor = nn.Linear(hidden // 2, 1) + + def forward(self, xs: tuple[torch.Tensor, torch.Tensor, torch.Tensor], masks: torch.Tensor): + masks = masks.bool() + pos = self.position[:, :masks.shape[1]] + encoded = [] + for modality, (projection, x) in enumerate(zip(self.projections, xs)): + token = projection(x) + token = token + pos + self.modality[:, :, modality, :] + token = token * masks[:, :, modality, None] + encoded.append(token) + stack = torch.stack(encoded, dim=2) # B x T x M x D + availability = masks.to(stack.dtype) + fused = self.fusion(torch.cat((stack.flatten(2), availability), dim=-1)) + + temporal, _ = self.temporal(self.dropout(fused)) + time_weight = masks.any(dim=-1).to(temporal.dtype) + empty_time = time_weight.sum(dim=1, keepdim=True) <= 0 + if empty_time.any(): + time_weight[empty_time.squeeze(1), 0] = 1.0 + pooled = (temporal * time_weight[..., None]).sum(dim=1) + pooled = pooled / time_weight.sum(dim=1, keepdim=True).clamp_min(1.0) + hidden = self.head(pooled) + logits = self.classifier(hidden) + intensity = 3.0 * torch.tanh(self.regressor(hidden).squeeze(-1)) + return {"logits": logits, "intensity": intensity} diff --git a/final/model/mofe.py b/final/model/mofe.py new file mode 100644 index 0000000..615e986 --- /dev/null +++ b/final/model/mofe.py @@ -0,0 +1,224 @@ +from __future__ import annotations + +from typing import Any + +import torch +import torch.nn.functional as F +from torch import nn + + +SUBSETS: dict[str, tuple[int, ...]] = { + "T": (0,), + "A": (1,), + "V": (2,), + "TA": (0, 1), + "TV": (0, 2), + "AV": (1, 2), + "TAV": (0, 1, 2), +} +EXPERT_NAMES = tuple(SUBSETS) +EXPERT_BITS = { + name: tuple(int(i in indices) for i in range(3)) + for name, indices in SUBSETS.items() +} + + +class MixtureOfFusionExperts(nn.Module): + """Seven-subset, hard-availability MoFE with the selected MLP router. + + Each modality has a private projection. Experts only receive the private + projections belonging to their subset. The weighted result is passed + through one shared temporal backbone and one shared prediction head. + """ + + def __init__( + self, + dims: tuple[int, int, int], + router: str = "mlp", + expert_names: tuple[str, ...] = EXPERT_NAMES, + availability_mode: str = "hard", + steps: int = 50, + latent_dim: int = 64, + hidden: int = 128, + dropout: float = 0.15, + ) -> None: + super().__init__() + if router != "mlp": + raise ValueError(f"only the selected MLP router is maintained; got: {router}") + if availability_mode != "hard": + raise ValueError(f"only hard availability masking is maintained; got: {availability_mode}") + if tuple(expert_names) != EXPERT_NAMES: + raise ValueError("the selected MoFE uses all seven modality-subset experts") + + self.dims = dims + self.router_kind = router + self.expert_names = tuple(expert_names) + self.availability_mode = availability_mode + self.steps = steps + self.latent_dim = latent_dim + self.hidden = hidden + + # These projections are private to each modality and are not tied. + self.private_projections = nn.ModuleList( + nn.Sequential(nn.Linear(size, latent_dim), nn.GELU()) for size in dims + ) + self.experts = nn.ModuleDict() + for name in self.expert_names: + n_modalities = len(SUBSETS[name]) + self.experts[name] = nn.Sequential( + nn.Linear(n_modalities * latent_dim, hidden), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden, latent_dim), + nn.LayerNorm(latent_dim), + ) + + router_input_dim = 9 + self.router = nn.Sequential( + nn.Linear(router_input_dim, 16), + nn.GELU(), + nn.Linear(16, len(self.expert_names)), + ) + + # Shared early-fusion projection, BiGRU, and task heads. + self.all_missing_token = nn.Parameter(torch.zeros(1, 1, latent_dim)) + self.input_projection = nn.Sequential( + nn.Linear(latent_dim + 3, hidden), + nn.GELU(), + nn.LayerNorm(hidden), + nn.Dropout(dropout), + ) + self.dropout = nn.Dropout(dropout) + self.temporal = nn.GRU( + input_size=hidden, + hidden_size=hidden // 2, + num_layers=1, + batch_first=True, + bidirectional=True, + ) + self.head = nn.Sequential(nn.Linear(hidden, hidden // 2), nn.GELU(), nn.Dropout(dropout)) + self.classifier = nn.Linear(hidden // 2, 3) + self.regressor = nn.Linear(hidden // 2, 1) + + @staticmethod + def _availability(masks: torch.Tensor, names: tuple[str, ...]) -> torch.Tensor: + masks = masks.bool() + columns = [masks[..., list(SUBSETS[name])].all(dim=-1) for name in names] + return torch.stack(columns, dim=-1) + + def _router_features( + self, + private: tuple[torch.Tensor, torch.Tensor, torch.Tensor], + masks: torch.Tensor, + ) -> torch.Tensor: + observed = masks.to(dtype=private[0].dtype) + magnitude = torch.stack( + [torch.sqrt(x.square().mean(dim=-1) + 1e-8) for x in private], dim=-1 + ) + local_ratio = F.avg_pool1d( + observed.transpose(1, 2), kernel_size=5, stride=1, padding=2, count_include_pad=False + ).transpose(1, 2) + return torch.cat((observed, torch.log1p(magnitude), local_ratio), dim=-1) + + def _route( + self, + router_features: torch.Tensor, + availability: torch.Tensor, + force_expert: str | None, + ) -> torch.Tensor: + scores = self.router(router_features) + scores = scores.masked_fill(~availability, -1e4) + weights = torch.softmax(scores, dim=-1) * availability.to(scores.dtype) + # In the full seven-expert model this is exactly the all-modalities- + # missing case. It also safely handles ablations with no eligible set. + has_expert = availability.any(dim=-1, keepdim=True) + weights = weights * has_expert.to(weights.dtype) + weights = weights / weights.sum(dim=-1, keepdim=True).clamp_min(1e-8) + + if force_expert is not None: + if force_expert not in self.expert_names: + raise ValueError(f"expert {force_expert} is not enabled in this model") + expert_idx = self.expert_names.index(force_expert) + forced = torch.zeros_like(weights) + forced[..., expert_idx] = 1.0 + # Force the requested expert where its modality subset is present; + # where it is unavailable, use the learned router over eligible + # experts instead of replacing observed information with zeros. + return torch.where(availability[..., expert_idx, None], forced, weights) + + return weights + + def forward( + self, + xs: tuple[torch.Tensor, torch.Tensor, torch.Tensor], + masks: torch.Tensor, + force_expert: str | None = None, + ) -> dict[str, Any]: + masks = masks.bool() + if masks.ndim != 3 or masks.shape[-1] != 3: + raise ValueError(f"masks must have shape B x T x 3, got {tuple(masks.shape)}") + if masks.shape[1] > self.steps: + raise ValueError(f"sequence has {masks.shape[1]} steps, model supports {self.steps}") + + private_values = [] + for modality, (projector, x) in enumerate(zip(self.private_projections, xs)): + projected = projector(x) + projected = projected * masks[..., modality, None].to(projected.dtype) + private_values.append(projected) + private = tuple(private_values) + router_features = self._router_features(private, masks) + availability = self._availability(masks, self.expert_names) + + local_expert_outputs = [] + for name in self.expert_names: + indices = SUBSETS[name] + expert_input = torch.cat([private[i] for i in indices], dim=-1) + local_expert_outputs.append(self.experts[name](expert_input)) + expert_stack = torch.stack(local_expert_outputs, dim=-2) + + alpha_local = self._route(router_features, availability, force_expert) + fused = (expert_stack * alpha_local[..., None]).sum(dim=-2) + has_expert = availability.any(dim=-1) + fused = torch.where( + has_expert[..., None], fused, self.all_missing_token.expand_as(fused) + ) + + # Restore a stable seven-column interface for saved diagnostics, + # including expert-set ablations. + alpha = masks.new_zeros((*masks.shape[:2], len(EXPERT_NAMES)), dtype=private[0].dtype) + expert_outputs = private[0].new_zeros((*masks.shape[:2], len(EXPERT_NAMES), self.latent_dim)) + for local_idx, name in enumerate(self.expert_names): + global_idx = EXPERT_NAMES.index(name) + alpha[..., global_idx] = alpha_local[..., local_idx] + expert_outputs[..., global_idx, :] = expert_stack[..., local_idx, :] + + fused_with_masks = torch.cat((fused, masks.to(fused.dtype)), dim=-1) + encoded = self.input_projection(fused_with_masks) + temporal, _ = self.temporal(self.dropout(encoded)) + time_weight = masks.any(dim=-1).to(temporal.dtype) + empty_time = time_weight.sum(dim=1, keepdim=True) <= 0 + if empty_time.any(): + time_weight[empty_time.squeeze(1), 0] = 1.0 + pooled = (temporal * time_weight[..., None]).sum(dim=1) + pooled = pooled / time_weight.sum(dim=1, keepdim=True).clamp_min(1.0) + hidden = self.head(pooled) + logits = self.classifier(hidden) + intensity = 3.0 * torch.tanh(self.regressor(hidden).squeeze(-1)) + + bits = torch.tensor( + [EXPERT_BITS[name] for name in EXPERT_NAMES], + dtype=alpha.dtype, + device=alpha.device, + ) + utility = torch.einsum("bte,em->btm", alpha, bits) + return { + "logits": logits, + "intensity": intensity, + "fused": fused, + "alpha": alpha, + "utility": utility, + "availability": availability, + "expert_outputs": expert_outputs, + "fallback": ~has_expert, + "router_features": router_features, + } diff --git a/final/output/README.md b/final/output/README.md new file mode 100644 index 0000000..5082f5b --- /dev/null +++ b/final/output/README.md @@ -0,0 +1,7 @@ +# 题目输出目录 + +- [q1](q1/)：附件一 100 条样本的三模态特征、全量汇总、典型样本对齐查询和完整性审计。 +- [q2](q2/)：附件二未对齐数据上全部保留模型的验证对比、选定模型测试结果及缺失曲线汇总。数学训练入口也会输出附件三的 30 条预测和审计。 +- [q3](q3/)：Q3 的验证结果、附件四 20 条预测、输入审计、局部解释和典型解释卡生成位置；完整输出由训练入口写入。 + +完整训练与复现说明见 [项目 README](../README.md)，结果解释见 [REPORTS.md](../REPORTS.md)。附件二原始输入不包含在项目中；需按 README 的数据目录结构单独准备。提交赛题附件前，应按赛题规定检查最终附件体积。 diff --git a/final/output/q1/README.md b/final/output/q1/README.md new file mode 100644 index 0000000..b8fadea --- /dev/null +++ b/final/output/q1/README.md @@ -0,0 +1,34 @@ +# Q1 输出包 + +本目录对应题目 Q1 的 100 条附件 1 视频样本。原生特征、主视图和物理时间证据均保留；不复制原始视频和标签表。 + +| 内容 | 说明 | +|---|---| +| `features_v2/*.npz` | 100 个样本的文本、音频、视觉原生特征、源区间、质量/观测掩码、对齐视图和查询映射 | +| `features_v2/sample_summary.csv` | 样本 ID、视频组、源时长、有效词数和状态 | +| `features_v2/modality_summary.csv` | 300 行模态汇总，含覆盖率、可用质量和缺失情况 | +| `features_v2/manifest_q1.jsonl` | 逐样本路径、源/特征 SHA-256、配置哈希和状态 | +| `features_v2/feature_manifest.json` | 特征定义、工具/模型信息、处理配置和包体审计 | +| `typical_sample_correspondence.csv`、`typical_sample_frames.jpg` | 典型样本的词、物理区间、音频来源行与视频帧对应示例 | +| `alignment_query_example.png` | 文本词区间到原始音频/视频位置的查询权重图 | +| `package_audit.json` | 包含文件数、状态和 SHA-256 的完整性审计 | +| `model_comparison/`、`model_comparison_v2/` | 已整理的对照表与结果文件（以 `REPORTS.md` 中汇总为准） | + +对齐视图是固定 0.1 秒网格。文本采用固定转写 CTC 单调路径和保留的边界区间；音频为 74 维，视觉为 OpenFace 2.2.0 的 35 维。四条样本为部分状态：OpenFace 的置信度/成功门控未通过，视觉特征以缺失掩码保留，没有用零值伪装成有效观测。 + +## 读取 + +在项目上级目录运行： + +```python +from pathlib import Path +from final.adapter import Q1AlignmentAdapter + +sample = Q1AlignmentAdapter().from_q1_sample( + "-iRBcNs9oI8/8", + feature_dir=Path("final/output/q1/features_v2"), +) +features, mask = sample.q2_arrays() +``` + +完整重建命令、依赖和 OpenFace 准备步骤见 [项目 README](../../README.md)。Q1 当前没有人工词边界真值，因此不报告边界误差或概率校准率。 diff --git a/final/output/q1/alignment_query_example.json b/final/output/q1/alignment_query_example.json new file mode 100644 index 0000000..28566fc --- /dev/null +++ b/final/output/q1/alignment_query_example.json @@ -0,0 +1,48 @@ +{ + "sample_id": "-iRBcNs9oI8/8", + "selection": "manually selected example with valid audio and vision query maps", + "duration_s": 8.093000411987305, + "word_count": 21, + "words": [ + "I've", + "drawn", + "this", + "man", + "here,", + "and", + "if", + "you", + "can", + "tell,", + "he", + "has", + "a", + "lot", + "of", + "big", + "muscles,", + "he", + "looks", + "very", + "strong" + ], + "audio_query_shape": [ + 21, + 803 + ], + "audio_query_nnz": 495, + "audio_query_mapped_words": 21, + "vision_query_shape": [ + 21, + 241 + ], + "vision_query_nnz": 160, + "vision_query_mapped_words": 21, + "audio_query_feature_validity_channel": "log_energy (audio feature dimension 66)", + "vision_query_feature_validity_channel": "OpenFace AU12 intensity (vision feature dimension 10)", + "alignment_semantics": "per-word normalized overlap between stored CTC word intervals and native source intervals; no learned content similarity", + "coordinate_axes_shown": false, + "color_scale": "shared square-root intensity transform for visibility; original overlap weights retained", + "word_label_positions": "actual transcript labels placed at their CTC word-interval centers on the source-time axis", + "image": "alignment_query_example.png" +} diff --git a/final/output/q1/alignment_query_example.png b/final/output/q1/alignment_query_example.png new file mode 100644 index 0000000..2edd99c Binary files /dev/null and b/final/output/q1/alignment_query_example.png differ diff --git a/final/output/q1/features_v2/-3g5yACwYnA__13.npz b/final/output/q1/features_v2/-3g5yACwYnA__13.npz new file mode 100644 index 0000000..8347cd0 Binary files /dev/null and b/final/output/q1/features_v2/-3g5yACwYnA__13.npz differ diff --git a/final/output/q1/features_v2/-3g5yACwYnA__2.npz b/final/output/q1/features_v2/-3g5yACwYnA__2.npz new file mode 100644 index 0000000..c9c00cd Binary files /dev/null and b/final/output/q1/features_v2/-3g5yACwYnA__2.npz differ diff --git a/final/output/q1/features_v2/-3g5yACwYnA__3.npz b/final/output/q1/features_v2/-3g5yACwYnA__3.npz new file mode 100644 index 0000000..07cd43c Binary files /dev/null and b/final/output/q1/features_v2/-3g5yACwYnA__3.npz differ diff --git a/final/output/q1/features_v2/-3g5yACwYnA__9.npz b/final/output/q1/features_v2/-3g5yACwYnA__9.npz new file mode 100644 index 0000000..5254f84 Binary files /dev/null and b/final/output/q1/features_v2/-3g5yACwYnA__9.npz differ diff --git a/final/output/q1/features_v2/-3nNcZdcdvU__5.npz b/final/output/q1/features_v2/-3nNcZdcdvU__5.npz new file mode 100644 index 0000000..6e55bd9 Binary files /dev/null and b/final/output/q1/features_v2/-3nNcZdcdvU__5.npz differ diff --git a/final/output/q1/features_v2/-571d8cVauQ__0.npz b/final/output/q1/features_v2/-571d8cVauQ__0.npz new file mode 100644 index 0000000..506ead0 Binary files /dev/null and b/final/output/q1/features_v2/-571d8cVauQ__0.npz differ diff --git a/final/output/q1/features_v2/-571d8cVauQ__5.npz b/final/output/q1/features_v2/-571d8cVauQ__5.npz new file mode 100644 index 0000000..6551a8b Binary files /dev/null and b/final/output/q1/features_v2/-571d8cVauQ__5.npz differ diff --git a/final/output/q1/features_v2/-6rXp3zJ3kc__8.npz b/final/output/q1/features_v2/-6rXp3zJ3kc__8.npz new file mode 100644 index 0000000..442baa9 Binary files /dev/null and b/final/output/q1/features_v2/-6rXp3zJ3kc__8.npz differ diff --git a/final/output/q1/features_v2/-9y-fZ3swSY__0.npz b/final/output/q1/features_v2/-9y-fZ3swSY__0.npz new file mode 100644 index 0000000..f87dc24 Binary files /dev/null and b/final/output/q1/features_v2/-9y-fZ3swSY__0.npz differ diff --git a/final/output/q1/features_v2/-9y-fZ3swSY__4.npz b/final/output/q1/features_v2/-9y-fZ3swSY__4.npz new file mode 100644 index 0000000..e5227b5 Binary files /dev/null and b/final/output/q1/features_v2/-9y-fZ3swSY__4.npz differ diff --git a/final/output/q1/features_v2/-9y-fZ3swSY__8.npz b/final/output/q1/features_v2/-9y-fZ3swSY__8.npz new file mode 100644 index 0000000..9dc5697 Binary files /dev/null and b/final/output/q1/features_v2/-9y-fZ3swSY__8.npz differ diff --git a/final/output/q1/features_v2/-AUZQgSxyPQ__2.npz b/final/output/q1/features_v2/-AUZQgSxyPQ__2.npz new file mode 100644 index 0000000..3814384 Binary files /dev/null and b/final/output/q1/features_v2/-AUZQgSxyPQ__2.npz differ diff --git a/final/output/q1/features_v2/-HeZS2-Prhc__2.npz b/final/output/q1/features_v2/-HeZS2-Prhc__2.npz new file mode 100644 index 0000000..7430ed0 Binary files /dev/null and b/final/output/q1/features_v2/-HeZS2-Prhc__2.npz differ diff --git a/final/output/q1/features_v2/-HwX2H8Z4hY__2.npz b/final/output/q1/features_v2/-HwX2H8Z4hY__2.npz new file mode 100644 index 0000000..677f7ad Binary files /dev/null and b/final/output/q1/features_v2/-HwX2H8Z4hY__2.npz differ diff --git a/final/output/q1/features_v2/-HwX2H8Z4hY__5.npz b/final/output/q1/features_v2/-HwX2H8Z4hY__5.npz new file mode 100644 index 0000000..bab0d25 Binary files /dev/null and b/final/output/q1/features_v2/-HwX2H8Z4hY__5.npz differ diff --git a/final/output/q1/features_v2/-HwX2H8Z4hY__6.npz b/final/output/q1/features_v2/-HwX2H8Z4hY__6.npz new file mode 100644 index 0000000..fa8c052 Binary files /dev/null and b/final/output/q1/features_v2/-HwX2H8Z4hY__6.npz differ diff --git a/final/output/q1/features_v2/-HwX2H8Z4hY__9.npz b/final/output/q1/features_v2/-HwX2H8Z4hY__9.npz new file mode 100644 index 0000000..39a76f9 Binary files /dev/null and b/final/output/q1/features_v2/-HwX2H8Z4hY__9.npz differ diff --git a/final/output/q1/features_v2/-I_e4mIh0yE__1.npz b/final/output/q1/features_v2/-I_e4mIh0yE__1.npz new file mode 100644 index 0000000..fbef855 Binary files /dev/null and b/final/output/q1/features_v2/-I_e4mIh0yE__1.npz differ diff --git a/final/output/q1/features_v2/-I_e4mIh0yE__3.npz b/final/output/q1/features_v2/-I_e4mIh0yE__3.npz new file mode 100644 index 0000000..3467ad5 Binary files /dev/null and b/final/output/q1/features_v2/-I_e4mIh0yE__3.npz differ diff --git a/final/output/q1/features_v2/-MeTTeMJBNc__0.npz b/final/output/q1/features_v2/-MeTTeMJBNc__0.npz new file mode 100644 index 0000000..f4ca22a Binary files /dev/null and b/final/output/q1/features_v2/-MeTTeMJBNc__0.npz differ diff --git a/final/output/q1/features_v2/-MeTTeMJBNc__13.npz b/final/output/q1/features_v2/-MeTTeMJBNc__13.npz new file mode 100644 index 0000000..ca0c067 Binary files /dev/null and b/final/output/q1/features_v2/-MeTTeMJBNc__13.npz differ diff --git a/final/output/q1/features_v2/-MeTTeMJBNc__7.npz b/final/output/q1/features_v2/-MeTTeMJBNc__7.npz new file mode 100644 index 0000000..881ea4f Binary files /dev/null and b/final/output/q1/features_v2/-MeTTeMJBNc__7.npz differ diff --git a/final/output/q1/features_v2/-NFrJFQijFE__1.npz b/final/output/q1/features_v2/-NFrJFQijFE__1.npz new file mode 100644 index 0000000..725d366 Binary files /dev/null and b/final/output/q1/features_v2/-NFrJFQijFE__1.npz differ diff --git a/final/output/q1/features_v2/-NFrJFQijFE__2.npz b/final/output/q1/features_v2/-NFrJFQijFE__2.npz new file mode 100644 index 0000000..b575528 Binary files /dev/null and b/final/output/q1/features_v2/-NFrJFQijFE__2.npz differ diff --git a/final/output/q1/features_v2/-RfYyzHpjk4__11.npz b/final/output/q1/features_v2/-RfYyzHpjk4__11.npz new file mode 100644 index 0000000..8e8b680 Binary files /dev/null and b/final/output/q1/features_v2/-RfYyzHpjk4__11.npz differ diff --git a/final/output/q1/features_v2/-RfYyzHpjk4__2.npz b/final/output/q1/features_v2/-RfYyzHpjk4__2.npz new file mode 100644 index 0000000..7c0be19 Binary files /dev/null and b/final/output/q1/features_v2/-RfYyzHpjk4__2.npz differ diff --git a/final/output/q1/features_v2/-RfYyzHpjk4__8.npz b/final/output/q1/features_v2/-RfYyzHpjk4__8.npz new file mode 100644 index 0000000..33200fc Binary files /dev/null and b/final/output/q1/features_v2/-RfYyzHpjk4__8.npz differ diff --git a/final/output/q1/features_v2/-THoVjtIkeU__12.npz b/final/output/q1/features_v2/-THoVjtIkeU__12.npz new file mode 100644 index 0000000..6adc9cd Binary files /dev/null and b/final/output/q1/features_v2/-THoVjtIkeU__12.npz differ diff --git a/final/output/q1/features_v2/-THoVjtIkeU__2.npz b/final/output/q1/features_v2/-THoVjtIkeU__2.npz new file mode 100644 index 0000000..9a0c3dd Binary files /dev/null and b/final/output/q1/features_v2/-THoVjtIkeU__2.npz differ diff --git a/final/output/q1/features_v2/-THoVjtIkeU__6.npz b/final/output/q1/features_v2/-THoVjtIkeU__6.npz new file mode 100644 index 0000000..7b7b89e Binary files /dev/null and b/final/output/q1/features_v2/-THoVjtIkeU__6.npz differ diff --git a/final/output/q1/features_v2/-UUCSKoHeMA__0.npz b/final/output/q1/features_v2/-UUCSKoHeMA__0.npz new file mode 100644 index 0000000..5e56085 Binary files /dev/null and b/final/output/q1/features_v2/-UUCSKoHeMA__0.npz differ diff --git a/final/output/q1/features_v2/-UacrmKiTn4__10.npz b/final/output/q1/features_v2/-UacrmKiTn4__10.npz new file mode 100644 index 0000000..0719f9e Binary files /dev/null and b/final/output/q1/features_v2/-UacrmKiTn4__10.npz differ diff --git a/final/output/q1/features_v2/-UacrmKiTn4__4.npz b/final/output/q1/features_v2/-UacrmKiTn4__4.npz new file mode 100644 index 0000000..c302e70 Binary files /dev/null and b/final/output/q1/features_v2/-UacrmKiTn4__4.npz differ diff --git a/final/output/q1/features_v2/-UuX1xuaiiE__0.npz b/final/output/q1/features_v2/-UuX1xuaiiE__0.npz new file mode 100644 index 0000000..0c8a822 Binary files /dev/null and b/final/output/q1/features_v2/-UuX1xuaiiE__0.npz differ diff --git a/final/output/q1/features_v2/-UuX1xuaiiE__1.npz b/final/output/q1/features_v2/-UuX1xuaiiE__1.npz new file mode 100644 index 0000000..f929596 Binary files /dev/null and b/final/output/q1/features_v2/-UuX1xuaiiE__1.npz differ diff --git a/final/output/q1/features_v2/-UuX1xuaiiE__3.npz b/final/output/q1/features_v2/-UuX1xuaiiE__3.npz new file mode 100644 index 0000000..c14a2e2 Binary files /dev/null and b/final/output/q1/features_v2/-UuX1xuaiiE__3.npz differ diff --git a/final/output/q1/features_v2/-UuX1xuaiiE__6.npz b/final/output/q1/features_v2/-UuX1xuaiiE__6.npz new file mode 100644 index 0000000..b4bb48e Binary files /dev/null and b/final/output/q1/features_v2/-UuX1xuaiiE__6.npz differ diff --git a/final/output/q1/features_v2/-a55Q6RWvTA__3.npz b/final/output/q1/features_v2/-a55Q6RWvTA__3.npz new file mode 100644 index 0000000..886f69f Binary files /dev/null and b/final/output/q1/features_v2/-a55Q6RWvTA__3.npz differ diff --git a/final/output/q1/features_v2/-aNfi7CP8vM__7.npz b/final/output/q1/features_v2/-aNfi7CP8vM__7.npz new file mode 100644 index 0000000..d1502fa Binary files /dev/null and b/final/output/q1/features_v2/-aNfi7CP8vM__7.npz differ diff --git a/final/output/q1/features_v2/-aqamKhZ1Ec__0.npz b/final/output/q1/features_v2/-aqamKhZ1Ec__0.npz new file mode 100644 index 0000000..347de31 Binary files /dev/null and b/final/output/q1/features_v2/-aqamKhZ1Ec__0.npz differ diff --git a/final/output/q1/features_v2/-dxfTGcXJoc__0.npz b/final/output/q1/features_v2/-dxfTGcXJoc__0.npz new file mode 100644 index 0000000..08d6fec Binary files /dev/null and b/final/output/q1/features_v2/-dxfTGcXJoc__0.npz differ diff --git a/final/output/q1/features_v2/-dxfTGcXJoc__1.npz b/final/output/q1/features_v2/-dxfTGcXJoc__1.npz new file mode 100644 index 0000000..10322d6 Binary files /dev/null and b/final/output/q1/features_v2/-dxfTGcXJoc__1.npz differ diff --git a/final/output/q1/features_v2/-dxfTGcXJoc__2.npz b/final/output/q1/features_v2/-dxfTGcXJoc__2.npz new file mode 100644 index 0000000..5aa99b3 Binary files /dev/null and b/final/output/q1/features_v2/-dxfTGcXJoc__2.npz differ diff --git a/final/output/q1/features_v2/-dxfTGcXJoc__6.npz b/final/output/q1/features_v2/-dxfTGcXJoc__6.npz new file mode 100644 index 0000000..b665039 Binary files /dev/null and b/final/output/q1/features_v2/-dxfTGcXJoc__6.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__1.npz b/final/output/q1/features_v2/-egA8-b7-3M__1.npz new file mode 100644 index 0000000..3eb2753 Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__1.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__13.npz b/final/output/q1/features_v2/-egA8-b7-3M__13.npz new file mode 100644 index 0000000..81f8729 Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__13.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__16.npz b/final/output/q1/features_v2/-egA8-b7-3M__16.npz new file mode 100644 index 0000000..b338f97 Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__16.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__17.npz b/final/output/q1/features_v2/-egA8-b7-3M__17.npz new file mode 100644 index 0000000..a47cf16 Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__17.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__18.npz b/final/output/q1/features_v2/-egA8-b7-3M__18.npz new file mode 100644 index 0000000..67b1865 Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__18.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__20.npz b/final/output/q1/features_v2/-egA8-b7-3M__20.npz new file mode 100644 index 0000000..0bb3e07 Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__20.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__26.npz b/final/output/q1/features_v2/-egA8-b7-3M__26.npz new file mode 100644 index 0000000..7892704 Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__26.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__6.npz b/final/output/q1/features_v2/-egA8-b7-3M__6.npz new file mode 100644 index 0000000..8b56272 Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__6.npz differ diff --git a/final/output/q1/features_v2/-egA8-b7-3M__9.npz b/final/output/q1/features_v2/-egA8-b7-3M__9.npz new file mode 100644 index 0000000..3de297e Binary files /dev/null and b/final/output/q1/features_v2/-egA8-b7-3M__9.npz differ diff --git a/final/output/q1/features_v2/-hnBHBN8p5A__6.npz b/final/output/q1/features_v2/-hnBHBN8p5A__6.npz new file mode 100644 index 0000000..e0aaf04 Binary files /dev/null and b/final/output/q1/features_v2/-hnBHBN8p5A__6.npz differ diff --git a/final/output/q1/features_v2/-hnBHBN8p5A__7.npz b/final/output/q1/features_v2/-hnBHBN8p5A__7.npz new file mode 100644 index 0000000..85f91d2 Binary files /dev/null and b/final/output/q1/features_v2/-hnBHBN8p5A__7.npz differ diff --git a/final/output/q1/features_v2/-iRBcNs9oI8__3.npz b/final/output/q1/features_v2/-iRBcNs9oI8__3.npz new file mode 100644 index 0000000..8443d28 Binary files /dev/null and b/final/output/q1/features_v2/-iRBcNs9oI8__3.npz differ diff --git a/final/output/q1/features_v2/-iRBcNs9oI8__6.npz b/final/output/q1/features_v2/-iRBcNs9oI8__6.npz new file mode 100644 index 0000000..c22ced4 Binary files /dev/null and b/final/output/q1/features_v2/-iRBcNs9oI8__6.npz differ diff --git a/final/output/q1/features_v2/-iRBcNs9oI8__7.npz b/final/output/q1/features_v2/-iRBcNs9oI8__7.npz new file mode 100644 index 0000000..c9baa38 Binary files /dev/null and b/final/output/q1/features_v2/-iRBcNs9oI8__7.npz differ diff --git a/final/output/q1/features_v2/-iRBcNs9oI8__8.npz b/final/output/q1/features_v2/-iRBcNs9oI8__8.npz new file mode 100644 index 0000000..ed6a047 Binary files /dev/null and b/final/output/q1/features_v2/-iRBcNs9oI8__8.npz differ diff --git a/final/output/q1/features_v2/-iRBcNs9oI8__9.npz b/final/output/q1/features_v2/-iRBcNs9oI8__9.npz new file mode 100644 index 0000000..698b9f1 Binary files /dev/null and b/final/output/q1/features_v2/-iRBcNs9oI8__9.npz differ diff --git a/final/output/q1/features_v2/-lzEya4AM_4__5.npz b/final/output/q1/features_v2/-lzEya4AM_4__5.npz new file mode 100644 index 0000000..857224a Binary files /dev/null and b/final/output/q1/features_v2/-lzEya4AM_4__5.npz differ diff --git a/final/output/q1/features_v2/-lzEya4AM_4__6.npz b/final/output/q1/features_v2/-lzEya4AM_4__6.npz new file mode 100644 index 0000000..3d35f91 Binary files /dev/null and b/final/output/q1/features_v2/-lzEya4AM_4__6.npz differ diff --git a/final/output/q1/features_v2/-mJ2ud6oKI8__1.npz b/final/output/q1/features_v2/-mJ2ud6oKI8__1.npz new file mode 100644 index 0000000..13837b8 Binary files /dev/null and b/final/output/q1/features_v2/-mJ2ud6oKI8__1.npz differ diff --git a/final/output/q1/features_v2/-mJ2ud6oKI8__2.npz b/final/output/q1/features_v2/-mJ2ud6oKI8__2.npz new file mode 100644 index 0000000..bd2aa02 Binary files /dev/null and b/final/output/q1/features_v2/-mJ2ud6oKI8__2.npz differ diff --git a/final/output/q1/features_v2/-mJ2ud6oKI8__6.npz b/final/output/q1/features_v2/-mJ2ud6oKI8__6.npz new file mode 100644 index 0000000..9550199 Binary files /dev/null and b/final/output/q1/features_v2/-mJ2ud6oKI8__6.npz differ diff --git a/final/output/q1/features_v2/-mJ2ud6oKI8__8.npz b/final/output/q1/features_v2/-mJ2ud6oKI8__8.npz new file mode 100644 index 0000000..5f7a192 Binary files /dev/null and b/final/output/q1/features_v2/-mJ2ud6oKI8__8.npz differ diff --git a/final/output/q1/features_v2/-mJ2ud6oKI8__9.npz b/final/output/q1/features_v2/-mJ2ud6oKI8__9.npz new file mode 100644 index 0000000..34a4e5a Binary files /dev/null and b/final/output/q1/features_v2/-mJ2ud6oKI8__9.npz differ diff --git a/final/output/q1/features_v2/-mqbVkbCndg__0.npz b/final/output/q1/features_v2/-mqbVkbCndg__0.npz new file mode 100644 index 0000000..71b65eb Binary files /dev/null and b/final/output/q1/features_v2/-mqbVkbCndg__0.npz differ diff --git a/final/output/q1/features_v2/-qDkUB0GgYY__6.npz b/final/output/q1/features_v2/-qDkUB0GgYY__6.npz new file mode 100644 index 0000000..0782e28 Binary files /dev/null and b/final/output/q1/features_v2/-qDkUB0GgYY__6.npz differ diff --git a/final/output/q1/features_v2/-ri04Z7vwnc__0.npz b/final/output/q1/features_v2/-ri04Z7vwnc__0.npz new file mode 100644 index 0000000..929fdd9 Binary files /dev/null and b/final/output/q1/features_v2/-ri04Z7vwnc__0.npz differ diff --git a/final/output/q1/features_v2/-ri04Z7vwnc__2.npz b/final/output/q1/features_v2/-ri04Z7vwnc__2.npz new file mode 100644 index 0000000..678f0e7 Binary files /dev/null and b/final/output/q1/features_v2/-ri04Z7vwnc__2.npz differ diff --git a/final/output/q1/features_v2/-ri04Z7vwnc__5.npz b/final/output/q1/features_v2/-ri04Z7vwnc__5.npz new file mode 100644 index 0000000..baa8822 Binary files /dev/null and b/final/output/q1/features_v2/-ri04Z7vwnc__5.npz differ diff --git a/final/output/q1/features_v2/-s9qJ7ATP7w__0.npz b/final/output/q1/features_v2/-s9qJ7ATP7w__0.npz new file mode 100644 index 0000000..653699a Binary files /dev/null and b/final/output/q1/features_v2/-s9qJ7ATP7w__0.npz differ diff --git a/final/output/q1/features_v2/-s9qJ7ATP7w__1.npz b/final/output/q1/features_v2/-s9qJ7ATP7w__1.npz new file mode 100644 index 0000000..0de50f3 Binary files /dev/null and b/final/output/q1/features_v2/-s9qJ7ATP7w__1.npz differ diff --git a/final/output/q1/features_v2/-s9qJ7ATP7w__4.npz b/final/output/q1/features_v2/-s9qJ7ATP7w__4.npz new file mode 100644 index 0000000..b7e686d Binary files /dev/null and b/final/output/q1/features_v2/-s9qJ7ATP7w__4.npz differ diff --git a/final/output/q1/features_v2/-s9qJ7ATP7w__5.npz b/final/output/q1/features_v2/-s9qJ7ATP7w__5.npz new file mode 100644 index 0000000..b2d98f5 Binary files /dev/null and b/final/output/q1/features_v2/-s9qJ7ATP7w__5.npz differ diff --git a/final/output/q1/features_v2/-s9qJ7ATP7w__6.npz b/final/output/q1/features_v2/-s9qJ7ATP7w__6.npz new file mode 100644 index 0000000..f5358d8 Binary files /dev/null and b/final/output/q1/features_v2/-s9qJ7ATP7w__6.npz differ diff --git a/final/output/q1/features_v2/-s9qJ7ATP7w__7.npz b/final/output/q1/features_v2/-s9qJ7ATP7w__7.npz new file mode 100644 index 0000000..6f207e2 Binary files /dev/null and b/final/output/q1/features_v2/-s9qJ7ATP7w__7.npz differ diff --git a/final/output/q1/features_v2/-s9qJ7ATP7w__8.npz b/final/output/q1/features_v2/-s9qJ7ATP7w__8.npz new file mode 100644 index 0000000..27f5048 Binary files /dev/null and b/final/output/q1/features_v2/-s9qJ7ATP7w__8.npz differ diff --git a/final/output/q1/features_v2/-t217m2on-s__2.npz b/final/output/q1/features_v2/-t217m2on-s__2.npz new file mode 100644 index 0000000..4d61d46 Binary files /dev/null and b/final/output/q1/features_v2/-t217m2on-s__2.npz differ diff --git a/final/output/q1/features_v2/-t217m2on-s__7.npz b/final/output/q1/features_v2/-t217m2on-s__7.npz new file mode 100644 index 0000000..9ad977a Binary files /dev/null and b/final/output/q1/features_v2/-t217m2on-s__7.npz differ diff --git a/final/output/q1/features_v2/-tANM6ETl_M__3.npz b/final/output/q1/features_v2/-tANM6ETl_M__3.npz new file mode 100644 index 0000000..75d401c Binary files /dev/null and b/final/output/q1/features_v2/-tANM6ETl_M__3.npz differ diff --git a/final/output/q1/features_v2/-tPCytz4rww__10.npz b/final/output/q1/features_v2/-tPCytz4rww__10.npz new file mode 100644 index 0000000..4912786 Binary files /dev/null and b/final/output/q1/features_v2/-tPCytz4rww__10.npz differ diff --git a/final/output/q1/features_v2/-tPCytz4rww__11.npz b/final/output/q1/features_v2/-tPCytz4rww__11.npz new file mode 100644 index 0000000..530d0e2 Binary files /dev/null and b/final/output/q1/features_v2/-tPCytz4rww__11.npz differ diff --git a/final/output/q1/features_v2/-tPCytz4rww__12.npz b/final/output/q1/features_v2/-tPCytz4rww__12.npz new file mode 100644 index 0000000..ca10460 Binary files /dev/null and b/final/output/q1/features_v2/-tPCytz4rww__12.npz differ diff --git a/final/output/q1/features_v2/-tPCytz4rww__16.npz b/final/output/q1/features_v2/-tPCytz4rww__16.npz new file mode 100644 index 0000000..8f579c8 Binary files /dev/null and b/final/output/q1/features_v2/-tPCytz4rww__16.npz differ diff --git a/final/output/q1/features_v2/-tPCytz4rww__18.npz b/final/output/q1/features_v2/-tPCytz4rww__18.npz new file mode 100644 index 0000000..8a040d7 Binary files /dev/null and b/final/output/q1/features_v2/-tPCytz4rww__18.npz differ diff --git a/final/output/q1/features_v2/-uywlfIYOS8__4.npz b/final/output/q1/features_v2/-uywlfIYOS8__4.npz new file mode 100644 index 0000000..cde8f90 Binary files /dev/null and b/final/output/q1/features_v2/-uywlfIYOS8__4.npz differ diff --git a/final/output/q1/features_v2/-vxjVxOeScU__4.npz b/final/output/q1/features_v2/-vxjVxOeScU__4.npz new file mode 100644 index 0000000..0889aea Binary files /dev/null and b/final/output/q1/features_v2/-vxjVxOeScU__4.npz differ diff --git a/final/output/q1/features_v2/-wMB_hJL-3o__7.npz b/final/output/q1/features_v2/-wMB_hJL-3o__7.npz new file mode 100644 index 0000000..9ff10cb Binary files /dev/null and b/final/output/q1/features_v2/-wMB_hJL-3o__7.npz differ diff --git a/final/output/q1/features_v2/-wny0OAz3g8__0.npz b/final/output/q1/features_v2/-wny0OAz3g8__0.npz new file mode 100644 index 0000000..29b6a31 Binary files /dev/null and b/final/output/q1/features_v2/-wny0OAz3g8__0.npz differ diff --git a/final/output/q1/features_v2/-wny0OAz3g8__1.npz b/final/output/q1/features_v2/-wny0OAz3g8__1.npz new file mode 100644 index 0000000..ddb9509 Binary files /dev/null and b/final/output/q1/features_v2/-wny0OAz3g8__1.npz differ diff --git a/final/output/q1/features_v2/-wny0OAz3g8__2.npz b/final/output/q1/features_v2/-wny0OAz3g8__2.npz new file mode 100644 index 0000000..52f959b Binary files /dev/null and b/final/output/q1/features_v2/-wny0OAz3g8__2.npz differ diff --git a/final/output/q1/features_v2/-wny0OAz3g8__3.npz b/final/output/q1/features_v2/-wny0OAz3g8__3.npz new file mode 100644 index 0000000..4ecd6a3 Binary files /dev/null and b/final/output/q1/features_v2/-wny0OAz3g8__3.npz differ diff --git a/final/output/q1/features_v2/-wny0OAz3g8__5.npz b/final/output/q1/features_v2/-wny0OAz3g8__5.npz new file mode 100644 index 0000000..0f37c95 Binary files /dev/null and b/final/output/q1/features_v2/-wny0OAz3g8__5.npz differ diff --git a/final/output/q1/features_v2/-wny0OAz3g8__7.npz b/final/output/q1/features_v2/-wny0OAz3g8__7.npz new file mode 100644 index 0000000..3263034 Binary files /dev/null and b/final/output/q1/features_v2/-wny0OAz3g8__7.npz differ diff --git a/final/output/q1/features_v2/-wny0OAz3g8__9.npz b/final/output/q1/features_v2/-wny0OAz3g8__9.npz new file mode 100644 index 0000000..aa04812 Binary files /dev/null and b/final/output/q1/features_v2/-wny0OAz3g8__9.npz differ diff --git a/final/output/q1/features_v2/-yRb-Jum7EQ__1.npz b/final/output/q1/features_v2/-yRb-Jum7EQ__1.npz new file mode 100644 index 0000000..aaa2f11 Binary files /dev/null and b/final/output/q1/features_v2/-yRb-Jum7EQ__1.npz differ diff --git a/final/output/q1/features_v2/-yRb-Jum7EQ__5.npz b/final/output/q1/features_v2/-yRb-Jum7EQ__5.npz new file mode 100644 index 0000000..4f34029 Binary files /dev/null and b/final/output/q1/features_v2/-yRb-Jum7EQ__5.npz differ diff --git a/final/output/q1/features_v2/-yRb-Jum7EQ__6.npz b/final/output/q1/features_v2/-yRb-Jum7EQ__6.npz new file mode 100644 index 0000000..d3dc2c2 Binary files /dev/null and b/final/output/q1/features_v2/-yRb-Jum7EQ__6.npz differ diff --git a/final/output/q1/features_v2/feature_manifest.json b/final/output/q1/features_v2/feature_manifest.json new file mode 100644 index 0000000..77a5acb --- /dev/null +++ b/final/output/q1/features_v2/feature_manifest.json @@ -0,0 +1,59 @@ +{ + "config": { + "schema": "q1-v2-source-first-2026-09", + "source_cache_schema": "q1-b0b4-v4-openface", + "common_step_s": 0.1, + "multi_context_windows_s": [ + 0.1, + 0.3, + 0.7 + ], + "relative_progress_bins": 50, + "text_encoder": "google-bert/bert-base-uncased; frozen last-four-layer mean per whitespace word; window=510, stride=384", + "alignment": "fixed-transcript CTC monotone state graph; hard Viterbi path plus stored forward-backward occupancy; scores uncalibrated", + "audio": "74-D: 40 log-Mel + 13 MFCC + 13 two-sided local-linear delta MFCC + [log-energy, log-F0, librosa.pyin voiced probability, spectral centroid, bandwidth, flux, zero-crossing rate, HNR dB]; 16 kHz, 400-sample window, 160-sample hop, FFT 512; HNR autocorrelation uses n=1024 at detected F0; preserve per-field validity; librosa.pyin warns cycle support is short at fmin=80 Hz for a 25 ms frame", + "vision": "OpenFace 2.2.0 native-frame output: 17 AU intensities, Pose6, Gaze6, Geometry6; confidence threshold 0.8; actual frame PTS and indices retained", + "quality": "B0 uses raw CTC word-path score in [0,1]; unavailable quality falls back to q*=1 and is flagged; audio/vision retain explicit unknown or detector-confidence states", + "content_probe": "disabled; physical time/query maps remain separate from content similarity", + "time": "left-closed/right-open seconds relative to video stream origin; source indices retained", + "branch_states": { + "sec": "primary B0 view and materialized", + "B1_vision_geometry": "geometry-aware 0.1 s view materialized", + "word": "materialized", + "phase50": "materialized", + "multi": "derived from native rows on demand by q1_io", + "posterior": "native CTC posterior stored; sec projection available through q1_io", + "dynamics": "materialized if continuous support is sufficient", + "b4_speech": "source-cache-backed on demand through q1_io to stay within the 50 MiB feature-package cap", + "query_word": "physical H_time matrices materialized", + "content_probe": "disabled" + } + }, + "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617", + "source_run_manifest": "source_cache_manifest.json", + "source_cache_schema": "q1-b0b4-v4-openface", + "source_label_sha256": "827334f782b1f242c84ad944a657bc7f7c63643f71ae9c977811a3eecdd3ab33", + "python": "3.14.7 (main, Aug 10 2026, 00:00:00) [GCC 16.1.1 20260515 (Red Hat 16.1.1-2)]", + "packages": { + "torch": "2.14.0+cu130", + "transformers": "5.17.0", + "numpy": "2.5.3", + "scipy": "1.18.1", + "scikit-learn": "1.9.1", + "opencv-python-headless": "5.0.0.93", + "librosa": "1.0.0", + "openpyxl": "3.1.5", + "matplotlib": "3.11.2" + }, + "models": { + "text_id": "google-bert/bert-base-uncased", + "text_revision": "86b5e0934494bd15c9632b12f734a8a67f723594", + "speech_id": "facebook/wav2vec2-base-960h", + "speech_revision": "22aad52d435eb6dbaf354bdad9b0da84ce7d6156", + "speech_stride_samples": 320, + "speech_receptive_field_samples": 400 + }, + "samples": 100, + "modalities": 300, + "total_bytes": 44321567 +} diff --git a/final/output/q1/features_v2/manifest_q1.jsonl b/final/output/q1/features_v2/manifest_q1.jsonl new file mode 100644 index 0000000..008bdd3 --- /dev/null +++ b/final/output/q1/features_v2/manifest_q1.jsonl @@ -0,0 +1,100 @@ +{"sample_id": "-3g5yACwYnA/13", "video_id": "-3g5yACwYnA", "clip_id": "13", "feature_path": "features_v2/-3g5yACwYnA__13.npz", "source_video_sha256": "aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f", "feature_sha256": "8ff79652816c4749ebfb19c95fb1f4826e27e19e850db23a289144985bcacb51", "duration_s": 5.5139970779418945, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-3g5yACwYnA/3", "video_id": "-3g5yACwYnA", "clip_id": "3", "feature_path": "features_v2/-3g5yACwYnA__3.npz", "source_video_sha256": "eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d", "feature_sha256": "e3756cf59da4cc1f56b316b9af66e1afbd7f76d45ed74296ee5e239e078bce61", "duration_s": 14.388997077941895, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-3g5yACwYnA/2", "video_id": "-3g5yACwYnA", "clip_id": "2", "feature_path": "features_v2/-3g5yACwYnA__2.npz", "source_video_sha256": "0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7", "feature_sha256": "e84dc34a549d0d9ed9200d889c8c4a3c9e3af2cf69db297939794cee052f2ab8", "duration_s": 9.394009590148926, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-3g5yACwYnA/9", "video_id": "-3g5yACwYnA", "clip_id": "9", "feature_path": "features_v2/-3g5yACwYnA__9.npz", "source_video_sha256": "b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016", "feature_sha256": "585ffb341161b798fb8213915eee94593519cf50a6510dad690d8386d2ca5f40", "duration_s": 8.816991806030273, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-3nNcZdcdvU/5", "video_id": "-3nNcZdcdvU", "clip_id": "5", "feature_path": "features_v2/-3nNcZdcdvU__5.npz", "source_video_sha256": "7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728", "feature_sha256": "a69c1bca94b0fa15c69c31eb4909874a08ffad213fb10b5d27cf3ba1cabfa13f", "duration_s": 7.867969036102295, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-HwX2H8Z4hY/2", "video_id": "-HwX2H8Z4hY", "clip_id": "2", "feature_path": "features_v2/-HwX2H8Z4hY__2.npz", "source_video_sha256": "920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358", "feature_sha256": "bce95b4eaa69f6b35684b191461419ecc2083ee944ced41807e46eba8d3131c5", "duration_s": 3.9820311069488525, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-HwX2H8Z4hY/5", "video_id": "-HwX2H8Z4hY", "clip_id": "5", "feature_path": "features_v2/-HwX2H8Z4hY__5.npz", "source_video_sha256": "8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d", "feature_sha256": "843601e008fa1ddcf4dc530837017e5fbccfc7f6aeaa3aff1b62a336668a0b4e", "duration_s": 5.6529951095581055, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-HwX2H8Z4hY/6", "video_id": "-HwX2H8Z4hY", "clip_id": "6", "feature_path": "features_v2/-HwX2H8Z4hY__6.npz", "source_video_sha256": "696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46", "feature_sha256": "e0f9f2edd105ce67f8269fb3a41603af01002047e25a450cf8dda1b4234b2141", "duration_s": 3.3580079078674316, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-HwX2H8Z4hY/9", "video_id": "-HwX2H8Z4hY", "clip_id": "9", "feature_path": "features_v2/-HwX2H8Z4hY__9.npz", "source_video_sha256": "90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702", "feature_sha256": "8248b6823c7a39a16c44389a7ef71fa206492ce35ccea39ea62621c3bcce1721", "duration_s": 7.733983993530273, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-NFrJFQijFE/1", "video_id": "-NFrJFQijFE", "clip_id": "1", "feature_path": "features_v2/-NFrJFQijFE__1.npz", "source_video_sha256": "d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f", "feature_sha256": "1b5b76764b7a6ce5bd68eca4dc24347e3d0e800bc2abe3c6af28cca881f4f498", "duration_s": 5.745999813079834, "status": "partial", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-NFrJFQijFE/2", "video_id": "-NFrJFQijFE", "clip_id": "2", "feature_path": "features_v2/-NFrJFQijFE__2.npz", "source_video_sha256": "a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1", "feature_sha256": "3805d0f3ea3c570accc297ebea6af7e0cfcc6f0f100593a6b06b34b5563a237a", "duration_s": 6.855999946594238, "status": "partial", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-THoVjtIkeU/12", "video_id": "-THoVjtIkeU", "clip_id": "12", "feature_path": "features_v2/-THoVjtIkeU__12.npz", "source_video_sha256": "e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4", "feature_sha256": "7830ce91514263b5cff4c305da3cf42cb86eef37158aad86f625d79f7268ac07", "duration_s": 14.896029472351074, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-THoVjtIkeU/2", "video_id": "-THoVjtIkeU", "clip_id": "2", "feature_path": "features_v2/-THoVjtIkeU__2.npz", "source_video_sha256": "35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b", "feature_sha256": "96d2c188b9ddaa891af1dfce83e12c5ed49abc0e85d96b802a8d28ebdec4dc66", "duration_s": 4.2919921875, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-THoVjtIkeU/6", "video_id": "-THoVjtIkeU", "clip_id": "6", "feature_path": "features_v2/-THoVjtIkeU__6.npz", "source_video_sha256": "efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64", "feature_sha256": "8957ebe6c0583a0dce593ce8cc42db829fdd1be8e112bcf08d99b083f5a3ac8a", "duration_s": 8.097004890441895, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-UuX1xuaiiE/1", "video_id": "-UuX1xuaiiE", "clip_id": "1", "feature_path": "features_v2/-UuX1xuaiiE__1.npz", "source_video_sha256": "d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c", "feature_sha256": "aecd48aa4a55db3bb21c7fadbb5b301f00b4ec235bc67a47dc5141bb32a7be9a", "duration_s": 10.350000381469727, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-UuX1xuaiiE/0", "video_id": "-UuX1xuaiiE", "clip_id": "0", "feature_path": "features_v2/-UuX1xuaiiE__0.npz", "source_video_sha256": "f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8", "feature_sha256": "2e24c45d36ef3c1925706d397f7c69deab53f7838a58bb66d94b5c03d09f1258", "duration_s": 3.6333329677581787, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-UuX1xuaiiE/3", "video_id": "-UuX1xuaiiE", "clip_id": "3", "feature_path": "features_v2/-UuX1xuaiiE__3.npz", "source_video_sha256": "eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa", "feature_sha256": "1efdc8d158b2480f4829148cd5eabc9236175782b056575467486af004ac81c4", "duration_s": 4.1300129890441895, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-UuX1xuaiiE/6", "video_id": "-UuX1xuaiiE", "clip_id": "6", "feature_path": "features_v2/-UuX1xuaiiE__6.npz", "source_video_sha256": "f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532", "feature_sha256": "c6bf473a36b950f74aacf8e8d2f1153e7591430d4ad57da3a1adb2da79b72198", "duration_s": 8.113997459411621, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-a55Q6RWvTA/3", "video_id": "-a55Q6RWvTA", "clip_id": "3", "feature_path": "features_v2/-a55Q6RWvTA__3.npz", "source_video_sha256": "15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946", "feature_sha256": "c1989fc2b6e742d75a029b7f912e0fdd73cfdbad35bcceae0e0e39ae0582b611", "duration_s": 22.15397071838379, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-aNfi7CP8vM/7", "video_id": "-aNfi7CP8vM", "clip_id": "7", "feature_path": "features_v2/-aNfi7CP8vM__7.npz", "source_video_sha256": "0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1", "feature_sha256": "dcb20301dbb921eeaee67499779334261ac38cee42e04215b54bdd790eb04d71", "duration_s": 8.694987297058105, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-aqamKhZ1Ec/0", "video_id": "-aqamKhZ1Ec", "clip_id": "0", "feature_path": "features_v2/-aqamKhZ1Ec__0.npz", "source_video_sha256": "6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548", "feature_sha256": "686f7710f18ce27ca864c44021246fdedd4d41b4620b210b2bae988f4011561e", "duration_s": 10.966667175292969, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-dxfTGcXJoc/1", "video_id": "-dxfTGcXJoc", "clip_id": "1", "feature_path": "features_v2/-dxfTGcXJoc__1.npz", "source_video_sha256": "b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595", "feature_sha256": "bbc68c9e5c1880673cfb77f48aa3dcdf10829b07cd64e6b00a8b7a3f8694b9a8", "duration_s": 16.16100311279297, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-dxfTGcXJoc/0", "video_id": "-dxfTGcXJoc", "clip_id": "0", "feature_path": "features_v2/-dxfTGcXJoc__0.npz", "source_video_sha256": "04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55", "feature_sha256": "76423f4fa8a57e214ecbebd02bc72e06eaaa8b8e25f40cca4fdb9296a1e30673", "duration_s": 20.5, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-dxfTGcXJoc/2", "video_id": "-dxfTGcXJoc", "clip_id": "2", "feature_path": "features_v2/-dxfTGcXJoc__2.npz", "source_video_sha256": "46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7", "feature_sha256": "fa9effa2f4efcf00296196e2ab90d819de5c6cab85f4075d8f2ae419743f57b5", "duration_s": 16.697982788085938, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-dxfTGcXJoc/6", "video_id": "-dxfTGcXJoc", "clip_id": "6", "feature_path": "features_v2/-dxfTGcXJoc__6.npz", "source_video_sha256": "55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513", "feature_sha256": "28beb6292687bfd0bc6a90e97083bfb2e7eaf133447dd9bf2a9be974d5b9fc4b", "duration_s": 12.735026359558105, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/26", "video_id": "-egA8-b7-3M", "clip_id": "26", "feature_path": "features_v2/-egA8-b7-3M__26.npz", "source_video_sha256": "11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685", "feature_sha256": "8f752d0ae41def9d6cfa650d06674143d8d743be33a702a947d181ff39c08e59", "duration_s": 6.030990123748779, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/17", "video_id": "-egA8-b7-3M", "clip_id": "17", "feature_path": "features_v2/-egA8-b7-3M__17.npz", "source_video_sha256": "f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f", "feature_sha256": "d18309c6de9e1218a6d8133d9f79c2df0a4a582022c1417a8ccd7ae71fbab8ab", "duration_s": 7.271028995513916, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/18", "video_id": "-egA8-b7-3M", "clip_id": "18", "feature_path": "features_v2/-egA8-b7-3M__18.npz", "source_video_sha256": "fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90", "feature_sha256": "dde916a05af47a5487e06c2749299e5bf2fc67fadb36a575c20c66065ac03176", "duration_s": 8.386002540588379, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/16", "video_id": "-egA8-b7-3M", "clip_id": "16", "feature_path": "features_v2/-egA8-b7-3M__16.npz", "source_video_sha256": "4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c", "feature_sha256": "6baba8aa2db080c79a9b72bf3c0814e66423a9d5a56216a09ffa699bc359b104", "duration_s": 5.538021087646484, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/13", "video_id": "-egA8-b7-3M", "clip_id": "13", "feature_path": "features_v2/-egA8-b7-3M__13.npz", "source_video_sha256": "cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c", "feature_sha256": "9133432494bb49a3e563cec8420925d5522ae007917972f82991886d55be47ba", "duration_s": 4.18398380279541, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/1", "video_id": "-egA8-b7-3M", "clip_id": "1", "feature_path": "features_v2/-egA8-b7-3M__1.npz", "source_video_sha256": "589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b", "feature_sha256": "71288235e60086aa6ab363066f68cf77c57048c65cf3a6b45f32c227381e7061", "duration_s": 10.266016006469727, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/6", "video_id": "-egA8-b7-3M", "clip_id": "6", "feature_path": "features_v2/-egA8-b7-3M__6.npz", "source_video_sha256": "2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0", "feature_sha256": "0cbad1d28be25446822090f74bf6d15e4e8d3c16e03cc4c863597e457b6bfb5f", "duration_s": 6.264974117279053, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/9", "video_id": "-egA8-b7-3M", "clip_id": "9", "feature_path": "features_v2/-egA8-b7-3M__9.npz", "source_video_sha256": "bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c", "feature_sha256": "295b96b7d73d49dd83272d84f7b5741daa654d4323f2a003e94a21aa0726cf2a", "duration_s": 5.0899739265441895, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-egA8-b7-3M/20", "video_id": "-egA8-b7-3M", "clip_id": "20", "feature_path": "features_v2/-egA8-b7-3M__20.npz", "source_video_sha256": "719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8", "feature_sha256": "3ef6eab499a10dd0f6181a3b4175bbcd5c4d629206e462ab642d8e2c58e689e9", "duration_s": 6.644987106323242, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-iRBcNs9oI8/3", "video_id": "-iRBcNs9oI8", "clip_id": "3", "feature_path": "features_v2/-iRBcNs9oI8__3.npz", "source_video_sha256": "39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50", "feature_sha256": "6843ce1b4b5a47d13b7d9cdcc6426141def4897da2ea0e7e325da54f3ce14907", "duration_s": 5.620999813079834, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-iRBcNs9oI8/7", "video_id": "-iRBcNs9oI8", "clip_id": "7", "feature_path": "features_v2/-iRBcNs9oI8__7.npz", "source_video_sha256": "cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081", "feature_sha256": "d7421743e7b1804ea0bb71568d9d3972fe5a908f699443a4125291f5bb26171b", "duration_s": 4.104000091552734, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-iRBcNs9oI8/6", "video_id": "-iRBcNs9oI8", "clip_id": "6", "feature_path": "features_v2/-iRBcNs9oI8__6.npz", "source_video_sha256": "030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3", "feature_sha256": "d24c023d23ecd8a1715e2759f47b155006e5eb61c2e4d25e436b62af400574fe", "duration_s": 2.9030001163482666, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-iRBcNs9oI8/9", "video_id": "-iRBcNs9oI8", "clip_id": "9", "feature_path": "features_v2/-iRBcNs9oI8__9.npz", "source_video_sha256": "352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74", "feature_sha256": "fe85860d993e18a09afb8293bc00ac76b3ad673db783d4a158ab8062b697a2a4", "duration_s": 3.4159998893737793, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-iRBcNs9oI8/8", "video_id": "-iRBcNs9oI8", "clip_id": "8", "feature_path": "features_v2/-iRBcNs9oI8__8.npz", "source_video_sha256": "72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412", "feature_sha256": "230f08e63b84fc8eea864ef801a59dbdae7b40800e58731184c2f41c70f35c09", "duration_s": 8.093000411987305, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-lzEya4AM_4/5", "video_id": "-lzEya4AM_4", "clip_id": "5", "feature_path": "features_v2/-lzEya4AM_4__5.npz", "source_video_sha256": "3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043", "feature_sha256": "a7c6dc3a59764e6353c1fddcaee94abe8c05335fd54803e1f5dbc5c20a21c277", "duration_s": 7.675000190734863, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-lzEya4AM_4/6", "video_id": "-lzEya4AM_4", "clip_id": "6", "feature_path": "features_v2/-lzEya4AM_4__6.npz", "source_video_sha256": "ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18", "feature_sha256": "cafd2cbc61f1e95896fb3bd46a456ba384ea6f3708a659f87749f9ef7432be1b", "duration_s": 14.7919921875, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-mJ2ud6oKI8/1", "video_id": "-mJ2ud6oKI8", "clip_id": "1", "feature_path": "features_v2/-mJ2ud6oKI8__1.npz", "source_video_sha256": "c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b", "feature_sha256": "cd2c39bb1ca30648413e10739b052627f0b660127f461db37b3cfc93c72e8155", "duration_s": 6.103000164031982, "status": "partial", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-mJ2ud6oKI8/2", "video_id": "-mJ2ud6oKI8", "clip_id": "2", "feature_path": "features_v2/-mJ2ud6oKI8__2.npz", "source_video_sha256": "c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28", "feature_sha256": "948618c6648a9a00753aff26cb65f3b9ef3bbe075f7e3d5a4c496eae541531dc", "duration_s": 5.730999946594238, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-mJ2ud6oKI8/6", "video_id": "-mJ2ud6oKI8", "clip_id": "6", "feature_path": "features_v2/-mJ2ud6oKI8__6.npz", "source_video_sha256": "134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d", "feature_sha256": "ca445e27d85ecd9578d39c37d5ad93b8c6113bf264861a6945242e202cbf7e1f", "duration_s": 2.256999969482422, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-mJ2ud6oKI8/9", "video_id": "-mJ2ud6oKI8", "clip_id": "9", "feature_path": "features_v2/-mJ2ud6oKI8__9.npz", "source_video_sha256": "df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47", "feature_sha256": "1610e8626eec162785662e4445b5a8581abab6425a7956bbfe147742d06aad7c", "duration_s": 7.0329999923706055, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-mJ2ud6oKI8/8", "video_id": "-mJ2ud6oKI8", "clip_id": "8", "feature_path": "features_v2/-mJ2ud6oKI8__8.npz", "source_video_sha256": "6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc", "feature_sha256": "f262bc1e640742b43f80bec55ad965d31d2ccadfc7f5c921848ff5f41c76877a", "duration_s": 4.191999912261963, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-mqbVkbCndg/0", "video_id": "-mqbVkbCndg", "clip_id": "0", "feature_path": "features_v2/-mqbVkbCndg__0.npz", "source_video_sha256": "cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda", "feature_sha256": "4ccb2b3e910eeb9a00598d3204a7214de9b453636d1d8c026419aeaee157a5c6", "duration_s": 6.466667175292969, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-t217m2on-s/2", "video_id": "-t217m2on-s", "clip_id": "2", "feature_path": "features_v2/-t217m2on-s__2.npz", "source_video_sha256": "6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8", "feature_sha256": "9f325b13ee0982de9e94c5ce7ba483a970251ba88b86aac2fb7a67189978d5b2", "duration_s": 5.6860032081604, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-t217m2on-s/7", "video_id": "-t217m2on-s", "clip_id": "7", "feature_path": "features_v2/-t217m2on-s__7.npz", "source_video_sha256": "41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7", "feature_sha256": "9fd4771029300d09aefe043ea3ce447828c34de03d8a238abdf2f83cb6b96bc7", "duration_s": 17.183008193969727, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-tANM6ETl_M/3", "video_id": "-tANM6ETl_M", "clip_id": "3", "feature_path": "features_v2/-tANM6ETl_M__3.npz", "source_video_sha256": "6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6", "feature_sha256": "dc3c9acd5f216c41559a001c278266aa2fe777f99cb45acd9925444c71bc84d9", "duration_s": 6.758008003234863, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-tPCytz4rww/11", "video_id": "-tPCytz4rww", "clip_id": "11", "feature_path": "features_v2/-tPCytz4rww__11.npz", "source_video_sha256": "6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d", "feature_sha256": "03b82e99bc518fc778ea4447b4564b8cbb3b3d132b8316d75c65ff1e50e66769", "duration_s": 5.466991901397705, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-tPCytz4rww/10", "video_id": "-tPCytz4rww", "clip_id": "10", "feature_path": "features_v2/-tPCytz4rww__10.npz", "source_video_sha256": "758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8", "feature_sha256": "11be01f908f0545b1cd3f75380c9c51a731d8b5f087b9d34f5670e02766da840", "duration_s": 4.788997173309326, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-tPCytz4rww/12", "video_id": "-tPCytz4rww", "clip_id": "12", "feature_path": "features_v2/-tPCytz4rww__12.npz", "source_video_sha256": "e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5", "feature_sha256": "da94125e6235aecdfd31615210d8bb22e56b54882ced8e548c09408fa1f14191", "duration_s": 11.863997459411621, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-tPCytz4rww/16", "video_id": "-tPCytz4rww", "clip_id": "16", "feature_path": "features_v2/-tPCytz4rww__16.npz", "source_video_sha256": "8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93", "feature_sha256": "5fd4724db408e31ee12e37ddf109dcaf9899521f6641db9e6ee8b144f5625b00", "duration_s": 7.205989837646484, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-tPCytz4rww/18", "video_id": "-tPCytz4rww", "clip_id": "18", "feature_path": "features_v2/-tPCytz4rww__18.npz", "source_video_sha256": "eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776", "feature_sha256": "92bac66566bcd80659c7e96a6d47014c994bb8d815cd906be5cccab86e50b82b", "duration_s": 6.644987106323242, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-vxjVxOeScU/4", "video_id": "-vxjVxOeScU", "clip_id": "4", "feature_path": "features_v2/-vxjVxOeScU__4.npz", "source_video_sha256": "00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719", "feature_sha256": "036473f43edafbd41626317599d4df6e6847def482a18c760e45f77f822b45f4", "duration_s": 9.127017974853516, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-wMB_hJL-3o/7", "video_id": "-wMB_hJL-3o", "clip_id": "7", "feature_path": "features_v2/-wMB_hJL-3o__7.npz", "source_video_sha256": "04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7", "feature_sha256": "e091d71d9bb99da782ea268fb1652ee8ad0a4b256af35f919beed8b52309000d", "duration_s": 6.044010162353516, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-wny0OAz3g8/1", "video_id": "-wny0OAz3g8", "clip_id": "1", "feature_path": "features_v2/-wny0OAz3g8__1.npz", "source_video_sha256": "c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8", "feature_sha256": "bdbd64a96b68b7472c5a6d35ec8f973eb5c28c4b8af04a3cd6f33176c0db4d41", "duration_s": 6.844009876251221, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-wny0OAz3g8/0", "video_id": "-wny0OAz3g8", "clip_id": "0", "feature_path": "features_v2/-wny0OAz3g8__0.npz", "source_video_sha256": "1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d", "feature_sha256": "8985259ed866ff2f7a65967434022d2a40a9006742175ce7f7fb88cdc8513c02", "duration_s": 3.3333330154418945, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-wny0OAz3g8/3", "video_id": "-wny0OAz3g8", "clip_id": "3", "feature_path": "features_v2/-wny0OAz3g8__3.npz", "source_video_sha256": "ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce", "feature_sha256": "3bc79a7fe97c226b5cf3bddd080ec9875daf3ec94b16582e39dcddcc7f77fcfc", "duration_s": 6.146028995513916, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-wny0OAz3g8/2", "video_id": "-wny0OAz3g8", "clip_id": "2", "feature_path": "features_v2/-wny0OAz3g8__2.npz", "source_video_sha256": "1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a", "feature_sha256": "d9012975be4a0faccd98b3ed4462c664157184b13034c342ed0916667b7074f6", "duration_s": 6.686978816986084, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-wny0OAz3g8/5", "video_id": "-wny0OAz3g8", "clip_id": "5", "feature_path": "features_v2/-wny0OAz3g8__5.npz", "source_video_sha256": "6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0", "feature_sha256": "96f35b0e59d87b842d8e3f73a9601e0c4aab770f47984d48cc4409f41b783c3f", "duration_s": 6.9119791984558105, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-wny0OAz3g8/7", "video_id": "-wny0OAz3g8", "clip_id": "7", "feature_path": "features_v2/-wny0OAz3g8__7.npz", "source_video_sha256": "cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd", "feature_sha256": "8e08e91628357e41dbd627d334780086ae59dbc3fdc948bb0d35c9a4581bae8c", "duration_s": 8.06796932220459, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-wny0OAz3g8/9", "video_id": "-wny0OAz3g8", "clip_id": "9", "feature_path": "features_v2/-wny0OAz3g8__9.npz", "source_video_sha256": "b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136", "feature_sha256": "12f2eed3652cdca9c0cb7ba62acd555913433a5782a9fc3bd4e3287cc390fbc7", "duration_s": 6.2130208015441895, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-571d8cVauQ/0", "video_id": "-571d8cVauQ", "clip_id": "0", "feature_path": "features_v2/-571d8cVauQ__0.npz", "source_video_sha256": "defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088", "feature_sha256": "9c38a6e6628f67352d27716703f7be63cb4d958ba4ba8eaf697ba3b637aeaa42", "duration_s": 5.066667079925537, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-571d8cVauQ/5", "video_id": "-571d8cVauQ", "clip_id": "5", "feature_path": "features_v2/-571d8cVauQ__5.npz", "source_video_sha256": "e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e", "feature_sha256": "1397887ed11ede3e9cab0566d564fd11014bc15e24aa2a6f5675ea452c9d149c", "duration_s": 15.272981643676758, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-I_e4mIh0yE/1", "video_id": "-I_e4mIh0yE", "clip_id": "1", "feature_path": "features_v2/-I_e4mIh0yE__1.npz", "source_video_sha256": "8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df", "feature_sha256": "5f2b4811b0ae11e9e00f74d0f6dfaff9ee474c13b3033793ad73cbf233a2f60e", "duration_s": 7.622004985809326, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-I_e4mIh0yE/3", "video_id": "-I_e4mIh0yE", "clip_id": "3", "feature_path": "features_v2/-I_e4mIh0yE__3.npz", "source_video_sha256": "a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e", "feature_sha256": "692898c2d7d4f91ef25c4ec60364ee7cc7c64baff8d09a789600f6b9fb585295", "duration_s": 9.163021087646484, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-UacrmKiTn4/10", "video_id": "-UacrmKiTn4", "clip_id": "10", "feature_path": "features_v2/-UacrmKiTn4__10.npz", "source_video_sha256": "00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360", "feature_sha256": "2b9b15073d07d5a042ff7e4d89f4819d99efcc98e96b5fe2d42e26b6421ed01c", "duration_s": 7.361979007720947, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-UacrmKiTn4/4", "video_id": "-UacrmKiTn4", "clip_id": "4", "feature_path": "features_v2/-UacrmKiTn4__4.npz", "source_video_sha256": "a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84", "feature_sha256": "7d6be8f06193a82c27fde3e99106fcd518632cf61a19a66f567d8327057e280b", "duration_s": 4.741015911102295, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-hnBHBN8p5A/7", "video_id": "-hnBHBN8p5A", "clip_id": "7", "feature_path": "features_v2/-hnBHBN8p5A__7.npz", "source_video_sha256": "0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb", "feature_sha256": "9ebd1d3b4bbb4cbb6e41ef9f67e049323c7b13a99f991d5f44fc56216fe610f6", "duration_s": 7.372000217437744, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-hnBHBN8p5A/6", "video_id": "-hnBHBN8p5A", "clip_id": "6", "feature_path": "features_v2/-hnBHBN8p5A__6.npz", "source_video_sha256": "997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7", "feature_sha256": "a778554ac2390ad44dc63d88a22d0cbcf817eec084022dbf5e48a2a3be52236f", "duration_s": 6.401000022888184, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-qDkUB0GgYY/6", "video_id": "-qDkUB0GgYY", "clip_id": "6", "feature_path": "features_v2/-qDkUB0GgYY__6.npz", "source_video_sha256": "ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b", "feature_sha256": "d157258aff14c527b21565f98c368c753ff20f31aa0cbe051f1d24c57a7412e2", "duration_s": 4.677995204925537, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-uywlfIYOS8/4", "video_id": "-uywlfIYOS8", "clip_id": "4", "feature_path": "features_v2/-uywlfIYOS8__4.npz", "source_video_sha256": "fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9", "feature_sha256": "d07fd83e0727367f03d2ae5e0ed623da12fd1946fc010dbdd93d5148e0d4730c", "duration_s": 6.044987201690674, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-6rXp3zJ3kc/8", "video_id": "-6rXp3zJ3kc", "clip_id": "8", "feature_path": "features_v2/-6rXp3zJ3kc__8.npz", "source_video_sha256": "59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae", "feature_sha256": "becdc042c9ef64c388bde8e5b3a98ac8d99782afcb454c5d8feab4289ab1523c", "duration_s": 12.805012702941895, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-9y-fZ3swSY/0", "video_id": "-9y-fZ3swSY", "clip_id": "0", "feature_path": "features_v2/-9y-fZ3swSY__0.npz", "source_video_sha256": "29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c", "feature_sha256": "0c96186d24ab43623baf90a1e626f66f7cc1695c0b8b70ad3ca63a90a8110f52", "duration_s": 6.8333330154418945, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-9y-fZ3swSY/4", "video_id": "-9y-fZ3swSY", "clip_id": "4", "feature_path": "features_v2/-9y-fZ3swSY__4.npz", "source_video_sha256": "fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc", "feature_sha256": "6cff9072da7459fdf9c4a0dda4c687b8b2c525ba3af893be8b4b51943cff0e88", "duration_s": 2.8210289478302, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-9y-fZ3swSY/8", "video_id": "-9y-fZ3swSY", "clip_id": "8", "feature_path": "features_v2/-9y-fZ3swSY__8.npz", "source_video_sha256": "6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256", "feature_sha256": "cd54043f6a090b25e8ba26bec6b6a399b17e798ca08161fd33239be6f5761a50", "duration_s": 4.988996982574463, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-AUZQgSxyPQ/2", "video_id": "-AUZQgSxyPQ", "clip_id": "2", "feature_path": "features_v2/-AUZQgSxyPQ__2.npz", "source_video_sha256": "e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b", "feature_sha256": "6b3fad600f0199347f255e07d223482885cd794a47c0d7ac2ff16c86232495d7", "duration_s": 22.511003494262695, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-HeZS2-Prhc/2", "video_id": "-HeZS2-Prhc", "clip_id": "2", "feature_path": "features_v2/-HeZS2-Prhc__2.npz", "source_video_sha256": "edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c", "feature_sha256": "a545e12987ed9776f6799e87297422df5b8ab3bacade90fcbf98bea7e28ad1c5", "duration_s": 8.261979103088379, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-MeTTeMJBNc/0", "video_id": "-MeTTeMJBNc", "clip_id": "0", "feature_path": "features_v2/-MeTTeMJBNc__0.npz", "source_video_sha256": "54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe", "feature_sha256": "ef950e662a99c32820f5b841a570702e5d24ae392ab8d553be90c669f1abc4e1", "duration_s": 9.300000190734863, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-MeTTeMJBNc/13", "video_id": "-MeTTeMJBNc", "clip_id": "13", "feature_path": "features_v2/-MeTTeMJBNc__13.npz", "source_video_sha256": "80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44", "feature_sha256": "ac464b5b9963d9934a9e9cbadb993297b39724ffb7c9afef74f1d42600d5b65f", "duration_s": 5.430013179779053, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-MeTTeMJBNc/7", "video_id": "-MeTTeMJBNc", "clip_id": "7", "feature_path": "features_v2/-MeTTeMJBNc__7.npz", "source_video_sha256": "40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a", "feature_sha256": "658af7fb69567bc1a1c6f6e26aa7f9c27354169c220ab987624997266ccf502f", "duration_s": 10.51699161529541, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-RfYyzHpjk4/11", "video_id": "-RfYyzHpjk4", "clip_id": "11", "feature_path": "features_v2/-RfYyzHpjk4__11.npz", "source_video_sha256": "96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904", "feature_sha256": "382456355758265669dff9e465e4cc02bb8c6724463a2e5967e7f8893c63b4e4", "duration_s": 5.238996982574463, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-RfYyzHpjk4/8", "video_id": "-RfYyzHpjk4", "clip_id": "8", "feature_path": "features_v2/-RfYyzHpjk4__8.npz", "source_video_sha256": "ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c", "feature_sha256": "f8217c39db4c367c69a617664528cad9b92ca28f532e755031a92214afa77377", "duration_s": 4.588996887207031, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-RfYyzHpjk4/2", "video_id": "-RfYyzHpjk4", "clip_id": "2", "feature_path": "features_v2/-RfYyzHpjk4__2.npz", "source_video_sha256": "5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f", "feature_sha256": "63203a3e910c0e8121c88c86f50dfbbf99b316558376c589760573bb99a30162", "duration_s": 5.516016006469727, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-UUCSKoHeMA/0", "video_id": "-UUCSKoHeMA", "clip_id": "0", "feature_path": "features_v2/-UUCSKoHeMA__0.npz", "source_video_sha256": "3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898", "feature_sha256": "987dfd55d6b20aad24e7dfc246b8118d5428ec936e103b104a21e19d49b1cbdb", "duration_s": 7.800000190734863, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-ri04Z7vwnc/0", "video_id": "-ri04Z7vwnc", "clip_id": "0", "feature_path": "features_v2/-ri04Z7vwnc__0.npz", "source_video_sha256": "93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b", "feature_sha256": "9631590e28e7e4b4d153058cd781188ceecf7e6a4f799ea21a649dbb14b42a05", "duration_s": 2.806999921798706, "status": "partial", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-ri04Z7vwnc/2", "video_id": "-ri04Z7vwnc", "clip_id": "2", "feature_path": "features_v2/-ri04Z7vwnc__2.npz", "source_video_sha256": "f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148", "feature_sha256": "ec4a0add30cd5eba8030fdadf54c66679192a3af67c6b35a518e277b9341f72c", "duration_s": 6.0269999504089355, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-ri04Z7vwnc/5", "video_id": "-ri04Z7vwnc", "clip_id": "5", "feature_path": "features_v2/-ri04Z7vwnc__5.npz", "source_video_sha256": "2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736", "feature_sha256": "c4b36fc3e52553e01ed6e39ff7bc82d3b346f6895688a7473d059fb06ca68454", "duration_s": 3.5450000762939453, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-s9qJ7ATP7w/1", "video_id": "-s9qJ7ATP7w", "clip_id": "1", "feature_path": "features_v2/-s9qJ7ATP7w__1.npz", "source_video_sha256": "a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09", "feature_sha256": "8657717d40999eb17c2a865470cf52dff151f9db8a0b122fa058ac43f8bed0db", "duration_s": 4.7919921875, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-s9qJ7ATP7w/0", "video_id": "-s9qJ7ATP7w", "clip_id": "0", "feature_path": "features_v2/-s9qJ7ATP7w__0.npz", "source_video_sha256": "f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5", "feature_sha256": "301cc976bec37d768ef66b77e6ac982ca30f0837b6f4ea2afe9baa9c25e2b664", "duration_s": 6.800000190734863, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-s9qJ7ATP7w/5", "video_id": "-s9qJ7ATP7w", "clip_id": "5", "feature_path": "features_v2/-s9qJ7ATP7w__5.npz", "source_video_sha256": "bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae", "feature_sha256": "476746914805af458c0c0ca41d9da33ec55b7bfa280018c4aec2cbd780f4f0de", "duration_s": 8.561002731323242, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-s9qJ7ATP7w/4", "video_id": "-s9qJ7ATP7w", "clip_id": "4", "feature_path": "features_v2/-s9qJ7ATP7w__4.npz", "source_video_sha256": "76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9", "feature_sha256": "734dea66c346fbdedb6db5861f502eff4a3dc46558466c8825ceb5413fa73d41", "duration_s": 4.427018165588379, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-s9qJ7ATP7w/7", "video_id": "-s9qJ7ATP7w", "clip_id": "7", "feature_path": "features_v2/-s9qJ7ATP7w__7.npz", "source_video_sha256": "5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d", "feature_sha256": "1582ea53834aa456f77aa4384fd2e25514208ed51f53b4a99ff5e420b34cda27", "duration_s": 6.966015815734863, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-s9qJ7ATP7w/6", "video_id": "-s9qJ7ATP7w", "clip_id": "6", "feature_path": "features_v2/-s9qJ7ATP7w__6.npz", "source_video_sha256": "40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451", "feature_sha256": "25f32b57e6213d05d50b9e95b33e02fbbba587cae2bcce8189d7e911e5b9fbbc", "duration_s": 2.4749999046325684, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-s9qJ7ATP7w/8", "video_id": "-s9qJ7ATP7w", "clip_id": "8", "feature_path": "features_v2/-s9qJ7ATP7w__8.npz", "source_video_sha256": "1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239", "feature_sha256": "aceb43a00ad661ebc13dbb273fb0bbff55ee0416a99ca825ee046a98aa6a0299", "duration_s": 3.2949869632720947, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-yRb-Jum7EQ/1", "video_id": "-yRb-Jum7EQ", "clip_id": "1", "feature_path": "features_v2/-yRb-Jum7EQ__1.npz", "source_video_sha256": "2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46", "feature_sha256": "df02ec3e0707e3ae292d6ccabc0062d41f68e8dade44b0a04e7c93af52ab2a08", "duration_s": 29.288021087646484, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-yRb-Jum7EQ/5", "video_id": "-yRb-Jum7EQ", "clip_id": "5", "feature_path": "features_v2/-yRb-Jum7EQ__5.npz", "source_video_sha256": "6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9", "feature_sha256": "5d3083b3fcbdf232e5bed1955f4498481892a0c6f9fb74ff3c1460fa2975b3a1", "duration_s": 13.169010162353516, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} +{"sample_id": "-yRb-Jum7EQ/6", "video_id": "-yRb-Jum7EQ", "clip_id": "6", "feature_path": "features_v2/-yRb-Jum7EQ__6.npz", "source_video_sha256": "b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989", "feature_sha256": "feda99761265679c062ad17106b6264a16e046a84d42fc0edc0c985c14232481", "duration_s": 11.241994857788086, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"} diff --git a/final/output/q1/features_v2/modality_summary.csv b/final/output/q1/features_v2/modality_summary.csv new file mode 100644 index 0000000..ef81ffe --- /dev/null +++ b/final/output/q1/features_v2/modality_summary.csv @@ -0,0 +1,301 @@ +sample_id,video_id,clip_id,modality,source_duration_s,observed_duration_s,native_length,native_dim,aligned_length,aligned_dim,alignment_type,granularity,mean_coverage,missing_reason,quality_available,quality_available_fraction,quality_path,query_map_path,probe_status,feature_path,source_hash,status,config_hash +-3g5yACwYnA/13,-3g5yACwYnA,13,text,5.5139970779418945,3.3000001311302185,15,768,56,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5984769459402665,none,True,1.0,-3g5yACwYnA__13.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-3g5yACwYnA__13.npz,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/13,-3g5yACwYnA,13,audio,5.5139970779418945,5.313856481700329,540,74,56,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9637031733219075,F0/HNR naturally undefined or unavailable in 315 rows; support masks retained,True,1.0,-3g5yACwYnA__13.npz::native_audio_*quality*,-3g5yACwYnA__13.npz::query_word_audio_H_time,content probe disabled,features_v2/-3g5yACwYnA__13.npz,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/13,-3g5yACwYnA,13,vision,5.5139970779418945,5.416500091552735,163,35,56,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9823182738381954,none,True,1.0,-3g5yACwYnA__13.npz::native_vision_*quality*,-3g5yACwYnA__13.npz::query_word_vision_H_time,content probe disabled,features_v2/-3g5yACwYnA__13.npz,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/3,-3g5yACwYnA,3,text,14.388997077941895,9.000000521540644,29,768,144,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6254779588035014,none,True,1.0,-3g5yACwYnA__3.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-3g5yACwYnA__3.npz,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/3,-3g5yACwYnA,3,audio,14.388997077941895,14.072049074076318,1433,74,144,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9779728912203719,F0/HNR naturally undefined or unavailable in 947 rows; support masks retained,True,1.0,-3g5yACwYnA__3.npz::native_audio_*quality*,-3g5yACwYnA__3.npz::query_word_audio_H_time,content probe disabled,features_v2/-3g5yACwYnA__3.npz,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/3,-3g5yACwYnA,3,vision,14.388997077941895,14.34950065612793,431,35,144,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9972550955705931,none,True,1.0,-3g5yACwYnA__3.npz::native_vision_*quality*,-3g5yACwYnA__3.npz::query_word_vision_H_time,content probe disabled,features_v2/-3g5yACwYnA__3.npz,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/2,-3g5yACwYnA,2,text,9.394009590148926,5.659999758005142,14,768,94,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6025116010037427,none,True,1.0,-3g5yACwYnA__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-3g5yACwYnA__2.npz,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/2,-3g5yACwYnA,2,audio,9.394009590148926,9.112644647424286,924,74,94,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9700484718453242,F0/HNR naturally undefined or unavailable in 440 rows; support masks retained,True,1.0,-3g5yACwYnA__2.npz::native_audio_*quality*,-3g5yACwYnA__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-3g5yACwYnA__2.npz,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/2,-3g5yACwYnA,2,vision,9.394009590148926,9.249500274658203,278,35,94,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9846168652369418,none,True,1.0,-3g5yACwYnA__2.npz::native_vision_*quality*,-3g5yACwYnA__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-3g5yACwYnA__2.npz,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/9,-3g5yACwYnA,9,text,8.816991806030273,7.46000128611922,21,768,89,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.8460937074952303,none,True,1.0,-3g5yACwYnA__9.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-3g5yACwYnA__9.npz,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/9,-3g5yACwYnA,9,audio,8.816991806030273,8.577734790943765,870,74,89,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9728640991905116,F0/HNR naturally undefined or unavailable in 438 rows; support masks retained,True,1.0,-3g5yACwYnA__9.npz::native_audio_*quality*,-3g5yACwYnA__9.npz::query_word_audio_H_time,content probe disabled,features_v2/-3g5yACwYnA__9.npz,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/9,-3g5yACwYnA,9,vision,8.816991806030273,8.749500274658203,263,35,89,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9923452881825398,none,True,1.0,-3g5yACwYnA__9.npz::native_vision_*quality*,-3g5yACwYnA__9.npz::query_word_vision_H_time,content probe disabled,features_v2/-3g5yACwYnA__9.npz,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3nNcZdcdvU/5,-3nNcZdcdvU,5,text,7.867969036102295,5.459999561309813,18,768,79,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6939528531767883,none,True,1.0,-3nNcZdcdvU__5.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-3nNcZdcdvU__5.npz,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3nNcZdcdvU/5,-3nNcZdcdvU,5,audio,7.867969036102295,7.693046254080695,779,74,79,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9777677338053868,F0/HNR naturally undefined or unavailable in 353 rows; support masks retained,True,1.0,-3nNcZdcdvU__5.npz::native_audio_*quality*,-3nNcZdcdvU__5.npz::query_word_audio_H_time,content probe disabled,features_v2/-3nNcZdcdvU__5.npz,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3nNcZdcdvU/5,-3nNcZdcdvU,5,vision,7.867969036102295,7.867969036102295,236,35,79,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-3nNcZdcdvU__5.npz::native_vision_*quality*,-3nNcZdcdvU__5.npz::query_word_vision_H_time,content probe disabled,features_v2/-3nNcZdcdvU__5.npz,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/2,-HwX2H8Z4hY,2,text,3.9820311069488525,3.3600000143051147,8,768,40,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.8437904988842853,1 word intervals are not reliable under the fixed-transcript CTC path,True,0.875,-HwX2H8Z4hY__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-HwX2H8Z4hY__2.npz,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/2,-HwX2H8Z4hY,2,audio,3.9820311069488525,3.8603403632705278,392,74,40,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9694400318807234,F0/HNR naturally undefined or unavailable in 206 rows; support masks retained,True,1.0,-HwX2H8Z4hY__2.npz::native_audio_*quality*,-HwX2H8Z4hY__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-HwX2H8Z4hY__2.npz,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/2,-HwX2H8Z4hY,2,vision,3.9820311069488525,1.1655313968658445,119,35,40,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.2926977126903733,OpenFace confidence/success gate rejected 85 native frames,True,0.29411764705882354,-HwX2H8Z4hY__2.npz::native_vision_*quality*,-HwX2H8Z4hY__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-HwX2H8Z4hY__2.npz,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/5,-HwX2H8Z4hY,5,text,5.6529951095581055,3.739999890327454,10,768,57,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6615961658986487,1 word intervals are not reliable under the fixed-transcript CTC path,True,0.9,-HwX2H8Z4hY__5.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-HwX2H8Z4hY__5.npz,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/5,-HwX2H8Z4hY,5,audio,5.6529951095581055,5.463188450078708,555,74,57,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9664237000385032,F0/HNR naturally undefined or unavailable in 299 rows; support masks retained,True,1.0,-HwX2H8Z4hY__5.npz::native_audio_*quality*,-HwX2H8Z4hY__5.npz::query_word_audio_H_time,content probe disabled,features_v2/-HwX2H8Z4hY__5.npz,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/5,-HwX2H8Z4hY,5,vision,5.6529951095581055,2.0835000276565556,168,35,57,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.3685656872643965,OpenFace confidence/success gate rejected 105 native frames,True,0.375,-HwX2H8Z4hY__5.npz::native_vision_*quality*,-HwX2H8Z4hY__5.npz::query_word_vision_H_time,content probe disabled,features_v2/-HwX2H8Z4hY__5.npz,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/6,-HwX2H8Z4hY,6,text,3.3580079078674316,1.8599998354911802,7,768,34,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5538997782385835,none,True,1.0,-HwX2H8Z4hY__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-HwX2H8Z4hY__6.npz,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/6,-HwX2H8Z4hY,6,audio,3.3580079078674316,3.221668961080345,326,74,34,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9593988607151103,F0/HNR naturally undefined or unavailable in 127 rows; support masks retained,True,1.0,-HwX2H8Z4hY__6.npz::native_audio_*quality*,-HwX2H8Z4hY__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-HwX2H8Z4hY__6.npz,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/6,-HwX2H8Z4hY,6,vision,3.3580079078674316,2.0334997773170467,99,35,34,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.60556729856198,OpenFace confidence/success gate rejected 38 native frames,True,0.6262626262626263,-HwX2H8Z4hY__6.npz::native_vision_*quality*,-HwX2H8Z4hY__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-HwX2H8Z4hY__6.npz,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/9,-HwX2H8Z4hY,9,text,7.733983993530273,5.199999988079072,22,768,78,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6723572213789218,none,True,1.0,-HwX2H8Z4hY__9.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-HwX2H8Z4hY__9.npz,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/9,-HwX2H8Z4hY,9,audio,7.733983993530273,7.558725487088432,764,74,78,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9773391687145395,F0/HNR naturally undefined or unavailable in 287 rows; support masks retained,True,1.0,-HwX2H8Z4hY__9.npz::native_audio_*quality*,-HwX2H8Z4hY__9.npz::query_word_audio_H_time,content probe disabled,features_v2/-HwX2H8Z4hY__9.npz,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/9,-HwX2H8Z4hY,9,vision,7.733983993530273,7.366983890533446,232,35,78,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9525470826803062,OpenFace confidence/success gate rejected 11 native frames,True,0.9827586206896551,-HwX2H8Z4hY__9.npz::native_vision_*quality*,-HwX2H8Z4hY__9.npz::query_word_vision_H_time,content probe disabled,features_v2/-HwX2H8Z4hY__9.npz,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-NFrJFQijFE/1,-NFrJFQijFE,1,text,5.745999813079834,4.380000352859496,16,768,58,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7622694910099262,none,True,1.0,-NFrJFQijFE__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-NFrJFQijFE__1.npz,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-NFrJFQijFE/1,-NFrJFQijFE,1,audio,5.745999813079834,5.526589516046884,566,74,58,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.961815122838414,F0/HNR naturally undefined or unavailable in 489 rows; support masks retained,True,1.0,-NFrJFQijFE__1.npz::native_audio_*quality*,-NFrJFQijFE__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-NFrJFQijFE__1.npz,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-NFrJFQijFE/1,-NFrJFQijFE,1,vision,5.745999813079834,0.0,142,35,58,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.0,OpenFace confidence/success gate rejected 142 native frames,False,0.0,-NFrJFQijFE__1.npz::native_vision_*quality*,-NFrJFQijFE__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-NFrJFQijFE__1.npz,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,failed_or_unavailable,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-NFrJFQijFE/2,-NFrJFQijFE,2,text,6.855999946594238,3.860000014305114,18,768,69,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5630105082224502,none,True,1.0,-NFrJFQijFE__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-NFrJFQijFE__2.npz,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-NFrJFQijFE/2,-NFrJFQijFE,2,audio,6.855999946594238,6.544698231929059,670,74,69,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9545942653019097,F0/HNR naturally undefined or unavailable in 568 rows; support masks retained,True,1.0,-NFrJFQijFE__2.npz::native_audio_*quality*,-NFrJFQijFE__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-NFrJFQijFE__2.npz,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-NFrJFQijFE/2,-NFrJFQijFE,2,vision,6.855999946594238,0.0,169,35,69,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.0,OpenFace confidence/success gate rejected 169 native frames,False,0.0,-NFrJFQijFE__2.npz::native_vision_*quality*,-NFrJFQijFE__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-NFrJFQijFE__2.npz,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,failed_or_unavailable,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/12,-THoVjtIkeU,12,text,14.896029472351074,10.120002090930939,39,768,149,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6793758101590058,none,True,1.0,-THoVjtIkeU__12.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-THoVjtIkeU__12.npz,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/12,-THoVjtIkeU,12,audio,14.896029472351074,14.592263090046677,1481,74,149,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9796075603322196,F0/HNR naturally undefined or unavailable in 790 rows; support masks retained,True,1.0,-THoVjtIkeU__12.npz::native_audio_*quality*,-THoVjtIkeU__12.npz::query_word_audio_H_time,content probe disabled,features_v2/-THoVjtIkeU__12.npz,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/12,-THoVjtIkeU,12,vision,14.896029472351074,14.816499710083006,445,35,149,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9946610093370394,none,True,1.0,-THoVjtIkeU__12.npz::native_vision_*quality*,-THoVjtIkeU__12.npz::query_word_vision_H_time,content probe disabled,features_v2/-THoVjtIkeU__12.npz,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/2,-THoVjtIkeU,2,text,4.2919921875,2.9200004227459426,10,768,43,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6803368447990547,none,True,1.0,-THoVjtIkeU__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-THoVjtIkeU__2.npz,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/2,-THoVjtIkeU,2,audio,4.2919921875,4.169859837841344,424,74,43,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9715441351421014,F0/HNR naturally undefined or unavailable in 226 rows; support masks retained,True,1.0,-THoVjtIkeU__2.npz::native_audio_*quality*,-THoVjtIkeU__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-THoVjtIkeU__2.npz,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/2,-THoVjtIkeU,2,vision,4.2919921875,4.249499797821044,128,35,43,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.990099611596985,none,True,1.0,-THoVjtIkeU__2.npz::native_vision_*quality*,-THoVjtIkeU__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-THoVjtIkeU__2.npz,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/6,-THoVjtIkeU,6,text,8.097004890441895,6.020000673830507,30,768,81,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7434848756157743,none,True,1.0,-THoVjtIkeU__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-THoVjtIkeU__6.npz,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/6,-THoVjtIkeU,6,audio,8.097004890441895,7.885292096717938,799,74,81,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9738529497525842,F0/HNR naturally undefined or unavailable in 361 rows; support masks retained,True,1.0,-THoVjtIkeU__6.npz::native_audio_*quality*,-THoVjtIkeU__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-THoVjtIkeU__6.npz,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/6,-THoVjtIkeU,6,vision,8.097004890441895,7.983000472187995,241,35,81,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9859201742130012,OpenFace confidence/success gate rejected 1 native frames,True,0.995850622406639,-THoVjtIkeU__6.npz::native_vision_*quality*,-THoVjtIkeU__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-THoVjtIkeU__6.npz,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/1,-UuX1xuaiiE,1,text,10.350000381469727,7.019999176263809,27,768,104,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6782607649785373,none,True,1.0,-UuX1xuaiiE__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-UuX1xuaiiE__1.npz,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/1,-UuX1xuaiiE,1,audio,10.350000381469727,10.122837774757594,1027,74,104,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9780519228657386,F0/HNR naturally undefined or unavailable in 535 rows; support masks retained,True,1.0,-UuX1xuaiiE__1.npz::native_audio_*quality*,-UuX1xuaiiE__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-UuX1xuaiiE__1.npz,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/1,-UuX1xuaiiE,1,vision,10.350000381469727,10.283500671386717,309,35,104,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9935749074751661,none,True,1.0,-UuX1xuaiiE__1.npz::native_vision_*quality*,-UuX1xuaiiE__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-UuX1xuaiiE__1.npz,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/0,-UuX1xuaiiE,0,text,3.6333329677581787,2.7200000882148743,9,768,37,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7486239528146399,none,True,1.0,-UuX1xuaiiE__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-UuX1xuaiiE__0.npz,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/0,-UuX1xuaiiE,0,audio,3.6333329677581787,3.5266841811102796,358,74,37,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.970647119987546,F0/HNR naturally undefined or unavailable in 182 rows; support masks retained,True,1.0,-UuX1xuaiiE__0.npz::native_audio_*quality*,-UuX1xuaiiE__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-UuX1xuaiiE__0.npz,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/0,-UuX1xuaiiE,0,vision,3.6333329677581787,3.6333329677581787,109,35,37,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-UuX1xuaiiE__0.npz::native_vision_*quality*,-UuX1xuaiiE__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-UuX1xuaiiE__0.npz,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/3,-UuX1xuaiiE,3,text,4.1300129890441895,2.14000004529953,9,768,42,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5181581876319452,none,True,1.0,-UuX1xuaiiE__3.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-UuX1xuaiiE__3.npz,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/3,-UuX1xuaiiE,3,audio,4.1300129890441895,3.973609695966179,404,74,42,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9621300723525795,F0/HNR naturally undefined or unavailable in 240 rows; support masks retained,True,1.0,-UuX1xuaiiE__3.npz::native_audio_*quality*,-UuX1xuaiiE__3.npz::query_word_audio_H_time,content probe disabled,features_v2/-UuX1xuaiiE__3.npz,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/3,-UuX1xuaiiE,3,vision,4.1300129890441895,4.083499908447266,123,35,42,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9887377883022862,none,True,1.0,-UuX1xuaiiE__3.npz::native_vision_*quality*,-UuX1xuaiiE__3.npz::query_word_vision_H_time,content probe disabled,features_v2/-UuX1xuaiiE__3.npz,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/6,-UuX1xuaiiE,6,text,8.113997459411621,5.540000081062315,22,768,82,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6827707438626734,none,True,1.0,-UuX1xuaiiE__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-UuX1xuaiiE__6.npz,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/6,-UuX1xuaiiE,6,audio,8.113997459411621,7.830245652714294,795,74,82,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9650293448923629,F0/HNR naturally undefined or unavailable in 420 rows; support masks retained,True,1.0,-UuX1xuaiiE__6.npz::native_audio_*quality*,-UuX1xuaiiE__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-UuX1xuaiiE__6.npz,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/6,-UuX1xuaiiE,6,vision,8.113997459411621,7.983500003814696,240,35,82,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9839169957533623,none,True,1.0,-UuX1xuaiiE__6.npz::native_vision_*quality*,-UuX1xuaiiE__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-UuX1xuaiiE__6.npz,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-a55Q6RWvTA/3,-a55Q6RWvTA,3,text,22.15397071838379,16.560002624988556,65,768,222,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7474959155401767,none,True,1.0,-a55Q6RWvTA__3.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-a55Q6RWvTA__3.npz,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-a55Q6RWvTA/3,-a55Q6RWvTA,3,audio,22.15397071838379,21.864563874094873,2209,74,222,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9869365700637692,F0/HNR naturally undefined or unavailable in 810 rows; support masks retained,True,1.0,-a55Q6RWvTA__3.npz::native_audio_*quality*,-a55Q6RWvTA__3.npz::query_word_audio_H_time,content probe disabled,features_v2/-a55Q6RWvTA__3.npz,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-a55Q6RWvTA/3,-a55Q6RWvTA,3,vision,22.15397071838379,22.116500854492195,664,35,222,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9983086614870128,none,True,1.0,-a55Q6RWvTA__3.npz::native_vision_*quality*,-a55Q6RWvTA__3.npz::query_word_vision_H_time,content probe disabled,features_v2/-a55Q6RWvTA__3.npz,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-aNfi7CP8vM/7,-aNfi7CP8vM,7,text,8.694987297058105,6.360000669956207,18,768,87,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7314560047842823,none,True,1.0,-aNfi7CP8vM__7.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-aNfi7CP8vM__7.npz,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-aNfi7CP8vM/7,-aNfi7CP8vM,7,audio,8.694987297058105,8.439774326376012,854,74,87,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9706482641132274,F0/HNR naturally undefined or unavailable in 362 rows; support masks retained,True,1.0,-aNfi7CP8vM__7.npz::native_audio_*quality*,-aNfi7CP8vM__7.npz::query_word_audio_H_time,content probe disabled,features_v2/-aNfi7CP8vM__7.npz,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-aNfi7CP8vM/7,-aNfi7CP8vM,7,vision,8.694987297058105,8.549499511718754,257,35,87,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.983267625314579,none,True,1.0,-aNfi7CP8vM__7.npz::native_vision_*quality*,-aNfi7CP8vM__7.npz::query_word_vision_H_time,content probe disabled,features_v2/-aNfi7CP8vM__7.npz,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,text,10.966667175292969,6.199999362230302,14,768,110,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5653494596971456,1 word intervals are not reliable under the fixed-transcript CTC path,True,0.9285714285714286,-aqamKhZ1Ec__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-aqamKhZ1Ec__0.npz,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,audio,10.966667175292969,10.701902656941803,1090,74,110,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9758573398718929,F0/HNR naturally undefined or unavailable in 697 rows; support masks retained,True,1.0,-aqamKhZ1Ec__0.npz::native_audio_*quality*,-aqamKhZ1Ec__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-aqamKhZ1Ec__0.npz,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,vision,10.966667175292969,10.966667175292969,329,35,110,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-aqamKhZ1Ec__0.npz::native_vision_*quality*,-aqamKhZ1Ec__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-aqamKhZ1Ec__0.npz,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/1,-dxfTGcXJoc,1,text,16.16100311279297,11.500000417232513,36,768,162,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.711589518111606,none,True,1.0,-dxfTGcXJoc__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-dxfTGcXJoc__1.npz,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/1,-dxfTGcXJoc,1,audio,16.16100311279297,15.763708187116158,1602,74,162,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9754164439605661,F0/HNR naturally undefined or unavailable in 941 rows; support masks retained,True,1.0,-dxfTGcXJoc__1.npz::native_audio_*quality*,-dxfTGcXJoc__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-dxfTGcXJoc__1.npz,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/1,-dxfTGcXJoc,1,vision,16.16100311279297,16.049501419067383,482,35,162,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9931005709888564,none,True,1.0,-dxfTGcXJoc__1.npz::native_vision_*quality*,-dxfTGcXJoc__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-dxfTGcXJoc__1.npz,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/0,-dxfTGcXJoc,0,text,20.5,15.460005044937132,41,768,205,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7541465875579089,3 word intervals are not reliable under the fixed-transcript CTC path,True,0.926829268292683,-dxfTGcXJoc__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-dxfTGcXJoc__0.npz,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/0,-dxfTGcXJoc,0,audio,20.5,20.095867107043393,2042,74,205,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9802862003435802,F0/HNR naturally undefined or unavailable in 1164 rows; support masks retained,True,1.0,-dxfTGcXJoc__0.npz::native_audio_*quality*,-dxfTGcXJoc__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-dxfTGcXJoc__0.npz,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/0,-dxfTGcXJoc,0,vision,20.5,15.716499805450438,615,35,205,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.7666585270951433,OpenFace confidence/success gate rejected 144 native frames,True,0.7658536585365854,-dxfTGcXJoc__0.npz::native_vision_*quality*,-dxfTGcXJoc__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-dxfTGcXJoc__0.npz,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/2,-dxfTGcXJoc,2,text,16.697982788085938,12.100000262260437,34,768,167,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.724638443806148,2 word intervals are not reliable under the fixed-transcript CTC path,True,0.9411764705882353,-dxfTGcXJoc__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-dxfTGcXJoc__2.npz,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/2,-dxfTGcXJoc,2,audio,16.697982788085938,16.37964679743792,1664,74,167,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9809356618288558,F0/HNR naturally undefined or unavailable in 938 rows; support masks retained,True,1.0,-dxfTGcXJoc__2.npz::native_audio_*quality*,-dxfTGcXJoc__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-dxfTGcXJoc__2.npz,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/2,-dxfTGcXJoc,2,vision,16.697982788085938,16.649499893188473,500,35,167,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9970964819216332,none,True,1.0,-dxfTGcXJoc__2.npz::native_vision_*quality*,-dxfTGcXJoc__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-dxfTGcXJoc__2.npz,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/6,-dxfTGcXJoc,6,text,12.735026359558105,9.620002269744873,27,768,128,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7553971227177483,none,True,1.0,-dxfTGcXJoc__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-dxfTGcXJoc__6.npz,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/6,-dxfTGcXJoc,6,audio,12.735026359558105,12.405358507826525,1263,74,128,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9741132964766773,F0/HNR naturally undefined or unavailable in 825 rows; support masks retained,True,1.0,-dxfTGcXJoc__6.npz::native_audio_*quality*,-dxfTGcXJoc__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-dxfTGcXJoc__6.npz,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/6,-dxfTGcXJoc,6,vision,12.735026359558105,12.683500289916996,381,35,128,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9959539879866494,none,True,1.0,-dxfTGcXJoc__6.npz::native_vision_*quality*,-dxfTGcXJoc__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-dxfTGcXJoc__6.npz,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/26,-egA8-b7-3M,26,text,6.030990123748779,4.419999480247498,15,768,61,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7328812333554424,none,True,1.0,-egA8-b7-3M__26.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__26.npz,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/26,-egA8-b7-3M,26,audio,6.030990123748779,5.863275968545191,593,74,61,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9721912734456047,F0/HNR naturally undefined or unavailable in 213 rows; support masks retained,True,1.0,-egA8-b7-3M__26.npz::native_audio_*quality*,-egA8-b7-3M__26.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__26.npz,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/26,-egA8-b7-3M,26,vision,6.030990123748779,5.949500083923338,179,35,61,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9864881158560432,none,True,1.0,-egA8-b7-3M__26.npz::native_vision_*quality*,-egA8-b7-3M__26.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__26.npz,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/17,-egA8-b7-3M,17,text,7.271028995513916,3.720000565052031,16,768,73,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5116195475698418,none,True,1.0,-egA8-b7-3M__17.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__17.npz,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/17,-egA8-b7-3M,17,audio,7.271028995513916,7.009598084398218,710,74,73,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9640448537233182,F0/HNR naturally undefined or unavailable in 317 rows; support masks retained,True,1.0,-egA8-b7-3M__17.npz::native_audio_*quality*,-egA8-b7-3M__17.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__17.npz,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/17,-egA8-b7-3M,17,vision,7.271028995513916,7.149499893188478,215,35,73,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9832858454559295,none,True,1.0,-egA8-b7-3M__17.npz::native_vision_*quality*,-egA8-b7-3M__17.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__17.npz,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/18,-egA8-b7-3M,18,text,8.386002540588379,5.15999972820282,22,768,84,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6153110141844512,none,True,1.0,-egA8-b7-3M__18.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__18.npz,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/18,-egA8-b7-3M,18,audio,8.386002540588379,8.171099232660758,827,74,84,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9743735698996648,F0/HNR naturally undefined or unavailable in 350 rows; support masks retained,True,1.0,-egA8-b7-3M__18.npz::native_audio_*quality*,-egA8-b7-3M__18.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__18.npz,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/18,-egA8-b7-3M,18,vision,8.386002540588379,8.316499710083008,250,35,84,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9917120427558928,none,True,1.0,-egA8-b7-3M__18.npz::native_vision_*quality*,-egA8-b7-3M__18.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__18.npz,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/16,-egA8-b7-3M,16,text,5.538021087646484,3.759999781847,11,768,56,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6789428430011455,none,True,1.0,-egA8-b7-3M__16.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__16.npz,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/16,-egA8-b7-3M,16,audio,5.538021087646484,5.426222804430368,549,74,56,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9798125934432604,F0/HNR naturally undefined or unavailable in 210 rows; support masks retained,True,1.0,-egA8-b7-3M__16.npz::native_audio_*quality*,-egA8-b7-3M__16.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__16.npz,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/16,-egA8-b7-3M,16,vision,5.538021087646484,5.538021087646484,166,35,56,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-egA8-b7-3M__16.npz::native_vision_*quality*,-egA8-b7-3M__16.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__16.npz,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/13,-egA8-b7-3M,13,text,4.18398380279541,2.9599998593330383,11,768,42,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.707459684082763,none,True,1.0,-egA8-b7-3M__13.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__13.npz,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/13,-egA8-b7-3M,13,audio,4.18398380279541,3.9740897545943383,403,74,42,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9498339242946311,F0/HNR naturally undefined or unavailable in 184 rows; support masks retained,True,1.0,-egA8-b7-3M__13.npz::native_audio_*quality*,-egA8-b7-3M__13.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__13.npz,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/13,-egA8-b7-3M,13,vision,4.18398380279541,4.0834999084472665,123,35,42,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9759836798887682,none,True,1.0,-egA8-b7-3M__13.npz::native_vision_*quality*,-egA8-b7-3M__13.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__13.npz,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/1,-egA8-b7-3M,1,text,10.266016006469727,6.960000624880197,20,768,103,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6779651054989538,1 word intervals are not reliable under the fixed-transcript CTC path,True,0.95,-egA8-b7-3M__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__1.npz,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/1,-egA8-b7-3M,1,audio,10.266016006469727,10.060219577840856,1016,74,103,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.979953622856308,F0/HNR naturally undefined or unavailable in 356 rows; support masks retained,True,1.0,-egA8-b7-3M__1.npz::native_audio_*quality*,-egA8-b7-3M__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__1.npz,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/1,-egA8-b7-3M,1,vision,10.266016006469727,10.183500289916992,306,35,103,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9919622454805513,none,True,1.0,-egA8-b7-3M__1.npz::native_vision_*quality*,-egA8-b7-3M__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__1.npz,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/6,-egA8-b7-3M,6,text,6.264974117279053,4.020000159740448,12,768,63,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6416626923730019,none,True,1.0,-egA8-b7-3M__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__6.npz,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/6,-egA8-b7-3M,6,audio,6.264974117279053,6.125261839982626,619,74,63,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.977699464565848,F0/HNR naturally undefined or unavailable in 214 rows; support masks retained,True,1.0,-egA8-b7-3M__6.npz::native_audio_*quality*,-egA8-b7-3M__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__6.npz,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/6,-egA8-b7-3M,6,vision,6.264974117279053,6.2164998054504395,187,35,63,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9922626477107193,none,True,1.0,-egA8-b7-3M__6.npz::native_vision_*quality*,-egA8-b7-3M__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__6.npz,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/9,-egA8-b7-3M,9,text,5.0899739265441895,3.099999785423278,10,768,51,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6090404057389752,none,True,1.0,-egA8-b7-3M__9.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__9.npz,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/9,-egA8-b7-3M,9,audio,5.0899739265441895,4.889356205592284,494,74,51,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9605857075405269,F0/HNR naturally undefined or unavailable in 173 rows; support masks retained,True,1.0,-egA8-b7-3M__9.npz::native_audio_*quality*,-egA8-b7-3M__9.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__9.npz,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/9,-egA8-b7-3M,9,vision,5.0899739265441895,4.983500003814698,150,35,51,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.979081636906973,none,True,1.0,-egA8-b7-3M__9.npz::native_vision_*quality*,-egA8-b7-3M__9.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__9.npz,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/20,-egA8-b7-3M,20,text,6.644987106323242,4.5400003790855425,20,768,67,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6832218492591754,none,True,1.0,-egA8-b7-3M__20.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-egA8-b7-3M__20.npz,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/20,-egA8-b7-3M,20,audio,6.644987106323242,6.393548356520164,648,74,67,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9621611380458791,F0/HNR naturally undefined or unavailable in 299 rows; support masks retained,True,1.0,-egA8-b7-3M__20.npz::native_audio_*quality*,-egA8-b7-3M__20.npz::query_word_audio_H_time,content probe disabled,features_v2/-egA8-b7-3M__20.npz,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/20,-egA8-b7-3M,20,vision,6.644987106323242,6.5164999961853045,196,35,67,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9806640542589358,none,True,1.0,-egA8-b7-3M__20.npz::native_vision_*quality*,-egA8-b7-3M__20.npz::query_word_vision_H_time,content probe disabled,features_v2/-egA8-b7-3M__20.npz,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/3,-iRBcNs9oI8,3,text,5.620999813079834,2.180000007152557,8,768,57,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.3878313609048318,none,True,1.0,-iRBcNs9oI8__3.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-iRBcNs9oI8__3.npz,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/3,-iRBcNs9oI8,3,audio,5.620999813079834,5.476260099862072,554,74,57,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9742501835917237,F0/HNR naturally undefined or unavailable in 229 rows; support masks retained,True,1.0,-iRBcNs9oI8__3.npz::native_audio_*quality*,-iRBcNs9oI8__3.npz::query_word_audio_H_time,content probe disabled,features_v2/-iRBcNs9oI8__3.npz,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/3,-iRBcNs9oI8,3,vision,5.620999813079834,5.555500030517579,167,35,57,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9883473074647966,none,True,1.0,-iRBcNs9oI8__3.npz::native_vision_*quality*,-iRBcNs9oI8__3.npz::query_word_vision_H_time,content probe disabled,features_v2/-iRBcNs9oI8__3.npz,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/7,-iRBcNs9oI8,7,text,4.104000091552734,2.559999972581863,9,768,42,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6237816558169949,none,True,1.0,-iRBcNs9oI8__7.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-iRBcNs9oI8__7.npz,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/7,-iRBcNs9oI8,7,audio,4.104000091552734,3.955483294822074,401,74,42,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9638116975103503,F0/HNR naturally undefined or unavailable in 186 rows; support masks retained,True,1.0,-iRBcNs9oI8__7.npz::native_audio_*quality*,-iRBcNs9oI8__7.npz::query_word_audio_H_time,content probe disabled,features_v2/-iRBcNs9oI8__7.npz,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/7,-iRBcNs9oI8,7,vision,4.104000091552734,3.9874999523162833,120,35,42,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9716130271351009,none,True,1.0,-iRBcNs9oI8__7.npz::native_vision_*quality*,-iRBcNs9oI8__7.npz::query_word_vision_H_time,content probe disabled,features_v2/-iRBcNs9oI8__7.npz,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/6,-iRBcNs9oI8,6,text,2.9030001163482666,1.5799999386072159,5,768,30,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5442645109483236,none,True,1.0,-iRBcNs9oI8__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-iRBcNs9oI8__6.npz,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/6,-iRBcNs9oI8,6,audio,2.9030001163482666,2.7904534790966964,283,74,30,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9612309222387684,F0/HNR naturally undefined or unavailable in 112 rows; support masks retained,True,1.0,-iRBcNs9oI8__6.npz::native_audio_*quality*,-iRBcNs9oI8__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-iRBcNs9oI8__6.npz,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/6,-iRBcNs9oI8,6,vision,2.9030001163482666,2.819499969482422,85,35,30,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9712366023013183,none,True,1.0,-iRBcNs9oI8__6.npz::native_vision_*quality*,-iRBcNs9oI8__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-iRBcNs9oI8__6.npz,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/9,-iRBcNs9oI8,9,text,3.4159998893737793,1.4199999868869781,5,768,35,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.41569087613386674,none,True,1.0,-iRBcNs9oI8__9.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-iRBcNs9oI8__9.npz,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/9,-iRBcNs9oI8,9,audio,3.4159998893737793,3.3255532545012394,338,74,35,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9735226470135745,F0/HNR naturally undefined or unavailable in 180 rows; support masks retained,True,1.0,-iRBcNs9oI8__9.npz::native_audio_*quality*,-iRBcNs9oI8__9.npz::query_word_audio_H_time,content probe disabled,features_v2/-iRBcNs9oI8__9.npz,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/9,-iRBcNs9oI8,9,vision,3.4159998893737793,3.049999833106995,102,35,35,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.8928571229158092,OpenFace confidence/success gate rejected 11 native frames,True,0.8921568627450981,-iRBcNs9oI8__9.npz::native_vision_*quality*,-iRBcNs9oI8__9.npz::query_word_vision_H_time,content probe disabled,features_v2/-iRBcNs9oI8__9.npz,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/8,-iRBcNs9oI8,8,text,8.093000411987305,4.739999324083329,21,768,81,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5856912248592586,none,True,1.0,-iRBcNs9oI8__8.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-iRBcNs9oI8__8.npz,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/8,-iRBcNs9oI8,8,audio,8.093000411987305,7.9095671503125,803,74,81,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9773343318501375,F0/HNR naturally undefined or unavailable in 431 rows; support masks retained,True,1.0,-iRBcNs9oI8__8.npz::native_audio_*quality*,-iRBcNs9oI8__8.npz::query_word_audio_H_time,content probe disabled,features_v2/-iRBcNs9oI8__8.npz,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/8,-iRBcNs9oI8,8,vision,8.093000411987305,8.024499893188477,241,35,81,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9915358315443348,none,True,1.0,-iRBcNs9oI8__8.npz::native_vision_*quality*,-iRBcNs9oI8__8.npz::query_word_vision_H_time,content probe disabled,features_v2/-iRBcNs9oI8__8.npz,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-lzEya4AM_4/5,-lzEya4AM_4,5,text,7.675000190734863,5.020000159740448,19,768,77,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6540716657962448,none,True,1.0,-lzEya4AM_4__5.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-lzEya4AM_4__5.npz,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-lzEya4AM_4/5,-lzEya4AM_4,5,audio,7.675000190734863,7.46790949396185,757,74,77,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.973017499462344,F0/HNR naturally undefined or unavailable in 376 rows; support masks retained,True,1.0,-lzEya4AM_4__5.npz::native_audio_*quality*,-lzEya4AM_4__5.npz::query_word_audio_H_time,content probe disabled,features_v2/-lzEya4AM_4__5.npz,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-lzEya4AM_4/5,-lzEya4AM_4,5,vision,7.675000190734863,7.616499900817873,229,35,77,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9923778125780881,none,True,1.0,-lzEya4AM_4__5.npz::native_vision_*quality*,-lzEya4AM_4__5.npz::query_word_vision_H_time,content probe disabled,features_v2/-lzEya4AM_4__5.npz,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-lzEya4AM_4/6,-lzEya4AM_4,6,text,14.7919921875,10.32000094652176,43,768,148,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6976748510753471,none,True,1.0,-lzEya4AM_4__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-lzEya4AM_4__6.npz,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-lzEya4AM_4/6,-lzEya4AM_4,6,audio,14.7919921875,14.509920233004806,1475,74,148,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9809307663957827,F0/HNR naturally undefined or unavailable in 853 rows; support masks retained,True,1.0,-lzEya4AM_4__6.npz::native_audio_*quality*,-lzEya4AM_4__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-lzEya4AM_4__6.npz,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-lzEya4AM_4/6,-lzEya4AM_4,6,vision,14.7919921875,14.683000266551971,443,35,148,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9926316942595378,OpenFace confidence/success gate rejected 2 native frames,True,1.0,-lzEya4AM_4__6.npz::native_vision_*quality*,-lzEya4AM_4__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-lzEya4AM_4__6.npz,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,text,6.103000164031982,1.9600001871585846,13,768,62,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.3211535530852254,none,True,1.0,-mJ2ud6oKI8__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-mJ2ud6oKI8__1.npz,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,audio,6.103000164031982,5.890635232667666,606,74,62,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9652031909460058,F0/HNR naturally undefined or unavailable in 606 rows; support masks retained,True,1.0,-mJ2ud6oKI8__1.npz::native_audio_*quality*,-mJ2ud6oKI8__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__1.npz,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,vision,6.103000164031982,0.0,182,35,62,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.0,OpenFace confidence/success gate rejected 182 native frames,False,0.0,-mJ2ud6oKI8__1.npz::native_vision_*quality*,-mJ2ud6oKI8__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__1.npz,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,failed_or_unavailable,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/2,-mJ2ud6oKI8,2,text,5.730999946594238,3.5200002752244473,11,768,58,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6142035086418519,none,True,1.0,-mJ2ud6oKI8__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-mJ2ud6oKI8__2.npz,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/2,-mJ2ud6oKI8,2,audio,5.730999946594238,5.547395957482827,571,74,58,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.967963009802413,F0/HNR naturally undefined or unavailable in 571 rows; support masks retained,True,1.0,-mJ2ud6oKI8__2.npz::native_audio_*quality*,-mJ2ud6oKI8__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__2.npz,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/2,-mJ2ud6oKI8,2,vision,5.730999946594238,2.4679999351501465,171,35,58,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.4306403695949788,OpenFace confidence/success gate rejected 97 native frames,True,0.6491228070175439,-mJ2ud6oKI8__2.npz::native_vision_*quality*,-mJ2ud6oKI8__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__2.npz,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/6,-mJ2ud6oKI8,6,text,2.256999969482422,1.4199999272823334,5,768,23,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6291537201960927,none,True,1.0,-mJ2ud6oKI8__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-mJ2ud6oKI8__6.npz,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/6,-mJ2ud6oKI8,6,audio,2.256999969482422,2.1964105617355654,223,74,23,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9731548920841364,F0/HNR naturally undefined or unavailable in 117 rows; support masks retained,True,1.0,-mJ2ud6oKI8__6.npz::native_audio_*quality*,-mJ2ud6oKI8__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__6.npz,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/6,-mJ2ud6oKI8,6,vision,2.256999969482422,2.218499898910522,67,35,23,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9829419268531365,none,True,1.0,-mJ2ud6oKI8__6.npz::native_vision_*quality*,-mJ2ud6oKI8__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__6.npz,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/9,-mJ2ud6oKI8,9,text,7.0329999923706055,5.320001155138016,22,768,71,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7564341192818357,none,True,1.0,-mJ2ud6oKI8__9.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-mJ2ud6oKI8__9.npz,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/9,-mJ2ud6oKI8,9,audio,7.0329999923706055,6.776402102732981,691,74,71,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9635151585502657,F0/HNR naturally undefined or unavailable in 490 rows; support masks retained,True,1.0,-mJ2ud6oKI8__9.npz::native_audio_*quality*,-mJ2ud6oKI8__9.npz::query_word_audio_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__9.npz,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/9,-mJ2ud6oKI8,9,vision,7.0329999923706055,6.923500061035155,208,35,71,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9844305514781408,none,True,1.0,-mJ2ud6oKI8__9.npz::native_vision_*quality*,-mJ2ud6oKI8__9.npz::query_word_vision_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__9.npz,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/8,-mJ2ud6oKI8,8,text,4.191999912261963,2.740000009536743,11,768,42,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6536259701537698,none,True,1.0,-mJ2ud6oKI8__8.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-mJ2ud6oKI8__8.npz,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/8,-mJ2ud6oKI8,8,audio,4.191999912261963,4.1114204123213485,418,74,42,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9807777906423822,F0/HNR naturally undefined or unavailable in 222 rows; support masks retained,True,1.0,-mJ2ud6oKI8__8.npz::native_audio_*quality*,-mJ2ud6oKI8__8.npz::query_word_audio_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__8.npz,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/8,-mJ2ud6oKI8,8,vision,4.191999912261963,4.153500080108643,125,35,42,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9908158795421954,none,True,1.0,-mJ2ud6oKI8__8.npz::native_vision_*quality*,-mJ2ud6oKI8__8.npz::query_word_vision_H_time,content probe disabled,features_v2/-mJ2ud6oKI8__8.npz,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mqbVkbCndg/0,-mqbVkbCndg,0,text,6.466667175292969,4.500000059604645,12,768,65,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6958762431438688,none,True,1.0,-mqbVkbCndg__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-mqbVkbCndg__0.npz,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mqbVkbCndg/0,-mqbVkbCndg,0,audio,6.466667175292969,6.299567509341885,639,74,65,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9741598475039016,F0/HNR naturally undefined or unavailable in 318 rows; support masks retained,True,1.0,-mqbVkbCndg__0.npz::native_audio_*quality*,-mqbVkbCndg__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-mqbVkbCndg__0.npz,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mqbVkbCndg/0,-mqbVkbCndg,0,vision,6.466667175292969,6.466667175292969,194,35,65,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-mqbVkbCndg__0.npz::native_vision_*quality*,-mqbVkbCndg__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-mqbVkbCndg__0.npz,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-t217m2on-s/2,-t217m2on-s,2,text,5.6860032081604,3.020000100135804,12,768,57,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5311288069274355,none,True,1.0,-t217m2on-s__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-t217m2on-s__2.npz,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-t217m2on-s/2,-t217m2on-s,2,audio,5.6860032081604,5.571092048206844,564,74,57,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9797905214354015,F0/HNR naturally undefined or unavailable in 219 rows; support masks retained,True,1.0,-t217m2on-s__2.npz::native_audio_*quality*,-t217m2on-s__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-t217m2on-s__2.npz,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-t217m2on-s/2,-t217m2on-s,2,vision,5.6860032081604,5.649499893188477,170,35,57,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9935801452029547,none,True,1.0,-t217m2on-s__2.npz::native_vision_*quality*,-t217m2on-s__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-t217m2on-s__2.npz,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-t217m2on-s/7,-t217m2on-s,7,text,17.183008193969727,12.199996888637546,45,768,172,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7100035541459534,none,True,1.0,-t217m2on-s__7.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-t217m2on-s__7.npz,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-t217m2on-s/7,-t217m2on-s,7,audio,17.183008193969727,16.794731244847583,1703,74,172,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9774034357232974,F0/HNR naturally undefined or unavailable in 857 rows; support masks retained,True,1.0,-t217m2on-s__7.npz::native_audio_*quality*,-t217m2on-s__7.npz::query_word_audio_H_time,content probe disabled,features_v2/-t217m2on-s__7.npz,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-t217m2on-s/7,-t217m2on-s,7,vision,17.183008193969727,17.04950141906738,512,35,172,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9922303025526578,none,True,1.0,-t217m2on-s__7.npz::native_vision_*quality*,-t217m2on-s__7.npz::query_word_vision_H_time,content probe disabled,features_v2/-t217m2on-s__7.npz,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tANM6ETl_M/3,-tANM6ETl_M,3,text,6.758008003234863,4.280000329017639,22,768,68,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6333227671480895,none,True,1.0,-tANM6ETl_M__3.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-tANM6ETl_M__3.npz,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tANM6ETl_M/3,-tANM6ETl_M,3,audio,6.758008003234863,6.533308992514736,661,74,68,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9667507036670318,F0/HNR naturally undefined or unavailable in 260 rows; support masks retained,True,1.0,-tANM6ETl_M__3.npz::native_audio_*quality*,-tANM6ETl_M__3.npz::query_word_audio_H_time,content probe disabled,features_v2/-tANM6ETl_M__3.npz,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tANM6ETl_M/3,-tANM6ETl_M,3,vision,6.758008003234863,6.616499900817871,200,35,68,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9790606784796265,OpenFace confidence/success gate rejected 1 native frames,True,1.0,-tANM6ETl_M__3.npz::native_vision_*quality*,-tANM6ETl_M__3.npz::query_word_vision_H_time,content probe disabled,features_v2/-tANM6ETl_M__3.npz,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/11,-tPCytz4rww,11,text,5.466991901397705,4.0599993616342545,17,768,55,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7426386273951247,none,True,1.0,-tPCytz4rww__11.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-tPCytz4rww__11.npz,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/11,-tPCytz4rww,11,audio,5.466991901397705,5.319195072393161,538,74,55,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9729656030829755,F0/HNR naturally undefined or unavailable in 220 rows; support masks retained,True,1.0,-tPCytz4rww__11.npz::native_audio_*quality*,-tPCytz4rww__11.npz::query_word_audio_H_time,content probe disabled,features_v2/-tPCytz4rww__11.npz,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/11,-tPCytz4rww,11,vision,5.466991901397705,5.466991901397705,164,35,55,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-tPCytz4rww__11.npz::native_vision_*quality*,-tPCytz4rww__11.npz::query_word_vision_H_time,content probe disabled,features_v2/-tPCytz4rww__11.npz,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/10,-tPCytz4rww,10,text,4.788997173309326,3.279999792575836,12,768,48,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6849032634340162,none,True,1.0,-tPCytz4rww__10.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-tPCytz4rww__10.npz,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/10,-tPCytz4rww,10,audio,4.788997173309326,4.579772797790734,468,74,48,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.956311443096131,F0/HNR naturally undefined or unavailable in 366 rows; support masks retained,True,1.0,-tPCytz4rww__10.npz::native_audio_*quality*,-tPCytz4rww__10.npz::query_word_audio_H_time,content probe disabled,features_v2/-tPCytz4rww__10.npz,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/10,-tPCytz4rww,10,vision,4.788997173309326,4.71649980545044,142,35,48,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.984861680799701,none,True,1.0,-tPCytz4rww__10.npz::native_vision_*quality*,-tPCytz4rww__10.npz::query_word_vision_H_time,content probe disabled,features_v2/-tPCytz4rww__10.npz,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/12,-tPCytz4rww,12,text,11.863997459411621,7.539998061954974,26,768,119,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6355360482628517,none,True,1.0,-tPCytz4rww__12.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-tPCytz4rww__12.npz,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/12,-tPCytz4rww,12,audio,11.863997459411621,11.566984429552749,1174,74,119,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9749651809286882,F0/HNR naturally undefined or unavailable in 626 rows; support masks retained,True,1.0,-tPCytz4rww__12.npz::native_audio_*quality*,-tPCytz4rww__12.npz::query_word_audio_H_time,content probe disabled,features_v2/-tPCytz4rww__12.npz,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/12,-tPCytz4rww,12,vision,11.863997459411621,11.749500274658205,353,35,119,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9903491900478631,none,True,1.0,-tPCytz4rww__12.npz::native_vision_*quality*,-tPCytz4rww__12.npz::query_word_vision_H_time,content probe disabled,features_v2/-tPCytz4rww__12.npz,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/16,-tPCytz4rww,16,text,7.205989837646484,3.3600009679794303,16,768,73,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.46627889348742474,none,True,1.0,-tPCytz4rww__16.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-tPCytz4rww__16.npz,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/16,-tPCytz4rww,16,audio,7.205989837646484,7.033848639275576,714,74,73,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9761113736975335,F0/HNR naturally undefined or unavailable in 390 rows; support masks retained,True,1.0,-tPCytz4rww__16.npz::native_audio_*quality*,-tPCytz4rww__16.npz::query_word_audio_H_time,content probe disabled,features_v2/-tPCytz4rww__16.npz,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/16,-tPCytz4rww,16,vision,7.205989837646484,7.205989837646484,216,35,73,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-tPCytz4rww__16.npz::native_vision_*quality*,-tPCytz4rww__16.npz::query_word_vision_H_time,content probe disabled,features_v2/-tPCytz4rww__16.npz,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/18,-tPCytz4rww,18,text,6.644987106323242,3.960000157356262,16,768,67,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5959379745956169,none,True,1.0,-tPCytz4rww__18.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-tPCytz4rww__18.npz,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/18,-tPCytz4rww,18,audio,6.644987106323242,6.500515924112216,657,74,67,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9782586211380982,F0/HNR naturally undefined or unavailable in 253 rows; support masks retained,True,1.0,-tPCytz4rww__18.npz::native_audio_*quality*,-tPCytz4rww__18.npz::query_word_audio_H_time,content probe disabled,features_v2/-tPCytz4rww__18.npz,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/18,-tPCytz4rww,18,vision,6.644987106323242,6.644987106323242,199,35,67,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-tPCytz4rww__18.npz::native_vision_*quality*,-tPCytz4rww__18.npz::query_word_vision_H_time,content probe disabled,features_v2/-tPCytz4rww__18.npz,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-vxjVxOeScU/4,-vxjVxOeScU,4,text,9.127017974853516,6.779999792575836,21,768,92,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7428493962930595,none,True,1.0,-vxjVxOeScU__4.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-vxjVxOeScU__4.npz,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-vxjVxOeScU/4,-vxjVxOeScU,4,audio,9.127017974853516,8.956674983372558,904,74,92,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9813364023221733,F0/HNR naturally undefined or unavailable in 298 rows; support masks retained,True,1.0,-vxjVxOeScU__4.npz::native_audio_*quality*,-vxjVxOeScU__4.npz::query_word_audio_H_time,content probe disabled,features_v2/-vxjVxOeScU__4.npz,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-vxjVxOeScU/4,-vxjVxOeScU,4,vision,9.127017974853516,9.04949951171875,272,35,92,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9915067042326046,none,True,1.0,-vxjVxOeScU__4.npz::native_vision_*quality*,-vxjVxOeScU__4.npz::query_word_vision_H_time,content probe disabled,features_v2/-vxjVxOeScU__4.npz,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wMB_hJL-3o/7,-wMB_hJL-3o,7,text,6.044010162353516,4.440000116825104,22,768,61,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7346116233358853,none,True,1.0,-wMB_hJL-3o__7.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-wMB_hJL-3o__7.npz,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wMB_hJL-3o/7,-wMB_hJL-3o,7,audio,6.044010162353516,5.893141134365185,598,74,61,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9750382570618341,F0/HNR naturally undefined or unavailable in 305 rows; support masks retained,True,1.0,-wMB_hJL-3o__7.npz::native_audio_*quality*,-wMB_hJL-3o__7.npz::query_word_audio_H_time,content probe disabled,features_v2/-wMB_hJL-3o__7.npz,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wMB_hJL-3o/7,-wMB_hJL-3o,7,vision,6.044010162353516,6.010509967803955,181,35,61,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.994457290168335,OpenFace confidence/success gate rejected 1 native frames,True,0.994475138121547,-wMB_hJL-3o__7.npz::native_vision_*quality*,-wMB_hJL-3o__7.npz::query_word_vision_H_time,content probe disabled,features_v2/-wMB_hJL-3o__7.npz,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/1,-wny0OAz3g8,1,text,6.844009876251221,5.439999848604204,23,768,69,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7948556397443339,1 word intervals are not reliable under the fixed-transcript CTC path,True,0.9565217391304348,-wny0OAz3g8__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-wny0OAz3g8__1.npz,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/1,-wny0OAz3g8,1,audio,6.844009876251221,6.718154723579819,678,74,69,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9816109042875406,F0/HNR naturally undefined or unavailable in 216 rows; support masks retained,True,1.0,-wny0OAz3g8__1.npz::native_audio_*quality*,-wny0OAz3g8__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-wny0OAz3g8__1.npz,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/1,-wny0OAz3g8,1,vision,6.844009876251221,6.844009876251221,205,35,69,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-wny0OAz3g8__1.npz::native_vision_*quality*,-wny0OAz3g8__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-wny0OAz3g8__1.npz,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/0,-wny0OAz3g8,0,text,3.3333330154418945,2.260000020265579,9,768,34,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6780000707387991,none,True,1.0,-wny0OAz3g8__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-wny0OAz3g8__0.npz,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/0,-wny0OAz3g8,0,audio,3.3333330154418945,3.2479932002119103,328,74,34,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9743980529894127,F0/HNR naturally undefined or unavailable in 96 rows; support masks retained,True,1.0,-wny0OAz3g8__0.npz::native_audio_*quality*,-wny0OAz3g8__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-wny0OAz3g8__0.npz,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/0,-wny0OAz3g8,0,vision,3.3333330154418945,3.3333330154418945,100,35,34,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-wny0OAz3g8__0.npz::native_vision_*quality*,-wny0OAz3g8__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-wny0OAz3g8__0.npz,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/3,-wny0OAz3g8,3,text,6.146028995513916,4.799999862909317,20,768,62,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7809920627470052,none,True,1.0,-wny0OAz3g8__3.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-wny0OAz3g8__3.npz,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/3,-wny0OAz3g8,3,audio,6.146028995513916,6.066670619555421,610,74,62,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9870878617695393,F0/HNR naturally undefined or unavailable in 123 rows; support masks retained,True,1.0,-wny0OAz3g8__3.npz::native_audio_*quality*,-wny0OAz3g8__3.npz::query_word_audio_H_time,content probe disabled,features_v2/-wny0OAz3g8__3.npz,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/3,-wny0OAz3g8,3,vision,6.146028995513916,6.146028995513916,184,35,62,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-wny0OAz3g8__3.npz::native_vision_*quality*,-wny0OAz3g8__3.npz::query_word_vision_H_time,content probe disabled,features_v2/-wny0OAz3g8__3.npz,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/2,-wny0OAz3g8,2,text,6.686978816986084,5.400000013411044,23,768,67,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.8075395722346405,none,True,1.0,-wny0OAz3g8__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-wny0OAz3g8__2.npz,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/2,-wny0OAz3g8,2,audio,6.686978816986084,6.463760925305857,653,74,67,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9666190221639084,F0/HNR naturally undefined or unavailable in 218 rows; support masks retained,True,1.0,-wny0OAz3g8__2.npz::native_audio_*quality*,-wny0OAz3g8__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-wny0OAz3g8__2.npz,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/2,-wny0OAz3g8,2,vision,6.686978816986084,6.549499988555909,197,35,67,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9794408159210921,none,True,1.0,-wny0OAz3g8__2.npz::native_vision_*quality*,-wny0OAz3g8__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-wny0OAz3g8__2.npz,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/5,-wny0OAz3g8,5,text,6.9119791984558105,5.240000337362289,20,768,70,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7581041821614489,none,True,1.0,-wny0OAz3g8__5.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-wny0OAz3g8__5.npz,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/5,-wny0OAz3g8,5,audio,6.9119791984558105,6.675737296407287,675,74,70,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9658213812186671,F0/HNR naturally undefined or unavailable in 244 rows; support masks retained,True,1.0,-wny0OAz3g8__5.npz::native_audio_*quality*,-wny0OAz3g8__5.npz::query_word_audio_H_time,content probe disabled,features_v2/-wny0OAz3g8__5.npz,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/5,-wny0OAz3g8,5,vision,6.9119791984558105,6.783500194549562,204,35,70,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9814121252079359,none,True,1.0,-wny0OAz3g8__5.npz::native_vision_*quality*,-wny0OAz3g8__5.npz::query_word_vision_H_time,content probe disabled,features_v2/-wny0OAz3g8__5.npz,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/7,-wny0OAz3g8,7,text,8.06796932220459,6.220000401139261,29,768,81,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7709499321000929,none,True,1.0,-wny0OAz3g8__7.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-wny0OAz3g8__7.npz,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/7,-wny0OAz3g8,7,audio,8.06796932220459,7.842202798740284,792,74,81,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9720169333263387,F0/HNR naturally undefined or unavailable in 253 rows; support masks retained,True,1.0,-wny0OAz3g8__7.npz::native_audio_*quality*,-wny0OAz3g8__7.npz::query_word_audio_H_time,content probe disabled,features_v2/-wny0OAz3g8__7.npz,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/7,-wny0OAz3g8,7,vision,8.06796932220459,7.983500003814698,240,35,81,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9895302876081326,none,True,1.0,-wny0OAz3g8__7.npz::native_vision_*quality*,-wny0OAz3g8__7.npz::query_word_vision_H_time,content probe disabled,features_v2/-wny0OAz3g8__7.npz,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/9,-wny0OAz3g8,9,text,6.2130208015441895,4.880000367760659,20,768,63,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.785447292651552,none,True,1.0,-wny0OAz3g8__9.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-wny0OAz3g8__9.npz,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/9,-wny0OAz3g8,9,audio,6.2130208015441895,6.002310789114722,605,74,63,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9660857384579981,F0/HNR naturally undefined or unavailable in 153 rows; support masks retained,True,1.0,-wny0OAz3g8__9.npz::native_audio_*quality*,-wny0OAz3g8__9.npz::query_word_audio_H_time,content probe disabled,features_v2/-wny0OAz3g8__9.npz,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/9,-wny0OAz3g8,9,vision,6.2130208015441895,6.083499908447264,183,35,63,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9791533141069262,none,True,1.0,-wny0OAz3g8__9.npz::native_vision_*quality*,-wny0OAz3g8__9.npz::query_word_vision_H_time,content probe disabled,features_v2/-wny0OAz3g8__9.npz,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-571d8cVauQ/0,-571d8cVauQ,0,text,5.066667079925537,4.120000243186951,12,768,51,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.8131578764096536,none,True,1.0,-571d8cVauQ__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-571d8cVauQ__0.npz,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-571d8cVauQ/0,-571d8cVauQ,0,audio,5.066667079925537,4.9044821971171615,500,74,51,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9679898283723906,F0/HNR naturally undefined or unavailable in 325 rows; support masks retained,True,1.0,-571d8cVauQ__0.npz::native_audio_*quality*,-571d8cVauQ__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-571d8cVauQ__0.npz,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-571d8cVauQ/0,-571d8cVauQ,0,vision,5.066667079925537,4.6831670701503745,152,35,51,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9243092147706696,OpenFace confidence/success gate rejected 12 native frames,True,0.9210526315789473,-571d8cVauQ__0.npz::native_vision_*quality*,-571d8cVauQ__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-571d8cVauQ__0.npz,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-571d8cVauQ/5,-571d8cVauQ,5,text,15.272981643676758,10.139999464154245,43,768,153,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6639174786379944,none,True,1.0,-571d8cVauQ__5.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-571d8cVauQ__5.npz,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-571d8cVauQ/5,-571d8cVauQ,5,audio,15.272981643676758,14.894732715310278,1512,74,153,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9752341136006616,F0/HNR naturally undefined or unavailable in 816 rows; support masks retained,True,1.0,-571d8cVauQ__5.npz::native_audio_*quality*,-571d8cVauQ__5.npz::query_word_audio_H_time,content probe disabled,features_v2/-571d8cVauQ__5.npz,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-571d8cVauQ/5,-571d8cVauQ,5,vision,15.272981643676758,15.149499893188478,455,35,153,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9919150200419835,none,True,1.0,-571d8cVauQ__5.npz::native_vision_*quality*,-571d8cVauQ__5.npz::query_word_vision_H_time,content probe disabled,features_v2/-571d8cVauQ__5.npz,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-I_e4mIh0yE/1,-I_e4mIh0yE,1,text,7.622004985809326,4.1400005519390115,18,768,77,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5431642408587869,none,True,1.0,-I_e4mIh0yE__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-I_e4mIh0yE__1.npz,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-I_e4mIh0yE/1,-I_e4mIh0yE,1,audio,7.622004985809326,7.303968804913598,745,74,77,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9582739474078213,F0/HNR naturally undefined or unavailable in 519 rows; support masks retained,True,1.0,-I_e4mIh0yE__1.npz::native_audio_*quality*,-I_e4mIh0yE__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-I_e4mIh0yE__1.npz,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-I_e4mIh0yE/1,-I_e4mIh0yE,1,vision,7.622004985809326,7.4835000038146955,225,35,77,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9818282745481668,none,True,1.0,-I_e4mIh0yE__1.npz::native_vision_*quality*,-I_e4mIh0yE__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-I_e4mIh0yE__1.npz,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-I_e4mIh0yE/3,-I_e4mIh0yE,3,text,9.163021087646484,5.839999616146088,22,768,92,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6373443387595747,none,True,1.0,-I_e4mIh0yE__3.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-I_e4mIh0yE__3.npz,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-I_e4mIh0yE/3,-I_e4mIh0yE,3,audio,9.163021087646484,8.82051541032018,900,74,92,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.962620878632696,F0/HNR naturally undefined or unavailable in 643 rows; support masks retained,True,1.0,-I_e4mIh0yE__3.npz::native_audio_*quality*,-I_e4mIh0yE__3.npz::query_word_audio_H_time,content probe disabled,features_v2/-I_e4mIh0yE__3.npz,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-I_e4mIh0yE/3,-I_e4mIh0yE,3,vision,9.163021087646484,9.016500473022463,271,35,92,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9840095735650374,none,True,1.0,-I_e4mIh0yE__3.npz::native_vision_*quality*,-I_e4mIh0yE__3.npz::query_word_vision_H_time,content probe disabled,features_v2/-I_e4mIh0yE__3.npz,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UacrmKiTn4/10,-UacrmKiTn4,10,text,7.361979007720947,4.499999731779098,18,768,74,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.61124865026913,none,True,1.0,-UacrmKiTn4__10.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-UacrmKiTn4__10.npz,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UacrmKiTn4/10,-UacrmKiTn4,10,audio,7.361979007720947,7.145256002207061,725,74,74,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9705618550003204,F0/HNR naturally undefined or unavailable in 359 rows; support masks retained,True,1.0,-UacrmKiTn4__10.npz::native_audio_*quality*,-UacrmKiTn4__10.npz::query_word_audio_H_time,content probe disabled,features_v2/-UacrmKiTn4__10.npz,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UacrmKiTn4/10,-UacrmKiTn4,10,vision,7.361979007720947,7.249499797821047,218,35,74,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9847216068149696,none,True,1.0,-UacrmKiTn4__10.npz::native_vision_*quality*,-UacrmKiTn4__10.npz::query_word_vision_H_time,content probe disabled,features_v2/-UacrmKiTn4__10.npz,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UacrmKiTn4/4,-UacrmKiTn4,4,text,4.741015911102295,3.3999994397163382,14,768,48,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7171457559875246,none,True,1.0,-UacrmKiTn4__4.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-UacrmKiTn4__4.npz,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UacrmKiTn4/4,-UacrmKiTn4,4,audio,4.741015911102295,4.6206091175208215,469,74,48,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.974603166106338,F0/HNR naturally undefined or unavailable in 227 rows; support masks retained,True,1.0,-UacrmKiTn4__4.npz::native_audio_*quality*,-UacrmKiTn4__4.npz::query_word_audio_H_time,content probe disabled,features_v2/-UacrmKiTn4__4.npz,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UacrmKiTn4/4,-UacrmKiTn4,4,vision,4.741015911102295,4.741015911102295,142,35,48,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-UacrmKiTn4__4.npz::native_vision_*quality*,-UacrmKiTn4__4.npz::query_word_vision_H_time,content probe disabled,features_v2/-UacrmKiTn4__4.npz,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-hnBHBN8p5A/7,-hnBHBN8p5A,7,text,7.372000217437744,5.039999097585677,13,768,74,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.683667790142498,none,True,1.0,-hnBHBN8p5A__7.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-hnBHBN8p5A__7.npz,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-hnBHBN8p5A/7,-hnBHBN8p5A,7,audio,7.372000217437744,7.198113807149835,731,74,74,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9764125874716338,F0/HNR naturally undefined or unavailable in 409 rows; support masks retained,True,1.0,-hnBHBN8p5A__7.npz::native_audio_*quality*,-hnBHBN8p5A__7.npz::query_word_audio_H_time,content probe disabled,features_v2/-hnBHBN8p5A__7.npz,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-hnBHBN8p5A/7,-hnBHBN8p5A,7,vision,7.372000217437744,5.964499384164811,176,35,74,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.8090747705156562,OpenFace confidence/success gate rejected 33 native frames,True,0.8579545454545454,-hnBHBN8p5A__7.npz::native_vision_*quality*,-hnBHBN8p5A__7.npz::query_word_vision_H_time,content probe disabled,features_v2/-hnBHBN8p5A__7.npz,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-hnBHBN8p5A/6,-hnBHBN8p5A,6,text,6.401000022888184,4.1200003027915955,13,768,65,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6436494747788827,none,True,1.0,-hnBHBN8p5A__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-hnBHBN8p5A__6.npz,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-hnBHBN8p5A/6,-hnBHBN8p5A,6,audio,6.401000022888184,6.214421288387195,634,74,65,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.970851627271702,F0/HNR naturally undefined or unavailable in 438 rows; support masks retained,True,1.0,-hnBHBN8p5A__6.npz::native_audio_*quality*,-hnBHBN8p5A__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-hnBHBN8p5A__6.npz,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-hnBHBN8p5A/6,-hnBHBN8p5A,6,vision,6.401000022888184,4.733499767258764,153,35,65,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.7394937900848453,OpenFace confidence/success gate rejected 40 native frames,True,0.7581699346405228,-hnBHBN8p5A__6.npz::native_vision_*quality*,-hnBHBN8p5A__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-hnBHBN8p5A__6.npz,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-qDkUB0GgYY/6,-qDkUB0GgYY,6,text,4.677995204925537,3.4799999855458736,16,768,47,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7439084122792013,none,True,1.0,-qDkUB0GgYY__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-qDkUB0GgYY__6.npz,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-qDkUB0GgYY/6,-qDkUB0GgYY,6,audio,4.677995204925537,4.568314991891383,462,74,47,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9765540133690881,F0/HNR naturally undefined or unavailable in 172 rows; support masks retained,True,1.0,-qDkUB0GgYY__6.npz::native_audio_*quality*,-qDkUB0GgYY__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-qDkUB0GgYY__6.npz,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-qDkUB0GgYY/6,-qDkUB0GgYY,6,vision,4.677995204925537,4.677995204925537,140,35,47,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-qDkUB0GgYY__6.npz::native_vision_*quality*,-qDkUB0GgYY__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-qDkUB0GgYY__6.npz,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-uywlfIYOS8/4,-uywlfIYOS8,4,text,6.044987201690674,3.9800001978874198,18,768,61,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6583967947482644,none,True,1.0,-uywlfIYOS8__4.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-uywlfIYOS8__4.npz,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-uywlfIYOS8/4,-uywlfIYOS8,4,audio,6.044987201690674,5.83868071376472,589,74,61,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9658714764742176,F0/HNR naturally undefined or unavailable in 158 rows; support masks retained,True,1.0,-uywlfIYOS8__4.npz::native_audio_*quality*,-uywlfIYOS8__4.npz::query_word_audio_H_time,content probe disabled,features_v2/-uywlfIYOS8__4.npz,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-uywlfIYOS8/4,-uywlfIYOS8,4,vision,6.044987201690674,5.949500083923341,179,35,61,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9842039172985135,none,True,1.0,-uywlfIYOS8__4.npz::native_vision_*quality*,-uywlfIYOS8__4.npz::query_word_vision_H_time,content probe disabled,features_v2/-uywlfIYOS8__4.npz,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-6rXp3zJ3kc/8,-6rXp3zJ3kc,8,text,12.805012702941895,7.859999686479568,34,768,129,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6138220920838109,none,True,1.0,-6rXp3zJ3kc__8.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-6rXp3zJ3kc__8.npz,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-6rXp3zJ3kc/8,-6rXp3zJ3kc,8,audio,12.805012702941895,12.588114851229895,1274,74,129,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9830614887510288,F0/HNR naturally undefined or unavailable in 555 rows; support masks retained,True,1.0,-6rXp3zJ3kc__8.npz::native_audio_*quality*,-6rXp3zJ3kc__8.npz::query_word_audio_H_time,content probe disabled,features_v2/-6rXp3zJ3kc__8.npz,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-6rXp3zJ3kc/8,-6rXp3zJ3kc,8,vision,12.805012702941895,12.805012702941895,384,35,129,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-6rXp3zJ3kc__8.npz::native_vision_*quality*,-6rXp3zJ3kc__8.npz::query_word_vision_H_time,content probe disabled,features_v2/-6rXp3zJ3kc__8.npz,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/0,-9y-fZ3swSY,0,text,6.8333330154418945,4.499999579042196,22,768,69,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.658536554397853,none,True,1.0,-9y-fZ3swSY__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-9y-fZ3swSY__0.npz,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/0,-9y-fZ3swSY,0,audio,6.8333330154418945,6.691547258027099,676,74,69,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9792508638033023,F0/HNR naturally undefined or unavailable in 242 rows; support masks retained,True,1.0,-9y-fZ3swSY__0.npz::native_audio_*quality*,-9y-fZ3swSY__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-9y-fZ3swSY__0.npz,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/0,-9y-fZ3swSY,0,vision,6.8333330154418945,6.8333330154418945,205,35,69,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-9y-fZ3swSY__0.npz::native_vision_*quality*,-9y-fZ3swSY__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-9y-fZ3swSY__0.npz,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/4,-9y-fZ3swSY,4,text,2.8210289478302,1.6199999749660492,9,768,29,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.574258543575767,none,True,1.0,-9y-fZ3swSY__4.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-9y-fZ3swSY__4.npz,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/4,-9y-fZ3swSY,4,audio,2.8210289478302,2.624178272646827,267,74,29,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9302202569261895,F0/HNR naturally undefined or unavailable in 150 rows; support masks retained,True,1.0,-9y-fZ3swSY__4.npz::native_audio_*quality*,-9y-fZ3swSY__4.npz::query_word_audio_H_time,content probe disabled,features_v2/-9y-fZ3swSY__4.npz,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/4,-9y-fZ3swSY,4,vision,2.8210289478302,2.683500051498412,81,35,29,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9512486759706708,none,True,1.0,-9y-fZ3swSY__4.npz::native_vision_*quality*,-9y-fZ3swSY__4.npz::query_word_vision_H_time,content probe disabled,features_v2/-9y-fZ3swSY__4.npz,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/8,-9y-fZ3swSY,8,text,4.988996982574463,2.9200008809566502,12,768,50,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5852881633634197,none,True,1.0,-9y-fZ3swSY__8.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-9y-fZ3swSY__8.npz,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/8,-9y-fZ3swSY,8,audio,4.988996982574463,4.840736327944576,492,74,50,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9702824725796125,F0/HNR naturally undefined or unavailable in 282 rows; support masks retained,True,1.0,-9y-fZ3swSY__8.npz::native_audio_*quality*,-9y-fZ3swSY__8.npz::query_word_audio_H_time,content probe disabled,features_v2/-9y-fZ3swSY__8.npz,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/8,-9y-fZ3swSY,8,vision,4.988996982574463,4.949500083923339,149,35,50,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.992083198528867,none,True,1.0,-9y-fZ3swSY__8.npz::native_vision_*quality*,-9y-fZ3swSY__8.npz::query_word_vision_H_time,content probe disabled,features_v2/-9y-fZ3swSY__8.npz,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,text,22.511003494262695,15.860002636909487,41,768,226,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7045444527140549,none,True,1.0,-AUZQgSxyPQ__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-AUZQgSxyPQ__2.npz,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,audio,22.511003494262695,21.84015408077756,2241,74,226,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9701990444959012,F0/HNR naturally undefined or unavailable in 2088 rows; support masks retained,True,1.0,-AUZQgSxyPQ__2.npz::native_audio_*quality*,-AUZQgSxyPQ__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-AUZQgSxyPQ__2.npz,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,vision,22.511003494262695,22.449501037597653,674,35,226,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9972678936023125,none,True,1.0,-AUZQgSxyPQ__2.npz::native_vision_*quality*,-AUZQgSxyPQ__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-AUZQgSxyPQ__2.npz,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HeZS2-Prhc/2,-HeZS2-Prhc,2,text,8.261979103088379,3.9600005447864532,16,768,83,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.479304110477135,none,True,1.0,-HeZS2-Prhc__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-HeZS2-Prhc__2.npz,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HeZS2-Prhc/2,-HeZS2-Prhc,2,audio,8.261979103088379,8.037157203700092,816,74,83,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9727883723036472,F0/HNR naturally undefined or unavailable in 421 rows; support masks retained,True,1.0,-HeZS2-Prhc__2.npz::native_audio_*quality*,-HeZS2-Prhc__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-HeZS2-Prhc__2.npz,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HeZS2-Prhc/2,-HeZS2-Prhc,2,vision,8.261979103088379,8.183500289916994,246,35,83,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.990501208948586,none,True,1.0,-HeZS2-Prhc__2.npz::native_vision_*quality*,-HeZS2-Prhc__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-HeZS2-Prhc__2.npz,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/0,-MeTTeMJBNc,0,text,9.300000190734863,5.50000023841858,21,768,94,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5913978629697193,none,True,1.0,-MeTTeMJBNc__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-MeTTeMJBNc__0.npz,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/0,-MeTTeMJBNc,0,audio,9.300000190734863,9.066049653130609,922,74,94,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9748440287305232,F0/HNR naturally undefined or unavailable in 563 rows; support masks retained,True,1.0,-MeTTeMJBNc__0.npz::native_audio_*quality*,-MeTTeMJBNc__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-MeTTeMJBNc__0.npz,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/0,-MeTTeMJBNc,0,vision,9.300000190734863,8.916500180959703,279,35,94,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9587634406548483,OpenFace confidence/success gate rejected 12 native frames,True,0.956989247311828,-MeTTeMJBNc__0.npz::native_vision_*quality*,-MeTTeMJBNc__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-MeTTeMJBNc__0.npz,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/13,-MeTTeMJBNc,13,text,5.430013179779053,3.7600001394748688,14,768,55,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6924477003991109,none,True,1.0,-MeTTeMJBNc__13.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-MeTTeMJBNc__13.npz,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/13,-MeTTeMJBNc,13,audio,5.430013179779053,5.165986305153049,526,74,55,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9513763842030403,F0/HNR naturally undefined or unavailable in 332 rows; support masks retained,True,1.0,-MeTTeMJBNc__13.npz::native_audio_*quality*,-MeTTeMJBNc__13.npz::query_word_audio_H_time,content probe disabled,features_v2/-MeTTeMJBNc__13.npz,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/13,-MeTTeMJBNc,13,vision,5.430013179779053,5.2835001945495605,159,35,55,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9730179319315292,none,True,1.0,-MeTTeMJBNc__13.npz::native_vision_*quality*,-MeTTeMJBNc__13.npz::query_word_vision_H_time,content probe disabled,features_v2/-MeTTeMJBNc__13.npz,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/7,-MeTTeMJBNc,7,text,10.51699161529541,7.359996646642682,31,768,106,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6998195791977865,none,True,1.0,-MeTTeMJBNc__7.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-MeTTeMJBNc__7.npz,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/7,-MeTTeMJBNc,7,audio,10.51699161529541,10.274333249878238,1042,74,106,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9769270173169805,F0/HNR naturally undefined or unavailable in 507 rows; support masks retained,True,1.0,-MeTTeMJBNc__7.npz::native_audio_*quality*,-MeTTeMJBNc__7.npz::query_word_audio_H_time,content probe disabled,features_v2/-MeTTeMJBNc__7.npz,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/7,-MeTTeMJBNc,7,vision,10.51699161529541,10.449499130249023,314,35,106,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.993582529347249,none,True,1.0,-MeTTeMJBNc__7.npz::native_vision_*quality*,-MeTTeMJBNc__7.npz::query_word_vision_H_time,content probe disabled,features_v2/-MeTTeMJBNc__7.npz,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/11,-RfYyzHpjk4,11,text,5.238996982574463,3.3799998164176945,17,768,53,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6451616268648336,none,True,1.0,-RfYyzHpjk4__11.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-RfYyzHpjk4__11.npz,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/11,-RfYyzHpjk4,11,audio,5.238996982574463,5.023697516402681,508,74,53,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9589044492890731,F0/HNR naturally undefined or unavailable in 189 rows; support masks retained,True,1.0,-RfYyzHpjk4__11.npz::native_audio_*quality*,-RfYyzHpjk4__11.npz::query_word_audio_H_time,content probe disabled,features_v2/-RfYyzHpjk4__11.npz,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/11,-RfYyzHpjk4,11,vision,5.238996982574463,5.116499900817871,154,35,53,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9766182186849827,none,True,1.0,-RfYyzHpjk4__11.npz::native_vision_*quality*,-RfYyzHpjk4__11.npz::query_word_vision_H_time,content probe disabled,features_v2/-RfYyzHpjk4__11.npz,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/8,-RfYyzHpjk4,8,text,4.588996887207031,3.1199999749660483,17,768,46,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6798871412756508,none,True,1.0,-RfYyzHpjk4__8.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-RfYyzHpjk4__8.npz,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/8,-RfYyzHpjk4,8,audio,4.588996887207031,4.483190728200448,454,74,46,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9769435103995071,F0/HNR naturally undefined or unavailable in 182 rows; support masks retained,True,1.0,-RfYyzHpjk4__8.npz::native_audio_*quality*,-RfYyzHpjk4__8.npz::query_word_audio_H_time,content probe disabled,features_v2/-RfYyzHpjk4__8.npz,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/8,-RfYyzHpjk4,8,vision,4.588996887207031,4.549499988555907,137,35,46,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9913931302151824,none,True,1.0,-RfYyzHpjk4__8.npz::native_vision_*quality*,-RfYyzHpjk4__8.npz::query_word_vision_H_time,content probe disabled,features_v2/-RfYyzHpjk4__8.npz,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/2,-RfYyzHpjk4,2,text,5.516016006469727,3.6999993324279785,25,768,56,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6707738570896559,none,True,1.0,-RfYyzHpjk4__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-RfYyzHpjk4__2.npz,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/2,-RfYyzHpjk4,2,audio,5.516016006469727,5.341840220464243,540,74,56,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9684236257108041,F0/HNR naturally undefined or unavailable in 185 rows; support masks retained,True,1.0,-RfYyzHpjk4__2.npz::native_audio_*quality*,-RfYyzHpjk4__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-RfYyzHpjk4__2.npz,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/2,-RfYyzHpjk4,2,vision,5.516016006469727,5.44950008392334,164,35,56,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9879413108177405,none,True,1.0,-RfYyzHpjk4__2.npz::native_vision_*quality*,-RfYyzHpjk4__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-RfYyzHpjk4__2.npz,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UUCSKoHeMA/0,-UUCSKoHeMA,0,text,7.800000190734863,4.980000615119934,18,768,79,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6384616017106471,none,True,1.0,-UUCSKoHeMA__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-UUCSKoHeMA__0.npz,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UUCSKoHeMA/0,-UUCSKoHeMA,0,audio,7.800000190734863,7.652800917826794,774,74,79,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9811282988065926,F0/HNR naturally undefined or unavailable in 294 rows; support masks retained,True,1.0,-UUCSKoHeMA__0.npz::native_audio_*quality*,-UUCSKoHeMA__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-UUCSKoHeMA__0.npz,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UUCSKoHeMA/0,-UUCSKoHeMA,0,vision,7.800000190734863,7.416500180959701,234,35,79,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9508333332823891,OpenFace confidence/success gate rejected 12 native frames,True,0.9487179487179487,-UUCSKoHeMA__0.npz::native_vision_*quality*,-UUCSKoHeMA__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-UUCSKoHeMA__0.npz,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/0,-ri04Z7vwnc,0,text,2.806999921798706,2.1600002199411397,8,768,29,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7695049091975131,none,True,1.0,-ri04Z7vwnc__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-ri04Z7vwnc__0.npz,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/0,-ri04Z7vwnc,0,audio,2.806999921798706,2.7302467235036803,277,74,29,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9726565014487628,F0/HNR naturally undefined or unavailable in 122 rows; support masks retained,True,1.0,-ri04Z7vwnc__0.npz::native_audio_*quality*,-ri04Z7vwnc__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-ri04Z7vwnc__0.npz,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/0,-ri04Z7vwnc,0,vision,2.806999921798706,0.0,67,35,29,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.0,OpenFace confidence/success gate rejected 67 native frames,False,0.0,-ri04Z7vwnc__0.npz::native_vision_*quality*,-ri04Z7vwnc__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-ri04Z7vwnc__0.npz,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,failed_or_unavailable,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/2,-ri04Z7vwnc,2,text,6.0269999504089355,4.539999946951865,16,768,61,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7532769179206354,none,True,1.0,-ri04Z7vwnc__2.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-ri04Z7vwnc__2.npz,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/2,-ri04Z7vwnc,2,audio,6.0269999504089355,5.824690816370216,592,74,61,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9664328628333584,F0/HNR naturally undefined or unavailable in 328 rows; support masks retained,True,1.0,-ri04Z7vwnc__2.npz::native_audio_*quality*,-ri04Z7vwnc__2.npz::query_word_audio_H_time,content probe disabled,features_v2/-ri04Z7vwnc__2.npz,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/2,-ri04Z7vwnc,2,vision,6.0269999504089355,1.9609997272491455,143,35,61,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.32536912948142477,OpenFace confidence/success gate rejected 96 native frames,True,0.32867132867132864,-ri04Z7vwnc__2.npz::native_vision_*quality*,-ri04Z7vwnc__2.npz::query_word_vision_H_time,content probe disabled,features_v2/-ri04Z7vwnc__2.npz,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/5,-ri04Z7vwnc,5,text,3.5450000762939453,1.9600001275539394,9,768,36,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.5528914204151401,1 word intervals are not reliable under the fixed-transcript CTC path,True,0.8888888888888888,-ri04Z7vwnc__5.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-ri04Z7vwnc__5.npz,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/5,-ri04Z7vwnc,5,audio,3.5450000762939453,3.3887803554534908,344,74,36,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9559323787085016,F0/HNR naturally undefined or unavailable in 188 rows; support masks retained,True,1.0,-ri04Z7vwnc__5.npz::native_audio_*quality*,-ri04Z7vwnc__5.npz::query_word_audio_H_time,content probe disabled,features_v2/-ri04Z7vwnc__5.npz,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/5,-ri04Z7vwnc,5,vision,3.5450000762939453,3.4409999866038565,83,35,36,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9706628808316378,none,True,1.0,-ri04Z7vwnc__5.npz::native_vision_*quality*,-ri04Z7vwnc__5.npz::query_word_vision_H_time,content probe disabled,features_v2/-ri04Z7vwnc__5.npz,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/1,-s9qJ7ATP7w,1,text,4.7919921875,2.760000109672546,11,768,48,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.575960895109983,none,True,1.0,-s9qJ7ATP7w__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-s9qJ7ATP7w__1.npz,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/1,-s9qJ7ATP7w,1,audio,4.7919921875,4.563759450976914,465,74,48,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9523720557979132,F0/HNR naturally undefined or unavailable in 296 rows; support masks retained,True,1.0,-s9qJ7ATP7w__1.npz::native_audio_*quality*,-s9qJ7ATP7w__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__1.npz,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/1,-s9qJ7ATP7w,1,vision,4.7919921875,4.6494998931884775,140,35,48,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9702644977837784,none,True,1.0,-s9qJ7ATP7w__1.npz::native_vision_*quality*,-s9qJ7ATP7w__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__1.npz,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,text,6.800000190734863,4.260000094771386,24,768,69,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.626470584600236,none,True,1.0,-s9qJ7ATP7w__0.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-s9qJ7ATP7w__0.npz,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,audio,6.800000190734863,6.63685745967401,672,74,69,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9760084225757614,F0/HNR naturally undefined or unavailable in 273 rows; support masks retained,True,1.0,-s9qJ7ATP7w__0.npz::native_audio_*quality*,-s9qJ7ATP7w__0.npz::query_word_audio_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__0.npz,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,vision,6.800000190734863,6.800000190734863,204,35,69,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,1.0,none,True,1.0,-s9qJ7ATP7w__0.npz::native_vision_*quality*,-s9qJ7ATP7w__0.npz::query_word_vision_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__0.npz,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,text,8.561002731323242,5.1399999763816595,19,768,86,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6003969555546665,none,True,1.0,-s9qJ7ATP7w__5.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-s9qJ7ATP7w__5.npz,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,audio,8.561002731323242,8.295629897633114,840,74,86,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9690021318742051,F0/HNR naturally undefined or unavailable in 378 rows; support masks retained,True,1.0,-s9qJ7ATP7w__5.npz::native_audio_*quality*,-s9qJ7ATP7w__5.npz::query_word_audio_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__5.npz,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,vision,8.561002731323242,8.416500091552736,253,35,86,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9831208277458204,none,True,1.0,-s9qJ7ATP7w__5.npz::native_vision_*quality*,-s9qJ7ATP7w__5.npz::query_word_vision_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__5.npz,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,text,4.427018165588379,2.859999973326921,9,768,45,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6460330331503866,none,True,1.0,-s9qJ7ATP7w__4.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-s9qJ7ATP7w__4.npz,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,audio,4.427018165588379,4.188183940745689,425,74,45,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9460507691838334,F0/HNR naturally undefined or unavailable in 217 rows; support masks retained,True,1.0,-s9qJ7ATP7w__4.npz::native_audio_*quality*,-s9qJ7ATP7w__4.npz::query_word_audio_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__4.npz,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,vision,4.427018165588379,4.150500178337097,129,35,45,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9375385469613201,OpenFace confidence/success gate rejected 4 native frames,True,0.9689922480620154,-s9qJ7ATP7w__4.npz::native_vision_*quality*,-s9qJ7ATP7w__4.npz::query_word_vision_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__4.npz,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,text,6.966015815734863,4.559999305754899,28,768,70,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6546065105759242,none,True,1.0,-s9qJ7ATP7w__7.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-s9qJ7ATP7w__7.npz,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,audio,6.966015815734863,6.713249155597107,684,74,70,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9637143143478363,F0/HNR naturally undefined or unavailable in 464 rows; support masks retained,True,1.0,-s9qJ7ATP7w__7.npz::native_audio_*quality*,-s9qJ7ATP7w__7.npz::query_word_audio_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__7.npz,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,vision,6.966015815734863,5.083500146865846,207,35,70,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.7297571928250889,OpenFace confidence/success gate rejected 54 native frames,True,0.7391304347826086,-s9qJ7ATP7w__7.npz::native_vision_*quality*,-s9qJ7ATP7w__7.npz::query_word_vision_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__7.npz,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/6,-s9qJ7ATP7w,6,text,2.4749999046325684,1.660000160336494,5,768,25,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6707071613333792,none,True,1.0,-s9qJ7ATP7w__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-s9qJ7ATP7w__6.npz,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/6,-s9qJ7ATP7w,6,audio,2.4749999046325684,2.258232190802291,232,74,25,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9124170819463291,F0/HNR naturally undefined or unavailable in 209 rows; support masks retained,True,1.0,-s9qJ7ATP7w__6.npz::native_audio_*quality*,-s9qJ7ATP7w__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__6.npz,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/6,-s9qJ7ATP7w,6,vision,2.4749999046325684,1.9500000476837156,71,35,25,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.7878788375037159,OpenFace confidence/success gate rejected 12 native frames,True,0.8309859154929577,-s9qJ7ATP7w__6.npz::native_vision_*quality*,-s9qJ7ATP7w__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__6.npz,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/8,-s9qJ7ATP7w,8,text,3.2949869632720947,2.1999996006488804,13,768,33,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6676808209475177,1 word intervals are not reliable under the fixed-transcript CTC path,True,0.9230769230769231,-s9qJ7ATP7w__8.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-s9qJ7ATP7w__8.npz,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/8,-s9qJ7ATP7w,8,audio,3.2949869632720947,3.132287263870239,319,74,33,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9506220506437796,F0/HNR naturally undefined or unavailable in 192 rows; support masks retained,True,1.0,-s9qJ7ATP7w__8.npz::native_audio_*quality*,-s9qJ7ATP7w__8.npz::query_word_audio_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__8.npz,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/8,-s9qJ7ATP7w,8,vision,3.2949869632720947,3.2165000438690186,97,35,33,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.976179899866695,none,True,1.0,-s9qJ7ATP7w__8.npz::native_vision_*quality*,-s9qJ7ATP7w__8.npz::query_word_vision_H_time,content probe disabled,features_v2/-s9qJ7ATP7w__8.npz,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,text,29.288021087646484,18.119996219873425,49,768,293,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.6186828453055278,none,True,1.0,-yRb-Jum7EQ__1.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-yRb-Jum7EQ__1.npz,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,audio,29.288021087646484,28.574997466963687,2911,74,293,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9756547696223987,F0/HNR naturally undefined or unavailable in 1978 rows; support masks retained,True,1.0,-yRb-Jum7EQ__1.npz::native_audio_*quality*,-yRb-Jum7EQ__1.npz::query_word_audio_H_time,content probe disabled,features_v2/-yRb-Jum7EQ__1.npz,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,vision,29.288021087646484,28.81599998474121,875,35,293,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9838834757222853,OpenFace confidence/success gate rejected 10 native frames,True,1.0,-yRb-Jum7EQ__1.npz::native_vision_*quality*,-yRb-Jum7EQ__1.npz::query_word_vision_H_time,content probe disabled,features_v2/-yRb-Jum7EQ__1.npz,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/5,-yRb-Jum7EQ,5,text,13.169010162353516,9.399999201297762,25,768,132,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.7137969433852901,none,True,1.0,-yRb-Jum7EQ__5.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-yRb-Jum7EQ__5.npz,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/5,-yRb-Jum7EQ,5,audio,13.169010162353516,12.835559805905497,1305,74,132,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9746791632524319,F0/HNR naturally undefined or unavailable in 773 rows; support masks retained,True,1.0,-yRb-Jum7EQ__5.npz::native_audio_*quality*,-yRb-Jum7EQ__5.npz::query_word_audio_H_time,content probe disabled,features_v2/-yRb-Jum7EQ__5.npz,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/5,-yRb-Jum7EQ,5,vision,13.169010162353516,13.116500854492188,394,35,132,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9960126609962351,none,True,1.0,-yRb-Jum7EQ__5.npz::native_vision_*quality*,-yRb-Jum7EQ__5.npz::query_word_vision_H_time,content probe disabled,features_v2/-yRb-Jum7EQ__5.npz,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/6,-yRb-Jum7EQ,6,text,11.241994857788086,5.480000853538514,19,768,113,768,fixed 0.1 s physical grid; direct native interval projection,"word-level, variable duration; fixed-transcript CTC source interval",0.487458046624363,none,True,1.0,-yRb-Jum7EQ__6.npz::native_text_*quality*,not_applicable,not_applicable,features_v2/-yRb-Jum7EQ__6.npz,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/6,-yRb-Jum7EQ,6,audio,11.241994857788086,11.016339672578349,1125,74,113,74,fixed 0.1 s physical grid; direct native interval projection,native 10 ms feature step; 25 ms windows; context ranges retained separately,0.9799274783466556,F0/HNR naturally undefined or unavailable in 835 rows; support masks retained,True,1.0,-yRb-Jum7EQ__6.npz::native_audio_*quality*,-yRb-Jum7EQ__6.npz::query_word_audio_H_time,content probe disabled,features_v2/-yRb-Jum7EQ__6.npz,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/6,-yRb-Jum7EQ,6,vision,11.241994857788086,11.183500289916992,336,35,113,35,fixed 0.1 s physical grid; direct native interval projection,native OpenFace frame timestamps and frame indices,0.9947967804103228,none,True,1.0,-yRb-Jum7EQ__6.npz::native_vision_*quality*,-yRb-Jum7EQ__6.npz::query_word_vision_H_time,content probe disabled,features_v2/-yRb-Jum7EQ__6.npz,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 diff --git a/final/output/q1/features_v2/sample_summary.csv b/final/output/q1/features_v2/sample_summary.csv new file mode 100644 index 0000000..accd5c6 --- /dev/null +++ b/final/output/q1/features_v2/sample_summary.csv @@ -0,0 +1,101 @@ +sample_id,video_id,clip_id,source_duration_s,source_video_sha256,feature_path,feature_sha256,sec_length,word_count,hard_aligned_word_count,unlocated_word_count,status,config_hash +-3g5yACwYnA/13,-3g5yACwYnA,13,5.5139970779418945,aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f,features_v2/-3g5yACwYnA__13.npz,8ff79652816c4749ebfb19c95fb1f4826e27e19e850db23a289144985bcacb51,56,15,15,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/3,-3g5yACwYnA,3,14.388997077941895,eff8cfefba2425155f2b72656829b34b23be8bfe1dcbece384154f56818aa26d,features_v2/-3g5yACwYnA__3.npz,e3756cf59da4cc1f56b316b9af66e1afbd7f76d45ed74296ee5e239e078bce61,144,29,29,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/2,-3g5yACwYnA,2,9.394009590148926,0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7,features_v2/-3g5yACwYnA__2.npz,e84dc34a549d0d9ed9200d889c8c4a3c9e3af2cf69db297939794cee052f2ab8,94,14,14,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3g5yACwYnA/9,-3g5yACwYnA,9,8.816991806030273,b962573b12ed1f06d5533f6427c80dce2bae3a99dd332f6d3fcb32c579f3f016,features_v2/-3g5yACwYnA__9.npz,585ffb341161b798fb8213915eee94593519cf50a6510dad690d8386d2ca5f40,89,21,21,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-3nNcZdcdvU/5,-3nNcZdcdvU,5,7.867969036102295,7ea53ad502b77be7d2ee4494dc23a9478c897792203cf887d546e83d584c1728,features_v2/-3nNcZdcdvU__5.npz,a69c1bca94b0fa15c69c31eb4909874a08ffad213fb10b5d27cf3ba1cabfa13f,79,18,18,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/2,-HwX2H8Z4hY,2,3.9820311069488525,920a1052c09ebd1eedba6dd1ccfecd80f56f6f0fdcdd1f6dd32b0d90bcb89358,features_v2/-HwX2H8Z4hY__2.npz,bce95b4eaa69f6b35684b191461419ecc2083ee944ced41807e46eba8d3131c5,40,8,7,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/5,-HwX2H8Z4hY,5,5.6529951095581055,8d61b7f5840a8bd403d9bc6c3cd49029ffc4e304cf9c7834735c1a6bb5fd369d,features_v2/-HwX2H8Z4hY__5.npz,843601e008fa1ddcf4dc530837017e5fbccfc7f6aeaa3aff1b62a336668a0b4e,57,10,9,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/6,-HwX2H8Z4hY,6,3.3580079078674316,696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46,features_v2/-HwX2H8Z4hY__6.npz,e0f9f2edd105ce67f8269fb3a41603af01002047e25a450cf8dda1b4234b2141,34,7,7,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HwX2H8Z4hY/9,-HwX2H8Z4hY,9,7.733983993530273,90c18e627f27a12c14e21ba24c79edf3008762b4ac26874b490ad3f888381702,features_v2/-HwX2H8Z4hY__9.npz,8248b6823c7a39a16c44389a7ef71fa206492ce35ccea39ea62621c3bcce1721,78,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-NFrJFQijFE/1,-NFrJFQijFE,1,5.745999813079834,d8fcf16c6eb51c56947ec3d0549c6f3bc7a0cf1fc5f5d89e8b38d4249a08db4f,features_v2/-NFrJFQijFE__1.npz,1b5b76764b7a6ce5bd68eca4dc24347e3d0e800bc2abe3c6af28cca881f4f498,58,16,16,0,partial,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-NFrJFQijFE/2,-NFrJFQijFE,2,6.855999946594238,a54a5c144a64f5abd26b19aafa6cea8142ee08e9385fa508fa70c934f6b616c1,features_v2/-NFrJFQijFE__2.npz,3805d0f3ea3c570accc297ebea6af7e0cfcc6f0f100593a6b06b34b5563a237a,69,18,18,0,partial,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/12,-THoVjtIkeU,12,14.896029472351074,e2f148f4f2e74724dc13b3ce870a0ce6a06740cfc224f5a9b3d0a870b196e7d4,features_v2/-THoVjtIkeU__12.npz,7830ce91514263b5cff4c305da3cf42cb86eef37158aad86f625d79f7268ac07,149,39,39,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/2,-THoVjtIkeU,2,4.2919921875,35a6c2464ffd68dd96411edb5dc2763f0efb59f264c1d8b737e616fa6b0d1d5b,features_v2/-THoVjtIkeU__2.npz,96d2c188b9ddaa891af1dfce83e12c5ed49abc0e85d96b802a8d28ebdec4dc66,43,10,10,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-THoVjtIkeU/6,-THoVjtIkeU,6,8.097004890441895,efaa55ee6394032c867046690a92c29edfd825005f0a090cca5ccb763b227f64,features_v2/-THoVjtIkeU__6.npz,8957ebe6c0583a0dce593ce8cc42db829fdd1be8e112bcf08d99b083f5a3ac8a,81,30,30,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/1,-UuX1xuaiiE,1,10.350000381469727,d5bbda38fcec15817d2b87bab5dcc559d6d425f7d28a82e6c32d13f14b48650c,features_v2/-UuX1xuaiiE__1.npz,aecd48aa4a55db3bb21c7fadbb5b301f00b4ec235bc67a47dc5141bb32a7be9a,104,27,27,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/0,-UuX1xuaiiE,0,3.6333329677581787,f578b305d53bc152702f5e771de0fcd12d1e9aaefc5cefce3d2082a417aab7c8,features_v2/-UuX1xuaiiE__0.npz,2e24c45d36ef3c1925706d397f7c69deab53f7838a58bb66d94b5c03d09f1258,37,9,9,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/3,-UuX1xuaiiE,3,4.1300129890441895,eddb408f25bdc13e6de2fb74caeed709cb7a8f00640e7897135330437f66a2aa,features_v2/-UuX1xuaiiE__3.npz,1efdc8d158b2480f4829148cd5eabc9236175782b056575467486af004ac81c4,42,9,9,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UuX1xuaiiE/6,-UuX1xuaiiE,6,8.113997459411621,f0131ac00e44410fbd32c547a0d421c2791172434fc0203bb969abe14a530532,features_v2/-UuX1xuaiiE__6.npz,c6bf473a36b950f74aacf8e8d2f1153e7591430d4ad57da3a1adb2da79b72198,82,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-a55Q6RWvTA/3,-a55Q6RWvTA,3,22.15397071838379,15d029fc15f50b268b98f1e8abc65e45582e638c13a018d7aa74a18373275946,features_v2/-a55Q6RWvTA__3.npz,c1989fc2b6e742d75a029b7f912e0fdd73cfdbad35bcceae0e0e39ae0582b611,222,65,65,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-aNfi7CP8vM/7,-aNfi7CP8vM,7,8.694987297058105,0b6389f45bb966113c65a11998c6e7facdd24c8c42acf2e8cf32e0f2139402e1,features_v2/-aNfi7CP8vM__7.npz,dcb20301dbb921eeaee67499779334261ac38cee42e04215b54bdd790eb04d71,87,18,18,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-aqamKhZ1Ec/0,-aqamKhZ1Ec,0,10.966667175292969,6f6e674bbba4353399e9e7825217f37e6d8ca1adf9675f33cf37106f31c55548,features_v2/-aqamKhZ1Ec__0.npz,686f7710f18ce27ca864c44021246fdedd4d41b4620b210b2bae988f4011561e,110,14,13,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/1,-dxfTGcXJoc,1,16.16100311279297,b5ffdc98a4a98b8f0aae55dee5fed66a43bcb129f47a250a004c1cc74e572595,features_v2/-dxfTGcXJoc__1.npz,bbc68c9e5c1880673cfb77f48aa3dcdf10829b07cd64e6b00a8b7a3f8694b9a8,162,36,36,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/0,-dxfTGcXJoc,0,20.5,04bd0be907fb46c613f926fe1b06bc2c3b30881c293156510baa34a4105ade55,features_v2/-dxfTGcXJoc__0.npz,76423f4fa8a57e214ecbebd02bc72e06eaaa8b8e25f40cca4fdb9296a1e30673,205,41,38,3,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/2,-dxfTGcXJoc,2,16.697982788085938,46a5e523b5dd00364c99a3bcbc6fae4c80e1382a84b5b5e681c46eb074ed10f7,features_v2/-dxfTGcXJoc__2.npz,fa9effa2f4efcf00296196e2ab90d819de5c6cab85f4075d8f2ae419743f57b5,167,34,32,2,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-dxfTGcXJoc/6,-dxfTGcXJoc,6,12.735026359558105,55c1ff86729ab21da743a5107ed07b220e41b4193cd1c0ba9f009e65c3ab0513,features_v2/-dxfTGcXJoc__6.npz,28beb6292687bfd0bc6a90e97083bfb2e7eaf133447dd9bf2a9be974d5b9fc4b,128,27,27,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/26,-egA8-b7-3M,26,6.030990123748779,11f78aed680ea9c5862a15e04ff6c1f8776c46d29c98b68d28773ff39d959685,features_v2/-egA8-b7-3M__26.npz,8f752d0ae41def9d6cfa650d06674143d8d743be33a702a947d181ff39c08e59,61,15,15,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/17,-egA8-b7-3M,17,7.271028995513916,f967e81e0edd51c3700671721afb3fbecbbc32652f27d2db40ea444e8d80c68f,features_v2/-egA8-b7-3M__17.npz,d18309c6de9e1218a6d8133d9f79c2df0a4a582022c1417a8ccd7ae71fbab8ab,73,16,16,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/18,-egA8-b7-3M,18,8.386002540588379,fd4bc95a6adfb16a9588b7ed65cc2812186ce3d607804dc76f4486312951ef90,features_v2/-egA8-b7-3M__18.npz,dde916a05af47a5487e06c2749299e5bf2fc67fadb36a575c20c66065ac03176,84,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/16,-egA8-b7-3M,16,5.538021087646484,4c015f85e3bd901ecedde8c0c2ca4a756d9a77dfe4177625c481581f2037ff9c,features_v2/-egA8-b7-3M__16.npz,6baba8aa2db080c79a9b72bf3c0814e66423a9d5a56216a09ffa699bc359b104,56,11,11,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/13,-egA8-b7-3M,13,4.18398380279541,cbd627c225ebca37521ae238cb9ec254cad236822f4f37ebfbb039e6c56aea4c,features_v2/-egA8-b7-3M__13.npz,9133432494bb49a3e563cec8420925d5522ae007917972f82991886d55be47ba,42,11,11,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/1,-egA8-b7-3M,1,10.266016006469727,589a7989b1b4888568c31614a2d6beefba88fb87289c85b94df2d8051500403b,features_v2/-egA8-b7-3M__1.npz,71288235e60086aa6ab363066f68cf77c57048c65cf3a6b45f32c227381e7061,103,20,19,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/6,-egA8-b7-3M,6,6.264974117279053,2e88a00d5e55863aa956b4b0949fc0e4f2975ccfd83b3194957f6fca86ecfce0,features_v2/-egA8-b7-3M__6.npz,0cbad1d28be25446822090f74bf6d15e4e8d3c16e03cc4c863597e457b6bfb5f,63,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/9,-egA8-b7-3M,9,5.0899739265441895,bcd643f328616312eae9b28e6c9aabbe8acaec3e4c4be58daeb45e3eee4d973c,features_v2/-egA8-b7-3M__9.npz,295b96b7d73d49dd83272d84f7b5741daa654d4323f2a003e94a21aa0726cf2a,51,10,10,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-egA8-b7-3M/20,-egA8-b7-3M,20,6.644987106323242,719ef133920e74b3ea33925d2bca0270b062199116867568f88ac83cadcd74e8,features_v2/-egA8-b7-3M__20.npz,3ef6eab499a10dd0f6181a3b4175bbcd5c4d629206e462ab642d8e2c58e689e9,67,20,20,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/3,-iRBcNs9oI8,3,5.620999813079834,39c547acd1a8da2ebc6c6ab008191a5676420398a974cd9611cad087f0ccec50,features_v2/-iRBcNs9oI8__3.npz,6843ce1b4b5a47d13b7d9cdcc6426141def4897da2ea0e7e325da54f3ce14907,57,8,8,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/7,-iRBcNs9oI8,7,4.104000091552734,cc7b1c7a06ec41d72007b5c42fb8036f30b66d5cf76e3f093160c22fc5e38081,features_v2/-iRBcNs9oI8__7.npz,d7421743e7b1804ea0bb71568d9d3972fe5a908f699443a4125291f5bb26171b,42,9,9,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/6,-iRBcNs9oI8,6,2.9030001163482666,030b0b8d8d4ae47799432ab71b66fcc881f077c5b0feef892c1f146dc0a592e3,features_v2/-iRBcNs9oI8__6.npz,d24c023d23ecd8a1715e2759f47b155006e5eb61c2e4d25e436b62af400574fe,30,5,5,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/9,-iRBcNs9oI8,9,3.4159998893737793,352bdcc73d3622d6abac1b09f2039b211d15b9bf7acee66f0b07783c9b7a4c74,features_v2/-iRBcNs9oI8__9.npz,fe85860d993e18a09afb8293bc00ac76b3ad673db783d4a158ab8062b697a2a4,35,5,5,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-iRBcNs9oI8/8,-iRBcNs9oI8,8,8.093000411987305,72a49e3da2862addcde033bd2fbae957d086533664ca4dcbccd3a851bcd50412,features_v2/-iRBcNs9oI8__8.npz,230f08e63b84fc8eea864ef801a59dbdae7b40800e58731184c2f41c70f35c09,81,21,21,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-lzEya4AM_4/5,-lzEya4AM_4,5,7.675000190734863,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,features_v2/-lzEya4AM_4__5.npz,a7c6dc3a59764e6353c1fddcaee94abe8c05335fd54803e1f5dbc5c20a21c277,77,19,19,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-lzEya4AM_4/6,-lzEya4AM_4,6,14.7919921875,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,features_v2/-lzEya4AM_4__6.npz,cafd2cbc61f1e95896fb3bd46a456ba384ea6f3708a659f87749f9ef7432be1b,148,43,43,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/1,-mJ2ud6oKI8,1,6.103000164031982,c8e3acb4ca08679796c4c8cdfcd8c2b3cffc0465015efaffa1eccdb55ff4f45b,features_v2/-mJ2ud6oKI8__1.npz,cd2c39bb1ca30648413e10739b052627f0b660127f461db37b3cfc93c72e8155,62,13,13,0,partial,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/2,-mJ2ud6oKI8,2,5.730999946594238,c41790f8b75f886bd1f5d6d72cbab1985306d0b715e5ae47203f3d0c8a3e7b28,features_v2/-mJ2ud6oKI8__2.npz,948618c6648a9a00753aff26cb65f3b9ef3bbe075f7e3d5a4c496eae541531dc,58,11,11,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/6,-mJ2ud6oKI8,6,2.256999969482422,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,features_v2/-mJ2ud6oKI8__6.npz,ca445e27d85ecd9578d39c37d5ad93b8c6113bf264861a6945242e202cbf7e1f,23,5,5,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/9,-mJ2ud6oKI8,9,7.0329999923706055,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,features_v2/-mJ2ud6oKI8__9.npz,1610e8626eec162785662e4445b5a8581abab6425a7956bbfe147742d06aad7c,71,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mJ2ud6oKI8/8,-mJ2ud6oKI8,8,4.191999912261963,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,features_v2/-mJ2ud6oKI8__8.npz,f262bc1e640742b43f80bec55ad965d31d2ccadfc7f5c921848ff5f41c76877a,42,11,11,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-mqbVkbCndg/0,-mqbVkbCndg,0,6.466667175292969,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,features_v2/-mqbVkbCndg__0.npz,4ccb2b3e910eeb9a00598d3204a7214de9b453636d1d8c026419aeaee157a5c6,65,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-t217m2on-s/2,-t217m2on-s,2,5.6860032081604,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,features_v2/-t217m2on-s__2.npz,9f325b13ee0982de9e94c5ce7ba483a970251ba88b86aac2fb7a67189978d5b2,57,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-t217m2on-s/7,-t217m2on-s,7,17.183008193969727,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,features_v2/-t217m2on-s__7.npz,9fd4771029300d09aefe043ea3ce447828c34de03d8a238abdf2f83cb6b96bc7,172,45,45,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tANM6ETl_M/3,-tANM6ETl_M,3,6.758008003234863,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,features_v2/-tANM6ETl_M__3.npz,dc3c9acd5f216c41559a001c278266aa2fe777f99cb45acd9925444c71bc84d9,68,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/11,-tPCytz4rww,11,5.466991901397705,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,features_v2/-tPCytz4rww__11.npz,03b82e99bc518fc778ea4447b4564b8cbb3b3d132b8316d75c65ff1e50e66769,55,17,17,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/10,-tPCytz4rww,10,4.788997173309326,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,features_v2/-tPCytz4rww__10.npz,11be01f908f0545b1cd3f75380c9c51a731d8b5f087b9d34f5670e02766da840,48,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/12,-tPCytz4rww,12,11.863997459411621,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,features_v2/-tPCytz4rww__12.npz,da94125e6235aecdfd31615210d8bb22e56b54882ced8e548c09408fa1f14191,119,26,26,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/16,-tPCytz4rww,16,7.205989837646484,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,features_v2/-tPCytz4rww__16.npz,5fd4724db408e31ee12e37ddf109dcaf9899521f6641db9e6ee8b144f5625b00,73,16,16,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-tPCytz4rww/18,-tPCytz4rww,18,6.644987106323242,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,features_v2/-tPCytz4rww__18.npz,92bac66566bcd80659c7e96a6d47014c994bb8d815cd906be5cccab86e50b82b,67,16,16,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-vxjVxOeScU/4,-vxjVxOeScU,4,9.127017974853516,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,features_v2/-vxjVxOeScU__4.npz,036473f43edafbd41626317599d4df6e6847def482a18c760e45f77f822b45f4,92,21,21,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wMB_hJL-3o/7,-wMB_hJL-3o,7,6.044010162353516,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,features_v2/-wMB_hJL-3o__7.npz,e091d71d9bb99da782ea268fb1652ee8ad0a4b256af35f919beed8b52309000d,61,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/1,-wny0OAz3g8,1,6.844009876251221,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,features_v2/-wny0OAz3g8__1.npz,bdbd64a96b68b7472c5a6d35ec8f973eb5c28c4b8af04a3cd6f33176c0db4d41,69,23,22,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/0,-wny0OAz3g8,0,3.3333330154418945,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,features_v2/-wny0OAz3g8__0.npz,8985259ed866ff2f7a65967434022d2a40a9006742175ce7f7fb88cdc8513c02,34,9,9,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/3,-wny0OAz3g8,3,6.146028995513916,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,features_v2/-wny0OAz3g8__3.npz,3bc79a7fe97c226b5cf3bddd080ec9875daf3ec94b16582e39dcddcc7f77fcfc,62,20,20,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/2,-wny0OAz3g8,2,6.686978816986084,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,features_v2/-wny0OAz3g8__2.npz,d9012975be4a0faccd98b3ed4462c664157184b13034c342ed0916667b7074f6,67,23,23,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/5,-wny0OAz3g8,5,6.9119791984558105,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,features_v2/-wny0OAz3g8__5.npz,96f35b0e59d87b842d8e3f73a9601e0c4aab770f47984d48cc4409f41b783c3f,70,20,20,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/7,-wny0OAz3g8,7,8.06796932220459,cbf9cac1048ce191aef781d0c4563eb97f9f3ddd52eb1f9551fa78d31d70e7cd,features_v2/-wny0OAz3g8__7.npz,8e08e91628357e41dbd627d334780086ae59dbc3fdc948bb0d35c9a4581bae8c,81,29,29,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-wny0OAz3g8/9,-wny0OAz3g8,9,6.2130208015441895,b76f70fe783ee76bf1b637137226c98e67d13a6b15ec725894175b36cd76a136,features_v2/-wny0OAz3g8__9.npz,12f2eed3652cdca9c0cb7ba62acd555913433a5782a9fc3bd4e3287cc390fbc7,63,20,20,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-571d8cVauQ/0,-571d8cVauQ,0,5.066667079925537,defc62e1530d9b0cd27f2acfe042b71dd84d7e8c85f1df261f04912091976088,features_v2/-571d8cVauQ__0.npz,9c38a6e6628f67352d27716703f7be63cb4d958ba4ba8eaf697ba3b637aeaa42,51,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-571d8cVauQ/5,-571d8cVauQ,5,15.272981643676758,e304c03fac1f477823fe0926c4fb421b9c4e4091f85718b729afe856c24a7d3e,features_v2/-571d8cVauQ__5.npz,1397887ed11ede3e9cab0566d564fd11014bc15e24aa2a6f5675ea452c9d149c,153,43,43,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-I_e4mIh0yE/1,-I_e4mIh0yE,1,7.622004985809326,8453a966d4bd1f0179eb86a35cd34c0864a3a3bc2fa86f37783060cc0d14f5df,features_v2/-I_e4mIh0yE__1.npz,5f2b4811b0ae11e9e00f74d0f6dfaff9ee474c13b3033793ad73cbf233a2f60e,77,18,18,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-I_e4mIh0yE/3,-I_e4mIh0yE,3,9.163021087646484,a015f02ae51d74c42ad27e2a0831263a303f0658ac8d984819e27ad61342b23e,features_v2/-I_e4mIh0yE__3.npz,692898c2d7d4f91ef25c4ec60364ee7cc7c64baff8d09a789600f6b9fb585295,92,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UacrmKiTn4/10,-UacrmKiTn4,10,7.361979007720947,00312e0602dbeac970de5d7dd0e8a970e514fe45d7fe6625b4f454b299e36360,features_v2/-UacrmKiTn4__10.npz,2b9b15073d07d5a042ff7e4d89f4819d99efcc98e96b5fe2d42e26b6421ed01c,74,18,18,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UacrmKiTn4/4,-UacrmKiTn4,4,4.741015911102295,a7f2ab0d6ee2e2a2d5cd4239f03e6cac67c618ddb744891323df3ff87e29af84,features_v2/-UacrmKiTn4__4.npz,7d6be8f06193a82c27fde3e99106fcd518632cf61a19a66f567d8327057e280b,48,14,14,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-hnBHBN8p5A/7,-hnBHBN8p5A,7,7.372000217437744,0ec6fb76226315d3fafbb483e3500e60cc8a6729617b10e4b4b76163595080fb,features_v2/-hnBHBN8p5A__7.npz,9ebd1d3b4bbb4cbb6e41ef9f67e049323c7b13a99f991d5f44fc56216fe610f6,74,13,13,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-hnBHBN8p5A/6,-hnBHBN8p5A,6,6.401000022888184,997f6808ac3973ddd71800e91c8e10755c10b5037186d84348a79e00d672fdc7,features_v2/-hnBHBN8p5A__6.npz,a778554ac2390ad44dc63d88a22d0cbcf817eec084022dbf5e48a2a3be52236f,65,13,13,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-qDkUB0GgYY/6,-qDkUB0GgYY,6,4.677995204925537,ab19bf4a761d3919b7113b1104ccaadbbf0c4e7b45694904d094280a2c329c8b,features_v2/-qDkUB0GgYY__6.npz,d157258aff14c527b21565f98c368c753ff20f31aa0cbe051f1d24c57a7412e2,47,16,16,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-uywlfIYOS8/4,-uywlfIYOS8,4,6.044987201690674,fde46f5222cf5cfec7af9c93214c972ce2d686b0bf9c90a291021601968986f9,features_v2/-uywlfIYOS8__4.npz,d07fd83e0727367f03d2ae5e0ed623da12fd1946fc010dbdd93d5148e0d4730c,61,18,18,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-6rXp3zJ3kc/8,-6rXp3zJ3kc,8,12.805012702941895,59b77a0f873b7fa95e7327b93d64bb5a4bdae84039321988ef70bba8e63ee1ae,features_v2/-6rXp3zJ3kc__8.npz,becdc042c9ef64c388bde8e5b3a98ac8d99782afcb454c5d8feab4289ab1523c,129,34,34,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/0,-9y-fZ3swSY,0,6.8333330154418945,29a1fd181293cdbe24e50edcad51e3cc0f27a5824d840bcc39d453b272fb6e3c,features_v2/-9y-fZ3swSY__0.npz,0c96186d24ab43623baf90a1e626f66f7cc1695c0b8b70ad3ca63a90a8110f52,69,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/4,-9y-fZ3swSY,4,2.8210289478302,fe04a26710e85c58bdaad5a48618c7e0f742d82b0d920eb4a046c8f14a6921dc,features_v2/-9y-fZ3swSY__4.npz,6cff9072da7459fdf9c4a0dda4c687b8b2c525ba3af893be8b4b51943cff0e88,29,9,9,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-9y-fZ3swSY/8,-9y-fZ3swSY,8,4.988996982574463,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,features_v2/-9y-fZ3swSY__8.npz,cd54043f6a090b25e8ba26bec6b6a399b17e798ca08161fd33239be6f5761a50,50,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-AUZQgSxyPQ/2,-AUZQgSxyPQ,2,22.511003494262695,e09034b30bb4f8fb9f9c2568afc20f2a764e327fe6ef4e13f21804eaac51bb7b,features_v2/-AUZQgSxyPQ__2.npz,6b3fad600f0199347f255e07d223482885cd794a47c0d7ac2ff16c86232495d7,226,41,41,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-HeZS2-Prhc/2,-HeZS2-Prhc,2,8.261979103088379,edb0fa22fdfbd990c8174764af78670bd2ecad799240d35d96a1124a3cd9dc0c,features_v2/-HeZS2-Prhc__2.npz,a545e12987ed9776f6799e87297422df5b8ab3bacade90fcbf98bea7e28ad1c5,83,16,16,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/0,-MeTTeMJBNc,0,9.300000190734863,54e2097f7bdb455d243e72254e5af7ed16160739f2f3cfd22f57d2729ec1acbe,features_v2/-MeTTeMJBNc__0.npz,ef950e662a99c32820f5b841a570702e5d24ae392ab8d553be90c669f1abc4e1,94,21,21,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/13,-MeTTeMJBNc,13,5.430013179779053,80c5d996e5d7b61c58e1fe32d706efcaf80c8c8cddb5c37a5c66d527786c2a44,features_v2/-MeTTeMJBNc__13.npz,ac464b5b9963d9934a9e9cbadb993297b39724ffb7c9afef74f1d42600d5b65f,55,14,14,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-MeTTeMJBNc/7,-MeTTeMJBNc,7,10.51699161529541,40716dc7402fd6398038eded58ce939ec76be6afbeea346b968bf4c427088f3a,features_v2/-MeTTeMJBNc__7.npz,658af7fb69567bc1a1c6f6e26aa7f9c27354169c220ab987624997266ccf502f,106,31,31,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/11,-RfYyzHpjk4,11,5.238996982574463,96e481cd732001239639c8cb4e7f94e570925e5a15940920ffab0cec1f13f904,features_v2/-RfYyzHpjk4__11.npz,382456355758265669dff9e465e4cc02bb8c6724463a2e5967e7f8893c63b4e4,53,17,17,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/8,-RfYyzHpjk4,8,4.588996887207031,ed8c672c532b8c7d1c830c82d6e2c14fcb3bbd3dc1a86670a03a6c8e83c02f3c,features_v2/-RfYyzHpjk4__8.npz,f8217c39db4c367c69a617664528cad9b92ca28f532e755031a92214afa77377,46,17,17,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-RfYyzHpjk4/2,-RfYyzHpjk4,2,5.516016006469727,5e428ee22de58c9d6561b9b4353bef9dfb811a5841d9ab07b5bc453d697ad17f,features_v2/-RfYyzHpjk4__2.npz,63203a3e910c0e8121c88c86f50dfbbf99b316558376c589760573bb99a30162,56,25,25,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-UUCSKoHeMA/0,-UUCSKoHeMA,0,7.800000190734863,3f9792888ec8e969f61cfb6cb4de7bdf7ef8944afe0a9d2a9d13586e1f13b898,features_v2/-UUCSKoHeMA__0.npz,987dfd55d6b20aad24e7dfc246b8118d5428ec936e103b104a21e19d49b1cbdb,79,18,18,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/0,-ri04Z7vwnc,0,2.806999921798706,93021c70c8fad20ad3fded80e7fc790a2f9c96835de58a3b694c6906de42684b,features_v2/-ri04Z7vwnc__0.npz,9631590e28e7e4b4d153058cd781188ceecf7e6a4f799ea21a649dbb14b42a05,29,8,8,0,partial,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/2,-ri04Z7vwnc,2,6.0269999504089355,f5993ce3a6ee56298462c08692a96f78c7d914d7264257ed401de7d6e17fd148,features_v2/-ri04Z7vwnc__2.npz,ec4a0add30cd5eba8030fdadf54c66679192a3af67c6b35a518e277b9341f72c,61,16,16,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-ri04Z7vwnc/5,-ri04Z7vwnc,5,3.5450000762939453,2a5e4319bf592a18c8eb5117f7c7ec30c6c79528f9eac9fe4fb85bf70f277736,features_v2/-ri04Z7vwnc__5.npz,c4b36fc3e52553e01ed6e39ff7bc82d3b346f6895688a7473d059fb06ca68454,36,9,8,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/1,-s9qJ7ATP7w,1,4.7919921875,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,features_v2/-s9qJ7ATP7w__1.npz,8657717d40999eb17c2a865470cf52dff151f9db8a0b122fa058ac43f8bed0db,48,11,11,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/0,-s9qJ7ATP7w,0,6.800000190734863,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,features_v2/-s9qJ7ATP7w__0.npz,301cc976bec37d768ef66b77e6ac982ca30f0837b6f4ea2afe9baa9c25e2b664,69,24,24,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/5,-s9qJ7ATP7w,5,8.561002731323242,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,features_v2/-s9qJ7ATP7w__5.npz,476746914805af458c0c0ca41d9da33ec55b7bfa280018c4aec2cbd780f4f0de,86,19,19,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/4,-s9qJ7ATP7w,4,4.427018165588379,76e0672ffce5857a789b41fb10606402f7da58c33579b1032a8282f86fb35bc9,features_v2/-s9qJ7ATP7w__4.npz,734dea66c346fbdedb6db5861f502eff4a3dc46558466c8825ceb5413fa73d41,45,9,9,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/7,-s9qJ7ATP7w,7,6.966015815734863,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,features_v2/-s9qJ7ATP7w__7.npz,1582ea53834aa456f77aa4384fd2e25514208ed51f53b4a99ff5e420b34cda27,70,28,28,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/6,-s9qJ7ATP7w,6,2.4749999046325684,40b684fd1b4559f0f82e72f9b47f4ce7c5e15059fe8d0d8ab112637cd3386451,features_v2/-s9qJ7ATP7w__6.npz,25f32b57e6213d05d50b9e95b33e02fbbba587cae2bcce8189d7e911e5b9fbbc,25,5,5,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-s9qJ7ATP7w/8,-s9qJ7ATP7w,8,3.2949869632720947,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,features_v2/-s9qJ7ATP7w__8.npz,aceb43a00ad661ebc13dbb273fb0bbff55ee0416a99ca825ee046a98aa6a0299,33,13,12,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/1,-yRb-Jum7EQ,1,29.288021087646484,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,features_v2/-yRb-Jum7EQ__1.npz,df02ec3e0707e3ae292d6ccabc0062d41f68e8dade44b0a04e7c93af52ab2a08,293,49,49,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/5,-yRb-Jum7EQ,5,13.169010162353516,6d6145293cf84d1fc8324dfa8d39465e2d7fc07affb67ff68deeabf6e3c410d9,features_v2/-yRb-Jum7EQ__5.npz,5d3083b3fcbdf232e5bed1955f4498481892a0c6f9fb74ff3c1460fa2975b3a1,132,25,25,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 +-yRb-Jum7EQ/6,-yRb-Jum7EQ,6,11.241994857788086,b51eb9ad06de804934cb6a315de591ba9ff7f8aa71bffff7f8001ab29945d989,features_v2/-yRb-Jum7EQ__6.npz,feda99761265679c062ad17106b6264a16e046a84d42fc0edc0c985c14232481,113,19,19,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617 diff --git a/final/output/q1/features_v2/typical_alignment_card.json b/final/output/q1/features_v2/typical_alignment_card.json new file mode 100644 index 0000000..257b83a --- /dev/null +++ b/final/output/q1/features_v2/typical_alignment_card.json @@ -0,0 +1,49 @@ +{ + "sample_id": "-tANM6ETl_M/3", + "source_video_sha256": "6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6", + "duration_s": 6.758008003234863, + "word": "you", + "word_char_span": [ + 26, + 29 + ], + "hard_interval_s": [ + 1.8624999523162842, + 2.0225000381469727 + ], + "boundary_interval_90_s": [ + 1.8224999904632568, + 1.8624999523162842, + 1.9225000143051147, + 2.0225000381469727 + ], + "audio_source_rows": [ + 185, + 186, + 187, + 188, + 189, + 190, + 191, + 192, + 193, + 194, + 195, + 196, + 197, + 198, + 199, + 200, + 201 + ], + "vision_source_rows": [ + 56, + 57, + 58, + 59, + 60, + 61 + ], + "manual_boundary_audit": "not available; no empirical boundary error or calibration claimed", + "vision_backend": "OpenFace 2.2.0" +} \ No newline at end of file diff --git a/final/output/q1/model_comparison/comparison_summary.csv b/final/output/q1/model_comparison/comparison_summary.csv new file mode 100644 index 0000000..4d9d19a --- /dev/null +++ b/final/output/q1/model_comparison/comparison_summary.csv @@ -0,0 +1,6 @@ +method,sample_count,video_group_count,oof_accuracy,oof_macro_f1,oof_mae,oof_rmse,oof_pearson,fold_accuracy_mean,fold_accuracy_sd,fold_macro_f1_mean,fold_macro_f1_sd,fold_mae_mean,fold_mae_sd,fold_rmse_mean,fold_rmse_sd,fold_pearson_mean,fold_pearson_sd +B0,100,37,0.59,0.33730158730158727,0.5345432417094708,0.732489121111015,0.42550845314348806,0.5900000000000001,0.13874436925511605,0.30250959643116504,0.12328311315733322,0.5345432417094707,0.1509440598896269,0.7021717712774651,0.2331713859134915,0.5178601432579046,0.0693447889958761 +B1,100,37,0.57,0.3083538083538084,0.5341934795305133,0.744394490195775,0.39809097710552227,0.5700000000000001,0.14404860290887933,0.2833554676255717,0.09714100671059138,0.5341934795305133,0.14505919692829797,0.7178215736202961,0.22038416878008044,0.5030623393785054,0.03792958723604798 +B2,100,37,0.55,0.27687837276878374,0.5308383707888424,0.7342310729072455,0.4232211505391657,0.55,0.14577379737113252,0.2663593056831732,0.06662075171997459,0.5308383707888424,0.1428834164916222,0.7056454349573301,0.22682534180070457,0.5469301159603699,0.08634745177982689 +B3,100,37,0.57,0.3083538083538084,0.5202135869115591,0.7371218880080749,0.4113934919986956,0.5700000000000001,0.14404860290887933,0.2833554676255717,0.09714100671059138,0.5202135869115592,0.1394995955025217,0.7111521056477064,0.21682758246306663,0.5156532365182802,0.06250980259239004 +B4,100,37,0.6,0.38820126144069805,0.5392817909643054,0.7627905806623468,0.34134008295313295,0.6,0.14577379737113252,0.3454724288057621,0.11319687165584016,0.5392817909643054,0.154798960029947,0.7336276515808532,0.2335592504675275,0.42790041610293833,0.06964606173684448 diff --git a/final/output/q1/model_comparison/fold_metrics.csv b/final/output/q1/model_comparison/fold_metrics.csv new file mode 100644 index 0000000..541ac65 --- /dev/null +++ b/final/output/q1/model_comparison/fold_metrics.csv @@ -0,0 +1,26 @@ +method,fold,train_samples,valid_samples,train_video_groups,valid_video_groups,feature_dimension,accuracy,macro_f1,mae,rmse,pearson +B0,1,80,20,31,6,4385,0.75,0.29411764705882354,0.40008344650268557,0.5260719631531688,0.4282455508385404 +B1,1,80,20,31,6,4385,0.75,0.29411764705882354,0.404778890311718,0.5102545703340213,0.5025732055401765 +B2,1,80,20,31,6,4385,0.75,0.29411764705882354,0.40554874762892723,0.49635824914543497,0.5251573968087152 +B3,1,80,20,31,6,4455,0.75,0.29411764705882354,0.40047709345817567,0.5227381465621478,0.4534944334978189 +B4,1,80,20,31,6,8225,0.8,0.41414141414141414,0.38890780285000803,0.5048429758830633,0.4807784199544402 +B0,2,80,20,30,7,4385,0.55,0.24444444444444444,0.7141662366688252,0.9440543822968819,0.5724797760279744 +B1,2,80,20,30,7,4385,0.6,0.25806451612903225,0.6969431169331074,0.9408874723828916,0.5447256164038773 +B2,2,80,20,30,7,4385,0.55,0.2528735632183908,0.6819145023822785,0.9246026596165687,0.5890930594687094 +B3,2,80,20,30,7,4455,0.6,0.25806451612903225,0.6838106364011765,0.9468942310321677,0.5641169095393826 +B4,2,80,20,30,7,8225,0.6,0.25,0.7017564930021762,0.9729294282855592,0.4758061583115507 +B0,3,80,20,29,8,4385,0.7,0.5119047619047619,0.6603802375495433,0.9513343469769703,0.45764170673589194 +B1,3,80,20,29,8,4385,0.65,0.44334975369458124,0.6796137057244778,0.9683893550579586,0.44885212546141806 +B2,3,80,20,29,8,4385,0.6,0.35555555555555557,0.6889978893101215,0.976480331076424,0.439491261114543 +B3,3,80,20,29,8,4455,0.65,0.44334975369458124,0.6552760511636734,0.9455118520762318,0.4613521776727222 +B4,3,80,20,29,8,8225,0.55,0.42124542124542125,0.6998446479439735,0.9910488675160706,0.3308335519309645 +B0,4,80,20,29,8,4385,0.4,0.19047619047619047,0.5209357619285584,0.6349209086686575,0.5599141805979567 +B1,4,80,20,29,8,4385,0.4,0.19047619047619047,0.48845834359526635,0.6251263063549318,0.5318831725972442 +B2,4,80,20,29,8,4385,0.35,0.1728395061728395,0.4634488061070442,0.5914937349952397,0.668684620535319 +B3,4,80,20,29,8,4455,0.4,0.19047619047619047,0.46604466289281843,0.6048342377796782,0.5952425112526142 +B4,4,80,20,29,8,8225,0.4,0.19753086419753085,0.5084194637835026,0.6563628690043487,0.37663363321003157 +B0,5,80,20,29,8,4385,0.55,0.2716049382716049,0.37715052589774134,0.45447725529164684,0.5710195020891593 +B1,5,80,20,29,8,4385,0.45,0.23076923076923075,0.40117334108799696,0.5444501639716764,0.4872775768898111 +B2,5,80,20,29,8,4385,0.5,0.25641025641025644,0.4142819085158408,0.5392921999529835,0.5122242418745627 +B3,5,80,20,29,8,4455,0.45,0.23076923076923075,0.39545949064195157,0.5357820607883065,0.504060150628863 +B4,5,80,20,29,8,8225,0.65,0.4444444444444445,0.3974805472418666,0.5429541172152239,0.4754503171077047 diff --git a/final/output/q1/model_comparison/run_manifest.json b/final/output/q1/model_comparison/run_manifest.json new file mode 100644 index 0000000..4814630 --- /dev/null +++ b/final/output/q1/model_comparison/run_manifest.json @@ -0,0 +1,22 @@ +{ + "version": "E题V2 Q1 B0-B4", + "samples": 100, + "video_groups": 37, + "folds": 5, + "grouping": "video_id GroupKFold; train/valid group disjoint", + "scalers": "B0 train-fold mean/std; B1-B4 train-fold median/MAD; no held-fold fitting", + "classifier": "LogisticRegression C=0.05", + "regressor": "Ridge alpha=25", + "branches": { + "B0": "quality-weighted physical projection", + "B1": "B0 plus train-fold robust scale and SO(3)/unit-gaze aggregation", + "B2": "B1 plus CTC forward-backward text occupancy", + "B3": "B1 plus native-row level-2 dynamics", + "B4": "B1 plus frozen Wav2Vec2 auxiliary representation" + }, + "B5": "query H_time audit only; not predictive score", + "B6": "content probe disabled", + "feature_manifest_sha256": "172d5d11964a800fff364e0592afb6959fa4bdc52f91106d4da428fcc175a054", + "source_run_manifest": "features_v2/manifest_q1.jsonl", + "bootstrap_repeats": 200 +} \ No newline at end of file diff --git a/final/output/q1/package_audit.json b/final/output/q1/package_audit.json new file mode 100644 index 0000000..01150fa --- /dev/null +++ b/final/output/q1/package_audit.json @@ -0,0 +1,27 @@ +{ + "source": "math/Q1/features_v2", + "sample_count": 100, + "video_group_count": 37, + "modality_rows": 300, + "feature_file_count": 100, + "sample_status_counts": { + "success": 96, + "partial": 4 + }, + "partial_sample_ids": [ + "-NFrJFQijFE/1", + "-NFrJFQijFE/2", + "-mJ2ud6oKI8/1", + "-ri04Z7vwnc/0" + ], + "feature_file_bytes": 44080979, + "feature_directory_bytes": 44321552, + "typical_sample_id": "-iRBcNs9oI8/8", + "typical_word_count": 21, + "checks": [ + "all 100 feature SHA-256 values match manifest", + "100 sample summary rows", + "300 modality summary rows", + "three modality rows per sample" + ] +} \ No newline at end of file diff --git a/final/Q1/results/model_comparison/run_manifest.json b/final/output/q1/source_cache_manifest.json similarity index 86% rename from final/Q1/results/model_comparison/run_manifest.json rename to final/output/q1/source_cache_manifest.json index f24a1b8..61645dd 100644 --- a/final/Q1/results/model_comparison/run_manifest.json +++ b/final/output/q1/source_cache_manifest.json @@ -1,16 +1,16 @@ { - "created_at_local": "2026-09-23 22:17:06 +0800", + "created_at_local": "2026-09-24 19:27:55 +0800", "python": "3.14.7 (main, Aug 10 2026, 00:00:00) [GCC 16.1.1 20260515 (Red Hat 16.1.1-2)]", "platform": "Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.43", - "uv_version": "uv 0.11.28 (x86_64-unknown-linux-gnu)", + "uv_version": "uv run parent; executable not on child PATH", "packages": { "torch": "2.14.0+cu130", "transformers": "5.17.0", "numpy": "2.5.3", "scipy": "1.18.1", "scikit-learn": "1.9.1", - "mediapipe": "1.0.1", "opencv-python-headless": "5.0.0.93", + "librosa": "1.0.0", "openpyxl": "3.1.5", "matplotlib": "3.11.2" }, @@ -25,9 +25,14 @@ "speech_stride_samples": 320, "speech_receptive_field_samples": 400 }, - "face_landmarker_asset_sha256": "64184e229b263107bc2b804c6625db1341ff2bb731874b0bcc2fe6544e0bc9ff", + "openface": { + "version": "2.2.0", + "executable": "FeatureExtraction", + "landmark_model": "main_clnf_general.txt", + "confidence_threshold": 0.8 + }, "inputs": { - "label_file": "/home/gloamxun/modeling_zhaocui/E题数据/附件1-数据集原始多模态样本/MOSEI数据集部分原始视频-100条/label-100.xlsx", + "label_file": "E题数据/附件1-数据集原始多模态样本/MOSEI数据集部分原始视频-100条/label-100.xlsx", "label_file_sha256": "827334f782b1f242c84ad944a657bc7f7c63643f71ae9c977811a3eecdd3ab33", "sample_count": 100, "video_sha256_by_sample": { @@ -214,7 +219,7 @@ "delta_mfcc_12", "log_energy", "log_f0_hz", - "voicing_strength", + "voiced_probability", "spectral_centroid_hz", "spectral_bandwidth_hz", "spectral_flux", @@ -222,49 +227,61 @@ "hnr_db" ], "vision_features": [ - "mp_blendshape_browDownLeft", - "mp_blendshape_browDownRight", - "mp_blendshape_browInnerUp", - "mp_blendshape_browOuterUpLeft", - "mp_blendshape_browOuterUpRight", - "mp_blendshape_eyeBlinkLeft", - "mp_blendshape_eyeBlinkRight", - "mp_blendshape_eyeSquintLeft", - "mp_blendshape_eyeSquintRight", - "mp_blendshape_eyeWideLeft", - "mp_blendshape_eyeWideRight", - "mp_blendshape_jawOpen", - "mp_blendshape_mouthFrownLeft", - "mp_blendshape_mouthFrownRight", - "mp_blendshape_mouthPucker", - "mp_blendshape_mouthSmileLeft", - "mp_blendshape_mouthSmileRight", - "pose_rotvec_x", - "pose_rotvec_y", - "pose_rotvec_z", - "pose_translation_x", - "pose_translation_y", - "pose_translation_z", - "gaze_left_x", - "gaze_left_y", - "gaze_left_z", - "gaze_right_x", - "gaze_right_y", - "gaze_right_z", - "eye_aperture_left", - "eye_aperture_right", - "mouth_aperture", - "mouth_width_face_ratio", - "brow_eye_distance_left", - "brow_eye_distance_right" + "AU01_r", + "AU02_r", + "AU04_r", + "AU05_r", + "AU06_r", + "AU07_r", + "AU09_r", + "AU10_r", + "AU12_r", + "AU14_r", + "AU15_r", + "AU17_r", + "AU20_r", + "AU23_r", + "AU25_r", + "AU26_r", + "AU45_r", + "pose_Rx", + "pose_Ry", + "pose_Rz", + "pose_Tx", + "pose_Ty", + "pose_Tz", + "gaze_0_x", + "gaze_0_y", + "gaze_0_z", + "gaze_1_x", + "gaze_1_y", + "gaze_1_z", + "geometry_eye_open_left", + "geometry_eye_open_right", + "geometry_mouth_open", + "geometry_mouth_width", + "geometry_brow_eye_left", + "geometry_brow_eye_right" ], - "vision_feature_note": "17 MediaPipe blendshape proxies and approximate gaze/landmark geometry; not OpenFace AU labels", + "vision_feature_note": "OpenFace 2.2.0 AU intensities, pose, gaze, and six geometry values defined in E题V2.pdf section 4.2.3", "grid_step_s": 0.1, "audio_window_samples": 400, "audio_step_samples": 160, "audio_fft": 512, "audio_mel_bands": 40, - "vision_sample_rate_hz": 5.0, + "pitch_estimator": { + "name": "librosa.pyin", + "version": "1.0.0", + "f0_range_hz": [ + 80.0, + 400.0 + ], + "frame_length_samples": 400, + "hop_length_samples": 160, + "center": false + }, + "hnr_estimator": "autocorrelation periodicity at pYIN-detected F0; dB", + "vision_sample_rate_hz": "native OpenFace frame timestamps; no fixed-rate assumption", "grouping": "GroupKFold by video_id", "fold_count": 5, "probe": { @@ -279,15 +296,15 @@ }, "bootstrap": { "unit": "video_id", - "repeats": 2000, + "repeats": 200, "seed": 20260923 }, "human_boundary_reference_count": 0, "boundary_interval_level": 0.9, - "hard_text_quality_weight": "per-sample median-normalized CTC path score clipped to [0.25, 4.0]; uncalibrated", - "cache_schema": "q1-b0b4-v3" + "hard_text_quality_weight": "raw fixed-transcript CTC word path score in [0,1]; unavailable scores use q*=1 and are flagged unavailable", + "cache_schema": "q1-b0b4-v4-openface" }, - "output_dir": "/home/gloamxun/modeling_zhaocui/math/Q1/results/model_comparison", - "typical_sample_id": "-9y-fZ3swSY/0", - "elapsed_seconds": 124.10033178329468 + "output_dir": "math/Q1/results/model_comparison", + "typical_sample_id": "-tANM6ETl_M/3", + "elapsed_seconds": 63.887396574020386 } diff --git a/final/output/q1/typical_sample_correspondence.csv b/final/output/q1/typical_sample_correspondence.csv new file mode 100644 index 0000000..29a3b68 --- /dev/null +++ b/final/output/q1/typical_sample_correspondence.csv @@ -0,0 +1,22 @@ +sample_id,word_index,word,text_observed,text_feature_dim,word_start_s,word_end_s,audio_source_first_row,audio_source_last_row,audio_start_s,audio_end_s,audio_first_sample,audio_last_sample_exclusive,audio_feature_dim,vision_source_first_row,vision_source_last_row,vision_start_s,vision_end_s,vision_first_frame_index,vision_last_frame_index,vision_observed_frames,vision_feature_dim +-iRBcNs9oI8/8,0,I've,True,768,0.082500,0.242500,7,23,0.077500,0.247500,1120,4080,74,2,7,0.050000,0.250500,3,8,6,35 +-iRBcNs9oI8/8,1,drawn,True,768,0.322500,0.662500,31,65,0.317500,0.667500,4960,10800,74,10,20,0.317000,0.684000,11,21,11,35 +-iRBcNs9oI8/8,2,this,True,768,1.022500,1.342500,101,133,1.017500,1.347500,16160,21680,74,31,40,1.017500,1.351500,32,41,10,35 +-iRBcNs9oI8/8,3,man,True,768,1.522500,1.682500,151,167,1.517500,1.687500,24160,27120,74,46,50,1.518500,1.685000,47,51,5,35 +-iRBcNs9oI8/8,4,"here,",True,768,1.902500,2.082500,189,207,1.897500,2.087500,30240,33520,74,57,62,1.885500,2.085500,58,63,6,35 +-iRBcNs9oI8/8,5,and,True,768,2.122500,2.262500,211,225,2.117500,2.267500,33760,36400,74,64,68,2.118500,2.285500,65,69,5,35 +-iRBcNs9oI8/8,6,if,True,768,2.322500,2.382500,231,237,2.317500,2.387500,36960,38320,74,70,71,2.319000,2.385500,71,72,2,35 +-iRBcNs9oI8/8,7,you,True,768,2.482500,2.642500,247,263,2.477500,2.647500,39520,42480,74,74,79,2.452500,2.652500,75,80,6,35 +-iRBcNs9oI8/8,8,can,True,768,2.742500,2.842500,273,283,2.737500,2.847500,43680,45680,74,82,85,2.719500,2.853000,83,86,4,35 +-iRBcNs9oI8/8,9,"tell,",True,768,2.882500,4.082500,287,407,2.877500,4.087500,45920,65520,74,86,122,2.853000,4.087500,87,123,37,35 +-iRBcNs9oI8/8,10,he,True,768,4.122500,4.182500,411,417,4.117500,4.187500,65760,67120,74,124,125,4.120500,4.187500,125,126,2,35 +-iRBcNs9oI8/8,11,has,True,768,4.222500,4.342500,421,433,4.217500,4.347500,67360,69680,74,127,130,4.221000,4.354500,128,131,4,35 +-iRBcNs9oI8/8,12,a,True,768,4.402500,4.422500,439,441,4.397500,4.427500,70240,70960,74,132,133,4.387500,4.454500,133,134,2,35 +-iRBcNs9oI8/8,13,lot,True,768,4.522500,4.682500,451,467,4.517500,4.687500,72160,75120,74,136,140,4.521500,4.688000,137,141,5,35 +-iRBcNs9oI8/8,14,of,True,768,4.742500,4.802500,473,479,4.737500,4.807500,75680,77040,74,142,144,4.721500,4.821500,143,145,3,35 +-iRBcNs9oI8/8,15,big,True,768,4.842500,4.962500,483,495,4.837500,4.967500,77280,79600,74,145,149,4.821500,4.988500,146,150,5,35 +-iRBcNs9oI8/8,16,"muscles,",True,768,5.062500,5.462500,505,545,5.057500,5.467500,80800,87600,74,152,164,5.055000,5.489000,153,165,13,35 +-iRBcNs9oI8/8,17,he,True,768,5.682500,5.802500,567,579,5.677500,5.807500,90720,93040,74,170,174,5.655500,5.822500,171,175,5,35 +-iRBcNs9oI8/8,18,looks,True,768,6.502500,6.762500,649,675,6.497500,6.767500,103840,108400,74,195,203,6.490000,6.790000,196,204,9,35 +-iRBcNs9oI8/8,19,very,True,768,7.202500,7.402500,719,739,7.197500,7.407500,115040,118640,74,216,222,7.190500,7.424000,217,223,7,35 +-iRBcNs9oI8/8,20,strong,True,768,7.602500,8.002500,759,799,7.597500,8.007500,121440,128240,74,228,240,7.591000,8.024500,229,241,13,35 diff --git a/final/output/q1/typical_sample_frames.jpg b/final/output/q1/typical_sample_frames.jpg new file mode 100644 index 0000000..796687e Binary files /dev/null and b/final/output/q1/typical_sample_frames.jpg differ diff --git a/final/output/q2/.gitkeep b/final/output/q2/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/final/output/q2/README.md b/final/output/q2/README.md new file mode 100644 index 0000000..64c8c91 --- /dev/null +++ b/final/output/q2/README.md @@ -0,0 +1,11 @@ +# Q2 未对齐数据对比输出 + +本目录汇总附件 2 `unaligned_50.pkl` 上的数学模型和两种保留深度学习模型对比。文本、音频、视觉特征均由 `final/adapter/` 投影到 50 个归一化进程位置；位置不是物理时间戳。 + +| 文件 | 内容 | +|---|---| +| `comparison_validation.csv` | 全部 14 个数学/深度模型版本在官方验证集的指标 | +| `comparison_test.csv` | 按预定选择规则保留的数学方案和两种深度模型测试指标 | +| `comparison_aurc.csv` | 单模态、同步、部分重叠、异步缺失模式的归一化 AURC-MAE | + +本目录是汇总结果。完整的权重、运行清单和情景审计位于 `final/experiments/q2/`。复现步骤、字段含义和限制见 [项目 README](../../README.md) 与 [REPORTS.md](../../REPORTS.md)。 diff --git a/final/output/q2/comparison_aurc.csv b/final/output/q2/comparison_aurc.csv new file mode 100644 index 0000000..dbb8a40 --- /dev/null +++ b/final/output/q2/comparison_aurc.csv @@ -0,0 +1,15 @@ +model,family,split,n,single,sync,partial,async +C0,math,official_valid,728,0.74245468084,0.764301057542,0.757756712334,0.7696260728 +C1,math,official_valid,728,0.743076794527,0.784873549766,0.734320150566,0.740601562173 +C2,math,official_valid,728,0.727868752149,0.744436296608,0.740885247081,0.751214732285 +C3,math,official_valid,728,0.744854990117,0.757338476158,0.756214402953,0.757743957955 +C4,math,official_valid,728,0.754417464746,0.761033324095,0.765972024063,0.776987042025 +C5,math,official_valid,728,0.792075799568,0.817620422195,0.808428116852,0.811767298445 +C6,math,official_valid,728,0.702788531864,0.723670054117,0.72921533774,0.728261026723 +C6_no_distance,math,official_valid,728,0.72420382512,0.738696028483,0.736639219843,0.73332169004 +C6_no_reconstruction,math,official_valid,728,0.712794456368,0.738238199286,0.727089334273,0.742886536621 +C6_pointmask,math,official_valid,728,0.682357162517,0.698807600141,0.686643826816,0.695143140271 +C7_distill,math,official_valid,728,0.721474159196,0.746756387096,0.741990897572,0.735862739309 +C7_group,math,official_valid,728,0.743706723652,0.745214598164,0.745320934303,0.750082046102 +EarlyConcat,deep_learning,official_valid,728,0.6613046329107035,0.6578565844284859,0.6492884830542397,0.655106630034653 +MoFE-7,deep_learning,official_valid,728,0.6509317685575335,0.6471657812189954,0.6417413313729995,0.6524663015746559 diff --git a/final/output/q2/comparison_test.csv b/final/output/q2/comparison_test.csv new file mode 100644 index 0000000..1965cbd --- /dev/null +++ b/final/output/q2/comparison_test.csv @@ -0,0 +1,4 @@ +model,family,split,n,accuracy,macro_f1,mae,rmse,pearson +C6,math,official_test,727,0.672627235213205,0.5484581573154621,0.7100059986114502,0.969292458045841,0.6424147486686707 +EarlyConcat,deep_learning,official_test,727,0.6698762035763411,0.5869825623561269,0.6850546002388,0.9016550912366708,0.6612713411018789 +MoFE-7,deep_learning,official_test,727,0.6629986244841816,0.5878736806271413,0.6702061891555786,0.8895326574633714,0.6446983125605155 diff --git a/final/output/q2/comparison_validation.csv b/final/output/q2/comparison_validation.csv new file mode 100644 index 0000000..ee51390 --- /dev/null +++ b/final/output/q2/comparison_validation.csv @@ -0,0 +1,15 @@ +model,family,split,n,accuracy,macro_f1,mae,rmse,pearson +C0,math,official_valid,728,0.5906593406593407,0.5573246854806906,0.7267872095108032,0.9493674330569902,0.5186371207237244 +C1,math,official_valid,728,0.5879120879120879,0.47389937106918234,0.7446202039718628,0.9822674244990333,0.5362412333488464 +C2,math,official_valid,728,0.5961538461538461,0.450273379924267,0.698158860206604,0.9260472748411018,0.5808078050613403 +C3,math,official_valid,728,0.5906593406593407,0.443346825657295,0.7317898869514465,0.985695966827773,0.5456575155258179 +C4,math,official_valid,728,0.554945054945055,0.4536351679548698,0.7454220056533813,1.0156130863371253,0.503315806388855 +C5,math,official_valid,728,0.5824175824175825,0.4401015447285379,0.7787972092628479,1.024799634376836,0.5123178958892822 +C6,math,official_valid,728,0.6085164835164835,0.518781225964892,0.6846672296524048,0.9208069125796768,0.6101511716842651 +C6_no_distance,math,official_valid,728,0.6071428571428571,0.4720788453840183,0.7117781043052673,0.9322639795095579,0.6074535250663757 +C6_no_reconstruction,math,official_valid,728,0.5837912087912088,0.48325805552048157,0.709170937538147,0.9543444540035635,0.5635251402854919 +C6_pointmask,math,official_valid,728,0.5810439560439561,0.5025832785659426,0.679211437702179,0.9175777170595291,0.5958787798881531 +C7_distill,math,official_valid,728,0.592032967032967,0.4870960207460408,0.7024686336517334,0.9442218762916826,0.5752816796302795 +C7_group,math,official_valid,728,0.6002747252747253,0.4509009833805456,0.7323286533355713,0.9355743977553851,0.6008110046386719 +EarlyConcat,deep_learning,official_valid,728,0.6236263736263736,0.5779072819396367,0.6614056825637817,0.8730861646443621,0.6092134453424162 +MoFE-7,deep_learning,official_valid,728,0.614010989010989,0.5661110562085216,0.6426358222961426,0.8499269790699532,0.6013646920407029 diff --git a/final/output/q3/.gitkeep b/final/output/q3/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/final/pyproject.toml b/final/pyproject.toml new file mode 100644 index 0000000..56e3172 --- /dev/null +++ b/final/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "multimodal-sentiment-modeling-deliverable" +version = "1.0.0" +description = "Standalone Q1-Q3 multimodal sentiment modeling and experiments" +requires-python = ">=3.11" +dependencies = [ + "numpy>=1.26,<3", + "scipy>=1.12", + "scikit-learn>=1.4", + "torch>=2.2", + "transformers>=4.44", + "librosa>=0.10.2", + "opencv-python-headless>=4.10", + "matplotlib>=3.8", + "openpyxl>=3.1", + "soundfile>=0.12", +] + +[tool.uv] +package = false diff --git a/final/q1/audit_unified_adapter.py b/final/q1/audit_unified_adapter.py new file mode 100644 index 0000000..29f620c --- /dev/null +++ b/final/q1/audit_unified_adapter.py @@ -0,0 +1,151 @@ +"""Full Q1 physical and official unaligned split audit; no model training.""" +from __future__ import annotations + +import hashlib +import json +import sys +from collections import Counter +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parent + +from ..adapter import Q1AlignmentAdapter +from ..q2.math.data import restricted_load + +MODALITIES = ("text", "audio", "vision") +RESULTS = ROOT / "output" / "q1" / "unified_adapter" +from ..data_paths import ATTACHMENT2 + +OFFICIAL = ATTACHMENT2 / "unaligned_50.pkl" + + +def _distribution(values: list[float]) -> dict[str, float | int]: + arr = np.asarray(values, np.float64) + return {"count": int(len(arr)), "min": float(arr.min()) if len(arr) else 0.0, + "p25": float(np.percentile(arr, 25)) if len(arr) else 0.0, + "median": float(np.median(arr)) if len(arr) else 0.0, + "p75": float(np.percentile(arr, 75)) if len(arr) else 0.0, + "max": float(arr.max()) if len(arr) else 0.0, + "mean": float(arr.mean()) if len(arr) else 0.0} + + +def _audit_split(name: str, split: dict, baseline: dict) -> dict: + adapter = Q1AlignmentAdapter() + n = len(split["id"]) + hashes = {m: hashlib.sha256() for m in MODALITIES} + mask_hash = hashlib.sha256() + lengths = {m: [] for m in MODALITIES} + observed_bins = {m: [] for m in MODALITIES} + all_missing_bins = Counter() + coverages = {m: [] for m in MODALITIES} + relations = {m: Counter() for m in MODALITIES} + all_missing = Counter() + conflicts = Counter() + text_padding_nonzero = 0 + max_provenance_error = 0.0 + max_nonfinite = 0 + modes = Counter() + shapes = {m: Counter() for m in MODALITIES} + for i in range(n): + sample = adapter.from_unaligned_record(split, i) + modes[sample.metadata["coordinate_mode"]] += 1 + stacked_mask = np.stack([sample.observed[m] for m in MODALITIES], axis=-1) + mask_hash.update(stacked_mask.tobytes(order="C")) + attention = np.asarray(split["text_bert"][i, 1], bool) + text_padding_nonzero += int(np.count_nonzero(np.any(split["text"][i] != 0, axis=1) & ~attention)) + for m in MODALITIES: + x = sample.features[m] + p = sample.provenance[m] + hashes[m].update(x.tobytes(order="C")) + shapes[m][str(list(x.shape))] += 1 + lengths[m].append(p.source_span) + observed_bins[m].append(int(sample.observed[m].sum())) + all_missing_bins[m] += int((~sample.observed[m]).sum()) + coverages[m].extend(sample.coverage[m].tolist()) + relations[m]["LK"] += 1 + all_missing[m] += int(not sample.observed[m].any()) + conflicts[m] += int(p.length_conflict) + if m == "vision": + all_missing["vision_tail_ambiguous"] += int(p.tail_ambiguous) + row_sums = np.asarray(p.source_weights.sum(axis=1)).reshape(-1) + if sample.observed[m].any(): + max_provenance_error = max(max_provenance_error, + float(np.max(np.abs(row_sums[sample.observed[m]] - 1.0)))) + max_provenance_error = max(max_provenance_error, + float(np.max(np.abs(row_sums[~sample.observed[m]]))) if (~sample.observed[m]).any() else 0.0) + max_nonfinite += int(np.count_nonzero(~np.isfinite(x))) + actual = {m: h.hexdigest() for m, h in hashes.items()} + expected = baseline[name]["sha256"] + return {"samples": n, "coordinate_modes": dict(modes), "feature_shapes": {m: dict(v) for m, v in shapes.items()}, + "source_length": {m: _distribution(v) for m, v in lengths.items()}, + "source_length_vs_K": {m: dict(v) for m, v in relations.items()}, + "observed_target_bins_per_sample": {m: _distribution(v) for m, v in observed_bins.items()}, + "coverage_per_target_bin": {m: _distribution(v) for m, v in coverages.items()}, + "text_nonzero_rows_outside_attention": text_padding_nonzero, + "length_conflict_samples": dict(conflicts), + "tail_ambiguous_samples": all_missing["vision_tail_ambiguous"], + "all_missing_target_bins": dict(all_missing_bins), + "all_missing_modality_samples": {m: all_missing[m] for m in MODALITIES}, + "nonfinite_output_values": max_nonfinite, + "max_provenance_row_sum_error": max_provenance_error, + "sha256": actual, "legacy_sha256": expected, + "exact_feature_equivalence": {m: actual[m] == expected[m] for m in MODALITIES}, + "mask_sha256": mask_hash.hexdigest(), + "exact_mask_equivalence": mask_hash.hexdigest() == baseline[name]["mask_sha256"]} + + +def _audit_physical() -> dict: + manifest = HERE / "features_v2" / "manifest_q1.jsonl" + ids = [json.loads(line)["sample_id"] for line in manifest.read_text(encoding="utf-8").splitlines()] + adapter = Q1AlignmentAdapter() + modes = Counter() + nonfinite = 0 + provenance_error = 0.0 + shapes = {m: Counter() for m in MODALITIES} + duration = [] + for sample_id in ids: + sample = adapter.from_q1_sample(sample_id) + modes[sample.metadata["coordinate_mode"]] += 1 + duration.append(sample.metadata["duration_s"]) + for m in MODALITIES: + x = sample.features[m] + shapes[m][str(list(x.shape))] += 1 + nonfinite += int(np.count_nonzero(~np.isfinite(x))) + sums = np.asarray(sample.provenance[m].source_weights.sum(axis=1)).ravel() + observed = sample.observed[m] + if observed.any(): + provenance_error = max(provenance_error, float(np.max(np.abs(sums[observed] - 1)))) + return {"samples": len(ids), "coordinate_modes": dict(modes), "feature_shapes": {m: dict(v) for m, v in shapes.items()}, + "duration_s": _distribution(duration), "nonfinite_output_values": nonfinite, + "max_provenance_row_sum_error": provenance_error, + "stored_dense_0_1_s_views_untouched": True} + + +def main() -> None: + RESULTS.mkdir(parents=True, exist_ok=True) + baseline = json.loads((RESULTS / "legacy_relative_baseline.json").read_text(encoding="utf-8")) + obj = restricted_load(OFFICIAL) + report = {"adapter": "q1-unified-1", "official_input": str(OFFICIAL.relative_to(ROOT)), + "relative": {name: _audit_split(name, obj[name], baseline) for name in ("train", "valid", "test")}} + del obj + report["physical"] = _audit_physical() + (RESULTS / "full_audit.json").write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") + equivalence = {"comparison": "SHA-256 of full float32 feature arrays and boolean masks against frozen old adapter output", + "splits": {name: {"samples": v["samples"], "exact_feature_equivalence": v["exact_feature_equivalence"], + "exact_mask_equivalence": v["exact_mask_equivalence"], + "old_feature_sha256": v["legacy_sha256"], "new_feature_sha256": v["sha256"], + "old_mask_sha256": baseline[name]["mask_sha256"], + "new_mask_sha256": v["mask_sha256"]} + for name, v in report["relative"].items()}} + (RESULTS / "equivalence_report.json").write_text(json.dumps(equivalence, indent=2), encoding="utf-8") + print(json.dumps({"relative": {name: {"samples": v["samples"], + "exact_feature_equivalence": v["exact_feature_equivalence"], + "exact_mask_equivalence": v["exact_mask_equivalence"]} for name, v in report["relative"].items()}, + "physical_samples": report["physical"]["samples"]}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/final/q1/build_v2.py b/final/q1/build_v2.py new file mode 100644 index 0000000..e1fe40d --- /dev/null +++ b/final/q1/build_v2.py @@ -0,0 +1,417 @@ +from __future__ import annotations + +import csv +import argparse +import hashlib +import json +import subprocess +import sys +from dataclasses import replace +from pathlib import Path +from typing import Any + +import numpy as np +from scipy import sparse + +ROOT = Path(__file__).resolve().parents[1] +Q1 = Path(__file__).resolve().parent +from . import compare_models as cm + +CACHE = Q1 / "cache" / "native" +RUN = ROOT / "output" / "q1" / "model_comparison" / "run_manifest.json" +LABELS = cm.LABEL_FILE +OUT = ROOT / "output" / "q1" / "features_v2" +MODALITIES = ("text", "audio", "vision") +CONFIG = { + "schema": "q1-v2-source-first-2026-09", + "source_cache_schema": "q1-b0b4-v4-openface", + "common_step_s": 0.1, + "multi_context_windows_s": [0.1, 0.3, 0.7], + "relative_progress_bins": 50, + "text_encoder": "google-bert/bert-base-uncased; frozen last-four-layer mean per whitespace word; window=510, stride=384", + "alignment": "fixed-transcript CTC monotone state graph; hard Viterbi path plus stored forward-backward occupancy; scores uncalibrated", + "audio": "74-D: 40 log-Mel + 13 MFCC + 13 two-sided local-linear delta MFCC + [log-energy, log-F0, librosa.pyin voiced probability, spectral centroid, bandwidth, flux, zero-crossing rate, HNR dB]; 16 kHz, 400-sample window, 160-sample hop, FFT 512; HNR autocorrelation uses n=1024 at detected F0; preserve per-field validity; librosa.pyin warns cycle support is short at fmin=80 Hz for a 25 ms frame", + "vision": "OpenFace 2.2.0 native-frame output: 17 AU intensities, Pose6, Gaze6, Geometry6; confidence threshold 0.8; actual frame PTS and indices retained", + "quality": "B0 uses raw CTC word-path score in [0,1]; unavailable quality falls back to q*=1 and is flagged; audio/vision retain explicit unknown or detector-confidence states", + "content_probe": "disabled; physical time/query maps remain separate from content similarity", + "time": "left-closed/right-open seconds relative to video stream origin; source indices retained", + "branch_states": {"sec": "primary B0 view and materialized", "B1_vision_geometry": "geometry-aware 0.1 s view materialized", "word": "materialized", "phase50": "materialized", "multi": "derived from native rows on demand by q1_io", "posterior": "native CTC posterior stored; sec projection available through q1_io", "dynamics": "materialized if continuous support is sufficient", "b4_speech": "source-cache-backed on demand through q1_io to stay within the 50 MiB feature-package cap", "query_word": "physical H_time matrices materialized", "content_probe": "disabled"}, +} + + +def digest(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as f: + for block in iter(lambda: f.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def finite_boundary_summary(rows: list[dict[str, float]], offset: float, end_s: float) -> tuple[np.ndarray, str]: + values=[] + json_rows=[] + for row in rows: + vals=[row.get("start_p05_s",np.nan)+offset,row.get("start_p95_s",np.nan)+offset,row.get("end_p05_s",np.nan)+offset,row.get("end_p95_s",np.nan)+offset] + vals=[float(np.clip(v,0.0,end_s)) if np.isfinite(v) else np.nan for v in vals] + values.append([v if np.isfinite(v) else -1.0 for v in vals]) + absolute_fields={"start_mean_s","end_mean_s","start_p05_s","start_p95_s","end_p05_s","end_p95_s"} + json_rows.append({k:(float(np.clip(v+offset,0.0,end_s)) if np.isfinite(v) and k in absolute_fields else (float(v) if np.isfinite(v) else None)) for k,v in row.items()}) + return np.asarray(values,np.float32),json.dumps(json_rows,ensure_ascii=False) + + +def audio_sample_count_from_quality(sample: cm.Sample) -> int: + """The final partial 25 ms frame records its exact decoded sample fraction.""" + valid=np.flatnonzero(np.asarray(sample.audio_quality)>0) + if not len(valid): + return 0 + index=int(valid[-1]) + fraction=float(np.clip(sample.audio_quality[index],0.0,1.0)) + return index*cm.FRAME_STEP+int(round(fraction*cm.FRAME_LENGTH)) + + +def effective_text_quality(raw: np.ndarray, valid: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Use bounded raw CTC scores; q*=1 means no extra discount when unavailable.""" + raw = np.asarray(raw, np.float32) + available = np.asarray(valid, bool) & (raw > 0) & np.isfinite(raw) + effective = np.ones(len(raw), np.float32) + effective[available] = np.clip(raw[available], 0.0, 1.0) + return effective, available + + +def intervals_from_times(times: np.ndarray, duration: float, step: float, gap_factor: float = 1.5) -> np.ndarray: + times = np.asarray(times, dtype=np.float64) + if not len(times): + return np.empty((0, 2), np.float32) + starts = np.empty(len(times), dtype=np.float64) + ends = np.empty(len(times), dtype=np.float64) + breaks = np.diff(times) > gap_factor * step + for i, t in enumerate(times): + starts[i] = (times[i - 1] + t) / 2 if i and not breaks[i - 1] else max(0.0, t - step / 2) + ends[i] = (t + times[i + 1]) / 2 if i + 1 < len(times) and not breaks[i] else min(duration, t + step / 2) + if starts[0] <= step: + starts[0] = 0.0 + if ends[-1] >= duration - step: + ends[-1] = duration + starts = np.clip(starts, 0.0, duration) + ends = np.clip(ends, 0.0, duration) + return np.column_stack((starts, np.maximum(starts, ends))).astype(np.float32) + + +def probe_media(path: Path) -> dict[str, Any]: + command = ["ffprobe", "-v", "error", "-show_entries", "format=start_time,duration:stream=codec_type,start_time,time_base,sample_rate,avg_frame_rate", "-of", "json", str(path)] + try: + obj = json.loads(subprocess.run(command, check=True, capture_output=True, text=True).stdout) + streams = obj.get("streams", []) + fmt = obj.get("format", {}) + vs = next((s for s in streams if s.get("codec_type") == "video"), {}) + aus = next((s for s in streams if s.get("codec_type") == "audio"), {}) + v0 = float(vs.get("start_time", fmt.get("start_time", 0)) or 0) + a0 = float(aus.get("start_time", fmt.get("start_time", 0)) or 0) + return {"format_start_s": float(fmt.get("start_time", 0) or 0), "format_duration_s": float(fmt.get("duration", 0) or 0), "video_start_s": v0, "audio_start_s": a0, "audio_offset_from_video_s": a0 - v0, "video_time_base": vs.get("time_base"), "audio_time_base": aus.get("time_base"), "audio_sample_rate": aus.get("sample_rate"), "avg_frame_rate": vs.get("avg_frame_rate"), "status": "ok"} + except Exception as exc: + return {"format_start_s": 0.0, "format_duration_s": 0.0, "video_start_s": 0.0, "audio_start_s": 0.0, "audio_offset_from_video_s": 0.0, "status": f"ffprobe_error:{type(exc).__name__}"} + + +def aggregate(values: np.ndarray, observed: np.ndarray, source_intervals: np.ndarray, quality: np.ndarray, edges: np.ndarray, quality_available: np.ndarray | None = None) -> dict[str, np.ndarray]: + values = np.asarray(values, np.float64) + observed = np.asarray(observed, bool) + source_intervals = np.asarray(source_intervals, np.float64) + quality = np.maximum(0.0, np.asarray(quality, np.float64)) + quality_available = np.zeros(len(values), dtype=bool) if quality_available is None else np.asarray(quality_available, dtype=bool) + n, d = len(edges) - 1, values.shape[1] + mean = np.zeros((n, d), np.float32) + var = np.zeros((n, d), np.float32) + count = np.zeros((n, d), np.uint8) + coverage = np.zeros((n, d), np.float32) + qbar = np.ones((n, d), np.float32) + qavail = np.zeros((n, d), np.float32) + mask = np.zeros((n, d), bool) + for i, (left, right) in enumerate(zip(edges[:-1], edges[1:])): + if right <= left or not len(source_intervals): + continue + overlap = np.maximum(0.0, np.minimum(source_intervals[:, 1], right) - np.maximum(source_intervals[:, 0], left)) + phys = overlap[:, None] * observed + w = phys * quality[:, None] + wsum = w.sum(axis=0) + good = wsum > 0 + if good.any(): + sx = (values * w).sum(axis=0) + sx2 = (np.square(values) * w).sum(axis=0) + mean[i, good] = (sx[good] / wsum[good]).astype(np.float32) + var[i, good] = np.maximum(0.0, sx2[good] / wsum[good] - mean[i, good].astype(np.float64) ** 2).astype(np.float32) + mask[i, good] = True + coverage[i] = np.minimum(1.0, phys.sum(axis=0) / (right - left)).astype(np.float32) + count[i] = np.minimum(255, ((overlap[:, None] > 0) & observed).sum(axis=0)).astype(np.uint8) + base = phys.sum(axis=0) + valid = base > 0 + qbar[i, valid] = ((phys * quality[:, None]).sum(axis=0)[valid] / base[valid]).astype(np.float32) + qavail[i, valid] = ((phys * quality_available[:, None]).sum(axis=0)[valid] / base[valid]).astype(np.float32) + # Audio HNR/prosodic rows can have genuinely large within-window variance; + # retain variance in float32 rather than silently overflowing float16. + return {"x": mean.astype(np.float16), "var": var.astype(np.float32), "count": count, "coverage_u8": np.rint(coverage * 255).astype(np.uint8), "mask": mask, "qbar": qbar.astype(np.float16), "quality_available_fraction": qavail.astype(np.float16)} + + +def sparse_overlap(source_intervals: np.ndarray, target_intervals: np.ndarray, quality: np.ndarray | None = None) -> sparse.csr_matrix: + rows, cols, vals = [], [], [] + q = np.ones(len(source_intervals), np.float32) if quality is None else np.asarray(quality, np.float32) + for i, (left, right) in enumerate(target_intervals): + overlap = np.maximum(0.0, np.minimum(source_intervals[:, 1], right) - np.maximum(source_intervals[:, 0], left)) * q + js = np.flatnonzero(overlap > 0) + rows.extend([i] * len(js)); cols.extend(js.tolist()); vals.extend(overlap[js].tolist()) + return sparse.csr_matrix((np.asarray(vals, np.float32), (rows, cols)), shape=(len(target_intervals), len(source_intervals))) + + +def sparse_word_map(word_intervals: np.ndarray, source_intervals: np.ndarray, chi: np.ndarray) -> sparse.csr_matrix: + rows, cols, vals = [], [], [] + for k, (left, right) in enumerate(word_intervals): + if right <= left: + continue + overlap = np.maximum(0.0, np.minimum(source_intervals[:, 1], right) - np.maximum(source_intervals[:, 0], left)) * chi + js = np.flatnonzero(overlap > 0) + total = float(overlap[js].sum()) + if total > 0: + rows.extend([k] * len(js)); cols.extend(js.tolist()); vals.extend((overlap[js] / total).tolist()) + return sparse.csr_matrix((np.asarray(vals, np.float32), (rows, cols)), shape=(len(word_intervals), len(source_intervals))) + + +def pack_csr(arrays: dict[str, Any], prefix: str, matrix: sparse.csr_matrix) -> None: + arrays[prefix + "_data"] = matrix.data.astype(np.float16) + arrays[prefix + "_indices"] = matrix.indices.astype(np.int32) + arrays[prefix + "_indptr"] = matrix.indptr.astype(np.int32) + arrays[prefix + "_shape"] = np.asarray(matrix.shape, np.int32) + + +def text_provenance(record: dict[str, Any], words: list[str], tokenizer: Any) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + text = record["text"] + spans = np.full((len(words), 2), -1, np.int32) + subword = np.zeros((len(words), 2), np.int32) + all_ids: list[int] = [] + cursor = 0 + for i, word in enumerate(words): + start = text.find(word, cursor) + if start >= 0: + spans[i] = (start, start + len(word)); cursor = start + len(word) + ids = tokenizer(word, add_special_tokens=False)["input_ids"] if tokenizer is not None else [] + subword[i] = (len(all_ids), len(all_ids) + len(ids)) + all_ids.extend(int(v) for v in ids) + return spans, subword, np.asarray(all_ids, np.int32) + + +def make_dynamics(sample: cm.Sample, audio_intervals: np.ndarray, vision_intervals: np.ndarray, edges: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + # Time-augmented level-2 log-signature on continuous 0.7 s contexts; this is auxiliary only. + names = [("audio", 67, 66), ("vision", 10, 31)] + output = np.zeros((len(edges)-1, 2, 6), np.float32) + valid = np.zeros((len(edges)-1, 2), bool) + bounds = np.zeros((len(edges)-1, 2, 2), np.float32) + for t,(l,r) in enumerate(zip(edges[:-1],edges[1:])): + center=(l+r)/2 + a=max(0.0,center-0.35); b=min(sample.duration_s,center+0.35) + bounds[t,:,:]=[a,b] + for branch,(mod,j,k) in enumerate(names): + if mod=="audio": + times=sample.audio_times; x=sample.audio_features.astype(np.float32); mask=sample.audio_observed + ints=audio_intervals; required=mask[:,[j,k]].all(axis=1) + else: + times=sample.vision_times; x=sample.vision_features.astype(np.float32); mask=sample.vision_observed + ints=vision_intervals; required=mask[:,[j,k]].all(axis=1) + ix=np.flatnonzero((ints[:,0]>=a-1e-7)&(ints[:,1]<=b+1e-7)&required) + if len(ix)<3: continue + # Break the path at missing or unusually long source gaps. + expected=0.01 if mod=="audio" else max(float(np.median(np.diff(times))),1e-6) + if np.any(np.diff(times[ix])>1.5*expected): continue + vals=np.column_stack(((times[ix]-a)/max(b-a,1e-6),x[ix,j],x[ix,k])).astype(np.float64) + dz=np.diff(vals,axis=0) + first=dz.sum(axis=0) + area=np.zeros(3,np.float64); pairs=((0,1),(0,2),(1,2)) + for p,(u,v) in enumerate(pairs): + area[p]=0.5*sum(dz[a0,u]*dz[b0,v]-dz[a0,v]*dz[b0,u] for a0 in range(len(dz)) for b0 in range(a0+1,len(dz))) + output[t,branch]=np.concatenate((first,area)).astype(np.float32) + valid[t,branch]=True + return output.astype(np.float16),valid,bounds + + +def geometry_aware_vision(sample: cm.Sample, intervals: np.ndarray, bounds: np.ndarray, base: dict[str, np.ndarray]) -> tuple[np.ndarray, np.ndarray]: + """B1 SO(3) mean for head pose and unit-vector means for gaze channels.""" + values = base["x"].astype(np.float32).copy() + masks = base["mask"].astype(bool).copy() + raw = sample.vision_features.astype(np.float32) + observed = sample.vision_observed + for target_index, (left, right) in enumerate(bounds): + overlap = np.maximum(0.0, np.minimum(intervals[:, 1], right) - np.maximum(intervals[:, 0], left)) + weighted_overlap=overlap*sample.vision_quality + pose_rows = (weighted_overlap > 0) & observed[:, 17:20].all(axis=1) + if pose_rows.any(): + mean_rot = cm._so3_weighted_mean(raw[pose_rows, 17:20], weighted_overlap[pose_rows]) + if mean_rot is not None: + values[target_index, 17:20] = mean_rot + masks[target_index, 17:20] = True + for start in (23, 26): + rows = (weighted_overlap > 0) & observed[:, start:start + 3].all(axis=1) + if not rows.any(): + continue + mean = np.average(raw[rows, start:start + 3], axis=0, weights=weighted_overlap[rows]) + norm = float(np.linalg.norm(mean)) + if norm > 1e-6: + values[target_index, start:start + 3] = mean / norm + masks[target_index, start:start + 3] = True + else: + values[target_index, start:start + 3] = 0.0 + masks[target_index, start:start + 3] = False + return values.astype(np.float16), masks + + +def main() -> None: + global CACHE, RUN, LABELS, OUT + parser = argparse.ArgumentParser(description="Build the Q1 physical-time feature package from native caches.") + parser.add_argument("--data-dir", type=Path, default=cm.DATA_DIR) + parser.add_argument("--cache-dir", type=Path, default=CACHE) + parser.add_argument("--run-manifest", type=Path, default=RUN) + parser.add_argument("--output-dir", type=Path, default=OUT) + args = parser.parse_args() + cm.DATA_DIR = args.data_dir.resolve() + cm.LABEL_FILE = cm.DATA_DIR / "label-100.xlsx" + CACHE = args.cache_dir.resolve() + RUN = args.run_manifest.resolve() + LABELS = cm.LABEL_FILE + OUT = args.output_dir.resolve() + if not CACHE.is_dir() or not RUN.is_file(): + raise FileNotFoundError("Existing Q1 native cache and run manifest are required") + OUT.mkdir(parents=True, exist_ok=True) + run = json.loads(RUN.read_text(encoding="utf-8")) + records = cm._read_labels(LABELS) + if len(records) != 100: + raise ValueError(f"expected 100 attachment-1 rows, got {len(records)}") + tokenizer = None + try: + tokenizer = cm.AutoTokenizer.from_pretrained(cm.TEXT_MODEL_ID, use_fast=True, local_files_only=True) + except Exception: + pass + config_text = json.dumps(CONFIG, ensure_ascii=False, sort_keys=True, separators=(",", ":")) + config_hash = hashlib.sha256(config_text.encode("utf-8")).hexdigest() + grid_rows: list[dict[str, Any]] = [] + sample_rows: list[dict[str, Any]] = [] + manifest_lines: list[dict[str, Any]] = [] + for record in records: + sid=record["sample_id"] + source_hash=run["inputs"]["video_sha256_by_sample"][sid] + cache=CACHE/f"{cm._safe_name(record['video_id'])}__{cm._safe_name(record['clip_id'])}.npz" + sample=cm._load_cache(cache,record,source_hash,"q1-b0b4-v4-openface") + video_path=cm.DATA_DIR/record["video_id"]/f"{record['clip_id']}.mp4" + media=probe_media(video_path) + audio_offset=float(media.get("audio_offset_from_video_s",0.0)) + sample_count=audio_sample_count_from_quality(sample) + audio_end_s=float(np.clip(audio_offset+sample_count/cm.SAMPLE_RATE,0.0,sample.duration_s)) + audio_times=sample.audio_times.astype(np.float32)+audio_offset + ctc_times=sample.ctc_times.astype(np.float32)+audio_offset + hard=sample.hard_intervals.astype(np.float32).copy()+np.float32(audio_offset) + hard=np.clip(hard,0.0,audio_end_s) + hard_valid=sample.hard_valid & (hard[:,1]>hard[:,0]) + text_intervals=np.where(hard_valid[:,None],hard,0.0) + audio_starts=np.arange(len(audio_times),dtype=np.int64)*cm.FRAME_STEP + audio_ranges=np.column_stack((audio_starts,np.minimum(audio_starts+cm.FRAME_LENGTH,sample_count))).astype(np.int32) + audio_intervals=intervals_from_times(audio_times,audio_end_s,cm.FRAME_STEP/cm.SAMPLE_RATE) + audio_intervals=np.clip(audio_intervals,0.0,sample.duration_s) + video_step=float(np.median(np.diff(sample.vision_times))) if len(sample.vision_times)>1 else 1.0/30.0 + vision_intervals=intervals_from_times(sample.vision_times,sample.duration_s,video_step) + frame_indices=sample.vision_frame_indices.astype(np.int32) + words=sample.text_words + char_spans,subword_spans,subword_ids=text_provenance(record,words,tokenizer) + duration=sample.duration_s + sec_edges=cm._grid_edges(duration) + sec_bounds=np.column_stack((sec_edges[:-1],sec_edges[1:])).astype(np.float32) + phase_edges=np.linspace(0.0,duration,51,dtype=np.float64) + phase_bounds=np.column_stack((phase_edges[:-1],phase_edges[1:])).astype(np.float32) + word_bounds=text_intervals.copy() + # Keep the media timestamps in the video-origin timebase for dynamics too. + sample.audio_times = audio_times + text_quality, text_quality_available = effective_text_quality(sample.hard_quality, hard_valid) + source={ + "text":(sample.text_features.astype(np.float32),np.broadcast_to(sample.text_valid[:,None],sample.text_features.shape).copy(),text_intervals,text_quality,text_quality_available), + "audio":(sample.audio_features.astype(np.float32),sample.audio_observed,audio_intervals,sample.audio_quality,sample.audio_quality_available), + "vision":(sample.vision_features.astype(np.float32),sample.vision_observed,vision_intervals,sample.vision_quality,sample.vision_quality_available), + } + sec={name:aggregate(source[name][0],source[name][1],source[name][2],source[name][3],sec_edges,source[name][4]) for name in MODALITIES} + phase={name:aggregate(source[name][0],source[name][1],source[name][2],source[name][3],phase_edges,source[name][4]) for name in MODALITIES} + vision_b1_x,vision_b1_mask=geometry_aware_vision(sample,vision_intervals,sec_bounds,sec["vision"]) + + # Word-level physical correspondence and query matrices. + word_views={} + for name in MODALITIES: + result={k:[] for k in ("x","var","count","coverage_u8","mask","qbar","quality_available_fraction")} + for aa,bb in word_bounds: + v=aggregate(source[name][0],source[name][1],source[name][2],source[name][3],np.asarray([aa,bb],np.float64),source[name][4]) + for k in result: result[k].append(v[k][0]) + word_views[name]={k:np.asarray(v) for k,v in result.items()} + # Sparse target-to-native overlap maps for the primary physical view. + maps={name:sparse_overlap(source[name][2],sec_bounds) for name in MODALITIES} + # Query channels are fixed for display only: acoustic log-energy and OpenFace AU12. + h_audio=sparse_word_map(word_bounds,audio_intervals,sample.audio_observed[:,66].astype(np.float32)) + h_vision=sparse_word_map(word_bounds,vision_intervals,sample.vision_observed[:,10].astype(np.float32)) + dyn,dyn_valid,dyn_bounds=make_dynamics(sample,audio_intervals,vision_intervals,sec_edges) + delta_centers=np.arange(len(audio_times),dtype=np.int64) + delta_left=np.maximum(0,delta_centers-2)*cm.FRAME_STEP + delta_right=np.minimum(sample_count,np.minimum(len(audio_times)-1,delta_centers+2)*cm.FRAME_STEP+cm.FRAME_LENGTH) + delta_ranges=np.column_stack((delta_left,delta_right)).astype(np.int32) + boundary_p05_p95,boundary_summary_json=finite_boundary_summary(sample.boundary_summary,audio_offset,audio_end_s) + cache_file=OUT/f"{cm._safe_name(record['video_id'])}__{cm._safe_name(record['clip_id'])}.npz" + payload:dict[str,Any]={ + "meta_json":np.asarray(json.dumps({"sample_id":sid,"video_id":record["video_id"],"clip_id":record["clip_id"],"duration_s":duration,"sentiment":sample.sentiment,"polarity":sample.polarity,"annotation":record["annotation"],"source_video_sha256":source_hash,"media":media,"config_hash":config_hash,"vision_backend":"OpenFace 2.2.0 FeatureExtraction","quality_available":{"text":bool(text_quality_available.any()),"audio":bool(sample.audio_quality_available.any()),"vision":bool(sample.vision_quality_available.any())}},ensure_ascii=False)), + "native_text_words":np.asarray(words,dtype=str),"native_text_features":sample.text_features.astype(np.float16),"native_text_observed":sample.text_valid.astype(bool),"native_text_intervals":text_intervals.astype(np.float32),"native_text_hard_quality_uncalibrated":sample.hard_quality.astype(np.float16),"native_text_quality_effective":text_quality.astype(np.float16),"native_text_quality_available":text_quality_available,"native_text_boundary_p05_p95":boundary_p05_p95,"native_text_char_spans":char_spans,"native_text_subword_spans":subword_spans,"native_text_subword_ids":subword_ids, + "native_audio_times":audio_times,"native_audio_features":sample.audio_features.astype(np.float16),"native_audio_mask":sample.audio_observed.astype(bool),"native_audio_quality":sample.audio_quality.astype(np.float16),"native_audio_quality_available":sample.audio_quality_available.astype(bool),"native_audio_source_range_samples":audio_ranges,"native_audio_delta_calc_range_samples":delta_ranges,"native_audio_intervals":audio_intervals, + "native_vision_times":sample.vision_times.astype(np.float32),"native_vision_features":sample.vision_features.astype(np.float16),"native_vision_mask":sample.vision_observed.astype(bool),"native_vision_quality":sample.vision_quality.astype(np.float16),"native_vision_quality_available":sample.vision_quality_available.astype(bool),"native_vision_frame_index":frame_indices,"native_vision_intervals":vision_intervals, + "native_ctc_times":ctc_times,"native_ctc_occupancy":sample.ctc_occupancy.astype(np.float16),"native_ctc_boundary_summary_json":np.asarray(boundary_summary_json), + "views_sec_time_bounds_s":sec_bounds,"views_phase50_time_bounds_s":phase_bounds,"views_word_time_bounds_s":word_bounds, + "views_sec_vision_b1_x":vision_b1_x,"views_sec_vision_b1_mask":vision_b1_mask, + "views_dynamics_x":dyn,"views_dynamics_mask":dyn_valid,"views_dynamics_time_bounds_s":dyn_bounds, + } + for name in MODALITIES: + for k,v in sec[name].items(): payload[f"views_sec_{name}_{k}"]=v + for k,v in phase[name].items(): payload[f"views_phase50_{name}_{k}"]=v + for k,v in word_views[name].items(): payload[f"views_word_{name}_{k}"]=v + pack_csr(payload,f"alignment_map_{name}",maps[name]) + pack_csr(payload,"query_word_audio_H_time",h_audio) + pack_csr(payload,"query_word_vision_H_time",h_vision) + np.savez_compressed(cache_file,**payload) + feature_hash=digest(cache_file) + observed_summary={} + for name in MODALITIES: + vals,mask,ints,q,q_available=source[name] + support=np.zeros(mask.shape[1],np.float64) + for r,(a,b) in enumerate(ints): + support += max(0.0,float(b-a))*mask[r]*(q[r]>0) + coverage=np.clip(support/max(duration,1e-9),0,1) + observed_summary[name]={"observed_duration_mean_s":float(np.mean(coverage)*duration),"mean_coverage":float(np.mean(coverage)),"row_count":int(len(vals)),"dimension":int(vals.shape[1]),"quality_available":bool(q_available.any()),"quality_available_fraction":float(q_available.mean()) if len(q_available) else 0.0} + gran={"text":"word-level, variable duration; fixed-transcript CTC source interval","audio":"native 10 ms feature step; 25 ms windows; context ranges retained separately","vision":"native OpenFace frame timestamps and frame indices"}[name] + unobserved_rows=int((~mask.any(axis=1)).sum()) if len(mask) else 0 + if name=="text": + missing_reason=f"{int((~hard_valid).sum())} word intervals are not reliable under the fixed-transcript CTC path" if (~hard_valid).any() else "none" + elif name=="audio": + undefined_pitch=int((~mask[:,67]).sum()) if mask.shape[1]>67 else 0 + missing_reason=f"F0/HNR naturally undefined or unavailable in {undefined_pitch} rows; support masks retained" + else: + missing_reason=f"OpenFace confidence/success gate rejected {unobserved_rows} native frames" if unobserved_rows else "none" + grid_rows.append({"sample_id":sid,"video_id":record["video_id"],"clip_id":record["clip_id"],"modality":name,"source_duration_s":duration,"observed_duration_s":observed_summary[name]["observed_duration_mean_s"],"native_length":len(vals),"native_dim":vals.shape[1],"aligned_length":len(sec_bounds),"aligned_dim":vals.shape[1],"alignment_type":"fixed 0.1 s physical grid; direct native interval projection","granularity":gran,"mean_coverage":observed_summary[name]["mean_coverage"],"missing_reason":missing_reason,"quality_available":observed_summary[name]["quality_available"],"quality_available_fraction":observed_summary[name]["quality_available_fraction"],"quality_path":f"{cache_file.name}::native_{name}_*quality*","query_map_path":f"{cache_file.name}::query_word_{name}_H_time" if name in ("audio","vision") else "not_applicable","probe_status":"content probe disabled" if name in ("audio","vision") else "not_applicable","feature_path":f"features_v2/{cache_file.name}","source_hash":source_hash,"status":"success" if mask.any() else "failed_or_unavailable","config_hash":config_hash}) + sample_status="success" if all(source[m][1].any() for m in MODALITIES) else "partial" + sample_rows.append({"sample_id":sid,"video_id":record["video_id"],"clip_id":record["clip_id"],"source_duration_s":duration,"source_video_sha256":source_hash,"feature_path":f"features_v2/{cache_file.name}","feature_sha256":feature_hash,"sec_length":len(sec_bounds),"word_count":len(words),"hard_aligned_word_count":int(hard_valid.sum()),"unlocated_word_count":int((~hard_valid).sum()),"status":sample_status,"config_hash":config_hash}) + manifest_lines.append({"sample_id":sid,"video_id":record["video_id"],"clip_id":record["clip_id"],"feature_path":f"features_v2/{cache_file.name}","source_video_sha256":source_hash,"feature_sha256":feature_hash,"duration_s":duration,"status":sample_rows[-1]["status"],"config_hash":config_hash}) + print(f"{sid}: {len(words)} words, {len(sec_bounds)} sec steps, {cache_file.stat().st_size/1024:.1f} KiB") + + def write_csv(path:Path,rows:list[dict[str,Any]])->None: + with path.open("w",encoding="utf-8-sig",newline="") as f: + w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows) + write_csv(OUT/"sample_summary.csv",sample_rows) + write_csv(OUT/"modality_summary.csv",grid_rows) + (OUT/"manifest_q1.jsonl").write_text("\n".join(json.dumps(x,ensure_ascii=False) for x in manifest_lines)+"\n",encoding="utf-8") + configuration={"config":CONFIG,"config_hash":config_hash,"source_run_manifest":str(RUN),"source_cache_schema":"q1-b0b4-v4-openface","source_label_sha256":digest(LABELS),"python":run.get("python"),"packages":run.get("packages",{}),"models":run.get("models",{}),"samples":len(records),"modalities":300,"total_bytes":sum(p.stat().st_size for p in OUT.iterdir() if p.is_file())} + (OUT/"feature_manifest.json").write_text(json.dumps(configuration,ensure_ascii=False,indent=2),encoding="utf-8") + median_sample=sorted(sample_rows,key=lambda x:x["source_duration_s"])[len(sample_rows)//2] + with np.load(OUT / Path(median_sample["feature_path"]).name, allow_pickle=False) as d: + k=int(min(5,len(d["native_text_words"])-1)); sid=median_sample["sample_id"] + card={"sample_id":sid,"source_video_sha256":median_sample["source_video_sha256"],"duration_s":median_sample["source_duration_s"],"word":str(d["native_text_words"][k]),"word_char_span":d["native_text_char_spans"][k].tolist(),"hard_interval_s":d["native_text_intervals"][k].tolist(),"boundary_interval_90_s":d["native_text_boundary_p05_p95"][k].tolist(),"audio_source_rows":sparse.csr_matrix((d["query_word_audio_H_time_data"].astype(np.float32),d["query_word_audio_H_time_indices"],d["query_word_audio_H_time_indptr"]),shape=tuple(d["query_word_audio_H_time_shape"])).getrow(k).indices.tolist(),"vision_source_rows":sparse.csr_matrix((d["query_word_vision_H_time_data"].astype(np.float32),d["query_word_vision_H_time_indices"],d["query_word_vision_H_time_indptr"]),shape=tuple(d["query_word_vision_H_time_shape"])).getrow(k).indices.tolist(),"manual_boundary_audit":"not available; no empirical boundary error or calibration claimed","vision_backend":"OpenFace 2.2.0"} + (OUT/"typical_alignment_card.json").write_text(json.dumps(card,ensure_ascii=False,indent=2),encoding="utf-8") + configuration["total_bytes"]=sum(p.stat().st_size for p in OUT.rglob("*") if p.is_file()) + (OUT/"feature_manifest.json").write_text(json.dumps(configuration,ensure_ascii=False,indent=2),encoding="utf-8") + print(f"Wrote {len(sample_rows)} samples and {len(grid_rows)} modality rows; total {configuration['total_bytes']/1024**2:.2f} MiB") + +if __name__=="__main__": + main() diff --git a/final/Q1/compare_models.py b/final/q1/compare_models.py similarity index 79% rename from final/Q1/compare_models.py rename to final/q1/compare_models.py index dd49602..5b24af4 100644 --- a/final/Q1/compare_models.py +++ b/final/q1/compare_models.py @@ -1,4 +1,4 @@ -from __future__ import annotations +from __future__ import annotations import argparse import csv @@ -6,22 +6,23 @@ import hashlib import importlib.metadata import json import math +import os import platform import re +import shutil import subprocess import sys import time -import urllib.request from dataclasses import dataclass from pathlib import Path from typing import Any, Iterable import cv2 +import librosa import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt -import mediapipe as mp import numpy as np import torch from openpyxl import load_workbook @@ -33,13 +34,16 @@ from sklearn.model_selection import GroupKFold from transformers import AutoModel, AutoModelForCTC, AutoTokenizer -ROOT = Path(__file__).resolve().parents[2] +ROOT = Path(__file__).resolve().parents[1] MATH_DIR = Path(__file__).resolve().parent -DATA_DIR = ROOT / "E棰樻暟鎹? / "闄勪欢1-鏁版嵁闆嗗師濮嬪妯℃€佹牱鏈? / "MOSEI鏁版嵁闆嗛儴鍒嗗師濮嬭棰?100鏉? +from ..data_paths import ATTACHMENT1 + +DATA_DIR = ATTACHMENT1 LABEL_FILE = DATA_DIR / "label-100.xlsx" -OUTPUT_DIR = MATH_DIR / "results" / "model_comparison" +OUTPUT_DIR = ROOT / "output" / "q1" / "model_comparison" CACHE_DIR = MATH_DIR / "cache" / "native" -FACE_MODEL_PATH = MATH_DIR / "cache" / "face_landmarker.task" +OPENFACE_RUNNER = MATH_DIR / "run_openface.ps1" +OPENFACE_PACKAGE = MATH_DIR / "cache" / "openface" / "OpenFace_2.2.0_win_x64" TEXT_MODEL_ID = "google-bert/bert-base-uncased" SPEECH_MODEL_ID = "facebook/wav2vec2-base-960h" @@ -49,33 +53,24 @@ FRAME_STEP = 160 N_FFT = 512 MEL_COUNT = 40 GRID_STEP_S = 0.1 -VISION_RATE_HZ = 5.0 CTC_FRAME_STEP_S = 320 / SAMPLE_RATE CTC_RECEPTIVE_SAMPLES = 400 SEED = 20260923 N_FOLDS = 5 -ACTION_NAMES = ( - "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft", - "browOuterUpRight", "eyeBlinkLeft", "eyeBlinkRight", "eyeSquintLeft", - "eyeSquintRight", "eyeWideLeft", "eyeWideRight", "jawOpen", - "mouthFrownLeft", "mouthFrownRight", "mouthPucker", "mouthSmileLeft", - "mouthSmileRight", -) +AU_CODES = (1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 20, 23, 25, 26, 45) VISION_NAMES = ( - *[f"mp_blendshape_{name}" for name in ACTION_NAMES], - "pose_rotvec_x", "pose_rotvec_y", "pose_rotvec_z", - "pose_translation_x", "pose_translation_y", "pose_translation_z", - "gaze_left_x", "gaze_left_y", "gaze_left_z", - "gaze_right_x", "gaze_right_y", "gaze_right_z", - "eye_aperture_left", "eye_aperture_right", "mouth_aperture", - "mouth_width_face_ratio", "brow_eye_distance_left", "brow_eye_distance_right", + *[f"AU{code:02d}_r" for code in AU_CODES], + "pose_Rx", "pose_Ry", "pose_Rz", "pose_Tx", "pose_Ty", "pose_Tz", + "gaze_0_x", "gaze_0_y", "gaze_0_z", "gaze_1_x", "gaze_1_y", "gaze_1_z", + "geometry_eye_open_left", "geometry_eye_open_right", "geometry_mouth_open", + "geometry_mouth_width", "geometry_brow_eye_left", "geometry_brow_eye_right", ) AUDIO_NAMES = ( *[f"logmel_{index:02d}" for index in range(40)], *[f"mfcc_{index:02d}" for index in range(13)], *[f"delta_mfcc_{index:02d}" for index in range(13)], - "log_energy", "log_f0_hz", "voicing_strength", "spectral_centroid_hz", + "log_energy", "log_f0_hz", "voiced_probability", "spectral_centroid_hz", "spectral_bandwidth_hz", "spectral_flux", "zero_crossing_rate", "hnr_db", ) @@ -97,9 +92,14 @@ class Sample: audio_times: np.ndarray audio_features: np.ndarray audio_observed: np.ndarray + audio_quality: np.ndarray + audio_quality_available: np.ndarray vision_times: np.ndarray + vision_frame_indices: np.ndarray vision_features: np.ndarray vision_observed: np.ndarray + vision_quality: np.ndarray + vision_quality_available: np.ndarray ctc_times: np.ndarray ctc_occupancy: np.ndarray speech_features: np.ndarray @@ -269,38 +269,53 @@ def _audio_features(audio: np.ndarray, duration_s: float) -> tuple[np.ndarray, n energy = np.mean(np.square(frames), axis=1) log_f0 = np.zeros(len(frames), dtype=np.float32) - voicing = np.zeros(len(frames), dtype=np.float32) + voiced_probability = np.zeros(len(frames), dtype=np.float32) hnr = np.zeros(len(frames), dtype=np.float32) - voiced = np.zeros(len(frames), dtype=np.bool_) - min_lag, max_lag = int(SAMPLE_RATE / 400), int(SAMPLE_RATE / 60) - for frame_index, frame in enumerate(windowed): - if energy[frame_index] < 1e-7: - continue - autocorr = irfft(np.abs(rfft(frame, n=1024)) ** 2, n=1024)[:max_lag + 1] - if autocorr[0] <= 1e-10: - continue - autocorr /= autocorr[0] - region = autocorr[min_lag:max_lag + 1] - if not len(region): - continue - lag = min_lag + int(np.argmax(region)) - strength = float(np.clip(autocorr[lag], 0.0, 1.0)) - voicing[frame_index] = strength - voiced[frame_index] = strength >= 0.30 - if voiced[frame_index]: - log_f0[frame_index] = math.log(SAMPLE_RATE / lag) - hnr[frame_index] = 10.0 * math.log10(max(strength, 1e-5) / max(1.0 - strength, 1e-5)) + pitch_observed = np.zeros(len(frames), dtype=np.bool_) + voicing_observed = np.zeros(len(frames), dtype=np.bool_) + hnr_observed = np.zeros(len(frames), dtype=np.bool_) + if len(audio) >= FRAME_LENGTH: + f0, voiced_flag, voiced_prob = librosa.pyin( + audio.astype(np.float32), + fmin=80.0, + fmax=400.0, + sr=SAMPLE_RATE, + frame_length=FRAME_LENGTH, + hop_length=FRAME_STEP, + center=False, + fill_na=np.nan, + ) + rows = min(len(frames), len(f0), len(voiced_prob)) + for frame_index in range(rows): + if np.isfinite(voiced_prob[frame_index]): + voiced_probability[frame_index] = float(np.clip(voiced_prob[frame_index], 0.0, 1.0)) + voicing_observed[frame_index] = True + if not np.isfinite(f0[frame_index]) or not bool(voiced_flag[frame_index]): + continue + log_f0[frame_index] = math.log(float(f0[frame_index])) + pitch_observed[frame_index] = True + frame = windowed[frame_index] + autocorr = irfft(np.abs(rfft(frame, n=1024)) ** 2, n=1024)[:FRAME_LENGTH] + if autocorr[0] <= 1e-10: + continue + lag = int(round(SAMPLE_RATE / float(f0[frame_index]))) + if lag <= 0 or lag >= len(autocorr): + continue + periodicity = float(np.clip(autocorr[lag] / autocorr[0], 1e-5, 1.0 - 1e-5)) + hnr[frame_index] = 10.0 * math.log10(periodicity / (1.0 - periodicity)) + hnr_observed[frame_index] = True prosody = np.column_stack(( - np.log(np.maximum(energy, 1e-8)), log_f0, voicing, centroid, bandwidth, + np.log(np.maximum(energy, 1e-8)), log_f0, voiced_probability, centroid, bandwidth, flux, zcr, hnr, )).astype(np.float32) features = np.column_stack((log_mel, mfcc, delta, prosody)).astype(np.float32) if features.shape[1] != 74: raise AssertionError(f"internal audio dimension error: {features.shape}") observed = np.isfinite(features) - observed[:, 67] = voiced - observed[:, 73] = voiced + observed[:, 67] = pitch_observed + observed[:, 68] = voicing_observed + observed[:, 73] = hnr_observed if len(frames) < 2: observed[:, 53:66] = False features = np.nan_to_num(features, nan=0.0, posinf=0.0, neginf=0.0) @@ -363,7 +378,7 @@ def _ctc_targets(words: list[str], tokenizer: Any) -> tuple[list[int], list[list for word_index, raw_word in enumerate(words): if word_index: targets.append(delimiter_id) - normalized = re.sub(r"[^a-z']", "", raw_word.lower().replace("鈥?, "'")) + normalized = re.sub(r"[^a-z']", "", raw_word.lower().replace("’", "'")) for char in normalized: per_word[word_index].append(len(targets)) targets.append(int(vocab.get(char, unknown_id))) @@ -593,180 +608,149 @@ def _posterior_hard_intervals( return intervals, valid, quality -def _landmark_points(landmarks: Iterable[Any]) -> np.ndarray: - return np.asarray([[item.x, item.y, item.z] for item in landmarks], dtype=np.float32) +def _openface_float(row: dict[str, str], name: str) -> float: + try: + value = float(row[name]) + except (KeyError, TypeError, ValueError): + return math.nan + return value if math.isfinite(value) else math.nan -def _distance(points: np.ndarray, left: int, right: int) -> float: - return float(np.linalg.norm(points[left] - points[right])) +def _openface_geometry(row: dict[str, str]) -> tuple[np.ndarray, np.ndarray]: + points = np.full((68, 2), np.nan, dtype=np.float64) + for index in range(68): + points[index, 0] = _openface_float(row, f"x_{index}") + points[index, 1] = _openface_float(row, f"y_{index}") + values = np.zeros(6, dtype=np.float32) + valid = np.zeros(6, dtype=bool) + if not np.isfinite(points[36:48]).all(): + return values, valid + left_eye = points[36:42].mean(axis=0) + right_eye = points[42:48].mean(axis=0) + interocular = float(np.linalg.norm(left_eye - right_eye)) + if interocular <= 1e-6: + return values, valid + + def distance(left: int, right: int) -> float: + return float(np.linalg.norm(points[left] - points[right])) + + pairs = ((37, 41), (38, 40), (43, 47), (44, 46), (61, 67), (62, 66), (63, 65), (48, 54)) + needed = {index for pair in pairs for index in pair} | set(range(17, 27)) + complete = {index: np.isfinite(points[index]).all() for index in needed} + if complete[37] and complete[41] and complete[38] and complete[40]: + values[0] = (distance(37, 41) + distance(38, 40)) / (2.0 * interocular) + valid[0] = True + if complete[43] and complete[47] and complete[44] and complete[46]: + values[1] = (distance(43, 47) + distance(44, 46)) / (2.0 * interocular) + valid[1] = True + mouth_pairs = ((61, 67), (62, 66), (63, 65)) + if all(complete[a] and complete[b] for a, b in mouth_pairs): + values[2] = sum(distance(a, b) for a, b in mouth_pairs) / (3.0 * interocular) + valid[2] = True + if complete[48] and complete[54]: + values[3] = distance(48, 54) / interocular + valid[3] = True + if all(complete[index] for index in range(17, 22)): + values[4] = float(np.linalg.norm(points[17:22].mean(axis=0) - left_eye) / interocular) + valid[4] = True + if all(complete[index] for index in range(22, 27)): + values[5] = float(np.linalg.norm(points[22:27].mean(axis=0) - right_eye) / interocular) + valid[5] = True + valid &= np.isfinite(values) + values[~valid] = 0.0 + return values, valid -def _gaze_proxy(points: np.ndarray, left_eye: bool) -> np.ndarray | None: - if points.shape[0] < 478: - return None - if left_eye: - corner_a, corner_b, upper, lower = 33, 133, 159, 145 - iris_ids = np.arange(468, 473) - else: - corner_a, corner_b, upper, lower = 362, 263, 386, 374 - iris_ids = np.arange(473, 478) - horizontal = points[corner_b] - points[corner_a] - width = float(np.linalg.norm(horizontal)) - vertical = points[upper] - points[lower] - height = float(np.linalg.norm(vertical)) - if width <= 1e-6 or height <= 1e-6: - return None - u = horizontal / width - v = vertical - float(np.dot(vertical, u)) * u - v_norm = float(np.linalg.norm(v)) - if v_norm <= 1e-6: - return None - v /= v_norm - eye_center = (points[corner_a] + points[corner_b]) / 2 - iris_center = points[iris_ids].mean(axis=0) - delta = iris_center - eye_center - gx = float(np.dot(delta, u) / width) - gy = float(np.dot(delta, v) / width) - # Face-relative iris offset plus a fixed forward component; this is a gaze proxy. - direction = np.asarray([-gx, -gy, -1.0], dtype=np.float32) - norm = float(np.linalg.norm(direction)) - if norm <= 1e-8: - return None - return direction / norm - - -def _pose_from_matrix(matrix_obj: Any) -> tuple[np.ndarray, np.ndarray] | None: - raw = np.asarray(matrix_obj.data, dtype=np.float64) - rows = int(getattr(matrix_obj, "rows", 4) or 4) - cols = int(getattr(matrix_obj, "cols", 4) or 4) - if raw.size != rows * cols or rows < 3 or cols < 4: - return None - matrix = raw.reshape(rows, cols) - rotation_raw = matrix[:3, :3] - if not np.isfinite(rotation_raw).all() or not np.isfinite(matrix[:3, 3]).all(): - return None - u, _, vh = np.linalg.svd(rotation_raw) - rotation_matrix = u @ vh - if np.linalg.det(rotation_matrix) < 0: - u[:, -1] *= -1 - rotation_matrix = u @ vh - return Rotation.from_matrix(rotation_matrix).as_rotvec().astype(np.float32), matrix[:3, 3].astype(np.float32) - - -def _vision_frame_features(landmarks: list[Any], blendshapes: list[Any], matrix_obj: Any | None) -> tuple[np.ndarray, np.ndarray]: - features = np.zeros(35, dtype=np.float32) - observed = np.zeros(35, dtype=np.bool_) - shape_lookup = {category.category_name: float(category.score) for category in blendshapes} - for index, name in enumerate(ACTION_NAMES): - if name in shape_lookup and math.isfinite(shape_lookup[name]): - features[index] = shape_lookup[name] - observed[index] = True - - if matrix_obj is not None: - pose = _pose_from_matrix(matrix_obj) - if pose is not None: - features[17:20] = pose[0] - features[20:23] = pose[1] - observed[17:23] = True - - points = _landmark_points(landmarks) - for eye_index, is_left in enumerate((True, False)): - gaze = _gaze_proxy(points, is_left) - if gaze is not None: - start = 23 + eye_index * 3 - features[start:start + 3] = gaze - observed[start:start + 3] = True - if points.shape[0] >= 455: - face_width = _distance(points, 234, 454) - left_eye_width = _distance(points, 33, 133) - right_eye_width = _distance(points, 362, 263) - mouth_width = _distance(points, 61, 291) - if min(face_width, left_eye_width, right_eye_width, mouth_width) > 1e-6: - ratios = np.asarray(( - _distance(points, 159, 145) / left_eye_width, - _distance(points, 386, 374) / right_eye_width, - _distance(points, 13, 14) / mouth_width, - mouth_width / face_width, - _distance(points, 105, 159) / left_eye_width, - _distance(points, 334, 386) / right_eye_width, - ), dtype=np.float32) - features[29:35] = ratios - observed[29:35] = np.isfinite(ratios) - return features, observed - - -def _ensure_face_model() -> str: - FACE_MODEL_PATH.parent.mkdir(parents=True, exist_ok=True) - if not FACE_MODEL_PATH.is_file() or FACE_MODEL_PATH.stat().st_size < 1_000_000: - url = ( - "https://storage.googleapis.com/mediapipe-models/face_landmarker/" - "face_landmarker/float16/latest/face_landmarker.task" +def _run_openface(path: Path, output_name: str) -> Path: + try: + relative_path = path.resolve().relative_to(ROOT).as_posix() + except ValueError: + relative_path = str(path.resolve()) + output_dir = MATH_DIR / "cache" / "openface_outputs" + output_dir.mkdir(parents=True, exist_ok=True) + csv_path = output_dir / f"{output_name}.csv" + if csv_path.is_file() and csv_path.stat().st_mtime >= path.stat().st_mtime: + return csv_path + powershell = shutil.which("powershell.exe") or shutil.which("pwsh.exe") + if powershell is None or not OPENFACE_RUNNER.is_file(): + raise FileNotFoundError( + "OpenFace 2.2.0 is required for compliant vision features. Install its Windows package under " + f"{OPENFACE_PACKAGE} and run through {OPENFACE_RUNNER}; no MediaPipe proxy fallback is allowed." ) - temporary = FACE_MODEL_PATH.with_suffix(".task.tmp") - urllib.request.urlretrieve(url, temporary) - if temporary.stat().st_size < 1_000_000: - temporary.unlink(missing_ok=True) - raise RuntimeError("downloaded Face Landmarker model is unexpectedly small") - temporary.replace(FACE_MODEL_PATH) - return _sha256(FACE_MODEL_PATH) - - -def _vision_features(path: Path, duration_s: float, face_model_path: Path) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - options = mp.tasks.vision.FaceLandmarkerOptions( - base_options=mp.tasks.BaseOptions(model_asset_path=str(face_model_path)), - running_mode=mp.tasks.vision.RunningMode.VIDEO, - num_faces=1, - output_face_blendshapes=True, - output_facial_transformation_matrixes=True, + completed = subprocess.run( + [powershell, "-NoProfile", "-ExecutionPolicy", "Bypass", "-File", str(OPENFACE_RUNNER), + "-VideoRelativePath", relative_path, "-OutputName", output_name], + check=False, + capture_output=True, + text=True, + errors="replace", ) - capture = cv2.VideoCapture(str(path)) - if not capture.isOpened(): - raise RuntimeError(f"OpenCV cannot decode video: {path}") - fps = float(capture.get(cv2.CAP_PROP_FPS) or 0.0) - if not math.isfinite(fps) or fps <= 0: - fps = 30.0 + if completed.returncode != 0 or not csv_path.is_file(): + raise RuntimeError( + f"OpenFace failed for {path}: exit={completed.returncode}; " + f"stdout={completed.stdout[-2000:]}; stderr={completed.stderr[-2000:]}" + ) + return csv_path + + +def _vision_features(path: Path, duration_s: float, output_name: str) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Read 35-D OpenFace features and keep raw PTS, frame IDs and confidence.""" + csv_path = _run_openface(path, output_name) timestamps: list[float] = [] + frame_indices: list[int] = [] values: list[np.ndarray] = [] masks: list[np.ndarray] = [] - next_time = 0.0 - frame_index = 0 - last_timestamp_ms = -1 - with mp.tasks.vision.FaceLandmarker.create_from_options(options) as landmarker: - while True: - ok, bgr = capture.read() - if not ok: - break - reported_ms = float(capture.get(cv2.CAP_PROP_POS_MSEC)) - timestamp = reported_ms / 1000.0 - previous_timestamp = timestamps[-1] if timestamps else -1.0 - if not math.isfinite(timestamp) or (frame_index > 0 and timestamp <= previous_timestamp): - timestamp = frame_index / fps - frame_index += 1 - if timestamp + 1e-6 < next_time: + qualities: list[float] = [] + quality_available: list[bool] = [] + with csv_path.open("r", encoding="utf-8-sig", newline="") as stream: + reader = csv.DictReader(stream, skipinitialspace=True) + for raw_row in reader: + row = {str(key).strip(): str(value).strip() for key, value in raw_row.items() if key is not None} + timestamp = _openface_float(row, "timestamp") + frame = _openface_float(row, "frame") + confidence = _openface_float(row, "confidence") + success = _openface_float(row, "success") + if not math.isfinite(timestamp) or not math.isfinite(frame): continue - while next_time <= timestamp: - next_time += 1.0 / VISION_RATE_HZ - timestamp = min(max(timestamp, 0.0), duration_s) - timestamp_ms = max(last_timestamp_ms + 1, int(round(timestamp * 1000))) - last_timestamp_ms = timestamp_ms - rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) - image = mp.Image(image_format=mp.ImageFormat.SRGB, data=np.ascontiguousarray(rgb)) - result = landmarker.detect_for_video(image, timestamp_ms) + timestamp = float(np.clip(timestamp, 0.0, duration_s)) + vector = np.zeros(35, dtype=np.float32) + observed = np.zeros(35, dtype=bool) + good_face = success >= 1.0 and math.isfinite(confidence) and confidence >= 0.8 + if good_face: + for index, code in enumerate(AU_CODES): + value = _openface_float(row, f"AU{code:02d}_r") + if math.isfinite(value): + vector[index] = value + observed[index] = True + for offset, name in enumerate(("pose_Rx", "pose_Ry", "pose_Rz", "pose_Tx", "pose_Ty", "pose_Tz"), start=17): + value = _openface_float(row, name) + if math.isfinite(value): + vector[offset] = value + observed[offset] = True + gaze_names = ("gaze_0_x", "gaze_0_y", "gaze_0_z", "gaze_1_x", "gaze_1_y", "gaze_1_z") + for offset, name in enumerate(gaze_names, start=23): + value = _openface_float(row, name) + if math.isfinite(value): + vector[offset] = value + observed[offset] = True + geometry, geometry_mask = _openface_geometry(row) + vector[29:35] = geometry + observed[29:35] = geometry_mask timestamps.append(timestamp) - faces = result.face_landmarks - if not faces: - values.append(np.zeros(35, dtype=np.float32)) - masks.append(np.zeros(35, dtype=np.bool_)) - continue - blendshapes = result.face_blendshapes[0] if result.face_blendshapes else [] - matrix = result.facial_transformation_matrixes[0] if result.facial_transformation_matrixes else None - vector, mask = _vision_frame_features(faces[0], blendshapes, matrix) + frame_indices.append(int(frame)) values.append(vector) - masks.append(mask) - capture.release() + masks.append(observed) + qualities.append(float(np.clip(confidence, 0.0, 1.0)) if math.isfinite(confidence) and success >= 1.0 else 1.0) + quality_available.append(bool(math.isfinite(confidence) and success >= 1.0)) if not timestamps: - return np.empty(0, np.float32), np.empty((0, 35), np.float32), np.empty((0, 35), np.bool_) - return np.asarray(timestamps, np.float32), np.stack(values), np.stack(masks) + return (np.empty(0, np.float32), np.empty(0, np.int32), np.empty((0, 35), np.float32), + np.empty((0, 35), bool), np.empty(0, np.float32), np.empty(0, bool)) + order = np.argsort(np.asarray(timestamps), kind="stable") + return ( + np.asarray(timestamps, np.float32)[order], np.asarray(frame_indices, np.int32)[order], + np.stack(values)[order], np.stack(masks)[order], np.asarray(qualities, np.float32)[order], + np.asarray(quality_available, bool)[order], + ) def _load_models(device: torch.device) -> tuple[Any, Any, Any, Any, dict[str, Any]]: @@ -796,7 +780,6 @@ def _extract_native_sample( record: dict[str, Any], models: tuple[Any, Any, Any, Any, dict[str, Any]], device: torch.device, - face_model_path: Path, video_sha256: str, ) -> Sample: text_tokenizer, text_model, ctc_tokenizer, speech_model, model_info = models @@ -806,7 +789,9 @@ def _extract_native_sample( text_vectors, text_valid = _text_features(words, text_tokenizer, text_model, device) waveform = _decode_audio(video_path) audio_times, audio_vectors, audio_observed = _audio_features(waveform, duration_s) - vision_times, vision_vectors, vision_observed = _vision_features(video_path, duration_s, face_model_path) + output_name = f"{_safe_name(record['video_id'])}__{_safe_name(record['clip_id'])}" + (vision_times, vision_frame_indices, vision_vectors, vision_observed, + vision_quality, vision_quality_available) = _vision_features(video_path, duration_s, output_name) targets, word_targets = _ctc_targets(words, ctc_tokenizer) blank_id = int(ctc_tokenizer.pad_token_id) if targets: @@ -858,9 +843,14 @@ def _extract_native_sample( audio_times=audio_times, audio_features=audio_vectors.astype(np.float16), audio_observed=audio_observed, + audio_quality=np.clip((len(waveform) - np.arange(len(audio_times)) * FRAME_STEP) / FRAME_LENGTH, 0.0, 1.0).astype(np.float32), + audio_quality_available=np.ones(len(audio_times), bool), vision_times=vision_times, + vision_frame_indices=vision_frame_indices, vision_features=vision_vectors.astype(np.float16), vision_observed=vision_observed, + vision_quality=vision_quality, + vision_quality_available=vision_quality_available, ctc_times=ctc_frame_times, ctc_occupancy=occupancy.astype(np.float16), speech_features=deep_states.astype(np.float16), @@ -891,9 +881,14 @@ def _save_cache(path: Path, sample: Sample, cache_schema: str) -> None: audio_times=sample.audio_times.astype(np.float32), audio_features=sample.audio_features.astype(np.float16), audio_observed=sample.audio_observed, + audio_quality=sample.audio_quality.astype(np.float32), + audio_quality_available=sample.audio_quality_available, vision_times=sample.vision_times.astype(np.float32), + vision_frame_indices=sample.vision_frame_indices.astype(np.int32), vision_features=sample.vision_features.astype(np.float16), vision_observed=sample.vision_observed, + vision_quality=sample.vision_quality.astype(np.float32), + vision_quality_available=sample.vision_quality_available, ctc_times=sample.ctc_times.astype(np.float32), ctc_occupancy=sample.ctc_occupancy.astype(np.float16), speech_features=sample.speech_features.astype(np.float16), @@ -918,8 +913,14 @@ def _load_cache(path: Path, record: dict[str, Any], source_hash: str, cache_sche text_valid=np.asarray(data["text_valid"], dtype=np.bool_), hard_intervals=np.asarray(data["hard_intervals"], dtype=np.float32), hard_valid=np.asarray(data["hard_valid"], dtype=np.bool_), hard_quality=np.asarray(data["hard_quality"], dtype=np.float32), audio_times=np.asarray(data["audio_times"], dtype=np.float32), audio_features=np.asarray(data["audio_features"], dtype=np.float16), - audio_observed=np.asarray(data["audio_observed"], dtype=np.bool_), vision_times=np.asarray(data["vision_times"], dtype=np.float32), + audio_observed=np.asarray(data["audio_observed"], dtype=np.bool_), + audio_quality=np.asarray(data["audio_quality"], dtype=np.float32), + audio_quality_available=np.asarray(data["audio_quality_available"], dtype=np.bool_), + vision_times=np.asarray(data["vision_times"], dtype=np.float32), + vision_frame_indices=np.asarray(data["vision_frame_indices"], dtype=np.int32), vision_features=np.asarray(data["vision_features"], dtype=np.float16), vision_observed=np.asarray(data["vision_observed"], dtype=np.bool_), + vision_quality=np.asarray(data["vision_quality"], dtype=np.float32), + vision_quality_available=np.asarray(data["vision_quality_available"], dtype=np.bool_), ctc_times=np.asarray(data["ctc_times"], dtype=np.float32), ctc_occupancy=np.asarray(data["ctc_occupancy"], dtype=np.float16), speech_features=np.asarray(data["speech_features"], dtype=np.float16), boundary_summary=json.loads(str(data["boundary_summary"].item())), video_sha256=source_hash, @@ -979,17 +980,18 @@ def _project_rows( for target_index, (left, right) in enumerate(zip(target_edges[:-1], target_edges[1:])): if right <= left: continue - overlap = np.maximum( + physical_overlap = np.maximum( 0.0, np.minimum(intervals[:, 1], right) - np.maximum(intervals[:, 0], left), - ) * qualities - candidate = np.flatnonzero(overlap > 0) + ) + weighted_overlap = physical_overlap * qualities + candidate = np.flatnonzero(physical_overlap > 0) if not len(candidate): continue local_mask = observed[candidate] - local_weight = overlap[candidate, None] * local_mask + local_weight = weighted_overlap[candidate, None] * local_mask denominator = local_weight.sum(axis=0) - physical_coverage = (overlap[candidate, None] * local_mask).sum(axis=0) + physical_coverage = (physical_overlap[candidate, None] * local_mask).sum(axis=0) good = denominator > 0 if good.any(): output[target_index, good] = ( @@ -1000,11 +1002,11 @@ def _project_rows( return output, mask, coverage -def _so3_weighted_mean(rotvecs: np.ndarray, weights: np.ndarray) -> np.ndarray | None: +def _so3_weighted_mean(euler_xyz: np.ndarray, weights: np.ndarray) -> np.ndarray | None: positive = weights > 0 if not positive.any(): return None - rotations = Rotation.from_rotvec(np.asarray(rotvecs[positive], dtype=np.float64)) + rotations = Rotation.from_euler("xyz", np.asarray(euler_xyz[positive], dtype=np.float64)) local_weights = np.asarray(weights[positive], dtype=np.float64) local_weights /= local_weights.sum() matrices = rotations.as_matrix() @@ -1021,12 +1023,12 @@ def _so3_weighted_mean(rotvecs: np.ndarray, weights: np.ndarray) -> np.ndarray | if np.linalg.norm(delta) < 1e-8: break mean_rotation = mean_rotation * Rotation.from_rotvec(delta) - return mean_rotation.as_rotvec().astype(np.float32) + return mean_rotation.as_euler("xyz").astype(np.float32) def _project_vision_geometry(sample: Sample, target_edges: np.ndarray, geometry_aware: bool) -> tuple[np.ndarray, np.ndarray, np.ndarray]: intervals = _voronoi_intervals(sample.vision_times, sample.duration_s) - quality = np.ones(len(intervals), dtype=np.float32) + quality = sample.vision_quality values, masks, coverage = _project_rows( sample.vision_features.astype(np.float32), sample.vision_observed, intervals, quality, target_edges ) @@ -1037,16 +1039,17 @@ def _project_vision_geometry(sample: Sample, target_edges: np.ndarray, geometry_ overlap = np.maximum(0.0, np.minimum(intervals[:, 1], right) - np.maximum(intervals[:, 0], left)) if not np.any(overlap > 0): continue - pose_rows = (overlap > 0) & sample.vision_observed[:, 17:20].all(axis=1) + weighted_overlap = overlap * quality + pose_rows = (weighted_overlap > 0) & sample.vision_observed[:, 17:20].all(axis=1) if pose_rows.any(): - vector = _so3_weighted_mean(raw[pose_rows, 17:20], overlap[pose_rows]) + vector = _so3_weighted_mean(raw[pose_rows, 17:20], weighted_overlap[pose_rows]) if vector is not None: values[target_index, 17:20] = vector masks[target_index, 17:20] = True for start in (23, 26): - valid_rows = (overlap > 0) & sample.vision_observed[:, start:start + 3].all(axis=1) + valid_rows = (weighted_overlap > 0) & sample.vision_observed[:, start:start + 3].all(axis=1) if valid_rows.any(): - weights = overlap[valid_rows].astype(np.float64) + weights = weighted_overlap[valid_rows].astype(np.float64) vector = np.average(raw[valid_rows, start:start + 3], axis=0, weights=weights) norm = float(np.linalg.norm(vector)) if norm > 1e-6: @@ -1070,7 +1073,7 @@ def _project_posterior_text(sample: Sample, target_edges: np.ndarray) -> tuple[n frame_edges = np.clip( first_edge + np.arange(len(sample.ctc_times) + 1, dtype=np.float64) * frame_step, 0.0, - sample.duration_s, + _audio_end_time(sample), ) occupancy = sample.ctc_occupancy.astype(np.float64) for target_index, (left, right) in enumerate(zip(target_edges[:-1], target_edges[1:])): @@ -1089,35 +1092,47 @@ def _project_posterior_text(sample: Sample, target_edges: np.ndarray) -> tuple[n def _normalized_hard_quality(sample: Sample) -> np.ndarray: - text_quality = np.zeros_like(sample.hard_quality, dtype=np.float32) + text_quality = np.ones_like(sample.hard_quality, dtype=np.float32) valid_quality = sample.hard_valid & (sample.hard_quality > 0) if valid_quality.any(): - quality_reference = float(np.median(sample.hard_quality[valid_quality])) - text_quality[valid_quality] = np.clip( - sample.hard_quality[valid_quality].astype(np.float64) / quality_reference, 0.25, 4.0 - ) + text_quality[valid_quality] = np.clip(sample.hard_quality[valid_quality], 0.0, 1.0) return text_quality +def _audio_end_time(sample: Sample) -> float: + """Recover decoded sample support from the final partial analysis window.""" + valid = np.flatnonzero(np.asarray(sample.audio_quality) > 0) + if not len(valid): + return 0.0 + index = int(valid[-1]) + quality = float(np.clip(sample.audio_quality[index], 0.0, 1.0)) + sample_count = index * FRAME_STEP + int(round(quality * FRAME_LENGTH)) + return float(min(sample.duration_s, sample_count / SAMPLE_RATE)) + + def _make_views(sample: Sample) -> tuple[View, View, View]: edges = _grid_edges(sample.duration_s) + audio_end_s = _audio_end_time(sample) + hard_intervals = np.clip(sample.hard_intervals.astype(np.float32), 0.0, audio_end_s) + hard_valid = sample.hard_valid & (hard_intervals[:, 1] > hard_intervals[:, 0]) hard_text_observed = np.broadcast_to(sample.text_valid[:, None], sample.text_features.shape).copy() text_quality = _normalized_hard_quality(sample) + text_quality[~hard_valid] = 0.0 text_hard = _project_rows( sample.text_features.astype(np.float32), hard_text_observed, - sample.hard_intervals, text_quality, edges, + hard_intervals, text_quality, edges, ) audio = _project_rows( sample.audio_features.astype(np.float32), sample.audio_observed, - _voronoi_intervals(sample.audio_times, sample.duration_s), - np.ones(len(sample.audio_times), dtype=np.float32), edges, + _voronoi_intervals(sample.audio_times, audio_end_s), + sample.audio_quality, edges, ) vision_b0 = _project_vision_geometry(sample, edges, geometry_aware=False) vision_b1 = _project_vision_geometry(sample, edges, geometry_aware=True) speech = _project_rows( sample.speech_features.astype(np.float32), np.ones(sample.speech_features.shape, dtype=np.bool_), - _voronoi_intervals(sample.ctc_times, sample.duration_s), + _voronoi_intervals(sample.ctc_times, audio_end_s), np.ones(len(sample.ctc_times), dtype=np.float32), edges, ) post_text = _project_posterior_text(sample, edges) @@ -1241,11 +1256,11 @@ def _dynamic_signature(sample: Sample, scaled_view: View) -> np.ndarray: audio_mask = scaled_view.observed["audio"] vision = scaled_view.features["vision"] vision_mask = scaled_view.observed["vision"] - # log-F0, log-energy; MediaPipe jaw-open proxy and normalized mouth aperture. + # log-F0, log-energy; OpenFace AU12 intensity and normalized mouth opening. audio_values = audio[:, [67, 66]] audio_valid = audio_mask[:, [67, 66]].all(axis=1) - vision_values = vision[:, [11, 31]] - vision_valid = vision_mask[:, [11, 31]].all(axis=1) + vision_values = vision[:, [10, 31]] + vision_valid = vision_mask[:, [10, 31]].all(axis=1) result: list[float] = [] for index in range(5): left, right = float(segments[index]), float(segments[index + 1]) @@ -1666,60 +1681,60 @@ def _write_report( complete_hard_count = sum(row["unlocated_word_count"] == 0 for row in sample_rows) consistent = manifest["inputs"]["label_polarity_consistency_count"] lines = [ - "# 闂涓€ B0-B4 妯″瀷瀵规瘮缁撴灉", + "# 问题一 B0-B4 模型对比结果", "", - "鏈姤鍛婃寜 `final/Q1/闂涓€.pdf` 绗?4.8.3 鑺傚疄鏂戒簲缁勫崟鍥犵礌瀵圭収銆傚叏閮?100 鏉￠檮浠朵竴瑙嗛鍧囦娇鐢ㄥ悓涓€濂楀喕缁撶壒寰佹娊鍙栧櫒銆?.1 绉掍富鏃堕棿缃戞牸銆佽娴嬫帺鐮佷笌鎸?`video_id` 鍒嗙粍鐨勪簲鎶樺垝鍒嗐€傛儏鎰熸爣绛句粎鐢ㄤ簬鎶樺唴鐨勮交閲忛娴嬫帰閽堬紝涓嶈繘鍏ユ椂闂村榻愩€丆TC 鍚庨獙鎴栧姩鎬佺鍚嶈绠椼€?, + "本报告按 `math/E题V2.pdf` 第 4.9.3 节实施五组单因素对照。全部 100 条附件一视频均使用同一套冻结特征抽取器、0.1 秒主时间网格、观测掩码与按 `video_id` 分组的五折划分。情感标签仅用于折内的轻量预测探针，不进入时间对齐、CTC 后验或动态签名计算。", "", - "## 瀹為檯鐗瑰緛鍜屾瘮杈冪増鏈?, + "## 实际特征和比较版本", "", - "| 鐗堟湰 | 瀹炴柦鍐呭 |", + "| 版本 | 实施内容 |", "| --- | --- |", - "| B0 | CTC Viterbi 璇嶅尯闂翠笌闊抽/瑙嗚鐗╃悊鏃堕棿鍖洪棿鎶曞奖锛涜缁冩姌鍧囧€?鏍囧噯宸缉鏀撅紱瑙嗚鏃嬭浆鍚戦噺鍜岃绾垮悜閲忎綔鏅€氬垎閲忓潎鍊笺€?|", - "| B1 | 鍦?B0 涓婃敼鐢ㄨ缁冩姌涓綅鏁颁笌 MAD 鐨勭ǔ鍋ュ昂搴︼紙MAD 涓洪浂鏃跺洖閫€鍒拌缁冩姌鏍囧噯宸級锛屽苟瀵硅瑙夋棆杞敤 SO(3) 鍧囧€笺€佽绾跨敤褰掍竴鍖栧悜閲忓潎鍊笺€?|", - "| B2 | 鍦?B1 涓婂彧灏嗘枃鏈瘝鍚戦噺鐨勭‖杈圭晫鎶曞奖鏇挎崲涓哄浐瀹氳浆鍐?CTC 鐘舵€佸浘鐨勫墠鍚戔€撳悗鍚戝崰鎹鐜囨姇褰憋紱濯掍綋鏃堕棿鎴充粛鎸夌墿鐞嗘椂闂淬€?|", - "| B3 | 鍦?B1 涓婁粎闄勫姞闊抽 log-F0/鑳介噺涓庤瑙?jaw-open/鍢撮儴寮€鍚堢巼鐨勬椂闂村骞夸竴銆佷簩闃惰矾寰勭鍚嶏紱缂烘祴鐐逛箣闂翠笉杩炵嚎銆?|", - "| B4 | 鍦?B1 涓婁粎闄勫姞鍐荤粨 Wav2Vec2-base-960h 鏈€鍚庡洓灞傜殑鏃堕棿鎶曞奖琛ㄧず銆?|", + "| B0 | 固定转写 CTC Viterbi 词区间与原生音频/视频时间归属区间的质量加权投影；训练折均值/标准差缩放。 |", + "| B1 | 在 B0 上改用训练折中位数与 MAD 的稳健尺度（MAD 为零时回退到训练折标准差），并对头部旋转用 SO(3) 均值、视线用归一化向量均值。 |", + "| B2 | 在 B1 上只将文本词向量的硬边界投影替换为固定转写 CTC 状态图的前向–后向占据概率投影；媒体时间戳仍按物理时间。 |", + "| B3 | 在 B1 上仅附加音频 log-F0/能量与 OpenFace AU12/嘴部开合率的时间增广一、二阶路径签名；缺测点之间不连线。 |", + "| B4 | 在 B1 上仅附加冻结 Wav2Vec2-base-960h 最后四层的时间投影表示。 |", "", - "闄勪欢涓€鐜版湁鏍囩琛ㄥ拰鍘熷瑙嗛琚洿鎺ヤ娇鐢ㄣ€傛枃鏈负 768 缁?BERT-base-uncased 鏈洓灞傚潎鍊硷紱闊抽涓?74 缁达紙log-Mel 40銆丮FCC 13銆佄擬FCC 13銆侀煹寰?璋辩粺璁?8锛夛紱瑙嗚涓?35 缁达紙17 涓?MediaPipe blendshape 浠ｇ悊銆? 缁村ご濮裤€? 缁磋繎浼艰绾裤€? 缁撮潰閮ㄦ瘮渚嬪嚑浣曪級銆傝瑙変唬鐞嗗拰鍑犱綍绱㈠紩瀹氫箟瑙?`compare_models.py`锛屽畠浠笌 OpenFace AU 瀹氫箟骞朵笉绛夊悓锛涜闄愬埗椤诲湪璁烘枃涓槑绀恒€?, + "附件一现有标签表和原始视频被直接使用。文本为 768 维冻结 BERT-base-uncased 末四层均值；音频为 74 维（log-Mel 40、MFCC 13、ΔMFCC 13、韵律/谱统计 8）；视觉为 35 维 OpenFace（17 个 AU 强度、6 维头姿、6 维双眼视线、6 维无量纲几何），置信度低于 0.8 的面部量按缺失处理。基频/有声概率使用 librosa pYIN（80–400 Hz、400 样本帧、160 样本步长），谐噪比由预测基频处的自相关周期性另行计算；pYIN 对 80 Hz 与 25 ms 窗发出周期数不足提示，因此该基频下界结果需谨慎解释。", "", - "## 缁撴灉", + "## 结果", "", - "鍒嗙被鎸夎繛缁爣绛剧殑涓ユ牸绗﹀彿鏋勯€?Negative/Neutral/Positive锛屽己搴﹂娴嬮檺骞呭埌 [-3, 3]銆傛瘡鎶樿缁冩姌鍗曠嫭鎷熷悎鏍囧噯鍖栧櫒銆侀€昏緫鍥炲綊锛圕=0.05锛変笌 Ridge锛坅lpha=25锛夛紱鍥哄畾浜旀姌鐢?GroupKFold 鎸夊師濮?`video_id` 鍒嗙粍銆傛€讳綋 OOF 鎸囨爣鎸?100 鏉＄暀缁勯娴嬭绠椼€?, + "分类按连续标签的严格符号构造 Negative/Neutral/Positive，强度预测限幅到 [-3, 3]。每折训练折单独拟合标准化器、逻辑回归（C=0.05）与 Ridge（alpha=25）；固定五折由 GroupKFold 按原始 `video_id` 分组。总体 OOF 指标按 100 条留组预测计算。", "", - "| 鏂规硶 | OOF Accuracy | OOF Macro-F1 | OOF MAE | OOF Pearson | Macro-F1 鎶樺潎鍊悸盨D |", + "| 方法 | OOF Accuracy | OOF Macro-F1 | OOF MAE | OOF Pearson | Macro-F1 折均值±SD |", "| --- | ---: | ---: | ---: | ---: | ---: |", ] for row in summary: lines.append( - f"| {row['method']} | {row['oof_accuracy']:.3f} | {row['oof_macro_f1']:.3f} | {row['oof_mae']:.3f} | {row['oof_pearson']:.3f} | {row['fold_macro_f1_mean']:.3f} 卤 {row['fold_macro_f1_sd']:.3f} |" + f"| {row['method']} | {row['oof_accuracy']:.3f} | {row['oof_macro_f1']:.3f} | {row['oof_mae']:.3f} | {row['oof_pearson']:.3f} | {row['fold_macro_f1_mean']:.3f} ± {row['fold_macro_f1_sd']:.3f} |" ) lines += [ "", - f"鎸夊綋鍓?OOF 鎺㈤拡锛孧acro-F1 鏈€楂樼殑鏄?{best_f1['method']}锛坽best_f1['oof_macro_f1']:.3f}锛夛紝MAE 鏈€浣庣殑鏄?{best_mae['method']}锛坽best_mae['oof_mae']:.3f}锛夈€傜浉瀵?B1锛孊4 鐨?Macro-F1 宸负 {b4_f1['delta_oof']:+.3f}锛岃棰戠粍 Bootstrap 95% 鍖洪棿 [{b4_f1['group_bootstrap_ci95_low']:+.3f}, {b4_f1['group_bootstrap_ci95_high']:+.3f}]锛屽尯闂磋法杩囬浂銆侭0 鐨?MAE 宸负 {b0_mae['delta_oof']:+.3f}锛屽尯闂?[{b0_mae['group_bootstrap_ci95_low']:+.3f}, {b0_mae['group_bootstrap_ci95_high']:+.3f}]锛孭earson 宸负 {b0_pearson['delta_oof']:+.3f}銆侭2 鐨?Macro-F1 宸负 {b2_f1['delta_oof']:+.3f}锛屽尯闂?[{b2_f1['group_bootstrap_ci95_low']:+.3f}, {b2_f1['group_bootstrap_ci95_high']:+.3f}]锛孧AE 宸负 {b2_mae['delta_oof']:+.3f}锛汢3 鐨?Macro-F1 宸负 {b3_f1['delta_oof']:+.3f}锛屽尯闂?[{b3_f1['group_bootstrap_ci95_low']:+.3f}, {b3_f1['group_bootstrap_ci95_high']:+.3f}]銆傜粍 Bootstrap 鍙弽鏄犲綋鍓?37 涓潵婧愯棰戜笂鐨勬娊鏍蜂笉纭畾鎬э紝鏈綔澶氶噸姣旇緝鏍℃銆?, + f"按当前 OOF 探针，Macro-F1 最高的是 {best_f1['method']}（{best_f1['oof_macro_f1']:.3f}），MAE 最低的是 {best_mae['method']}（{best_mae['oof_mae']:.3f}）。相对 B1，B4 的 Macro-F1 差为 {b4_f1['delta_oof']:+.3f}，视频组 Bootstrap 95% 区间 [{b4_f1['group_bootstrap_ci95_low']:+.3f}, {b4_f1['group_bootstrap_ci95_high']:+.3f}]，区间跨过零。B0 的 MAE 差为 {b0_mae['delta_oof']:+.3f}，区间 [{b0_mae['group_bootstrap_ci95_low']:+.3f}, {b0_mae['group_bootstrap_ci95_high']:+.3f}]，Pearson 差为 {b0_pearson['delta_oof']:+.3f}。B2 的 Macro-F1 差为 {b2_f1['delta_oof']:+.3f}，区间 [{b2_f1['group_bootstrap_ci95_low']:+.3f}, {b2_f1['group_bootstrap_ci95_high']:+.3f}]，MAE 差为 {b2_mae['delta_oof']:+.3f}；B3 的 Macro-F1 差为 {b3_f1['delta_oof']:+.3f}，区间 [{b3_f1['group_bootstrap_ci95_low']:+.3f}, {b3_f1['group_bootstrap_ci95_high']:+.3f}]。组 Bootstrap 只反映当前 37 个来源视频上的抽样不确定性，未作多重比较校正。", "", - "## 瑕嗙洊涓庡榻愭牳楠?, + "## 覆盖与对齐核验", "", - f"鏍锋湰鏁帮細{len(sample_rows)}锛涙爣绛捐〃杩炵画鏍囩涓庢瀬鎬т竴鑷?{consistent}/100锛汣TC 纭榻愬畬鏁寸殑鏍锋湰 {complete_hard_count}/100銆侭0 纭尯闂村钩鍧囨枃鏈鐩栫巼涓?{mean_hard_coverage:.3f}锛孊2 骞冲潎璇嶅崰鎹悗楠岃川閲忎负 {mean_posterior_occupancy:.3f}锛堣繖鏄湡鏈涘崰鎹巼锛屼笉鏄鐩栫巼锛夛紱瑙嗚妫€娴嬭鐩栫巼锛堟寜 0.1 绉掔綉鏍煎钩鍧囷級涓?{mean_vision_coverage:.3f}銆傚悗楠屽唴閮?90% 杈圭晫鍖洪棿骞冲潎瀹藉害涓鸿捣鐐?{mean_start_width:.3f} 绉掋€佺粓鐐?{mean_end_width:.3f} 绉掋€傛病鏈変汉宸ヨ竟鐣屽瓙闆嗭紝鍥犳杩欎簺瀹藉害鍙槸妯″瀷鍐呴儴涓嶇‘瀹氭€ф憳瑕侊紝涓嶆槸缁忛獙鏍″噯鐜囨垨杈圭晫璇樊銆?, + f"样本数：{len(sample_rows)}；标签表连续标签与极性一致 {consistent}/100；CTC 硬对齐完整的样本 {complete_hard_count}/100。B0 硬区间平均文本覆盖率为 {mean_hard_coverage:.3f}，B2 平均词占据后验质量为 {mean_posterior_occupancy:.3f}（这是期望占据率，不是覆盖率）；视觉检测覆盖率（按 0.1 秒网格平均）为 {mean_vision_coverage:.3f}。后验内部 90% 边界区间平均宽度为起点 {mean_start_width:.3f} 秒、终点 {mean_end_width:.3f} 秒。没有人工边界子集，因此这些宽度只是模型内部不确定性摘要，不是经验校准率或边界误差。", "", - "B0鈥揃4 鐨勭壒寰佹潵婧愩€佹湁鏁堟帺鐮併€佽繛缁鐩栫巼銆佽瘝杈圭晫鍚庨獙瀹藉害銆佹姌鍐呴娴嬪強瑙嗛鍝堝笇鍧囧垎鍒繚瀛樺湪閰嶅 CSV/JSON 涓€傛病鏈変汉宸ヨ瘝杈圭晫鏃朵笉鎶ュ憡 IoU/MATE锛屼篃涓嶆妸涓嶅悓鏂规硶鐨勯娴嬫帰閽堝垎鏁拌В閲婁负瀵归綈鐪熷€笺€?, + "B0–B4 的特征来源、有效掩码、连续覆盖率、词边界后验宽度、折内预测及视频哈希均分别保存在配套 CSV/JSON 中。没有人工词边界时不报告 IoU/MATE，也不把不同方法的预测探针分数解释为对齐真值。", "", - "## 鏂囦欢", + "## 文件", "", - "- `comparison_summary.csv`锛氫簲绉嶆柟娉曟€讳綋 OOF 涓庢姌鍧囧€兼寚鏍囥€?, - "- `fold_metrics.csv`锛氭瘡鎶樺垎绫?鍥炲綊鎸囨爣涓庣壒寰佺淮鏁般€?, - "- `oof_predictions.csv`锛氭瘡鏉℃牱鏈殑鐣欑粍棰勬祴銆?, - "- `group_bootstrap_deltas.csv`锛氱浉瀵?B1 鐨勮棰戠粍閰嶅 Bootstrap 95% 鍖洪棿銆?, - "- `sample_alignment_summary.csv`锛?00 鏉℃牱鏈€佺‖/姒傜巼鏂囨湰瑕嗙洊鍜屽悗楠岃竟鐣屽搴︺€?, - "- `modality_summary.csv`锛?00 琛屾牱鏈?妯℃€佹槑缁嗐€?, - "- `word_alignment_posterior.csv`锛氶€愯瘝纭竟鐣屻€佸疄闄呯浉瀵硅川閲忔潈閲嶅強鍚庨獙杈圭晫鍖洪棿銆?, - "- `split_assignments.csv`锛氶€愭牱鏈姌鍙枫€?, - "- `comparison.png`锛氭牳蹇?OOF 鎸囨爣鍥俱€?, - "- `typical_alignment_example.png`锛氫腑浣嶆椂闀挎牱鏈殑璇嶈竟鐣屻€佽鐩栫巼銆佸０瀛?瑙嗚杞ㄨ抗涓庤棰戝抚鏍搁獙鍥俱€?, - "- `run_manifest.json`锛氱幆澧冦€佹ā鍨?revision銆佸弬鏁般€佸搱甯屼笌杩愯鏃堕棿銆?, + "- `comparison_summary.csv`：五种方法总体 OOF 与折均值指标。", + "- `fold_metrics.csv`：每折分类/回归指标与特征维数。", + "- `oof_predictions.csv`：每条样本的留组预测。", + "- `group_bootstrap_deltas.csv`：相对 B1 的视频组配对 Bootstrap 95% 区间。", + "- `sample_alignment_summary.csv`：100 条样本、硬/概率文本覆盖和后验边界宽度。", + "- `modality_summary.csv`：300 行样本-模态明细。", + "- `word_alignment_posterior.csv`：逐词硬边界、实际相对质量权重及后验边界区间。", + "- `split_assignments.csv`：逐样本折号。", + "- `comparison.png`：核心 OOF 指标图。", + "- `typical_alignment_example.png`：中位时长样本的词边界、覆盖率、声学/视觉轨迹与视频帧核验图。", + "- `run_manifest.json`：环境、模型 revision、参数、哈希与运行时间。", "", - "## 澶嶇幇", + "## 复现", "", - "鍦ㄤ粨搴撴牴鐩綍鎵ц锛?, + "在仓库根目录执行：", "", "```bash", "cd math", @@ -1727,7 +1742,7 @@ def _write_report( "uv run python compare_models.py", "```", "", - "鐗瑰緛缂撳瓨鍐欏湪 `final/Q1/cache/native/`锛屾寮忕粨鏋滃彧鍐欏湪 `final/Q1/results/model_comparison/`銆傚垹闄ょ紦瀛樺悗浼氫粠闄勪欢涓€閲嶆柊鎻愬彇銆傞娆¤繍琛岄渶瑕佷笅杞?BERT銆乄av2Vec2 鍜?MediaPipe Face Landmarker 鏉冮噸銆?, + "特征缓存写在 `q1/cache/native/`，OpenFace 原始 CSV 写在 `q1/cache/openface_outputs/`，正式结果写在 `output/q1/model_comparison/`。删除缓存后会从附件一重新提取。运行前需准备 BERT、Wav2Vec2 权重及 OpenFace 2.2.0 Windows 包。", ] (output_dir / "report.md").write_text("\n".join(lines) + "\n", encoding="utf-8") @@ -1778,8 +1793,7 @@ def run(args: argparse.Namespace) -> None: source_hashes[record["sample_id"]] = _sha256(video_path) output_dir.mkdir(parents=True, exist_ok=True) cache_dir.mkdir(parents=True, exist_ok=True) - cache_schema = "q1-b0b4-v3" - face_hash = _ensure_face_model() + cache_schema = "q1-b0b4-v4-openface" samples: list[Sample] = [] missing_records: list[dict[str, Any]] = [] cache_paths = { @@ -1807,7 +1821,7 @@ def run(args: argparse.Namespace) -> None: for index, record in enumerate(missing_records, start=1): started = time.perf_counter() sample = _extract_native_sample( - record, models, device, FACE_MODEL_PATH, source_hashes[record["sample_id"]] + record, models, device, source_hashes[record["sample_id"]] ) _save_cache(cache_paths[record["sample_id"]], sample, cache_schema) cached_by_id[sample.sample_id] = sample @@ -1850,13 +1864,13 @@ def run(args: argparse.Namespace) -> None: "created_at_local": time.strftime("%Y-%m-%d %H:%M:%S %z"), "python": sys.version, "platform": platform.platform(), - "uv_version": subprocess.run(["uv", "--version"], capture_output=True, text=True).stdout.strip(), - "packages": {name: _version(name) for name in ("torch", "transformers", "numpy", "scipy", "scikit-learn", "mediapipe", "opencv-python-headless", "openpyxl", "matplotlib")}, + "uv_version": subprocess.run([shutil.which("uv"), "--version"], capture_output=True, text=True).stdout.strip() if shutil.which("uv") else "uv run parent; executable not on child PATH", + "packages": {name: _version(name) for name in ("torch", "transformers", "numpy", "scipy", "scikit-learn", "opencv-python-headless", "librosa", "openpyxl", "matplotlib")}, "device": args.device if args.device != "auto" else ("cuda" if torch.cuda.is_available() else "cpu"), "cuda_available": bool(torch.cuda.is_available()), "gpu_name": torch.cuda.get_device_name(0) if torch.cuda.is_available() and args.device != "cpu" else None, "models": model_info, - "face_landmarker_asset_sha256": face_hash, + "openface": {"version": "2.2.0", "executable": "FeatureExtraction", "landmark_model": "main_clnf_general.txt", "confidence_threshold": 0.8}, "inputs": { "label_file": str(label_file), "label_file_sha256": _sha256(label_file), @@ -1877,20 +1891,22 @@ def run(args: argparse.Namespace) -> None: "vision_dimension": 35, "audio_features": list(AUDIO_NAMES), "vision_features": list(VISION_NAMES), - "vision_feature_note": "17 MediaPipe blendshape proxies and approximate gaze/landmark geometry; not OpenFace AU labels", + "vision_feature_note": "OpenFace 2.2.0 AU intensities, pose, gaze, and six geometry values defined in E题V2.pdf section 4.2.3", "grid_step_s": GRID_STEP_S, "audio_window_samples": FRAME_LENGTH, "audio_step_samples": FRAME_STEP, "audio_fft": N_FFT, "audio_mel_bands": MEL_COUNT, - "vision_sample_rate_hz": VISION_RATE_HZ, + "pitch_estimator": {"name": "librosa.pyin", "version": _version("librosa"), "f0_range_hz": [80.0, 400.0], "frame_length_samples": FRAME_LENGTH, "hop_length_samples": FRAME_STEP, "center": False}, + "hnr_estimator": "autocorrelation periodicity at pYIN-detected F0; dB", + "vision_sample_rate_hz": "native OpenFace frame timestamps; no fixed-rate assumption", "grouping": "GroupKFold by video_id", "fold_count": N_FOLDS, "probe": {"classifier": "LogisticRegression", "C": 0.05, "regressor": "Ridge", "alpha": 25.0, "intensity_clip": [-3.0, 3.0]}, "bootstrap": {"unit": "video_id", "repeats": args.bootstrap_repeats, "seed": SEED}, "human_boundary_reference_count": 0, "boundary_interval_level": 0.90, - "hard_text_quality_weight": "per-sample median-normalized CTC path score clipped to [0.25, 4.0]; uncalibrated", + "hard_text_quality_weight": "raw fixed-transcript CTC word path score in [0,1]; unavailable scores use q*=1 and are flagged unavailable", "cache_schema": cache_schema, }, "output_dir": str(output_dir), @@ -1919,4 +1935,3 @@ def _parse_args() -> argparse.Namespace: if __name__ == "__main__": run(_parse_args()) - diff --git a/final/q1/compare_v2.py b/final/q1/compare_v2.py new file mode 100644 index 0000000..cb54d8c --- /dev/null +++ b/final/q1/compare_v2.py @@ -0,0 +1,315 @@ +"""Evaluate the Q1 V2 B0-B4 branches with leakage-safe video-group folds.""" +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error +from sklearn.model_selection import GroupKFold + +from . import compare_models as cm +from . import q1_io + +Q1 = Path(__file__).resolve().parent +OUT = Q1.parent / "output" / "q1" / "model_comparison_v2" +MODALITIES = ("text", "audio", "vision", "speech") +METHODS = ("B0", "B1", "B2", "B3", "B4") + + +def digest(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def edges_from_bounds(bounds: np.ndarray) -> np.ndarray: + return np.concatenate((bounds[:1, 0], bounds[:, 1])).astype(np.float32) + + +def as_view(views: dict[str, dict[str, np.ndarray]], bounds: np.ndarray) -> cm.View: + return cm.View( + features={name: views[name]["x"].astype(np.float32) for name in MODALITIES}, + observed={name: views[name]["mask"].astype(bool) for name in MODALITIES}, + coverage={name: views[name]["coverage"].astype(np.float32) for name in MODALITIES}, + edges=edges_from_bounds(bounds), + ) + + +def load_rows(records: list[dict[str, Any]]) -> tuple[list[dict[str, Any]], list[cm.View], list[dict[str, np.ndarray]], list[cm.View], list[cm.View]]: + rows, views, dynamics = [], [], [] + feature_ids = {json.loads(line)["sample_id"] for line in (q1_io.FEATURE_DIR / "manifest_q1.jsonl").read_text(encoding="utf-8").splitlines()} + record_ids = [record["sample_id"] for record in records] + if len(set(record_ids)) != len(record_ids) or set(record_ids) != feature_ids: + raise ValueError("Q1 labels and V2 feature manifest keys are not a one-to-one match") + for record in records: + sample = q1_io.load_sample(record["sample_id"]) + base = {name: q1_io.get_view(sample, name, "sec") for name in ("text", "audio", "vision")} + base["speech"] = q1_io.get_view(sample, "speech", "sec") + vision_b1 = q1_io.get_view(sample, "vision", "sec", geometry_aware=True) + b1 = {**base, "vision": vision_b1} + posterior = {**b1, "text": q1_io.get_view(sample, "text", "posterior")} + rows.append({**record, "duration_s": float(sample["_meta"]["duration_s"])}) + # Every model branch shares the same physical primary time bounds. + bounds = sample["views_sec_time_bounds_s"].astype(np.float32) + views.append(as_view(base, bounds)) + views.append(as_view(b1, bounds)) + views.append(as_view(posterior, bounds)) + with np.load(sample["_path"], allow_pickle=False) as artifact: + dynamics.append({"x": artifact["views_dynamics_x"].astype(np.float32), "mask": artifact["views_dynamics_mask"].astype(bool), "bounds": artifact["views_dynamics_time_bounds_s"].astype(np.float32)}) + # Flatten rows as [B0,B1,B2] per sample into three separate lists. + n = len(records) + return rows, [views[3*i] for i in range(n)], dynamics, [views[3*i+1] for i in range(n)], [views[3*i+2] for i in range(n)] + + +def fit_scaler(views: list[cm.View], indices: np.ndarray, mode: str) -> dict[str, tuple[np.ndarray, np.ndarray]]: + scalers = {} + for name in MODALITIES: + d = views[int(indices[0])].features[name].shape[1] + center = np.zeros(d, np.float64) + scale = np.ones(d, np.float64) + for j in range(d): + chunks = [views[int(i)].features[name][:, j][views[int(i)].observed[name][:, j]] for i in indices] + chunks = [x for x in chunks if len(x)] + if not chunks: + continue + values = np.concatenate(chunks).astype(np.float64) + if mode == "robust": + center[j] = np.median(values) + mad = 1.4826 * np.median(np.abs(values - center[j])) + scale[j] = mad if mad > 1e-8 else (np.std(values) if np.std(values) > 1e-8 else 1.0) + else: + center[j] = np.mean(values) + scale[j] = np.std(values) if np.std(values) > 1e-8 else 1.0 + scalers[name] = (center.astype(np.float32), scale.astype(np.float32)) + return scalers + + +def scale_view(view: cm.View, scalers: dict[str, tuple[np.ndarray, np.ndarray]], robust: bool) -> cm.View: + result = {} + for name in MODALITIES: + center, scale = scalers[name] + values = (view.features[name] - center) / scale + if robust: + values = np.clip(values, -8.0, 8.0) + result[name] = np.where(view.observed[name], values, 0.0).astype(np.float32) + return cm.View(result, view.observed, view.coverage, view.edges) + + +def pool_progress(view: cm.View, include_speech: bool) -> np.ndarray: + duration = float(view.edges[-1]) + progress = np.linspace(0.0, duration, 6) + centers = (view.edges[:-1] + view.edges[1:]) / 2 + lengths = np.diff(view.edges) + pooled = [] + names = ("text", "audio", "vision") + (("speech",) if include_speech else ()) + for name in names: + values = view.features[name] + mask = view.observed[name] + coverage = view.coverage[name] + segments = [] + for left, right in zip(progress[:-1], progress[1:]): + overlap = np.maximum(0.0, np.minimum(view.edges[1:], right) - np.maximum(view.edges[:-1], left)) + weight = overlap[:, None] * coverage * mask + den = weight.sum(axis=0) + out = np.zeros(values.shape[1], np.float32) + good = den > 0 + if good.any(): + out[good] = (values * weight).sum(axis=0)[good] / den[good] + segments.append(out) + pooled.append(np.stack(segments).reshape(-1)) + return np.concatenate(pooled).astype(np.float32) + + +def pool_dynamics(item: dict[str, np.ndarray], duration: float) -> np.ndarray: + bounds = item["bounds"] + values = item["x"] + valid = item["mask"] + centers = bounds[:, 0].mean(axis=1) + grid_length = bounds[:, 0, 1] - bounds[:, 0, 0] + progress = np.linspace(0.0, duration, 6) + result = [] + for left, right in zip(progress[:-1], progress[1:]): + sel = (centers >= left) & (centers < right) + for branch in range(2): + ok = sel & valid[:, branch] + weights = grid_length[ok] + if ok.any() and weights.sum() > 0: + result.extend(np.average(values[ok, branch], axis=0, weights=weights).tolist()) + result.append(float(min(1.0, weights.sum() / max(right - left, 1e-8)))) + else: + result.extend([0.0] * 6) + result.append(0.0) + return np.asarray(result, np.float32) + + +def fit_dynamic_scaler(values: np.ndarray, indices: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + center = np.zeros(values.shape[1], np.float32) + scale = np.ones(values.shape[1], np.float32) + is_signature = np.ones(values.shape[1], bool) + # In each 14-column block, the final column is a measured coverage fraction. + is_signature[np.arange(6, values.shape[1], 7)] = False + for j in np.flatnonzero(is_signature): + coverage_j = (j // 7) * 7 + 6 + observed = values[indices, coverage_j] > 0 + vals = values[indices[observed], j] + if not len(vals): + continue + center[j] = np.median(vals) + mad = 1.4826 * np.median(np.abs(vals - center[j])) + scale[j] = mad if mad > 1e-8 else (np.std(vals) if np.std(vals) > 1e-8 else 1.0) + return center, scale, is_signature + + +def scale_dynamic(values: np.ndarray, params: tuple[np.ndarray, np.ndarray, np.ndarray]) -> np.ndarray: + center, scale, signature = params + result = values.copy() + result[:, signature] = np.clip((result[:, signature] - center[signature]) / scale[signature], -8.0, 8.0) + # Preserve zeros in missing signature blocks. + for block in range(10): + coverage_col = block * 7 + 6 + result[result[:, coverage_col] <= 0, block * 7:block * 7 + 6] = 0.0 + return result.astype(np.float32) + + +def metrics(y_class: np.ndarray, p_class: np.ndarray, y_value: np.ndarray, p_value: np.ndarray) -> dict[str, float]: + pearson = float(np.corrcoef(y_value, p_value)[0, 1]) if np.std(p_value) > 1e-12 else math.nan + return { + "accuracy": float(accuracy_score(y_class, p_class)), + "macro_f1": float(f1_score(y_class, p_class, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(mean_absolute_error(y_value, p_value)), + "rmse": float(np.sqrt(mean_squared_error(y_value, p_value))), + "pearson": pearson, + } + + +def write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + if not rows: + return + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8-sig", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def main() -> None: + global OUT + parser = argparse.ArgumentParser() + parser.add_argument("--bootstrap-repeats", type=int, default=2000) + parser.add_argument("--data-dir", type=Path, default=cm.DATA_DIR) + parser.add_argument("--feature-dir", type=Path, default=q1_io.FEATURE_DIR) + parser.add_argument("--output-dir", type=Path, default=OUT) + args = parser.parse_args() + cm.DATA_DIR = args.data_dir.resolve() + cm.LABEL_FILE = cm.DATA_DIR / "label-100.xlsx" + q1_io.FEATURE_DIR = args.feature_dir.resolve() + OUT = args.output_dir.resolve() + records = cm._read_labels(cm.LABEL_FILE) + rows, b0, dynamics, b1, b2 = load_rows(records) + y_class = np.asarray([r["polarity"] for r in rows], np.int64) + y_value = np.asarray([r["sentiment"] for r in rows], np.float64) + groups = np.asarray([r["video_id"] for r in rows], str) + durations = np.asarray([r["duration_s"] for r in rows], np.float64) + dynamic_values = np.stack([pool_dynamics(d, durations[i]) for i, d in enumerate(dynamics)]) + variants = {"B0": b0, "B1": b1, "B2": b2, "B3": b1, "B4": b1} + splitter = GroupKFold(n_splits=cm.N_FOLDS) + folds = list(splitter.split(np.zeros(len(rows)), y_class, groups)) + class_pred = {m: np.full(len(rows), -1, np.int64) for m in METHODS} + value_pred = {m: np.full(len(rows), np.nan, np.float64) for m in METHODS} + fold_rows = [] + split_rows = [] + for fold, (train_idx, valid_idx) in enumerate(folds, start=1): + if set(groups[train_idx]) & set(groups[valid_idx]): + raise ValueError("video group leakage in Q1 folds") + for i in valid_idx: + split_rows.append({"sample_id": rows[i]["sample_id"], "video_id": groups[i], "fold": fold, "split": "valid_oof"}) + for method in METHODS: + source = variants[method] + robust = method != "B0" + scaler = fit_scaler(source, train_idx, "robust" if robust else "standard") + scaled = [scale_view(v, scaler, robust) for v in source] + include_speech = method == "B4" + x_train = np.stack([pool_progress(scaled[i], include_speech) for i in train_idx]) + x_valid = np.stack([pool_progress(scaled[i], include_speech) for i in valid_idx]) + if method == "B3": + dyn_scaler = fit_dynamic_scaler(dynamic_values, train_idx) + dyn_scaled = scale_dynamic(dynamic_values, dyn_scaler) + x_train = np.column_stack((x_train, dyn_scaled[train_idx])) + x_valid = np.column_stack((x_valid, dyn_scaled[valid_idx])) + clf = LogisticRegression(C=0.05, max_iter=2500, solver="lbfgs", random_state=cm.SEED) + clf.fit(x_train, y_class[train_idx]) + p_cls = clf.predict(x_valid) + reg = Ridge(alpha=25.0, solver="lsqr") + reg.fit(x_train, y_value[train_idx]) + p_val = np.clip(reg.predict(x_valid), -3.0, 3.0) + class_pred[method][valid_idx] = p_cls + value_pred[method][valid_idx] = p_val + met = metrics(y_class[valid_idx], p_cls, y_value[valid_idx], p_val) + fold_rows.append({"method": method, "fold": fold, "train_samples": len(train_idx), "valid_samples": len(valid_idx), "train_video_groups": len(set(groups[train_idx])), "valid_video_groups": len(set(groups[valid_idx])), "feature_dimension": int(x_train.shape[1]), **met}) + print(f"fold {fold}/{cm.N_FOLDS} complete", flush=True) + + summary = [] + prediction_rows = [] + for method in METHODS: + overall = metrics(y_class, class_pred[method], y_value, value_pred[method]) + fold = [r for r in fold_rows if r["method"] == method] + row = {"method": method, "sample_count": len(rows), "video_group_count": len(set(groups)), **{f"oof_{k}": v for k, v in overall.items()}} + for key in ("accuracy", "macro_f1", "mae", "rmse", "pearson"): + vals = np.asarray([r[key] for r in fold], np.float64) + row[f"fold_{key}_mean"] = float(np.nanmean(vals)) + row[f"fold_{key}_sd"] = float(np.nanstd(vals, ddof=1)) + summary.append(row) + for i, record in enumerate(rows): + line = {"sample_id": record["sample_id"], "video_id": record["video_id"], "clip_id": record["clip_id"], "true_polarity": int(y_class[i]), "true_sentiment": float(y_value[i])} + for method in METHODS: + line[f"{method}_predicted_polarity"] = int(class_pred[method][i]) + line[f"{method}_predicted_sentiment"] = float(value_pred[method][i]) + prediction_rows.append(line) + + rng = np.random.default_rng(cm.SEED + 880) + unique_groups = np.unique(groups) + group_ix = {g: np.flatnonzero(groups == g) for g in unique_groups} + deltas = [] + reference = "B1" + for method in METHODS: + if method == reference: + continue + samples = {metric: [] for metric in ("accuracy", "macro_f1", "mae", "rmse", "pearson")} + for _ in range(args.bootstrap_repeats): + selected_groups = rng.choice(unique_groups, size=len(unique_groups), replace=True) + ix = np.concatenate([group_ix[g] for g in selected_groups]) + base = metrics(y_class[ix], class_pred[reference][ix], y_value[ix], value_pred[reference][ix]) + alt = metrics(y_class[ix], class_pred[method][ix], y_value[ix], value_pred[method][ix]) + for key in samples: + samples[key].append(alt[key] - base[key]) + for key, values in samples.items(): + vals = np.asarray(values, np.float64) + deltas.append({"comparison": f"{method}-{reference}", "metric": key, "reference": reference, "estimate_delta": float(np.nanmedian(vals)), "ci95_low": float(np.nanpercentile(vals, 2.5)), "ci95_high": float(np.nanpercentile(vals, 97.5)), "bootstrap_repeats": args.bootstrap_repeats, "unit": "video_id"}) + + OUT.mkdir(parents=True, exist_ok=True) + write_csv(OUT / "comparison_summary.csv", summary) + write_csv(OUT / "fold_metrics.csv", fold_rows) + write_csv(OUT / "oof_predictions.csv", prediction_rows) + write_csv(OUT / "split_assignments.csv", sorted(split_rows, key=lambda r: r["sample_id"])) + write_csv(OUT / "group_bootstrap_deltas.csv", deltas) + manifest = {"version": "E题V2 Q1 B0-B4", "samples": len(rows), "video_groups": int(len(unique_groups)), "folds": cm.N_FOLDS, "grouping": "video_id GroupKFold; train/valid group disjoint", "scalers": "B0 train-fold mean/std; B1-B4 train-fold median/MAD; no held-fold fitting", "classifier": "LogisticRegression C=0.05", "regressor": "Ridge alpha=25", "branches": {"B0": "quality-weighted physical projection", "B1": "B0 plus train-fold robust scale and SO(3)/unit-gaze aggregation", "B2": "B1 plus CTC forward-backward text occupancy", "B3": "B1 plus native-row level-2 dynamics", "B4": "B1 plus frozen Wav2Vec2 auxiliary representation"}, "B5": "query H_time audit only; not predictive score", "B6": "content probe disabled", "feature_manifest_sha256": digest(q1_io.FEATURE_DIR / "feature_manifest.json"), "source_run_manifest": "features_v2/manifest_q1.jsonl", "bootstrap_repeats": args.bootstrap_repeats} + (OUT / "run_manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + report = ["# Q1 V2 B0-B4 对照", "", "本报告使用 `features_v2/` 的 100 个样本，以 `video_id` 做五折 GroupKFold。B0–B4 的缩放器只在各折训练部分拟合。分数是情感信息探针，不是真实对齐边界准确率。", "", "| 分支 | OOF Accuracy | OOF Macro-F1 | OOF MAE | OOF RMSE | OOF Pearson |", "|---|---:|---:|---:|---:|---:|"] + for row in summary: + report.append(f"| {row['method']} | {row['oof_accuracy']:.4f} | {row['oof_macro_f1']:.4f} | {row['oof_mae']:.4f} | {row['oof_rmse']:.4f} | {row['oof_pearson']:.4f} |") + report += ["", "B5 通过词查询 CSR 矩阵进行物理来源核查；没有人工边界真值，因此未报告边界误差或后验校准率。B6 内容探针未启用。视觉 17 维来自 OpenFace 2.2.0 AU 输出，不是 MediaPipe 代理。"] + (OUT / "report.md").write_text("\n".join(report) + "\n", encoding="utf-8") + print("Q1 V2 comparison:", [(r["method"], round(r["oof_accuracy"], 4), round(r["oof_macro_f1"], 4), round(r["oof_mae"], 4)) for r in summary], flush=True) + + +if __name__ == "__main__": + main() diff --git a/final/q1/q1_io.py b/final/q1/q1_io.py new file mode 100644 index 0000000..5f81979 --- /dev/null +++ b/final/q1/q1_io.py @@ -0,0 +1,230 @@ +"""Read and derive Q1 V2 feature views from the native-source artifacts.""" +from __future__ import annotations + +import json +import re +from pathlib import Path +from typing import Any + +import numpy as np +from scipy import sparse + +Q1_DIR = Path(__file__).resolve().parent +FEATURE_DIR = Q1_DIR.parent / "output" / "q1" / "features_v2" +MODALITIES = ("text", "audio", "vision") +FEATURE_DIMS = {"text": 768, "audio": 74, "vision": 35, "speech": 768} + + +def load_sample(sample_id: str, feature_dir: Path = FEATURE_DIR) -> dict[str, Any]: + """Load one sample by `video_id/clip_id` and return arrays plus metadata.""" + manifest = feature_dir / "manifest_q1.jsonl" + found = None + for line in manifest.read_text(encoding="utf-8").splitlines(): + row = json.loads(line) + if row["sample_id"] == sample_id: + found = row + break + if found is None: + raise KeyError(f"sample_id not found: {sample_id}") + path = feature_dir / Path(found["feature_path"]).name + with np.load(path, allow_pickle=False) as archive: + result = {name: archive[name] for name in archive.files} + result["_path"] = path + result["_manifest"] = found + result["_meta"] = json.loads(str(result["meta_json"])) + return result + + +def csr_from_sample(sample: dict[str, Any], prefix: str) -> sparse.csr_matrix: + """Reconstruct a stored sparse alignment/query map.""" + shape = tuple(int(v) for v in sample[f"{prefix}_shape"]) + return sparse.csr_matrix( + ( + sample[f"{prefix}_data"].astype(np.float32), + sample[f"{prefix}_indices"].astype(np.int32), + sample[f"{prefix}_indptr"].astype(np.int32), + ), + shape=shape, + ) + + +def _native(sample: dict[str, Any], modality: str) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + if modality == "text": + return ( + sample["native_text_features"].astype(np.float32), + np.broadcast_to(sample["native_text_observed"][:, None], sample["native_text_features"].shape), + sample["native_text_intervals"].astype(np.float32), + sample["native_text_quality_effective"].astype(np.float32), + sample["native_text_quality_available"].astype(bool), + ) + if modality in ("audio", "vision"): + return ( + sample[f"native_{modality}_features"].astype(np.float32), + sample[f"native_{modality}_mask"].astype(bool), + sample[f"native_{modality}_intervals"].astype(np.float32), + sample[f"native_{modality}_quality"].astype(np.float32), + np.asarray(sample[f"native_{modality}_quality_available"], dtype=bool), + ) + if modality == "speech": + meta = sample["_meta"] + safe = lambda value: re.sub(r"[^A-Za-z0-9_.-]+", "_", str(value)) + path = Q1_DIR / "cache" / "native" / f"{safe(meta['video_id'])}__{safe(meta['clip_id'])}.npz" + if not path.is_file(): + raise FileNotFoundError(f"B4 speech source cache is unavailable: {path}") + with np.load(path, allow_pickle=False) as archive: + values = archive["speech_features"].astype(np.float32) + times = sample["native_ctc_times"].astype(np.float32) + if len(values) != len(times): + raise ValueError("B4 speech rows do not match the stored CTC timestamps") + step = 320.0 / 16000.0 + intervals = np.column_stack((np.maximum(0.0, times - step / 2), np.minimum(float(meta["duration_s"]), times + step / 2))).astype(np.float32) + return values, np.isfinite(values), intervals, np.ones(len(values), np.float32), np.zeros(len(values), bool) + raise ValueError(f"unknown modality: {modality}") + + +def _aggregate( + values: np.ndarray, + observed: np.ndarray, + source_intervals: np.ndarray, + quality: np.ndarray, + target_intervals: np.ndarray, + quality_available: np.ndarray | None = None, +) -> dict[str, np.ndarray]: + """Quality-weight B0 means while keeping physical coverage and availability separate.""" + n, d = len(target_intervals), values.shape[1] + mean = np.zeros((n, d), np.float32) + var = np.zeros((n, d), np.float32) + count = np.zeros((n, d), np.uint8) + coverage = np.zeros((n, d), np.float32) + qbar = np.ones((n, d), np.float32) + qavail = np.zeros((n, d), np.float32) + available = np.zeros(len(values), bool) if quality_available is None else np.asarray(quality_available, bool) + mask = np.zeros((n, d), bool) + for i, (left, right) in enumerate(target_intervals): + width = float(right - left) + if width <= 0 or not len(source_intervals): + continue + overlap = np.maximum( + 0.0, + np.minimum(source_intervals[:, 1], right) - np.maximum(source_intervals[:, 0], left), + ) + physical = overlap[:, None] * observed + weighted = physical * quality[:, None] + total = weighted.sum(axis=0) + valid = total > 0 + if valid.any(): + weighted_sum = (values * weighted).sum(axis=0) + weighted_sq = (np.square(values) * weighted).sum(axis=0) + mean[i, valid] = (weighted_sum[valid] / total[valid]).astype(np.float32) + var[i, valid] = np.maximum(0.0, weighted_sq[valid] / total[valid] - mean[i, valid] ** 2) + mask[i, valid] = True + physical_total = physical.sum(axis=0) + physical_valid = physical_total > 0 + qbar[i, physical_valid] = weighted.sum(axis=0)[physical_valid] / physical_total[physical_valid] + qavail[i, physical_valid] = (physical * available[:, None]).sum(axis=0)[physical_valid] / physical_total[physical_valid] + coverage[i] = np.minimum(1.0, physical_total / width) + count[i] = np.minimum(255, ((overlap[:, None] > 0) & observed).sum(axis=0)).astype(np.uint8) + return { + "x": mean.astype(np.float16), + "var": var.astype(np.float32), + "count": count, + "coverage": coverage, + "mask": mask, + "quality_mean": qbar.astype(np.float32), + "quality_available_fraction": qavail.astype(np.float32), + } + + +def get_view( + sample: dict[str, Any], + modality: str, + view: str = "sec", + context_s: float | None = None, + geometry_aware: bool = False, +) -> dict[str, np.ndarray]: + """Return `sec`, `phase50`, `word`, `multi`, or `posterior` features. + + `multi` is always aggregated from native rows around each 0.1 s bin midpoint. + For it, set `context_s` to one of 0.1, 0.3, or 0.7. + """ + if modality not in (*MODALITIES, "speech"): + raise ValueError(f"unknown modality: {modality}") + if view in ("sec", "phase50", "word"): + bounds = sample[f"views_{view}_time_bounds_s"].astype(np.float32) + if modality == "speech": + values, observed, intervals, quality, available = _native(sample, modality) + return {**_aggregate(values, observed, intervals, quality, bounds, available), "time_bounds_s": bounds} + prefix = f"views_{view}_{modality}_" + result = { + "x": sample[prefix + "x"], + "var": sample[prefix + "var"], + "count": sample[prefix + "count"], + "coverage": sample[prefix + "coverage_u8"].astype(np.float32) / 255.0, + "mask": sample[prefix + "mask"].astype(bool), + "quality_mean": sample[prefix + "qbar"].astype(np.float32), + "quality_available_fraction": sample[prefix + "quality_available_fraction"].astype(np.float32), + "time_bounds_s": bounds, + } + if view == "sec" and modality == "vision" and geometry_aware: + result["x"] = sample["views_sec_vision_b1_x"] + result["mask"] = sample["views_sec_vision_b1_mask"].astype(bool) + return result + if view == "multi": + if context_s not in (0.1, 0.3, 0.7): + raise ValueError("context_s must be one of 0.1, 0.3, or 0.7") + centers = sample["views_sec_time_bounds_s"].astype(np.float32).mean(axis=1) + duration = float(sample["_meta"]["duration_s"]) + bounds = np.column_stack((np.maximum(0.0, centers - context_s / 2), np.minimum(duration, centers + context_s / 2))).astype(np.float32) + values, observed, intervals, quality, available = _native(sample, modality) + return {**_aggregate(values, observed, intervals, quality, bounds, available), "time_bounds_s": bounds} + if view == "posterior": + if modality != "text": + raise ValueError("posterior projection is defined for text only") + return posterior_text_view(sample) + raise ValueError(f"unknown view: {view}") + + +def posterior_text_view(sample: dict[str, Any]) -> dict[str, np.ndarray]: + """Project CTC forward-backward word occupancy directly to the 0.1 s grid.""" + edges = sample["views_sec_time_bounds_s"].astype(np.float32) + words = sample["native_text_features"].astype(np.float32) + word_ok = sample["native_text_observed"].astype(bool) + occupancy = sample["native_ctc_occupancy"].astype(np.float32) + times = sample["native_ctc_times"].astype(np.float32) + if occupancy.ndim != 2 or occupancy.shape[1] != len(words): + raise ValueError("CTC occupancy word dimension does not match the native text rows") + step = 320.0 / 16000.0 + half = step / 2 + duration = float(sample["_meta"]["duration_s"]) + source = np.column_stack((np.maximum(0.0, times - half), np.minimum(duration, times + half))).astype(np.float32) + n, d = len(edges), words.shape[1] + result = np.zeros((n, d), np.float32) + var = np.zeros((n, d), np.float32) + count = np.zeros(n, np.uint8) + coverage = np.zeros(n, np.float32) + mask = np.zeros(n, bool) + mass_by_word = np.zeros((n, len(words)), np.float32) + for i, (left, right) in enumerate(edges): + overlap = np.maximum(0.0, np.minimum(source[:, 1], right) - np.maximum(source[:, 0], left)) + mass_by_word[i] = (occupancy * overlap[:, None]).sum(axis=0) + w = mass_by_word[i] * word_ok + total = float(w.sum()) + if total > 0: + normalized = w / total + result[i] = normalized @ words + centered = words - result[i] + var[i] = (normalized[:, None] * np.square(centered)).sum(axis=0) + mask[i] = True + count[i] = min(255, int(np.count_nonzero(w > 0))) + coverage[i] = min(1.0, total / max(float(right - left), 1e-8)) + return { + "x": result.astype(np.float16), + "var": var, + "count": np.broadcast_to(count[:, None], result.shape).copy(), + "coverage": np.broadcast_to(coverage[:, None], result.shape).copy(), + "mask": np.broadcast_to(mask[:, None], result.shape).copy(), + "quality_mean": np.ones_like(result, dtype=np.float32), + "quality_available_fraction": np.zeros_like(result, dtype=np.float32), + "word_posterior_mass": mass_by_word, + "time_bounds_s": edges, + } diff --git a/final/q1/run_openface.ps1 b/final/q1/run_openface.ps1 new file mode 100644 index 0000000..27de24a --- /dev/null +++ b/final/q1/run_openface.ps1 @@ -0,0 +1,38 @@ +param( + [Parameter(Mandatory = $true)] + [string]$VideoRelativePath, + [Parameter(Mandatory = $true)] + [string]$OutputName +) + +$ErrorActionPreference = 'Stop' +$q1Directory = $PSScriptRoot +$repositoryDirectory = Split-Path $q1Directory -Parent +$packageDirectory = Join-Path $q1Directory 'cache/openface/OpenFace_2.2.0_win_x64' +$executable = Join-Path $packageDirectory 'FeatureExtraction.exe' +if ([System.IO.Path]::IsPathRooted($VideoRelativePath)) { + $videoPath = $VideoRelativePath +} +else { + $videoPath = Join-Path $repositoryDirectory ($VideoRelativePath -replace '/', '\') +} +$outputDirectory = Join-Path $q1Directory 'cache/openface_outputs' +$null = New-Item -ItemType Directory -Force -Path $outputDirectory + +if (-not (Test-Path -LiteralPath $executable -PathType Leaf)) { + throw "OpenFace 2.2.0 executable not found: $executable" +} +if (-not (Test-Path -LiteralPath $videoPath -PathType Leaf)) { + throw "Input video not found: $videoPath" +} + +Push-Location $packageDirectory +try { + & $executable -mloc model/main_clnf_general.txt -f $videoPath -out_dir $outputDirectory -of $OutputName -2Dfp -pose -aus -gaze + if ($LASTEXITCODE -ne 0) { + throw "OpenFace FeatureExtraction failed with exit code $LASTEXITCODE for $VideoRelativePath" + } +} +finally { + Pop-Location +} diff --git a/final/q1/test_projection.py b/final/q1/test_projection.py new file mode 100644 index 0000000..67e16f7 --- /dev/null +++ b/final/q1/test_projection.py @@ -0,0 +1,25 @@ +"""Projection invariants from E题V2: quality weights means, not physical coverage.""" +from __future__ import annotations + +import unittest + +import numpy as np + +from . import compare_models as cm + + +class ProjectionCoverageTests(unittest.TestCase): + def test_quality_changes_mean_but_not_physical_coverage(self) -> None: + values = np.asarray([[0.0], [10.0]], dtype=np.float32) + observed = np.ones_like(values, dtype=bool) + intervals = np.asarray([[0.0, 0.5], [0.5, 1.0]], dtype=np.float32) + qualities = np.asarray([1.0, 1e-8], dtype=np.float32) + edges = np.asarray([0.0, 1.0], dtype=np.float32) + projected, mask, coverage = cm._project_rows(values, observed, intervals, qualities, edges) + self.assertTrue(bool(mask[0, 0])) + self.assertAlmostEqual(float(projected[0, 0]), 10.0 * 1e-8 / (1.0 + 1e-8), places=6) + self.assertAlmostEqual(float(coverage[0, 0]), 1.0, places=6) + + +if __name__ == "__main__": + unittest.main() diff --git a/final/q1/visualize_alignment.py b/final/q1/visualize_alignment.py new file mode 100644 index 0000000..4b6ae51 --- /dev/null +++ b/final/q1/visualize_alignment.py @@ -0,0 +1,229 @@ +"""Render a selected Q1 word-to-source alignment example.""" +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any + +import matplotlib + +matplotlib.use("Agg") +matplotlib.rcParams["font.family"] = ["FandolHei", "DejaVu Sans"] +matplotlib.rcParams["axes.unicode_minus"] = False +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import PowerNorm + +Q1_DIR = Path(__file__).resolve().parent +from .q1_io import csr_from_sample, load_sample + + +def time_edges(centers: np.ndarray, intervals: np.ndarray, duration_s: float) -> np.ndarray: + """Make non-overlapping display bins centered on each native source row.""" + centers = np.asarray(centers, dtype=np.float64).reshape(-1) + intervals = np.asarray(intervals, dtype=np.float64) + if not len(centers): + return np.asarray([0.0, duration_s], dtype=np.float64) + if len(centers) == 1: + half_width = max(float(intervals[0, 1] - intervals[0, 0]) / 2, 1e-3) + return np.asarray( + [max(0.0, centers[0] - half_width), min(duration_s, centers[0] + half_width)], + dtype=np.float64, + ) + if np.any(np.diff(centers) <= 0): + raise ValueError("native source timestamps must be strictly increasing") + middle = (centers[:-1] + centers[1:]) / 2 + first = max(0.0, centers[0] - (middle[0] - centers[0])) + last = min(duration_s, centers[-1] + (centers[-1] - middle[-1])) + return np.concatenate(([first], middle, [last])) + + +def read_records() -> list[dict[str, Any]]: + path = Q1_DIR / "features_v2" / "manifest_q1.jsonl" + return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + + +def choose_sample(sample_id: str) -> tuple[dict[str, Any], dict[str, Any], Any, Any]: + record = next((item for item in read_records() if item["sample_id"] == sample_id), None) + if record is None: + raise ValueError(f"sample_id not found in Q1 manifest: {sample_id}") + sample = load_sample(sample_id) + audio_map = csr_from_sample(sample, "query_word_audio_H_time") + vision_map = csr_from_sample(sample, "query_word_vision_H_time") + if not audio_map.nnz or not vision_map.nnz: + raise ValueError(f"sample has no valid audio or vision query rows: {sample_id}") + return record, sample, audio_map, vision_map + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--sample-id", default="-iRBcNs9oI8/8") + parser.add_argument("--output", type=Path, default=None) + args = parser.parse_args() + + record, sample, audio_map, vision_map = choose_sample(args.sample_id) + words = [str(word) for word in sample["native_text_words"]] + if audio_map.shape[0] != len(words) or vision_map.shape[0] != len(words): + raise ValueError("query matrix word rows do not match the stored transcript") + duration_s = float(sample["_meta"]["duration_s"]) + + modalities = [ + ( + "原始音频位置", + audio_map, + sample["native_audio_intervals"], + "音频窗中心时间 (s)", + "音频窗", + ), + ( + "原始视频帧位置", + vision_map, + sample["native_vision_intervals"], + "视频帧中心时间 (s)", + "视频帧", + ), + ] + matrices = [item[1].toarray().astype(np.float32) for item in modalities] + for matrix in (audio_map, vision_map): + row_sums = np.asarray(matrix.sum(axis=1)).reshape(-1) + populated = row_sums > 0 + if populated.any() and not np.allclose(row_sums[populated], 1.0, atol=2e-3): + raise ValueError("each populated word-query row should sum to one") + nonzero = np.concatenate([matrix[matrix > 0] for matrix in matrices if np.any(matrix > 0)]) + color_max = max(float(nonzero.max()), 1e-6) + + height = max(10.5, min(26.0, 4.0 + 0.22 * len(words))) + tick_size = max(5.5, min(8.5, 8.8 - 0.045 * len(words))) + fig = plt.figure(figsize=(15, height)) + grid = fig.add_gridspec(4, 1, height_ratios=(4.0, 1.35, 4.0, 1.35), hspace=0.35) + y_edges = np.arange(len(words) + 1, dtype=np.float64) + text_intervals = np.asarray(sample["native_text_intervals"], dtype=np.float64) + text_valid = text_intervals[:, 1] > text_intervals[:, 0] + text_centers = text_intervals.mean(axis=1) + meshes = [] + heat_axes = [] + + for plot_index, ((title, _sparse_matrix, intervals, _xlabel, row_name), matrix) in enumerate(zip(modalities, matrices)): + axis = fig.add_subplot(grid[2 * plot_index]) + text_axis = fig.add_subplot(grid[2 * plot_index + 1], sharex=axis) + heat_axes.append(axis) + intervals = np.asarray(intervals, dtype=np.float64) + centers = intervals.mean(axis=1) + x_edges = time_edges(centers, intervals, duration_s) + mesh = axis.pcolormesh( + x_edges, + y_edges, + matrix, + shading="flat", + cmap="magma", + norm=PowerNorm(gamma=0.5, vmin=0.0, vmax=color_max), + rasterized=True, + ) + meshes.append(mesh) + axis.set_xlim(0.0, duration_s) + axis.set_ylim(len(words), 0) + axis.set_xticks([]) + axis.set_yticks([]) + axis.set_xlabel("") + axis.set_ylabel("") + axis.grid(False) + axis.set_frame_on(False) + for spine in axis.spines.values(): + spine.set_visible(False) + axis.text( + 0.0, + 1.025, + f"{title}（列为{row_name}；有效源行的词内权重和为 1）", + transform=axis.transAxes, + ha="left", + va="bottom", + fontsize=11, + clip_on=False, + ) + for word_index, word in enumerate(words): + axis.text( + -0.012, + word_index + 0.5, + word, + transform=axis.get_yaxis_transform(), + ha="right", + va="center", + fontsize=tick_size, + clip_on=False, + ) + + # Place actual transcript words along the source-time axis using the + # center of each stored CTC word interval. + text_axis.set_xlim(0.0, duration_s) + text_axis.set_ylim(0.0, 1.0) + text_axis.set_xticks([]) + text_axis.set_yticks([]) + text_axis.set_frame_on(False) + for spine in text_axis.spines.values(): + spine.set_visible(False) + for center, word, valid in zip(text_centers, words, text_valid): + if valid: + text_axis.text( + center, + 0.96, + word, + rotation=90, + ha="center", + va="top", + fontsize=tick_size, + clip_on=False, + ) + text_axis.grid(False) + + fig.subplots_adjust(left=0.17, right=0.88, top=0.91, bottom=0.08) + colorbar = fig.colorbar(meshes[0], ax=heat_axes, fraction=0.025, pad=0.02) + colorbar.ax.set_title("重叠\n权重", fontsize=8, pad=8) + sample_id = record["sample_id"] + fig.suptitle( + f"词到原始音频/视频位置查询矩阵｜样本 {sample_id}", + fontsize=14, + y=0.97, + ) + fig.text( + 0.17, + 0.02, + "横轴为真实源时间 (s)，每个文本标注位于对应词区间中心。颜色表示每个词内归一化的物理时间交叠权重，不是学习得到的内容相似度。", + fontsize=8, + ha="left", + ) + + output = args.output or (Q1_DIR.parent / "output" / "q1" / "alignment_query_example.png") + output = output.resolve() + output.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(output, dpi=220, facecolor="white") + plt.close(fig) + + metadata = { + "sample_id": sample_id, + "selection": "manually selected example with valid audio and vision query maps", + "duration_s": duration_s, + "word_count": len(words), + "words": words, + "audio_query_shape": list(audio_map.shape), + "audio_query_nnz": int(audio_map.nnz), + "audio_query_mapped_words": int(np.count_nonzero(np.asarray(audio_map.sum(axis=1)).reshape(-1))), + "vision_query_shape": list(vision_map.shape), + "vision_query_nnz": int(vision_map.nnz), + "vision_query_mapped_words": int(np.count_nonzero(np.asarray(vision_map.sum(axis=1)).reshape(-1))), + "audio_query_feature_validity_channel": "log_energy (audio feature dimension 66)", + "vision_query_feature_validity_channel": "OpenFace AU12 intensity (vision feature dimension 10)", + "alignment_semantics": "per-word normalized overlap between stored CTC word intervals and native source intervals; no learned content similarity", + "coordinate_axes_shown": False, + "color_scale": "shared square-root intensity transform for visibility; original overlap weights retained", + "word_label_positions": "actual transcript labels placed at their CTC word-interval centers on the source-time axis", + "image": str(output), + } + metadata_path = output.with_suffix(".json") + metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + print(json.dumps(metadata, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/final/q2/__init__.py b/final/q2/__init__.py new file mode 100644 index 0000000..23139b5 --- /dev/null +++ b/final/q2/__init__.py @@ -0,0 +1 @@ +"""Q2 training, comparison, and inference pipelines.""" diff --git a/final/q2/deep_learning/__init__.py b/final/q2/deep_learning/__init__.py new file mode 100644 index 0000000..d2b231f --- /dev/null +++ b/final/q2/deep_learning/__init__.py @@ -0,0 +1 @@ +"""Maintained Q2 deep-learning schemes.""" diff --git a/final/q2/deep_learning/q2/__init__.py b/final/q2/deep_learning/q2/__init__.py new file mode 100644 index 0000000..8214e10 --- /dev/null +++ b/final/q2/deep_learning/q2/__init__.py @@ -0,0 +1 @@ +"""Q2 multimodal emotion-recognition experiments.""" diff --git a/final/q2/deep_learning/q2/data.py b/final/q2/deep_learning/q2/data.py new file mode 100644 index 0000000..59bff02 --- /dev/null +++ b/final/q2/deep_learning/q2/data.py @@ -0,0 +1,214 @@ +from __future__ import annotations + +import pickle +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np + +from ....data_paths import ATTACHMENT2, PROJECT_ROOT + + +ROOT = PROJECT_ROOT +MODALITIES = ("text", "audio", "vision") + + +@dataclass +class Split: + x: tuple[np.ndarray, np.ndarray, np.ndarray] + mask: np.ndarray # N x T x 3 + y_cls: np.ndarray + y_reg: np.ndarray + ids: list[str] + + @property + def n(self) -> int: + return len(self.y_cls) + + @property + def steps(self) -> int: + return int(self.x[0].shape[1]) + + +@dataclass +class RobustStats: + center: tuple[np.ndarray, np.ndarray, np.ndarray] + scale: tuple[np.ndarray, np.ndarray, np.ndarray] + + def save(self, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + path, + text_center=self.center[0], text_scale=self.scale[0], + audio_center=self.center[1], audio_scale=self.scale[1], + vision_center=self.center[2], vision_scale=self.scale[2], + ) + + @classmethod + def load(cls, path: Path) -> "RobustStats": + with np.load(path) as data: + return cls( + tuple(data[f"{m}_center"].astype(np.float32) for m in MODALITIES), + tuple(data[f"{m}_scale"].astype(np.float32) for m in MODALITIES), + ) + + +def _unpickle(path: Path) -> dict[str, Any]: + with path.open("rb") as stream: + return pickle.load(stream, encoding="latin1") + + +def _ids_and_targets(part: dict[str, Any]) -> tuple[list[str], np.ndarray, np.ndarray]: + ids = [str(x) for x in part["id"]] + y_cls = np.asarray(part["classification_labels"], dtype=np.int64).reshape(-1) + y_reg = np.asarray(part["regression_labels"], dtype=np.float32).reshape(-1) + return ids, y_cls, y_reg + + +def _text_mask(part: dict[str, Any]) -> np.ndarray: + tokens = np.asarray(part["text_bert"]) + if tokens.ndim != 3 or tokens.shape[1] < 2: + raise ValueError(f"unexpected text_bert shape: {tokens.shape}") + # MOSEI text_bert rows are input_ids, input_mask, segment_ids. + return tokens[:, 1, :].astype(bool) + + +def load_aligned(path: Path | None = None) -> dict[str, Split]: + path = path or ATTACHMENT2 / "aligned_50.pkl" + raw = _unpickle(path) + result: dict[str, Split] = {} + for name in ("train", "valid"): + part = raw[name] + xs = tuple(np.asarray(part[m], dtype=np.float32) for m in MODALITIES) + masks = [ + _text_mask(part), + np.any(np.isfinite(xs[1]) & (xs[1] != 0), axis=-1), + np.any(np.isfinite(xs[2]) & (xs[2] != 0), axis=-1), + ] + mask = np.stack(masks, axis=-1) + ids, y_cls, y_reg = _ids_and_targets(part) + if any(x.shape[1] != 50 for x in xs): + raise ValueError(f"{name} aligned feature tensors must have 50 slots") + result[name] = Split(xs, mask, y_cls, y_reg, ids) + train_videos = {x.split("$_$", 1)[0] for x in result["train"].ids} + valid_videos = {x.split("$_$", 1)[0] for x in result["valid"].ids} + overlap = train_videos & valid_videos + if overlap: + raise ValueError(f"official train/valid split leaks {len(overlap)} source video ids") + return result + + +def _resample_rows_to_50(values: np.ndarray, lengths: list[int] | np.ndarray) -> tuple[np.ndarray, np.ndarray]: + n, source_steps, dim = values.shape + output = np.zeros((n, 50, dim), dtype=np.float32) + mask = np.zeros((n, 50), dtype=bool) + lengths_arr = np.asarray(lengths, dtype=np.int64).reshape(-1) + for i in range(n): + length = int(np.clip(lengths_arr[i], 0, source_steps)) + if length == 0: + continue + source = np.nan_to_num(values[i, :length], nan=0.0, posinf=0.0, neginf=0.0) + observed = np.any(source != 0, axis=-1) + for j in range(50): + left = int(np.floor(j * length / 50)) + right = max(left + 1, int(np.ceil((j + 1) * length / 50))) + right = min(right, length) + use = observed[left:right] + if use.any(): + output[i, j] = source[left:right][use].mean(axis=0) + mask[i, j] = True + return output, mask + + +def load_fixed_window(path: Path | None = None) -> dict[str, Split]: + """Build a matched 50-slot equal-window control from the unaligned file.""" + path = path or ATTACHMENT2 / "unaligned_50.pkl" + raw = _unpickle(path) + result: dict[str, Split] = {} + for name in ("train", "valid"): + part = raw[name] + text = np.asarray(part["text"], dtype=np.float32) + audio, audio_mask = _resample_rows_to_50(part["audio"], part["audio_lengths"]) + vision, vision_mask = _resample_rows_to_50(part["vision"], part["vision_lengths"]) + text_mask = _text_mask(part) + xs = (text, audio, vision) + mask = np.stack((text_mask, audio_mask, vision_mask), axis=-1) + ids, y_cls, y_reg = _ids_and_targets(part) + result[name] = Split(xs, mask, y_cls, y_reg, ids) + return result + + +def fit_robust_stats(split: Split) -> RobustStats: + centers: list[np.ndarray] = [] + scales: list[np.ndarray] = [] + for modality in range(3): + observed = split.mask[:, :, modality].reshape(-1) + values = split.x[modality].reshape(-1, split.x[modality].shape[-1])[observed] + if not len(values): + raise ValueError(f"no observed values for {MODALITIES[modality]}") + values = np.nan_to_num(values, nan=0.0, posinf=0.0, neginf=0.0) + center = np.median(values, axis=0) + mad = np.median(np.abs(values - center), axis=0) + scale = 1.4826 * mad + std = np.std(values, axis=0) + scale = np.where(scale > 1e-6, scale, std) + scale = np.where(scale > 1e-6, scale, 1.0) + centers.append(center.astype(np.float32)) + scales.append(scale.astype(np.float32)) + return RobustStats(tuple(centers), tuple(scales)) + + +def apply_robust_stats(split: Split, stats: RobustStats) -> Split: + xs: list[np.ndarray] = [] + for modality in range(3): + values = (split.x[modality] - stats.center[modality]) / stats.scale[modality] + values = np.nan_to_num(values, nan=0.0, posinf=0.0, neginf=0.0) + values *= split.mask[:, :, modality, None] + xs.append(values.astype(np.float32, copy=False)) + return Split(tuple(xs), split.mask.copy(), split.y_cls, split.y_reg, split.ids) + + +def corrupt_masks( + base: np.ndarray, + ratio: float, + modalities: tuple[int, ...], + seed: int, +) -> np.ndarray: + result = base.copy() + rng = np.random.default_rng(seed) + n, steps, _ = result.shape + width = max(1, min(steps, int(round(ratio * steps)))) + starts = rng.integers(0, steps - width + 1, size=n) + for row, start in enumerate(starts.tolist()): + result[row, start:start + width, list(modalities)] = False + return result + + +def augment_masks(base: np.ndarray, rng: np.random.Generator) -> np.ndarray: + result = base.copy() + n, steps, _ = result.shape + for row in range(n): + if rng.random() >= 0.85: + continue + count = int(rng.integers(1, 4)) + modalities = rng.choice(3, size=count, replace=False) + ratio = float(rng.choice((0.10, 0.20, 0.30))) + width = max(1, int(round(ratio * steps))) + start = int(rng.integers(0, steps - width + 1)) + result[row, start:start + width, modalities] = False + return result + + +def shift_audio_vision(split: Split, seed: int, max_shift: int = 10) -> Split: + rng = np.random.default_rng(seed) + xs = [x.copy() for x in split.x] + masks = split.mask.copy() + for row in range(split.n): + for modality in (1, 2): + shift = int(rng.integers(1, max_shift + 1)) + if rng.random() < 0.5: + shift = -shift + xs[modality][row] = np.roll(xs[modality][row], shift, axis=0) + masks[row, :, modality] = np.roll(masks[row, :, modality], shift) + return Split(tuple(xs), masks, split.y_cls, split.y_reg, split.ids) diff --git a/final/q2/deep_learning/q2/evaluate_math_protocol.py b/final/q2/deep_learning/q2/evaluate_math_protocol.py new file mode 100644 index 0000000..751b359 --- /dev/null +++ b/final/q2/deep_learning/q2/evaluate_math_protocol.py @@ -0,0 +1,614 @@ +"""Score the frozen EarlyConcat and MoFE checkpoints using the math-Q2 protocol. + +This script performs no training and selects no models. It evaluates the saved +three-seed checkpoints on the official labeled test split once, and reuses the +fixed 42-scenario validation-mask audit as the controlled-missingness protocol. +This source is retained for protocol helpers used by the standalone training runner. +""" +from __future__ import annotations + +import csv +import argparse +import hashlib +import json +import math +import statistics +import time +from collections import defaultdict +from pathlib import Path +from typing import Any + +import numpy as np +import torch +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error +from torch import nn + +from .data import ( + ATTACHMENT2, + MODALITIES, + RobustStats, + Split, + _ids_and_targets, + _text_mask, + _unpickle, + apply_robust_stats, + fit_robust_stats, + load_aligned, +) +from .models import AlignedFusionModel +from .mofe import MixtureOfFusionExperts +from .train_mofe import MODEL_CONFIG, _predict, _device_for, EARLYCONCAT, MOFE7_MLP + + +Q2_ROOT = Path(__file__).resolve().parents[1] +REPO_ROOT = Q2_ROOT.parents[1] +REFERENCE_DIR = Q2_ROOT / "outputs" / "followups" / "R01_selected_model_reevaluation" +OUTPUT_DIR = Q2_ROOT / "outputs" / "followups" / "R02_math_protocol_evaluation" +SEEDS = (42, 3407, 2026) +BOOTSTRAP_REPS = 1000 +TEST_BOOTSTRAP_SEED = 20260925 +AURC_BOOTSTRAP_SEED = 20260926 +SCENARIO_SEED = 20261833 +METHODS = (EARLYCONCAT, MOFE7_MLP) +CURVE_MODES = ("single", "sync", "partial", "async") +CURVE_RATES = (0.0, 0.1, 0.3, 0.5, 0.7) + + +def write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + if not rows: + return + path.parent.mkdir(parents=True, exist_ok=True) + fields = list(dict.fromkeys(key for row in rows for key in row)) + with path.open("w", newline="", encoding="utf-8-sig") as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def split_from_part(part: dict[str, Any]) -> Split: + xs = tuple(np.asarray(part[name], dtype=np.float32) for name in MODALITIES) + masks = [ + _text_mask(part), + np.any(np.isfinite(xs[1]) & (xs[1] != 0), axis=-1), + np.any(np.isfinite(xs[2]) & (xs[2] != 0), axis=-1), + ] + ids, y_cls, y_reg = _ids_and_targets(part) + return Split(xs, np.stack(masks, axis=-1), y_cls, y_reg, ids) + + +def load_splits(feature_path: Path) -> dict[str, Split]: + raw = _unpickle(feature_path) + usual = load_aligned(feature_path) + splits = {"train": usual["train"], "valid": usual["valid"], "test": split_from_part(raw["test"])} + groups = { + name: {sample_id.split("$_$", 1)[0] for sample_id in split.ids} + for name, split in splits.items() + } + for first, second in (("train", "valid"), ("train", "test"), ("valid", "test")): + overlap = groups[first] & groups[second] + if overlap: + raise ValueError(f"official {first}/{second} source-video groups overlap: {len(overlap)}") + for name, split in splits.items(): + expected = np.where(split.y_reg < 0, 0, np.where(split.y_reg == 0, 1, 2)) + if not np.array_equal(expected, split.y_cls): + raise ValueError(f"{name}: classification labels disagree with strict sign of regression labels") + return splits + + +def metrics(split: Split, logits: np.ndarray, intensity: np.ndarray, indices: np.ndarray | None = None) -> dict[str, float]: + if indices is None: + indices = np.arange(split.n) + y_cls = split.y_cls[indices] + y_reg = split.y_reg[indices] + pred_cls = np.asarray(logits)[indices].argmax(axis=-1) + pred_reg = np.clip(np.asarray(intensity).reshape(-1)[indices], -3.0, 3.0) + return { + "accuracy": float(accuracy_score(y_cls, pred_cls)), + "macro_f1": float(f1_score(y_cls, pred_cls, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(mean_absolute_error(y_reg, pred_reg)), + "rmse": float(math.sqrt(mean_squared_error(y_reg, pred_reg))), + "pearson": float(np.corrcoef(y_reg, pred_reg)[0, 1]) if np.std(y_reg) > 0 and np.std(pred_reg) > 0 else float("nan"), + } + + +def load_model(method: str, seed: int, dims: tuple[int, int, int], device: torch.device) -> nn.Module: + if method == EARLYCONCAT: + checkpoint = REFERENCE_DIR / "models" / "baselines" / "concat" / f"seed_{seed}" / "model_best.pt" + model: nn.Module = AlignedFusionModel("concat", dims=dims).to(device) + state = torch.load(checkpoint, map_location=device, weights_only=False) + if state.get("kind") != "concat" or int(state.get("seed", -1)) != seed: + raise ValueError(f"unexpected EarlyConcat checkpoint: {checkpoint}") + elif method == MOFE7_MLP: + checkpoint = REFERENCE_DIR / "models" / MOFE7_MLP / f"seed_{seed}" / "model_best.pt" + model = MixtureOfFusionExperts(dims=dims, **MODEL_CONFIG).to(device) + state = torch.load(checkpoint, map_location=device, weights_only=False) + if state.get("config") != MODEL_CONFIG or int(state.get("seed", -1)) != seed: + raise ValueError(f"unexpected MoFE checkpoint: {checkpoint}") + else: + raise ValueError(f"unknown model {method}") + if tuple(state.get("dims", ())) != dims: + raise ValueError(f"feature dimensions do not match checkpoint: {checkpoint}") + model.load_state_dict(state["state_dict"]) + model.eval() + return model + + +def best_interval(visible: np.ndarray, wanted: int, cap: int, location: str, rng: np.random.Generator) -> tuple[int, int] | None: + steps = len(visible) + candidates: list[tuple[int, int, int, int]] = [] + for left in range(steps): + hits = 0 + for right in range(left, steps): + hits += int(visible[right]) + count = min(hits, cap) + if count: + candidates.append((abs(count - wanted), right - left + 1, left, right)) + if not candidates: + return None + best = min((error, span) for error, span, _, _ in candidates) + tied = [(left, right) for error, span, left, right in candidates if (error, span) == best] + if location == "start": + return min(tied, key=lambda pair: (pair[0], pair[1])) + if location == "end": + return max(tied, key=lambda pair: (pair[1], pair[0])) + if location == "middle": + center = (steps - 1) / 2 + return min(tied, key=lambda pair: (abs((pair[0] + pair[1]) / 2 - center), pair[0])) + if location != "random": + raise ValueError(f"unknown interval location: {location}") + return tied[int(rng.integers(0, len(tied)))] + + +def spread_short_spans(visible: np.ndarray, wanted: int, cap: int) -> np.ndarray: + positions = np.flatnonzero(visible) + count = min(int(wanted), int(cap), len(positions)) + chosen = np.zeros(len(visible), dtype=bool) + if count <= 0: + return chosen + n_spans = min(3, count) + chunks = np.array_split(positions, n_spans) + allocations = [count // n_spans + int(i < count % n_spans) for i in range(n_spans)] + for chunk, amount in zip(chunks, allocations): + if amount <= 0 or len(chunk) == 0: + continue + amount = min(amount, len(chunk)) + start = max(0, (len(chunk) - amount) // 2) + chosen[chunk[start:start + amount]] = True + return chosen + + +def continuous_mask( + original: np.ndarray, + rate: float, + mode: str, + rng: np.random.Generator, + *, + modalities: tuple[int, ...] | None = None, + location: str = "random", + span_structure: str = "long", +) -> np.ndarray: + """Reproduce math/Q2 continuous masking on this model's observed positions.""" + observed = np.asarray(original, dtype=bool) + result = observed.copy() + if rate <= 0 or mode == "none": + return result + steps, modality_count = observed.shape + present = [m for m in range(modality_count) if observed[:, m].any()] + if not present: + return result + if modalities is not None: + selected = [int(m) for m in modalities if int(m) in present] + if not selected: + return result + elif mode == "single": + selected = [int(rng.choice(present))] + elif mode in {"sync", "partial", "async"}: + if len(present) == 1: + selected = present + else: + count = int(rng.integers(2, min(3, len(present)) + 1)) + selected = sorted(int(v) for v in rng.choice(present, size=count, replace=False)) + else: + raise ValueError(f"unknown mask mode: {mode}") + + def max_hide(modality: int) -> int: + count = int(observed[:, modality].sum()) + keep = max(1, int(math.ceil(0.2 * count))) + return max(0, count - keep) + + target = {m: min(max_hide(m), int(round(rate * int(observed[:, m].sum())))) for m in selected} + if mode == "sync": + span = max(1, int(round(rate * steps))) + if location == "start": + left = 0 + elif location == "end": + left = steps - span + elif location == "middle": + left = (steps - span) // 2 + elif location == "random": + left = int(rng.integers(0, max(1, steps - span + 1))) + else: + raise ValueError(f"unknown interval location: {location}") + right = min(steps - 1, left + span - 1) + for m in selected: + candidates = np.flatnonzero(observed[left:right + 1, m]) + left + amount = min(len(candidates), max_hide(m), target[m]) + if amount: + offset = 0 if location != "end" else len(candidates) - amount + result[candidates[max(0, offset):max(0, offset) + amount], m] = False + else: + common_span = max(1, int(round(rate * steps))) + for rank, m in enumerate(selected): + wanted = target[m] + if wanted <= 0: + continue + cap = max_hide(m) + if span_structure == "multi_short": + hide = spread_short_spans(observed[:, m], wanted, cap) + elif span_structure != "long": + raise ValueError(f"unknown span structure: {span_structure}") + elif mode == "single" and location != "random": + # Place a contiguous block at the requested relative location + # among observed positions, while keeping the selected-source + # missing amount fixed. This avoids treating padding as time. + interval = best_interval(observed[:, m], wanted, cap, location, rng) + hide = np.zeros(steps, dtype=bool) + if interval is not None: + left, right = interval + candidates = np.flatnonzero(observed[left:right + 1, m]) + left + amount = min(len(candidates), wanted, cap) + if amount: + offset = 0 if location != "end" else len(candidates) - amount + hide[candidates[max(0, offset):max(0, offset) + amount]] = True + elif mode in {"partial", "async"}: + if mode == "partial": + base_left = int(rng.integers(0, max(1, steps - common_span + 1))) if location == "random" else ( + 0 if location == "start" else steps - common_span if location == "end" else (steps - common_span) // 2 + ) + offset = int(round(rank * common_span * 0.5)) + else: + base_left = 0 if location == "random" else ( + 0 if location == "start" else steps - common_span if location == "end" else (steps - common_span) // 2 + ) + available = max(1, steps - common_span + 1) + offsets = np.rint(np.linspace(0, max(0, available - 1), len(selected))).astype(int) + if location == "random": + rng.shuffle(offsets) + offset = int(offsets[rank]) + left = min(max(0, base_left + offset), max(0, steps - common_span)) + right = min(steps - 1, left + common_span - 1) + hide = np.zeros(steps, dtype=bool) + candidates = np.flatnonzero(observed[left:right + 1, m]) + left + amount = min(len(candidates), wanted, cap) + if amount: + hide[candidates[:amount]] = True + else: + interval = best_interval(observed[:, m], wanted, cap, location, rng) + hide = np.zeros(steps, dtype=bool) + if interval is not None: + left, right = interval + candidates = np.flatnonzero(observed[left:right + 1, m]) + left + amount = min(len(candidates), wanted, cap) + if amount: + offset = 0 if location != "end" else len(candidates) - amount + hide[candidates[max(0, offset):max(0, offset) + amount]] = True + result[hide, m] = False + return result + + +def scenario_seed(seed: int, sample_id: str, key: str) -> int: + return int.from_bytes(hashlib.sha256(f"{seed}:{sample_id}:{key}".encode()).digest()[:8], "little") + + +def make_scenarios(valid: Split, seed: int = SCENARIO_SEED) -> dict[str, np.ndarray]: + scenarios = {"0.0/none": valid.mask.copy()} + for rate in CURVE_RATES[1:]: + for mode in CURVE_MODES: + key = f"{rate:.1f}/{mode}" + scenarios[key] = np.stack([ + continuous_mask(mask, rate, mode, np.random.default_rng(scenario_seed(seed, sample_id, key))) + for sample_id, mask in zip(valid.ids, valid.mask) + ]) + modality_sets = (((0,), "T"), ((1,), "A"), ((2,), "V"), ((0, 1), "TA"), ((0, 2), "TV"), ((1, 2), "AV"), ((0, 1, 2), "TAV")) + for selected, label in modality_sets: + key = f"0.3/modality_{label}" + scenarios[key] = np.stack([ + continuous_mask(mask, 0.3, "sync", np.random.default_rng(scenario_seed(seed, sample_id, key)), modalities=selected) + for sample_id, mask in zip(valid.ids, valid.mask) + ]) + for modality_index, label in enumerate(("T", "A", "V")): + for location in ("start", "middle", "end"): + key = f"0.3/location_{location}_{label}" + scenarios[key] = np.stack([ + continuous_mask(mask, 0.3, "single", np.random.default_rng(scenario_seed(seed, sample_id, key)), modalities=(modality_index,), location=location) + for sample_id, mask in zip(valid.ids, valid.mask) + ]) + for structure in ("long", "multi_short"): + key = f"0.3/span_{structure}_{label}" + scenarios[key] = np.stack([ + continuous_mask(mask, 0.3, "single", np.random.default_rng(scenario_seed(seed, sample_id, key)), modalities=(modality_index,), span_structure=structure) + for sample_id, mask in zip(valid.ids, valid.mask) + ]) + for mode in ("sync", "partial", "async"): + key = f"0.3/synchrony_{mode}" + scenarios[key] = np.stack([ + continuous_mask(mask, 0.3, mode, np.random.default_rng(scenario_seed(seed, sample_id, key)), modalities=(0, 1, 2)) + for sample_id, mask in zip(valid.ids, valid.mask) + ]) + return scenarios + + +def actual_additional_rates(base: np.ndarray, scenarios: dict[str, np.ndarray]) -> dict[str, np.ndarray]: + result = {} + observed = base.sum(axis=1) + for scenario, current in scenarios.items(): + newly_hidden = base & ~current + hidden_count = newly_hidden.sum(axis=1) + by_modality = np.divide( + hidden_count, + observed, + out=np.full(hidden_count.shape, np.nan, dtype=np.float64), + where=observed > 0, + ) + result[scenario] = np.nanmean(by_modality, axis=1) + return result + + +def aurc_from_curve(rates: list[float], maes: list[float]) -> float: + order = np.argsort(np.asarray(rates), kind="stable") + x = np.asarray(rates, dtype=np.float64)[order] + y = np.asarray(maes, dtype=np.float64)[order] + unique_x, inverse = np.unique(x, return_inverse=True) + unique_y = np.asarray([y[inverse == i].mean() for i in range(len(unique_x))]) + if len(unique_x) <= 1 or unique_x[-1] <= 0: + return float(maes[0]) + return float(np.trapezoid(unique_y, unique_x) / unique_x[-1]) + + +def curve_scenarios(mode: str) -> list[str]: + return ["0.0/none"] + [f"{rate:.1f}/{mode}" for rate in CURVE_RATES[1:]] + + +def group_indices(ids: list[str]) -> tuple[list[str], dict[str, np.ndarray]]: + groups = sorted({sample_id.split("$_$", 1)[0] for sample_id in ids}) + mapping = {group: np.flatnonzero(np.asarray([x.split("$_$", 1)[0] == group for x in ids])) for group in groups} + return groups, mapping + + +def bootstrap_clean_test( + split: Split, + preds: dict[tuple[str, int], dict[str, np.ndarray]], +) -> list[dict[str, Any]]: + groups, mapping = group_indices(split.ids) + rng = np.random.default_rng(TEST_BOOTSTRAP_SEED) + draws: dict[str, list[float]] = defaultdict(list) + for _ in range(BOOTSTRAP_REPS): + chosen = rng.choice(groups, size=len(groups), replace=True) + indices = np.concatenate([mapping[group] for group in chosen]) + per_method = {} + for method in METHODS: + per_seed = [metrics(split, preds[(method, seed)]["logits"], preds[(method, seed)]["intensity"], indices) for seed in SEEDS] + per_method[method] = {key: float(np.mean([row[key] for row in per_seed])) for key in per_seed[0]} + for metric in per_method[EARLYCONCAT]: + draws[metric].append(per_method[MOFE7_MLP][metric] - per_method[EARLYCONCAT][metric]) + rows = [] + for metric, values in draws.items(): + rows.append({ + "comparison": "MoFE-7 + MLP Router minus EarlyConcat + BiGRU", + "metric": metric, + "delta_mean_over_seeds": float(np.mean([r[metric] for r in [ + metrics(split, preds[(MOFE7_MLP, seed)]["logits"], preds[(MOFE7_MLP, seed)]["intensity"]) + for seed in SEEDS + ]]) - np.mean([r[metric] for r in [ + metrics(split, preds[(EARLYCONCAT, seed)]["logits"], preds[(EARLYCONCAT, seed)]["intensity"]) + for seed in SEEDS + ]])), + "bootstrap_ci_2p5": float(np.quantile(values, 0.025)), + "bootstrap_ci_97p5": float(np.quantile(values, 0.975)), + "bootstrap_probability_delta_gt_0": float(np.mean(np.asarray(values) > 0)), + "replicates": BOOTSTRAP_REPS, + "resampling_unit": "source video id", + "paired": True, + "seed": TEST_BOOTSTRAP_SEED, + }) + return rows + + +def bootstrap_aurc( + valid: Split, + predictions: dict[tuple[str, int, str], dict[str, np.ndarray]], + scenarios: dict[str, np.ndarray], + rates_by_sample: dict[str, np.ndarray], +) -> list[dict[str, Any]]: + groups, mapping = group_indices(valid.ids) + rng = np.random.default_rng(AURC_BOOTSTRAP_SEED) + delta_by_mode: dict[str, list[float]] = {mode: [] for mode in CURVE_MODES} + for _ in range(BOOTSTRAP_REPS): + chosen = rng.choice(groups, size=len(groups), replace=True) + indices = np.concatenate([mapping[group] for group in chosen]) + for mode in CURVE_MODES: + keys = curve_scenarios(mode) + model_aucs: dict[str, list[float]] = {method: [] for method in METHODS} + for method in METHODS: + for seed in SEEDS: + xs = [float(np.nanmean(rates_by_sample[key][indices])) for key in keys] + ys = [float(np.abs(valid.y_reg[indices] - predictions[(method, seed, key)]["intensity"][indices]).mean()) for key in keys] + model_aucs[method].append(aurc_from_curve(xs, ys)) + delta_by_mode[mode].append(float(np.mean(model_aucs[MOFE7_MLP]) - np.mean(model_aucs[EARLYCONCAT]))) + point = {} + for mode in CURVE_MODES: + model_aucs = {} + for method in METHODS: + model_aucs[method] = [] + for seed in SEEDS: + keys = curve_scenarios(mode) + xs = [float(np.nanmean(rates_by_sample[key])) for key in keys] + ys = [float(np.abs(valid.y_reg - predictions[(method, seed, key)]["intensity"]).mean()) for key in keys] + model_aucs[method].append(aurc_from_curve(xs, ys)) + point[mode] = float(np.mean(model_aucs[MOFE7_MLP]) - np.mean(model_aucs[EARLYCONCAT])) + rows = [] + for mode, values in delta_by_mode.items(): + rows.append({ + "mode": mode, + "delta_aurc_mae_mofe_minus_earlyconcat": point[mode], + "bootstrap_ci_2p5": float(np.quantile(values, 0.025)), + "bootstrap_ci_97p5": float(np.quantile(values, 0.975)), + "bootstrap_probability_delta_lt_0": float(np.mean(np.asarray(values) < 0)), + "replicates": BOOTSTRAP_REPS, + "resampling_unit": "source video id", + "paired": True, + "seed": AURC_BOOTSTRAP_SEED, + }) + return rows + + +def run(device_name: str = "auto", batch_size: int = 64, masks_only: bool = False) -> None: + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + device = _device_for(device_name) + torch.set_num_threads(4) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + feature_path = ATTACHMENT2 / "aligned_50.pkl" + with (REFERENCE_DIR / "run_manifest.json").open("r", encoding="utf-8") as stream: + reference_manifest = json.load(stream) + if sha256(feature_path) != reference_manifest["feature_sha256"]: + raise ValueError("current official feature file hash differs from the checkpoint evaluation manifest") + + raw_splits = load_splits(feature_path) + train = raw_splits["train"] + valid = raw_splits["valid"] + test = raw_splits["test"] + scaler_path = REFERENCE_DIR / "aligned_robust_stats.npz" + stats = RobustStats.load(scaler_path) + computed = fit_robust_stats(train) + scaler_diff = max( + max(float(np.max(np.abs(a - b))) for a, b in zip(computed.center, stats.center)), + max(float(np.max(np.abs(a - b))) for a, b in zip(computed.scale, stats.scale)), + ) + if scaler_diff > 1e-6: + raise ValueError(f"checkpoint scaler is not the train-only scaler (max difference {scaler_diff})") + valid = apply_robust_stats(valid, stats) + test = apply_robust_stats(test, stats) + dims = tuple(x.shape[-1] for x in train.x) + + if not masks_only: + # Final, clean official-test evaluation; no retraining or selection occurs here. + test_predictions: dict[tuple[str, int], dict[str, np.ndarray]] = {} + test_rows: list[dict[str, Any]] = [] + for method in METHODS: + for seed in SEEDS: + model = load_model(method, seed, dims, device) + prediction = _predict(model, test, test.mask, device, batch_size) + test_predictions[(method, seed)] = prediction + test_rows.append({"method": method, "seed": seed, "n_test": test.n, **metrics(test, prediction["logits"], prediction["intensity"])}) + del model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + summary_rows = [] + for method in METHODS: + subset = [row for row in test_rows if row["method"] == method] + for metric in ("accuracy", "macro_f1", "mae", "rmse", "pearson"): + values = [float(row[metric]) for row in subset] + summary_rows.append({"method": method, "metric": metric, "mean": float(np.mean(values)), "sd_across_seeds": float(np.std(values, ddof=1))}) + write_csv(OUTPUT_DIR / "official_test_metrics_by_seed.csv", test_rows) + write_csv(OUTPUT_DIR / "official_test_summary.csv", summary_rows) + write_csv(OUTPUT_DIR / "official_test_paired_bootstrap.csv", bootstrap_clean_test(test, test_predictions)) + + # Reproduce the math-Q2 42-scenario design with a per-sample stable seed, + # while applying it to the observation masks used to train these models. + scenario_masks = make_scenarios(valid) + rates_by_sample = actual_additional_rates(valid.mask, scenario_masks) + condition_predictions: dict[tuple[str, int, str], dict[str, np.ndarray]] = {} + condition_rows: list[dict[str, Any]] = [] + for method in METHODS: + for seed in SEEDS: + model = load_model(method, seed, dims, device) + for scenario, masks in scenario_masks.items(): + prediction = _predict(model, valid, masks, device, batch_size) + condition_predictions[(method, seed, scenario)] = prediction + values = metrics(valid, prediction["logits"], prediction["intensity"]) + condition_rows.append({ + "method": method, + "seed": seed, + "scenario": scenario, + "realized_additional_global_rate": float(np.nanmean(rates_by_sample[scenario])), + "n_valid": valid.n, + **values, + }) + del model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + write_csv(OUTPUT_DIR / "controlled_metrics_by_scenario.csv", condition_rows) + + auc_rows: list[dict[str, Any]] = [] + for method in METHODS: + for seed in SEEDS: + for mode in CURVE_MODES: + keys = curve_scenarios(mode) + xs = [float(np.nanmean(rates_by_sample[key])) for key in keys] + ys = [float(np.abs(valid.y_reg - condition_predictions[(method, seed, key)]["intensity"]).mean()) for key in keys] + auc_rows.append({"method": method, "seed": seed, "mask_mode": mode, "aurc_mae": aurc_from_curve(xs, ys), "rates_realized": json.dumps(xs)}) + write_csv(OUTPUT_DIR / "aurc_mae_by_mode_seed.csv", auc_rows) + auc_summary = [] + for method in METHODS: + for mode in CURVE_MODES: + values = [row["aurc_mae"] for row in auc_rows if row["method"] == method and row["mask_mode"] == mode] + auc_summary.append({"method": method, "mask_mode": mode, "mean": float(np.mean(values)), "sd_across_seeds": float(np.std(values, ddof=1))}) + write_csv(OUTPUT_DIR / "aurc_mae_summary.csv", auc_summary) + write_csv(OUTPUT_DIR / "aurc_mae_paired_bootstrap.csv", bootstrap_aurc(valid, condition_predictions, scenario_masks, rates_by_sample)) + + manifest = { + "experiment": "Frozen EarlyConcat vs MoFE-7 evaluation under math/Q2 test protocol", + "created_unix": time.time(), + "device": str(device), + "cuda_device": torch.cuda.get_device_name(0) if device.type == "cuda" else None, + "feature_file": str(feature_path), + "feature_sha256": sha256(feature_path), + "representation": "official aligned_50 ordered positions; not physical-time bins", + "train_valid_test_counts": {name: split.n for name, split in raw_splits.items()}, + "source_video_groups": {name: len({sample_id.split("$_$", 1)[0] for sample_id in split.ids}) for name, split in raw_splits.items()}, + "official_group_splits_disjoint": True, + "test_evaluation": ( + "one final clean evaluation on official labeled test split; no training/model selection/calibration" + if not masks_only else "test outputs preserved from the earlier single evaluation; no test prediction was rerun" + ), + "test_prediction_performed_this_invocation": not masks_only, + "seeds": list(SEEDS), + "checkpoint_source": str(REFERENCE_DIR / "models"), + "train_only_scaler": str(scaler_path), + "scaler_max_abs_difference_from_train_refit": scaler_diff, + "test_labels_used_for_training_or_selection": False, + "controlled_missingness": { + "scenario_seed": SCENARIO_SEED, + "scenario_design": "math/Q2 42-scenario design regenerated on the Q2 models' BERT attention-mask base", + "scenarios": len(scenario_masks), + "AURC": "normalized trapezoidal area of MAE over realized equal-modality-weighted added missing rate, at 0/.1/.3/.5/.7 for single/sync/partial/async", + }, + "bootstrap": { + "replicates": BOOTSTRAP_REPS, + "test_seed": TEST_BOOTSTRAP_SEED, + "aurc_seed": AURC_BOOTSTRAP_SEED, + "unit": "source video id", + "paired": True, + }, + } + (OUTPUT_DIR / "run_manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8") + print(f"wrote math-protocol comparison to {OUTPUT_DIR}") + print(f"n_test={test.n}; n_valid={valid.n}; device={device}; scenarios={len(scenario_masks)}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--masks-only", action="store_true", help="Recompute validation mask scenarios without rerunning official-test inference") + args = parser.parse_args() + run(masks_only=args.masks_only) diff --git a/final/q2/deep_learning/q2/models.py b/final/q2/deep_learning/q2/models.py new file mode 100644 index 0000000..4ab3d11 --- /dev/null +++ b/final/q2/deep_learning/q2/models.py @@ -0,0 +1,4 @@ +"""Compatibility import for the maintained model registry.""" +from ....model.early_concat import AlignedFusionModel + +__all__ = ["AlignedFusionModel"] diff --git a/final/q2/deep_learning/q2/mofe.py b/final/q2/deep_learning/q2/mofe.py new file mode 100644 index 0000000..05581f3 --- /dev/null +++ b/final/q2/deep_learning/q2/mofe.py @@ -0,0 +1,4 @@ +"""Compatibility import for the maintained model registry.""" +from ....model.mofe import EXPERT_NAMES, SUBSETS, MixtureOfFusionExperts + +__all__ = ["EXPERT_NAMES", "SUBSETS", "MixtureOfFusionExperts"] diff --git a/final/q2/deep_learning/q2/train_compare.py b/final/q2/deep_learning/q2/train_compare.py new file mode 100644 index 0000000..7716b60 --- /dev/null +++ b/final/q2/deep_learning/q2/train_compare.py @@ -0,0 +1,490 @@ +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import random +import shutil +import time +from collections import Counter +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error +from torch import nn + +from .data import ( + ATTACHMENT2, + ROOT, + MODALITIES, + RobustStats, + Split, + apply_robust_stats, + augment_masks, + corrupt_masks, + fit_robust_stats, + load_aligned, + load_fixed_window, + shift_audio_vision, +) +from .models import AlignedFusionModel + + +PATTERNS = { + "text": (0,), + "audio": (1,), + "vision": (2,), + "audio_vision": (1, 2), + "all_modalities": (0, 1, 2), +} +KINDS = ("concat",) + + +def seed_everything(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def _tensor_split(split: Split, device: torch.device) -> tuple[tuple[torch.Tensor, ...], torch.Tensor, torch.Tensor, torch.Tensor]: + xs = tuple(torch.as_tensor(x, dtype=torch.float32, device=device) for x in split.x) + mask = torch.as_tensor(split.mask, dtype=torch.bool, device=device) + y_cls = torch.as_tensor(split.y_cls, dtype=torch.long, device=device) + y_reg = torch.as_tensor(split.y_reg, dtype=torch.float32, device=device) + return xs, mask, y_cls, y_reg + + +def _loss(output: dict[str, torch.Tensor], y_cls: torch.Tensor, y_reg: torch.Tensor) -> torch.Tensor: + class_loss = F.cross_entropy(output["logits"], y_cls) + intensity_loss = F.smooth_l1_loss(output["intensity"] / 3.0, y_reg / 3.0) + return class_loss + 0.5 * intensity_loss + + +@torch.inference_mode() +def _score_arrays( + model: AlignedFusionModel, + split: Split, + mask: np.ndarray, + device: torch.device, + batch_size: int = 128, +) -> tuple[dict[str, float], dict[str, np.ndarray]]: + model.eval() + predictions: dict[str, list[np.ndarray]] = {"logits": [], "intensity": []} + xs = split.x + for start in range(0, split.n, batch_size): + end = min(start + batch_size, split.n) + xb = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in xs) + mb = torch.as_tensor(mask[start:end], dtype=torch.bool, device=device) + output = model(xb, mb) + predictions["logits"].append(output["logits"].float().cpu().numpy()) + predictions["intensity"].append(output["intensity"].float().cpu().numpy()) + logits = np.concatenate(predictions["logits"], axis=0) + intensity = np.clip(np.concatenate(predictions["intensity"], axis=0), -3.0, 3.0) + pred_cls = logits.argmax(axis=-1) + pearson = _pearson(split.y_reg, intensity) + metrics = { + "accuracy": float(accuracy_score(split.y_cls, pred_cls)), + "macro_f1": float(f1_score(split.y_cls, pred_cls, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(mean_absolute_error(split.y_reg, intensity)), + "pearson": pearson, + } + return metrics, {"logits": logits, "intensity": intensity, "class": pred_cls} + + +def _pearson(y: np.ndarray, pred: np.ndarray) -> float: + a = np.asarray(y, dtype=np.float64) + b = np.asarray(pred, dtype=np.float64) + if a.std() < 1e-12 or b.std() < 1e-12: + return 0.0 + return float(np.corrcoef(a, b)[0, 1]) + + +def _validation_loss(model: AlignedFusionModel, valid: Split, device: torch.device, batch_size: int) -> float: + model.eval() + xs, masks, y_cls, y_reg = _tensor_split(valid, device) + losses: list[float] = [] + with torch.inference_mode(): + for start in range(0, valid.n, batch_size): + idx = slice(start, min(start + batch_size, valid.n)) + output = model(tuple(x[idx] for x in xs), masks[idx]) + losses.append(float(_loss(output, y_cls[idx], y_reg[idx]).item())) + return float(np.average(losses, weights=[min(batch_size, valid.n - i) for i in range(0, valid.n, batch_size)])) + + +def _write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + return + fields = list(dict.fromkeys(key for row in rows for key in row)) + with path.open("w", newline="", encoding="utf-8-sig") as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def _train_one( + kind: str, + train: Split, + valid: Split, + output_dir: Path, + device: torch.device, + seed: int, + epochs: int, + patience: int, + batch_size: int, +) -> tuple[AlignedFusionModel, int, list[dict[str, float]]]: + seed_everything(seed) + dims = tuple(int(x.shape[-1]) for x in train.x) + model = AlignedFusionModel(kind, dims=dims).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=1.5e-4, weight_decay=1e-4) + train_tensors = _tensor_split(train, device) + xs, base_masks, y_cls, y_reg = train_tensors + rng = np.random.default_rng(seed + 809) + best_loss = math.inf + best_epoch = 0 + stale_epochs = 0 + history: list[dict[str, float]] = [] + checkpoint_path = output_dir / "model_best.pt" + output_dir.mkdir(parents=True, exist_ok=True) + + for epoch in range(1, epochs + 1): + model.train() + order = rng.permutation(train.n) + batch_losses: list[float] = [] + for start in range(0, train.n, batch_size): + ids_np = order[start:start + batch_size] + ids = torch.as_tensor(ids_np, dtype=torch.long, device=device) + masks_np = augment_masks(train.mask[ids_np], rng) + masks = torch.as_tensor(masks_np, dtype=torch.bool, device=device) + output = model(tuple(x.index_select(0, ids) for x in xs), masks) + loss = _loss(output, y_cls.index_select(0, ids), y_reg.index_select(0, ids)) + optimizer.zero_grad(set_to_none=True) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + optimizer.step() + batch_losses.append(float(loss.detach().item())) + valid_loss = _validation_loss(model, valid, device, batch_size) + row = {"epoch": float(epoch), "train_loss": float(np.mean(batch_losses)), "valid_clean_loss": valid_loss} + history.append(row) + print(f"[{kind}] epoch={epoch:02d} train={row['train_loss']:.4f} valid={valid_loss:.4f}", flush=True) + if valid_loss < best_loss - 1e-4: + best_loss = valid_loss + best_epoch = epoch + stale_epochs = 0 + torch.save({"kind": kind, "dims": dims, "state_dict": model.state_dict(), "seed": seed, "best_epoch": epoch}, checkpoint_path) + else: + stale_epochs += 1 + if stale_epochs >= patience: + break + + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + model.load_state_dict(saved["state_dict"]) + model.eval() + _write_csv(output_dir / "training_history.csv", history) + return model, best_epoch, history + + +def _conditions(valid: Split, seed: int) -> list[tuple[str, float, np.ndarray]]: + result = [("clean", 0.0, valid.mask.copy())] + for rate in (0.10, 0.20, 0.30): + for pattern_id, (pattern, mods) in enumerate(PATTERNS.items()): + result.append((pattern, rate, corrupt_masks(valid.mask, rate, mods, seed + pattern_id * 101 + int(rate * 1000)))) + return result + + +def _eval_conditions( + model: AlignedFusionModel, + valid: Split, + device: torch.device, + seed: int, + seed_run: int, + method: str, + representation: str, +) -> list[dict[str, Any]]: + rows = [] + for condition, rate, masks in _conditions(valid, seed): + metrics, _ = _score_arrays(model, valid, masks, device) + rows.append({"method": method, "representation": representation, "seed": seed_run, "condition": condition, + "missing_rate": rate, "n_valid": valid.n, **metrics}) + print(f"[{method}/{representation}] {condition:14s} rate={rate:.1f} " + f"F1={metrics['macro_f1']:.3f} MAE={metrics['mae']:.3f} " + f"P={metrics['pearson']:.3f}", flush=True) + return rows + + +def _summary(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + groups = list(dict.fromkeys((row["method"], row["representation"]) for row in rows)) + summary: list[dict[str, Any]] = [] + for method, representation in groups: + matching = [r for r in rows if r["method"] == method and r["representation"] == representation] + local = [r for r in matching if r["condition"] != "clean" and r["missing_rate"] > 0] + clean = [r for r in matching if r["condition"] == "clean"] + seeds = sorted({int(r.get("seed", 0)) for r in matching}) + + def per_seed_mean(selected: list[dict[str, Any]], metric: str) -> list[float]: + return [float(np.mean([r[metric] for r in selected if int(r.get("seed", 0)) == seed])) + for seed in seeds if any(int(r.get("seed", 0)) == seed for r in selected)] + + clean_f1 = per_seed_mean(clean, "macro_f1") + clean_accuracy = per_seed_mean(clean, "accuracy") + clean_mae = per_seed_mean(clean, "mae") + clean_pearson = per_seed_mean(clean, "pearson") + corrupt_f1 = per_seed_mean(local, "macro_f1") + corrupt_accuracy = per_seed_mean(local, "accuracy") + corrupt_mae = per_seed_mean(local, "mae") + corrupt_pearson = per_seed_mean(local, "pearson") + row: dict[str, Any] = { + "method": method, + "representation": representation, + "n_seeds": len(seeds), + "clean_accuracy": float(np.mean(clean_accuracy)), + "clean_accuracy_sd": float(np.std(clean_accuracy, ddof=1)) if len(clean_accuracy) > 1 else 0.0, + "clean_macro_f1": float(np.mean(clean_f1)), + "clean_macro_f1_sd": float(np.std(clean_f1, ddof=1)) if len(clean_f1) > 1 else 0.0, + "clean_mae": float(np.mean(clean_mae)), + "clean_mae_sd": float(np.std(clean_mae, ddof=1)) if len(clean_mae) > 1 else 0.0, + "clean_pearson": float(np.mean(clean_pearson)), + "clean_pearson_sd": float(np.std(clean_pearson, ddof=1)) if len(clean_pearson) > 1 else 0.0, + "corrupt_accuracy_mean": float(np.mean(corrupt_accuracy)), + "corrupt_accuracy_sd": float(np.std(corrupt_accuracy, ddof=1)) if len(corrupt_accuracy) > 1 else 0.0, + "corrupt_macro_f1_mean": float(np.mean(corrupt_f1)), + "corrupt_macro_f1_sd": float(np.std(corrupt_f1, ddof=1)) if len(corrupt_f1) > 1 else 0.0, + "corrupt_macro_f1_worst": float(np.min([r["macro_f1"] for r in local])), + "corrupt_mae_mean": float(np.mean(corrupt_mae)), + "corrupt_mae_sd": float(np.std(corrupt_mae, ddof=1)) if len(corrupt_mae) > 1 else 0.0, + "corrupt_pearson_mean": float(np.mean(corrupt_pearson)), + "corrupt_pearson_sd": float(np.std(corrupt_pearson, ddof=1)) if len(corrupt_pearson) > 1 else 0.0, + } + for rate in (0.10, 0.20, 0.30): + at_rate = [r for r in local if r["missing_rate"] == rate] + f1_by_seed = per_seed_mean(at_rate, "macro_f1") + accuracy_by_seed = per_seed_mean(at_rate, "accuracy") + mae_by_seed = per_seed_mean(at_rate, "mae") + row[f"f1_rate_{int(rate * 100)}"] = float(np.mean(f1_by_seed)) + row[f"accuracy_rate_{int(rate * 100)}"] = float(np.mean(accuracy_by_seed)) + row[f"mae_rate_{int(rate * 100)}"] = float(np.mean(mae_by_seed)) + summary.append(row) + for row in summary: + row["pareto_nondominated"] = not any( + other is not row and other["representation"] == row["representation"] + and other["corrupt_macro_f1_mean"] >= row["corrupt_macro_f1_mean"] + and other["corrupt_mae_mean"] <= row["corrupt_mae_mean"] + and other["corrupt_pearson_mean"] >= row["corrupt_pearson_mean"] + and ( + other["corrupt_macro_f1_mean"] > row["corrupt_macro_f1_mean"] + or other["corrupt_mae_mean"] < row["corrupt_mae_mean"] + or other["corrupt_pearson_mean"] > row["corrupt_pearson_mean"] + ) + for other in summary + ) + return summary + + +def _plot(summary: list[dict[str, Any]], rows: list[dict[str, Any]], path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + colors = {"concat": "#4e79a7"} + fig, axes = plt.subplots(1, 2, figsize=(11, 4.4), constrained_layout=True) + for row in summary: + kind = row["method"] + y_f1 = [row["clean_macro_f1"]] + [row[f"f1_rate_{r}"] for r in (10, 20, 30)] + y_mae = [row["clean_mae"]] + [row[f"mae_rate_{r}"] for r in (10, 20, 30)] + axes[0].plot([0, 10, 20, 30], y_f1, marker="o", label=kind, color=colors.get(kind)) + axes[1].plot([0, 10, 20, 30], y_mae, marker="o", label=kind, color=colors.get(kind)) + axes[0].set(title="Polarity under contiguous local missingness", xlabel="masked slots (%)", ylabel="Macro-F1 (higher is better)") + axes[1].set(title="Intensity under contiguous local missingness", xlabel="masked slots (%)", ylabel="MAE (lower is better)") + for ax in axes: + ax.grid(alpha=0.25) + ax.legend(frameon=False) + fig.savefig(path, dpi=180) + plt.close(fig) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _run(args: argparse.Namespace) -> None: + seed_everything(args.seeds[0]) + if args.device == "auto": + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + else: + device = torch.device(args.device) + torch.set_num_threads(args.threads) + output = Path(args.output_dir) + output.mkdir(parents=True, exist_ok=True) + aligned_raw = load_aligned() + stats = fit_robust_stats(aligned_raw["train"]) + stats.save(output / "aligned_robust_stats.npz") + aligned = {k: apply_robust_stats(v, stats) for k, v in aligned_raw.items()} + audit = { + "source": str(ATTACHMENT2 / "aligned_50.pkl"), + "train_samples": aligned["train"].n, + "valid_samples": aligned["valid"].n, + "train_classes": np.bincount(aligned["train"].y_cls, minlength=3).tolist(), + "valid_classes": np.bincount(aligned["valid"].y_cls, minlength=3).tolist(), + "mean_observed_slots": { + MODALITIES[m]: float(aligned["train"].mask[:, :, m].sum(axis=1).mean()) for m in range(3) + }, + "train_valid_video_overlap": 0, + } + with (output / "data_audit.json").open("w", encoding="utf-8") as stream: + json.dump(audit, stream, ensure_ascii=False, indent=2) + print(f"device={device}; train={audit['train_samples']}; valid={audit['valid_samples']}; audit={audit}", flush=True) + + metric_rows: list[dict[str, Any]] = [] + best_epochs: dict[str, int] = {} + for kind in KINDS: + for seed in args.seeds: + seed_dir = output / "models" / "aligned" / kind / f"seed_{seed}" + model, best_epoch, _ = _train_one( + kind, aligned["train"], aligned["valid"], seed_dir, + device, seed, args.epochs, args.patience, args.batch_size, + ) + best_epochs[f"{kind}_seed_{seed}"] = best_epoch + metric_rows.extend(_eval_conditions(model, aligned["valid"], device, seed + 13, seed, kind, "provided_word_aligned_50")) + if seed == args.seeds[0]: + shutil.copy2(seed_dir / "model_best.pt", output / "models" / "aligned" / kind / "model_best.pt") + del model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + summary = _summary(metric_rows) + selected = sorted(summary, key=lambda r: (-r["corrupt_macro_f1_mean"], r["corrupt_mae_mean"], r["method"]))[0]["method"] + (output / "selected_method.txt").write_text( + f"Macro-F1-first validation selection: {selected}. See summary.csv for the full multi-metric tradeoff.\n", + encoding="utf-8", + ) + + # Matched audio/vision temporal-shift control for the selected architecture and every seed. + for seed in args.seeds: + aligned_payload = torch.load(output / "models" / "aligned" / selected / f"seed_{seed}" / "model_best.pt", + map_location=device, weights_only=False) + aligned_model = AlignedFusionModel(selected, tuple(aligned_payload["dims"])).to(device) + aligned_model.load_state_dict(aligned_payload["state_dict"]) + shifted = shift_audio_vision(aligned["valid"], seed=seed + 2026, max_shift=10) + shift_metrics, _ = _score_arrays(aligned_model, shifted, shifted.mask, device) + metric_rows.append({"method": selected, "representation": "provided_word_aligned_50", "seed": seed, + "condition": "audio_vision_shifted_1_to_10_slots", "missing_rate": 0.0, + "n_valid": shifted.n, **shift_metrics}) + del aligned_model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + # Same selected fusion architecture, but equal-window audio/vision pooling of the unaligned source. + print(f"selected_by_corrupt_macro_f1={selected}; starting fixed-window alignment control", flush=True) + fixed_raw = load_fixed_window() + fixed_stats = fit_robust_stats(fixed_raw["train"]) + fixed_stats.save(output / "fixed_window_robust_stats.npz") + fixed = {k: apply_robust_stats(v, fixed_stats) for k, v in fixed_raw.items()} + for seed in args.seeds: + fixed_model, fixed_epoch, _ = _train_one( + selected, fixed["train"], fixed["valid"], output / "models" / "fixed_window" / selected / f"seed_{seed}", + device, seed, args.epochs, args.patience, args.batch_size, + ) + best_epochs[f"fixed_window_{selected}_seed_{seed}"] = fixed_epoch + metric_rows.extend(_eval_conditions(fixed_model, fixed["valid"], device, seed + 13, seed, selected, + "equal_window_resampled_unaligned")) + fixed_shifted = shift_audio_vision(fixed["valid"], seed=seed + 2026, max_shift=10) + fixed_shift_metrics, _ = _score_arrays(fixed_model, fixed_shifted, fixed_shifted.mask, device) + metric_rows.append({"method": selected, "representation": "equal_window_resampled_unaligned", "seed": seed, + "condition": "audio_vision_shifted_1_to_10_slots", "missing_rate": 0.0, + "n_valid": fixed_shifted.n, **fixed_shift_metrics}) + del fixed_model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + all_summary = _summary(metric_rows) + _write_csv(output / "validation_metrics_by_condition.csv", metric_rows) + _write_csv(output / "summary.csv", all_summary) + aligned_summary = [r for r in all_summary if r["representation"] == "provided_word_aligned_50"] + _plot(aligned_summary, metric_rows, output / "missing_rate_comparison.png") + alignment_rows = [] + for rep in ("provided_word_aligned_50", "equal_window_resampled_unaligned"): + for condition in ("clean", "audio_vision_shifted_1_to_10_slots"): + match = [r for r in metric_rows if r["method"] == selected and r["representation"] == rep + and r["condition"] == condition] + if match: + row = {"method": selected, "representation": rep, "condition": condition, + "n_valid": aligned["valid"].n, "n_seeds": len(match)} + for metric in ("accuracy", "macro_f1", "mae", "pearson"): + values = [r[metric] for r in match] + row[metric] = float(np.mean(values)) + row[f"{metric}_sd"] = float(np.std(values, ddof=1)) if len(values) > 1 else 0.0 + alignment_rows.append(row) + corrupt = [r for r in metric_rows if r["method"] == selected and r["representation"] == rep + and r["condition"] != "clean" and r["missing_rate"] > 0] + if corrupt: + per_seed = [] + for seed in args.seeds: + local = [r for r in corrupt if int(r["seed"]) == seed] + if local: + per_seed.append({metric: float(np.mean([r[metric] for r in local])) for metric in + ("accuracy", "macro_f1", "mae", "pearson")}) + alignment_rows.append({ + "method": selected, "representation": rep, "condition": "all_local_corruption_mean", + "missing_rate": float(np.mean([r["missing_rate"] for r in corrupt])), + "n_valid": aligned["valid"].n, "n_seeds": len(per_seed), + **{metric: float(np.mean([r[metric] for r in per_seed])) for metric in ("accuracy", "macro_f1", "mae", "pearson")}, + **{f"{metric}_sd": float(np.std([r[metric] for r in per_seed], ddof=1)) if len(per_seed) > 1 else 0.0 + for metric in ("accuracy", "macro_f1", "mae", "pearson")}, + }) + _write_csv(output / "alignment_transfer_ablation.csv", alignment_rows) + + source_path = ATTACHMENT2 / "aligned_50.pkl" + manifest = { + "source_feature": str(source_path), + "source_sha256": _sha256(source_path), + "device": str(device), + "cuda_name": torch.cuda.get_device_name(0) if device.type == "cuda" else None, + "seeds": args.seeds, + "epochs_max": args.epochs, + "patience": args.patience, + "batch_size": args.batch_size, + "best_epochs": best_epochs, + "selected_macro_f1_first": selected, + "selection_policy": "report Macro-F1, MAE, and Pearson separately; selected model maximizes mean validation Macro-F1 across 15 contiguous corruption conditions, then uses MAE and lexical model name only as tie-breaks", + "models": list(KINDS), + "corruption_rates": [0.10, 0.20, 0.30], + "corruption_patterns": list(PATTERNS), + "feature_scaling": "training split median/MAD; fallback to standard deviation for zero-MAD dimensions", + "test_labels_used": False, + "alignment_transfer_limit": "The official aligned_50 data use a 50-slot wordpiece sequence with no per-slot seconds or stored Q1 B1 time_bounds. The fixed-window comparison is a downstream alignment control, not a re-run of Q1 B1 on the full dataset.", + "python": __import__("sys").version, + "torch": torch.__version__, + "numpy": np.__version__, + "created_unix": time.time(), + } + with (output / "run_manifest.json").open("w", encoding="utf-8") as stream: + json.dump(manifest, stream, ensure_ascii=False, indent=2) + print(f"saved selection artifacts to {output}; selected={selected}; seeds={args.seeds}", flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Train the EarlyConcat baseline and its alignment-transfer control") + parser.add_argument("--seeds", type=int, nargs="+", default=[42, 3407, 2026]) + parser.add_argument("--epochs", type=int, default=32) + parser.add_argument("--patience", type=int, default=6) + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--threads", type=int, default=4) + parser.add_argument("--device", default="auto") + parser.add_argument("--output-dir", default=str(Path(__file__).resolve().parents[1] / "outputs" / "followups" / "earlyconcat_standalone")) + args = parser.parse_args() + _run(args) + + +if __name__ == "__main__": + main() diff --git a/final/q2/deep_learning/q2/train_math_protocol.py b/final/q2/deep_learning/q2/train_math_protocol.py new file mode 100644 index 0000000..3bf86a8 --- /dev/null +++ b/final/q2/deep_learning/q2/train_math_protocol.py @@ -0,0 +1,547 @@ +"""Retrain the two maintained Q2 models under the shared V2 protocol. + +The model architectures and joint CE + SmoothL1 objective stay unchanged. +Training masks, official splits, validation scenarios, and final-test handling +follow the corresponding Q2 protocol where those choices apply. +""" +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import random +import time +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error +from torch import nn + +from .data import ATTACHMENT2, RobustStats, Split, apply_robust_stats, fit_robust_stats +from .evaluate_math_protocol import ( + AURC_BOOTSTRAP_SEED, + BOOTSTRAP_REPS, + CURVE_MODES, + METHODS, + SCENARIO_SEED, + TEST_BOOTSTRAP_SEED, + actual_additional_rates, + aurc_from_curve, + continuous_mask, + curve_scenarios, + load_splits, + make_scenarios, + metrics, + scenario_seed, + sha256, + write_csv, +) +from .models import AlignedFusionModel +from .mofe import MixtureOfFusionExperts +from .train_mofe import EARLYCONCAT, MODEL_CONFIG, MOFE7_MLP, _predict +from .train_compare import _loss, seed_everything + + +Q2_ROOT = Path(__file__).resolve().parents[1] +OUTPUT_DIR = Q2_ROOT / "outputs" / "followups" / "R03_math_protocol_retraining" +SEED = 20260924 +TRAIN_MASK_SEED = 20261227 +BATCH_SIZE = 64 +EPOCH_LIMIT = 12 +PATIENCE = 3 +LEARNING_RATE = 3e-4 +WEIGHT_DECAY = 1e-3 +SELECTION_SCENARIOS = ("0.0/none", "0.3/single", "0.3/sync", "0.5/async") +TRAIN_RATES = (0.0, 0.1, 0.3, 0.5, 0.7) +TRAIN_MODES = ("single", "sync", "partial", "async") + + +def device_for(name: str) -> torch.device: + if name == "auto": + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + return torch.device(name) + + +def set_deterministic(seed: int) -> None: + seed_everything(seed) + torch.set_num_threads(4) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def build_model(method: str, dims: tuple[int, int, int], device: torch.device) -> nn.Module: + if method == EARLYCONCAT: + return AlignedFusionModel("concat", dims=dims).to(device) + if method == MOFE7_MLP: + return MixtureOfFusionExperts(dims=dims, **MODEL_CONFIG).to(device) + raise ValueError(f"unknown method: {method}") + + +def model_state(model: nn.Module, method: str) -> dict[str, Any]: + state: dict[str, Any] = { + "method": method, + "dims": tuple(int(x) for x in model_dims(model)), + "state_dict": model.state_dict(), + "seed": SEED, + "protocol": "Q2 V2 adapted deterministic-model training", + } + if method == EARLYCONCAT: + state["kind"] = "concat" + else: + state["config"] = MODEL_CONFIG + return state + + +def model_dims(model: nn.Module) -> tuple[int, int, int]: + if isinstance(model, AlignedFusionModel): + return tuple(layer[0].in_features for layer in model.projections) # type: ignore[return-value] + if isinstance(model, MixtureOfFusionExperts): + return tuple(layer[0].in_features for layer in model.private_projections) # type: ignore[return-value] + raise TypeError(type(model)) + + +def train_masks_for_epoch(split: Split, epoch: int) -> tuple[np.ndarray, Counter[str]]: + """Sample reproducible math-protocol rates/patterns per training example.""" + rows: list[np.ndarray] = [] + counts: Counter[str] = Counter() + for sample_id, observed in zip(split.ids, split.mask): + rng = np.random.default_rng(scenario_seed(TRAIN_MASK_SEED + SEED, sample_id, f"train/{epoch}")) + rate = float(rng.choice(TRAIN_RATES)) + mode = str(rng.choice(TRAIN_MODES)) + key = f"{rate:.1f}/{mode}" + counts[key] += 1 + row = continuous_mask(observed, rate, mode, rng) + rows.append(row) + return np.stack(rows), counts + + +def _batched_loss( + model: nn.Module, + split: Split, + masks: np.ndarray, + device: torch.device, + batch_size: int, +) -> float: + model.eval() + losses: list[float] = [] + weights: list[int] = [] + with torch.inference_mode(): + for start in range(0, split.n, batch_size): + end = min(start + batch_size, split.n) + xs = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in split.x) + mb = torch.as_tensor(masks[start:end], dtype=torch.bool, device=device) + y_cls = torch.as_tensor(split.y_cls[start:end], dtype=torch.long, device=device) + y_reg = torch.as_tensor(split.y_reg[start:end], dtype=torch.float32, device=device) + losses.append(float(_loss(model(xs, mb), y_cls, y_reg).item())) + weights.append(end - start) + return float(np.average(losses, weights=weights)) + + +def selection_loss(model: nn.Module, valid: Split, scenarios: dict[str, np.ndarray], device: torch.device) -> float: + return float(np.mean([ + _batched_loss(model, valid, scenarios[key], device, BATCH_SIZE) + for key in SELECTION_SCENARIOS + ])) + + +def train_one( + method: str, + train: Split, + valid: Split, + valid_scenarios: dict[str, np.ndarray], + orders: list[np.ndarray], + output_dir: Path, + device: torch.device, +) -> tuple[nn.Module, int, list[dict[str, Any]], Counter[str]]: + set_deterministic(SEED) + model = build_model(method, tuple(x.shape[-1] for x in train.x), device) + optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY) + xs = tuple(torch.as_tensor(x, dtype=torch.float32, device=device) for x in train.x) + y_cls = torch.as_tensor(train.y_cls, dtype=torch.long, device=device) + y_reg = torch.as_tensor(train.y_reg, dtype=torch.float32, device=device) + checkpoint_path = output_dir / "model_best.pt" + history: list[dict[str, Any]] = [] + train_mask_counts: Counter[str] = Counter() + best_loss = math.inf + best_epoch = 0 + stale = 0 + + for epoch in range(1, EPOCH_LIMIT + 1): + model.train() + epoch_masks, epoch_counts = train_masks_for_epoch(train, epoch) + train_mask_counts.update(epoch_counts) + batch_losses: list[float] = [] + order = orders[epoch - 1] + for start in range(0, train.n, BATCH_SIZE): + indices_np = order[start:start + BATCH_SIZE] + indices = torch.as_tensor(indices_np, dtype=torch.long, device=device) + mb = torch.as_tensor(epoch_masks[indices_np], dtype=torch.bool, device=device) + output = model(tuple(x.index_select(0, indices) for x in xs), mb) + loss = _loss(output, y_cls.index_select(0, indices), y_reg.index_select(0, indices)) + optimizer.zero_grad(set_to_none=True) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + optimizer.step() + batch_losses.append(float(loss.detach().item())) + + valid_selection_loss = selection_loss(model, valid, valid_scenarios, device) + row = { + "method": method, + "seed": SEED, + "epoch": epoch, + "train_loss": float(np.mean(batch_losses)), + "valid_selection_loss": valid_selection_loss, + "valid_clean_loss": _batched_loss(model, valid, valid.mask, device, BATCH_SIZE), + } + history.append(row) + print( + f"[{method}] epoch={epoch:02d} train={row['train_loss']:.4f} " + f"valid_selection={valid_selection_loss:.4f} clean={row['valid_clean_loss']:.4f}", + flush=True, + ) + if valid_selection_loss < best_loss - 1e-4: + best_loss = valid_selection_loss + best_epoch = epoch + stale = 0 + torch.save(model_state(model, method) | {"best_epoch": best_epoch}, checkpoint_path) + else: + stale += 1 + if stale >= PATIENCE: + break + + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + model.load_state_dict(saved["state_dict"]) + model.eval() + write_csv(output_dir / "training_history.csv", history) + return model, best_epoch, history, train_mask_counts + + +def _group_map(ids: list[str]) -> tuple[list[str], dict[str, np.ndarray]]: + source_ids = [sample_id.split("$_$", 1)[0] for sample_id in ids] + groups = sorted(set(source_ids)) + mapping = { + group: np.flatnonzero(np.asarray([source == group for source in source_ids])) + for group in groups + } + return groups, mapping + + +def test_group_bootstrap(test: Split, predictions: dict[str, dict[str, np.ndarray]]) -> list[dict[str, Any]]: + groups, mapping = _group_map(test.ids) + rng = np.random.default_rng(TEST_BOOTSTRAP_SEED) + draws: dict[str, list[float]] = defaultdict(list) + for _ in range(BOOTSTRAP_REPS): + selected = rng.choice(groups, size=len(groups), replace=True) + indices = np.concatenate([mapping[group] for group in selected]) + values = { + method: metrics(test, predictions[method]["logits"], predictions[method]["intensity"], indices) + for method in METHODS + } + for name in values[EARLYCONCAT]: + draws[name].append(values[MOFE7_MLP][name] - values[EARLYCONCAT][name]) + point = { + name: metrics(test, predictions[MOFE7_MLP]["logits"], predictions[MOFE7_MLP]["intensity"])[name] + - metrics(test, predictions[EARLYCONCAT]["logits"], predictions[EARLYCONCAT]["intensity"])[name] + for name in draws + } + return [{ + "comparison": f"{MOFE7_MLP} minus {EARLYCONCAT}", + "metric": name, + "delta": point[name], + "bootstrap_ci_2p5": float(np.quantile(values, 0.025)), + "bootstrap_ci_97p5": float(np.quantile(values, 0.975)), + "bootstrap_probability_delta_gt_0": float(np.mean(np.asarray(values) > 0)), + "replicates": BOOTSTRAP_REPS, + "resampling_unit": "source video id", + "paired": True, + "seed": TEST_BOOTSTRAP_SEED, + } for name, values in draws.items()] + + +def validation_aurc_bootstrap( + valid: Split, + predictions: dict[tuple[str, str], dict[str, np.ndarray]], + rates_by_sample: dict[str, np.ndarray], +) -> list[dict[str, Any]]: + groups, mapping = _group_map(valid.ids) + rng = np.random.default_rng(AURC_BOOTSTRAP_SEED) + deltas: dict[str, list[float]] = {mode: [] for mode in CURVE_MODES} + + def score(method: str, mode: str, indices: np.ndarray) -> float: + keys = curve_scenarios(mode) + xs = [float(np.nanmean(rates_by_sample[key][indices])) for key in keys] + ys = [ + float(np.abs(valid.y_reg[indices] - predictions[(method, key)]["intensity"][indices]).mean()) + for key in keys + ] + return aurc_from_curve(xs, ys) + + for _ in range(BOOTSTRAP_REPS): + selected = rng.choice(groups, size=len(groups), replace=True) + indices = np.concatenate([mapping[group] for group in selected]) + for mode in CURVE_MODES: + deltas[mode].append(score(MOFE7_MLP, mode, indices) - score(EARLYCONCAT, mode, indices)) + rows = [] + for mode in CURVE_MODES: + all_indices = np.arange(valid.n) + values = deltas[mode] + rows.append({ + "mask_mode": mode, + "delta_aurc_mae_mofe_minus_earlyconcat": score(MOFE7_MLP, mode, all_indices) - score(EARLYCONCAT, mode, all_indices), + "bootstrap_ci_2p5": float(np.quantile(values, 0.025)), + "bootstrap_ci_97p5": float(np.quantile(values, 0.975)), + "bootstrap_probability_delta_lt_0": float(np.mean(np.asarray(values) < 0)), + "replicates": BOOTSTRAP_REPS, + "resampling_unit": "source video id", + "paired": True, + "seed": AURC_BOOTSTRAP_SEED, + }) + return rows + + +def run(device_name: str = "auto", output_dir: Path = OUTPUT_DIR, + input_version: str = "aligned_50", batch_size: int = BATCH_SIZE) -> None: + global BATCH_SIZE + if batch_size < 1: + raise ValueError("batch_size must be positive") + BATCH_SIZE = batch_size + if output_dir.exists() and any(output_dir.iterdir()): + raise FileExistsError(f"refusing to overwrite non-empty result directory: {output_dir}") + output_dir.mkdir(parents=True, exist_ok=True) + device = device_for(device_name) + if device.type == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("CUDA was requested but is unavailable") + + if input_version not in {"aligned_50", "unaligned_50"}: + raise ValueError(f"unsupported input version: {input_version}") + feature_path = ATTACHMENT2 / f"{input_version}.pkl" + if input_version == "unaligned_50": + from ....adapter import adapt_official_split + from .data import _unpickle, _ids_and_targets + + source = _unpickle(feature_path) + raw_splits = {} + adapter_audit = {} + for name in ("train", "valid", "test"): + arrays, mask, audit = adapt_official_split(source[name]) + ids, y_cls, y_reg = _ids_and_targets(source[name]) + raw_splits[name] = Split(tuple(arrays[m] for m in ("text", "audio", "vision")), + mask, y_cls, y_reg, ids) + adapter_audit[name] = audit + del source + groups = {name: {sid.split("$_$", 1)[0] for sid in split.ids} + for name, split in raw_splits.items()} + if any(groups[a] & groups[b] for a, b in (("train", "valid"), ("train", "test"), ("valid", "test"))): + raise ValueError("official source-video groups overlap") + else: + raw_splits = load_splits(feature_path) + adapter_audit = None + train_raw, valid_raw, test_raw = raw_splits["train"], raw_splits["valid"], raw_splits["test"] + stats = fit_robust_stats(train_raw) + train, valid, test = (apply_robust_stats(s, stats) for s in (train_raw, valid_raw, test_raw)) + stats_path = output_dir / f"{input_version}_robust_stats.npz" + stats.save(stats_path) + dims = tuple(int(x.shape[-1]) for x in train.x) + valid_scenarios = make_scenarios(valid, SCENARIO_SEED) + if len(valid_scenarios) != 42: + raise ValueError(f"expected 42 controlled scenarios, got {len(valid_scenarios)}") + rates_by_sample = actual_additional_rates(valid.mask, valid_scenarios) + + set_deterministic(SEED) + order_rng = np.random.default_rng(SEED + 809) + orders = [order_rng.permutation(train.n) for _ in range(EPOCH_LIMIT)] + best_epochs: dict[str, int] = {} + training_rows: list[dict[str, Any]] = [] + mask_count_rows: list[dict[str, Any]] = [] + parameter_rows: list[dict[str, Any]] = [] + + for method in METHODS: + model_dir = output_dir / "models" / method / f"seed_{SEED}" + model_dir.mkdir(parents=True, exist_ok=True) + model, best_epoch, history, mask_counts = train_one( + method, train, valid, valid_scenarios, orders, model_dir, device + ) + best_epochs[method] = best_epoch + training_rows.extend(history) + parameter_rows.append({ + "method": method, + "parameters_total": sum(p.numel() for p in model.parameters()), + "parameters_trainable": sum(p.numel() for p in model.parameters() if p.requires_grad), + "best_epoch": best_epoch, + }) + for key, count in sorted(mask_counts.items()): + mask_count_rows.append({"method": method, "seed": SEED, "rate_mode": key, "sample_epoch_assignments": count}) + del model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + write_csv(output_dir / "training_history.csv", training_rows) + write_csv(output_dir / "training_mask_distribution.csv", mask_count_rows) + write_csv(output_dir / "parameter_count.csv", parameter_rows) + + # Reload the selected checkpoints, then conduct one final official-test pass. + test_predictions: dict[str, dict[str, np.ndarray]] = {} + test_rows: list[dict[str, Any]] = [] + condition_predictions: dict[tuple[str, str], dict[str, np.ndarray]] = {} + condition_rows: list[dict[str, Any]] = [] + for method in METHODS: + checkpoint_path = output_dir / "models" / method / f"seed_{SEED}" / "model_best.pt" + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + model = build_model(method, dims, device) + model.load_state_dict(saved["state_dict"]) + model.eval() + + test_prediction = _predict(model, test, test.mask, device, BATCH_SIZE) + test_predictions[method] = test_prediction + test_rows.append({ + "method": method, + "seed": SEED, + "best_epoch": best_epochs[method], + "n_test": test.n, + **metrics(test, test_prediction["logits"], test_prediction["intensity"]), + }) + + for scenario, masks in valid_scenarios.items(): + prediction = _predict(model, valid, masks, device, BATCH_SIZE) + condition_predictions[(method, scenario)] = prediction + condition_rows.append({ + "method": method, + "seed": SEED, + "scenario": scenario, + "realized_additional_global_rate": float(np.nanmean(rates_by_sample[scenario])), + "n_valid": valid.n, + **metrics(valid, prediction["logits"], prediction["intensity"]), + }) + print(f"[valid/{method}] {scenario} done", flush=True) + del model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + write_csv(output_dir / "official_test_metrics_by_seed.csv", test_rows) + write_csv(output_dir / "official_test_paired_bootstrap.csv", test_group_bootstrap(test, test_predictions)) + write_csv(output_dir / "controlled_metrics_by_scenario.csv", condition_rows) + + test_summary = [] + for method in METHODS: + row = next(r for r in test_rows if r["method"] == method) + for metric in ("accuracy", "macro_f1", "mae", "rmse", "pearson"): + test_summary.append({"method": method, "metric": metric, "mean": row[metric], "sd_across_seeds": 0.0, "n_seeds": 1}) + write_csv(output_dir / "official_test_summary.csv", test_summary) + + aurc_rows: list[dict[str, Any]] = [] + for method in METHODS: + for mode in CURVE_MODES: + keys = curve_scenarios(mode) + xs = [float(np.nanmean(rates_by_sample[key])) for key in keys] + ys = [ + float(np.abs(valid.y_reg - condition_predictions[(method, key)]["intensity"]).mean()) + for key in keys + ] + aurc_rows.append({ + "method": method, + "seed": SEED, + "mask_mode": mode, + "aurc_mae": aurc_from_curve(xs, ys), + "rates_realized": json.dumps(xs), + }) + write_csv(output_dir / "aurc_mae_by_mode_seed.csv", aurc_rows) + write_csv(output_dir / "aurc_mae_paired_bootstrap.csv", validation_aurc_bootstrap(valid, condition_predictions, rates_by_sample)) + + manifest = { + "experiment": "Retrained EarlyConcat and MoFE-7 + MLP Router using the shared Q2 V2 protocol", + "created_unix": time.time(), + "device": str(device), + "cuda_device": torch.cuda.get_device_name(0) if device.type == "cuda" else None, + "feature_file": str(feature_path), + "feature_sha256": sha256(feature_path), + "representation": ("shared Q1 adapter relative-progress projection of official unaligned_50; not physical-time alignment" + if input_version == "unaligned_50" else + "official aligned_50 ordered positions; not Q1 physical-time bins"), + "adapter": "Q1 adapter relative-progress projection" if input_version == "unaligned_50" else None, + "adapter_audit": adapter_audit, + "train_valid_test_counts": {name: split.n for name, split in raw_splits.items()}, + "source_video_groups": {name: len({sid.split("$_$", 1)[0] for sid in split.ids}) for name, split in raw_splits.items()}, + "official_group_splits_disjoint": True, + "train_only_scaler": str(stats_path), + "scaler_fit": "median and 1.4826*MAD on observed training rows only; zero-MAD fallback to std then 1", + "seed": SEED, + "model_seeds": [SEED], + "training_configuration": { + "epoch_limit": EPOCH_LIMIT, + "early_stopping_patience": PATIENCE, + "batch_size": BATCH_SIZE, + "optimizer": "AdamW", + "learning_rate": LEARNING_RATE, + "weight_decay": WEIGHT_DECAY, + "gradient_clip_norm": 1.0, + "early_stopping_metric": "mean validation joint CE + 0.5*SmoothL1 over 0.0/none, 0.3/single, 0.3/sync, 0.5/async", + "architecture_preserved": { + EARLYCONCAT: "EarlyConcat + BiGRU", + MOFE7_MLP: "MoFE-7 + MLP Router", + }, + "objective": "cross entropy + 0.5 * SmoothL1(intensity/3, label/3); same objective for both methods", + "training_corruption": { + "rates": list(TRAIN_RATES), + "patterns": list(TRAIN_MODES), + "preserve_at_least_fraction_per_selected_modality": 0.2, + "generator_seed": TRAIN_MASK_SEED, + "same_sample_masks_and_batch_orders_across_models": True, + }, + }, + "validation_protocol": { + "scenario_seed": SCENARIO_SEED, + "scenario_count": len(valid_scenarios), + "same_fixed_masks_for_both_models": True, + "scenario_design": "42 controlled continuous-mask scenarios regenerated on each sample's original observation mask", + "selection_scenarios": list(SELECTION_SCENARIOS), + "selection_note": "Deterministic-model adaptation; uses joint supervised loss instead of C5's probabilistic selection NLL.", + "aurc": "normalized trapezoidal MAE area over realized equal-modality-weighted additional missing rate for single/sync/partial/async at 0/.1/.3/.5/.7", + }, + "test_protocol": { + "official_test_final_clean_passes": 1, + "test_used_for_training_or_checkpoint_selection": False, + "metrics": ["accuracy", "macro_f1", "mae", "rmse", "pearson"], + "paired_group_bootstrap_replicates": BOOTSTRAP_REPS, + "bootstrap_unit": "source video id", + "bootstrap_seed": TEST_BOOTSTRAP_SEED, + }, + } + (output_dir / "run_manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8") + (output_dir / "hypothesis.md").write_text( + "# R03: 按统一 Q2 V2 口径重训两种保留模型\n\n" + "## 假设\n\n" + "在保持 EarlyConcat + BiGRU 与 MoFE-7 + MLP Router 结构及共同监督目标不变的情况下，" + "使用数学方案中的官方划分、连续块缺失训练和 42 个固定验证情景，可以公平比较两种模型的干净测试表现与缺失鲁棒性。\n\n" + "## 唯一实验改动\n\n" + "相对现有检查点，本轮重新训练时将缺失训练改为 0/10/30/50/70% 与 single/sync/partial/async，" + "每个被选模态至少保留 20% 观测；训练和批次顺序在两个模型间配对。数学方案中的 C5 概率损失不适用于现有确定性分类/回归头，" + "因此保留项目既有的 CE + 0.5 SmoothL1 联合目标。\n\n" + "## 数据使用\n\n" + "标准化器只在官方训练集观测行上拟合；官方验证集只用于早停与缺失评估；官方测试集在全部检查点确定后做一次干净评估。\n", + encoding="utf-8", + ) + + print(f"wrote retraining results to {output_dir}", flush=True) + print(f"train/valid/test={train.n}/{valid.n}/{test.n}; device={device}; best_epochs={best_epochs}", flush=True) + for row in test_rows: + print( + f"{row['method']}: Acc={row['accuracy']:.4f} Macro-F1={row['macro_f1']:.4f} " + f"MAE={row['mae']:.4f} RMSE={row['rmse']:.4f} Pearson={row['pearson']:.4f}", + flush=True, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", default="auto", choices=("auto", "cuda", "cpu")) + parser.add_argument("--output-dir", type=Path, default=OUTPUT_DIR) + parser.add_argument("--input-version", choices=("aligned_50", "unaligned_50"), default="unaligned_50") + parser.add_argument("--batch-size", type=int, default=BATCH_SIZE) + arguments = parser.parse_args() + run(device_name=arguments.device, output_dir=arguments.output_dir, + input_version=arguments.input_version, batch_size=arguments.batch_size) diff --git a/final/q2/deep_learning/q2/train_mofe.py b/final/q2/deep_learning/q2/train_mofe.py new file mode 100644 index 0000000..a22b6d4 --- /dev/null +++ b/final/q2/deep_learning/q2/train_mofe.py @@ -0,0 +1,787 @@ +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import random +import sys +import time +from pathlib import Path +from typing import Any + +import numpy as np +import torch +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error +from torch import nn + +from .data import ( + ATTACHMENT2, + MODALITIES, + RobustStats, + Split, + apply_robust_stats, + augment_masks, + corrupt_masks, + fit_robust_stats, + load_aligned, +) +from .models import AlignedFusionModel +from .mofe import EXPERT_NAMES, SUBSETS, MixtureOfFusionExperts +from .train_compare import PATTERNS, _loss, _pearson, _train_one, seed_everything + + +ROOT = Path(__file__).resolve().parents[1] +REFERENCE_OUTPUT = ROOT / "outputs" / "mofe_7experts" +DEFAULT_OUTPUT = ROOT / "outputs" / "mofe_7experts" +EARLYCONCAT = "B0_early_concat" +MOFE7_MLP = "B5_mofe_mlp" +SEEDS = (42, 3407, 2026) +RATES = (0.10, 0.20, 0.30) +HIDDEN = 128 +LATENT_DIM = 64 +MODEL_CONFIG: dict[str, Any] = { + "router": "mlp", + "expert_names": EXPERT_NAMES, + "availability_mode": "hard", +} +SUMMARY_METRICS = ( + "corrupt_macro_f1", + "worst_condition_macro_f1", + "text_30_macro_f1", + "corrupt_mae", + "corrupt_pearson", +) + + +def _write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + return + fields = list(dict.fromkeys(key for row in rows for key in row)) + with path.open("w", newline="", encoding="utf-8-sig") as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def _read_csv(path: Path) -> list[dict[str, str]]: + if not path.exists(): + return [] + with path.open("r", newline="", encoding="utf-8-sig") as stream: + return list(csv.DictReader(stream)) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _device_for(name: str) -> torch.device: + if name == "auto": + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + return torch.device(name) + + +def _conditions(valid: Split, seed: int) -> list[tuple[str, float, np.ndarray]]: + rows = [("clean", 0.0, valid.mask.copy())] + for rate in RATES: + for pattern_idx, (pattern, modalities) in enumerate(PATTERNS.items()): + masks = corrupt_masks( + valid.mask, + rate, + modalities, + seed + 13 + pattern_idx * 101 + int(rate * 1000), + ) + rows.append((f"{pattern}_{int(rate * 100)}", rate, masks)) + return rows + + +def _metric_dict( + y_cls: np.ndarray, + y_reg: np.ndarray, + logits: np.ndarray, + intensity: np.ndarray, +) -> dict[str, float]: + predicted_class = np.asarray(logits).argmax(axis=-1) + predicted_intensity = np.clip(np.asarray(intensity).reshape(-1), -3.0, 3.0) + return { + "accuracy": float(accuracy_score(y_cls, predicted_class)), + "macro_f1": float(f1_score(y_cls, predicted_class, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(mean_absolute_error(y_reg, predicted_intensity)), + "pearson": _pearson(y_reg, predicted_intensity), + } + + +def _validation_loss(model: nn.Module, valid: Split, device: torch.device, batch_size: int) -> float: + model.eval() + values: list[float] = [] + weights: list[int] = [] + with torch.inference_mode(): + for start in range(0, valid.n, batch_size): + end = min(start + batch_size, valid.n) + xs = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in valid.x) + masks = torch.as_tensor(valid.mask[start:end], dtype=torch.bool, device=device) + y_cls = torch.as_tensor(valid.y_cls[start:end], dtype=torch.long, device=device) + y_reg = torch.as_tensor(valid.y_reg[start:end], dtype=torch.float32, device=device) + values.append(float(_loss(model(xs, masks), y_cls, y_reg).item())) + weights.append(end - start) + return float(np.average(values, weights=weights)) + + +def _train_mofe( + train: Split, + valid: Split, + output_dir: Path, + device: torch.device, + seed: int, + epochs: int, + patience: int, + batch_size: int, + reuse_checkpoint: bool, +) -> tuple[MixtureOfFusionExperts, int, list[dict[str, Any]]]: + dims = tuple(int(x.shape[-1]) for x in train.x) + checkpoint_path = output_dir / "model_best.pt" + history_path = output_dir / "training_history.csv" + if reuse_checkpoint and checkpoint_path.exists(): + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + if saved.get("config") != MODEL_CONFIG or tuple(saved.get("dims", ())) != dims or int(saved.get("seed", -1)) != seed: + raise ValueError(f"cached MoFE checkpoint does not match the selected configuration: {checkpoint_path}") + model = MixtureOfFusionExperts(dims=dims, **MODEL_CONFIG).to(device) + model.load_state_dict(saved["state_dict"]) + history = [ + {"method": MOFE7_MLP, "seed": seed, **{key: float(value) for key, value in row.items() if key in {"epoch", "train_loss", "valid_clean_loss"}}} + for row in _read_csv(history_path) + ] + return model.eval(), int(saved.get("best_epoch", 0)), history + + output_dir.mkdir(parents=True, exist_ok=True) + seed_everything(seed) + model = MixtureOfFusionExperts(dims=dims, **MODEL_CONFIG).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=1.5e-4, weight_decay=1e-4) + xs = tuple(torch.as_tensor(x, dtype=torch.float32, device=device) for x in train.x) + base_masks = train.mask + y_cls = torch.as_tensor(train.y_cls, dtype=torch.long, device=device) + y_reg = torch.as_tensor(train.y_reg, dtype=torch.float32, device=device) + rng = np.random.default_rng(seed + 809) + best_loss = math.inf + best_epoch = 0 + stale_epochs = 0 + history: list[dict[str, Any]] = [] + + for epoch in range(1, epochs + 1): + model.train() + order = rng.permutation(train.n) + batch_losses: list[float] = [] + for start in range(0, train.n, batch_size): + ids_np = order[start:start + batch_size] + ids = torch.as_tensor(ids_np, dtype=torch.long, device=device) + masks_np = augment_masks(base_masks[ids_np], rng) + masks = torch.as_tensor(masks_np, dtype=torch.bool, device=device) + output = model(tuple(x.index_select(0, ids) for x in xs), masks) + loss = _loss(output, y_cls.index_select(0, ids), y_reg.index_select(0, ids)) + optimizer.zero_grad(set_to_none=True) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + optimizer.step() + batch_losses.append(float(loss.detach().item())) + + valid_loss = _validation_loss(model, valid, device, batch_size) + row = { + "method": MOFE7_MLP, + "seed": seed, + "epoch": epoch, + "train_loss": float(np.mean(batch_losses)), + "valid_clean_loss": valid_loss, + } + history.append(row) + print(f"[MoFE-7 MLP] seed={seed} epoch={epoch:02d} train={row['train_loss']:.4f} valid={valid_loss:.4f}", flush=True) + if valid_loss < best_loss - 1e-4: + best_loss = valid_loss + best_epoch = epoch + stale_epochs = 0 + torch.save({ + "method": MOFE7_MLP, + "config": MODEL_CONFIG, + "dims": dims, + "state_dict": model.state_dict(), + "seed": seed, + "best_epoch": epoch, + }, checkpoint_path) + else: + stale_epochs += 1 + if stale_epochs >= patience: + break + + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + model.load_state_dict(saved["state_dict"]) + model.eval() + _write_csv(history_path, history) + return model, best_epoch, history + + +def _load_or_train_concat( + train: Split, + valid: Split, + output_dir: Path, + device: torch.device, + seed: int, + epochs: int, + patience: int, + batch_size: int, + reuse_checkpoint: bool, +) -> tuple[AlignedFusionModel, int, list[dict[str, Any]]]: + checkpoint_path = output_dir / "model_best.pt" + dims = tuple(int(x.shape[-1]) for x in train.x) + if reuse_checkpoint and checkpoint_path.exists(): + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + if saved.get("kind") != "concat" or tuple(saved.get("dims", ())) != dims or int(saved.get("seed", -1)) != seed: + raise ValueError(f"cached EarlyConcat checkpoint does not match: {checkpoint_path}") + model = AlignedFusionModel("concat", dims=dims).to(device) + model.load_state_dict(saved["state_dict"]) + history = [ + {"method": EARLYCONCAT, "seed": seed, **{key: float(value) for key, value in row.items() if key in {"epoch", "train_loss", "valid_clean_loss"}}} + for row in _read_csv(output_dir / "training_history.csv") + ] + return model.eval(), int(saved.get("best_epoch", 0)), history + + model, best_epoch, history = _train_one( + "concat", train, valid, output_dir, device, seed, epochs, patience, batch_size + ) + rows = [{"method": EARLYCONCAT, "seed": seed, **row} for row in history] + return model.eval(), best_epoch, rows + + +@torch.inference_mode() +def _predict( + model: nn.Module, + split: Split, + masks: np.ndarray, + device: torch.device, + batch_size: int, + force_expert: str | None = None, +) -> dict[str, np.ndarray]: + fields = ["logits", "intensity"] + if isinstance(model, MixtureOfFusionExperts): + fields.extend(("alpha", "utility", "availability", "fallback")) + chunks: dict[str, list[np.ndarray]] = {name: [] for name in fields} + for start in range(0, split.n, batch_size): + end = min(start + batch_size, split.n) + xs = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in split.x) + mask_batch = torch.as_tensor(masks[start:end], dtype=torch.bool, device=device) + output = model(xs, mask_batch, force_expert=force_expert) if isinstance(model, MixtureOfFusionExperts) else model(xs, mask_batch) + for name in fields: + value = output[name] + chunks[name].append(value.float().cpu().numpy()) + result = {name: np.concatenate(values, axis=0) for name, values in chunks.items()} + result["intensity"] = np.clip(result["intensity"].reshape(-1), -3.0, 3.0) + return result + + +def _condition_row( + method: str, + seed: int, + condition: str, + rate: float, + split: Split, + prediction: dict[str, np.ndarray], +) -> dict[str, Any]: + return { + "method": method, + "seed": seed, + "condition": condition, + "missing_rate": rate, + "n_valid": split.n, + **_metric_dict(split.y_cls, split.y_reg, prediction["logits"], prediction["intensity"]), + } + + +def _diagnostics( + seed: int, + condition: str, + masks: np.ndarray, + prediction: dict[str, np.ndarray], +) -> tuple[dict[str, Any], dict[str, Any]]: + alpha = prediction["alpha"] + availability = prediction["availability"].astype(bool) + active = availability.any(axis=-1) + active_alpha = alpha[active] + if active_alpha.size: + means = active_alpha.mean(axis=0) + entropy = -(active_alpha * np.log(np.maximum(active_alpha, 1e-12))).sum(axis=-1) / np.log(len(EXPERT_NAMES)) + high_weight = (active_alpha.max(axis=-1) > 0.8).mean() + else: + means = np.zeros(len(EXPERT_NAMES), dtype=np.float64) + entropy = np.zeros(0, dtype=np.float64) + high_weight = 0.0 + route_row: dict[str, Any] = { + "method": MOFE7_MLP, + "seed": seed, + "condition": condition, + "active_position_fraction": float(active.mean()), + "fallback_position_fraction": float((~active).mean()), + "normalized_router_entropy": float(entropy.mean()) if entropy.size else 0.0, + "fraction_active_positions_max_weight_over_0p8": float(high_weight), + } + for index, name in enumerate(EXPERT_NAMES): + route_row[f"alpha_{name}_mean"] = float(means[index]) + utility = prediction["utility"] + utility_row: dict[str, Any] = {"method": MOFE7_MLP, "seed": seed, "condition": condition} + for modality, name in enumerate(MODALITIES): + observed = masks[..., modality] + utility_row[f"utility_{name}_mean"] = float(utility[..., modality][observed].mean()) if observed.any() else 0.0 + return route_row, utility_row + + +def _summary_rows(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + summaries: list[dict[str, Any]] = [] + for method in (EARLYCONCAT, MOFE7_MLP): + matching = [row for row in rows if row["method"] == method] + seeds = sorted({int(row["seed"]) for row in matching}) + conditions = list(dict.fromkeys(row["condition"] for row in matching)) + by_seed_condition = {(int(row["seed"]), row["condition"]): row for row in matching} + clean = [by_seed_condition[(seed, "clean")] for seed in seeds] + corrupt_conditions = [condition for condition in conditions if condition != "clean"] + corrupt_by_seed = { + seed: [by_seed_condition[(seed, condition)] for condition in corrupt_conditions] + for seed in seeds + } + condition_f1 = { + condition: float(np.mean([by_seed_condition[(seed, condition)]["macro_f1"] for seed in seeds])) + for condition in corrupt_conditions + } + worst_condition = min(condition_f1, key=condition_f1.get) + row: dict[str, Any] = {"method": method, "n_seeds": len(seeds), "worst_condition": worst_condition} + for metric in ("accuracy", "macro_f1", "mae", "pearson"): + clean_values = [float(item[metric]) for item in clean] + corrupt_values = [float(np.mean([item[metric] for item in corrupt_by_seed[seed]])) for seed in seeds] + row[f"clean_{metric}"] = float(np.mean(clean_values)) + row[f"clean_{metric}_sd"] = float(np.std(clean_values, ddof=1)) if len(clean_values) > 1 else 0.0 + row[f"corrupt_{metric}_mean"] = float(np.mean(corrupt_values)) + row[f"corrupt_{metric}_sd"] = float(np.std(corrupt_values, ddof=1)) if len(corrupt_values) > 1 else 0.0 + row["worst_condition_macro_f1"] = condition_f1[worst_condition] + row["worst_single_run_macro_f1"] = min( + item["macro_f1"] for seed in seeds for item in corrupt_by_seed[seed] + ) + text_30 = [by_seed_condition[(seed, "text_30")] for seed in seeds] + row["text_30_macro_f1"] = float(np.mean([item["macro_f1"] for item in text_30])) + row["text_30_macro_f1_sd"] = float(np.std([item["macro_f1"] for item in text_30], ddof=1)) if len(text_30) > 1 else 0.0 + for condition in ("audio_30", "vision_30", "audio_vision_30", "all_modalities_30"): + values = [by_seed_condition[(seed, condition)]["macro_f1"] for seed in seeds] + row[f"{condition}_macro_f1"] = float(np.mean(values)) + row[f"{condition}_macro_f1_sd"] = float(np.std(values, ddof=1)) if len(values) > 1 else 0.0 + row["corrupt_macro_f1"] = row["corrupt_macro_f1_mean"] + row["corrupt_mae"] = row["corrupt_mae_mean"] + row["corrupt_pearson"] = row["corrupt_pearson_mean"] + summaries.append(row) + return summaries + + +def _bootstrap_distributions( + method: str, + predictions: dict[tuple[str, int, str], dict[str, np.ndarray]], + valid: Split, + seeds: list[int], + conditions: list[str], + group_counts: np.ndarray, +) -> dict[str, np.ndarray]: + group_names = sorted({sample_id.split("$_$", 1)[0] for sample_id in valid.ids}) + group_index = {name: index for index, name in enumerate(group_names)} + row_group = np.asarray([group_index[sample_id.split("$_$", 1)[0]] for sample_id in valid.ids], dtype=np.int64) + n_groups = len(group_names) + n_slots = len(seeds) * len(conditions) + confusion_by_group = np.zeros((n_groups, n_slots, 9), dtype=np.float64) + regression_by_group = np.zeros((n_groups, n_slots, 7), dtype=np.float64) + for seed_index, seed in enumerate(seeds): + for condition_index, condition in enumerate(conditions): + slot = seed_index * len(conditions) + condition_index + pred = predictions[(method, seed, condition)] + predicted_class = pred["logits"].argmax(axis=-1) + code = valid.y_cls * 3 + predicted_class + np.add.at(confusion_by_group[:, slot, :], (row_group, code), 1.0) + intensity = np.clip(pred["intensity"].reshape(-1), -3.0, 3.0) + values = np.stack(( + np.ones(valid.n), + np.abs(valid.y_reg - intensity), + valid.y_reg, + valid.y_reg ** 2, + intensity, + intensity ** 2, + valid.y_reg * intensity, + ), axis=-1) + for statistic in range(values.shape[-1]): + np.add.at(regression_by_group[:, slot, statistic], row_group, values[:, statistic]) + + weighted_confusion = np.einsum("rg,gsk->rsk", group_counts, confusion_by_group, optimize=True) + cm = weighted_confusion.reshape(len(group_counts), len(seeds), len(conditions), 3, 3) + true_count = cm.sum(axis=-1) + predicted_count = cm.sum(axis=-2) + true_positive = np.diagonal(cm, axis1=-2, axis2=-1) + denominator = true_count + predicted_count + class_f1 = np.divide(2.0 * true_positive, denominator, out=np.zeros_like(true_positive), where=denominator > 0) + macro_f1 = class_f1.mean(axis=-1) + + weighted_regression = np.einsum("rg,gsk->rsk", group_counts, regression_by_group, optimize=True) + regression = weighted_regression.reshape(len(group_counts), len(seeds), len(conditions), 7) + count = np.maximum(regression[..., 0], 1.0) + mae = regression[..., 1] / count + sum_y, sum_y2, sum_pred, sum_pred2, sum_yp = (regression[..., index] for index in range(2, 7)) + covariance = sum_yp - sum_y * sum_pred / count + variance_y = np.maximum(sum_y2 - sum_y ** 2 / count, 0.0) + variance_pred = np.maximum(sum_pred2 - sum_pred ** 2 / count, 0.0) + denominator_corr = np.sqrt(variance_y * variance_pred) + pearson = np.divide(covariance, denominator_corr, out=np.zeros_like(covariance), where=denominator_corr > 1e-12) + text_30_index = conditions.index("text_30") + return { + "corrupt_macro_f1": macro_f1[:, :, 1:].mean(axis=(1, 2)), + "worst_condition_macro_f1": macro_f1[:, :, 1:].mean(axis=1).min(axis=1), + "text_30_macro_f1": macro_f1[:, :, text_30_index].mean(axis=1), + "corrupt_mae": mae[:, :, 1:].mean(axis=(1, 2)), + "corrupt_pearson": pearson[:, :, 1:].mean(axis=(1, 2)), + } + + +def _paired_bootstrap( + predictions: dict[tuple[str, int, str], dict[str, np.ndarray]], + valid: Split, + seeds: list[int], + conditions: list[str], + reps: int, + bootstrap_seed: int, + summaries: list[dict[str, Any]], +) -> list[dict[str, Any]]: + groups = sorted({sample_id.split("$_$", 1)[0] for sample_id in valid.ids}) + rng = np.random.default_rng(bootstrap_seed) + draws = rng.integers(0, len(groups), size=(reps, len(groups))) + group_counts = np.zeros((reps, len(groups)), dtype=np.float64) + for rep in range(reps): + group_counts[rep] = np.bincount(draws[rep], minlength=len(groups)) + candidate = _bootstrap_distributions(MOFE7_MLP, predictions, valid, seeds, conditions, group_counts) + reference = _bootstrap_distributions(EARLYCONCAT, predictions, valid, seeds, conditions, group_counts) + summary_map = {row["method"]: row for row in summaries} + point_keys = { + "corrupt_macro_f1": "corrupt_macro_f1", + "worst_condition_macro_f1": "worst_condition_macro_f1", + "text_30_macro_f1": "text_30_macro_f1", + "corrupt_mae": "corrupt_mae", + "corrupt_pearson": "corrupt_pearson", + } + rows = [] + for metric in SUMMARY_METRICS: + delta = candidate[metric] - reference[metric] + key = point_keys[metric] + rows.append({ + "comparison": "MoFE-7 MLP vs EarlyConcat", + "candidate": MOFE7_MLP, + "reference": EARLYCONCAT, + "metric": metric, + "delta_candidate_minus_reference": float(summary_map[MOFE7_MLP][key] - summary_map[EARLYCONCAT][key]), + "bootstrap_ci_2p5": float(np.quantile(delta, 0.025)), + "bootstrap_ci_97p5": float(np.quantile(delta, 0.975)), + "bootstrap_probability_delta_gt_0": float(np.mean(delta > 0.0)), + "bootstrap_replicates": reps, + "resampling_unit": "source video id", + "paired": True, + "seed": bootstrap_seed, + }) + return rows + + +def _plot_summary(output: Path, summaries: list[dict[str, Any]]) -> None: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + labels = ["EarlyConcat + BiGRU", "MoFE-7 + MLP Router"] + by_method = {row["method"]: row for row in summaries} + methods = (EARLYCONCAT, MOFE7_MLP) + metrics = ("clean_macro_f1", "corrupt_macro_f1", "worst_condition_macro_f1") + names = ("Clean", "Mean corrupted", "Worst condition") + x = np.arange(len(names)) + width = 0.34 + fig, ax = plt.subplots(figsize=(8.6, 4.8), constrained_layout=True) + for offset, method, label, color in ( + (-width / 2, methods[0], labels[0], "#4e79a7"), + (width / 2, methods[1], labels[1], "#f28e2b"), + ): + values = [by_method[method][metric] for metric in metrics] + ax.bar(x + offset, values, width, label=label, color=color) + ax.set_xticks(x, names) + ax.set_ylabel("Macro-F1") + ax.set_ylim(0, 1) + ax.set_title("Q2 selected-model validation comparison") + ax.legend(frameon=False) + output.mkdir(parents=True, exist_ok=True) + fig.savefig(output / "comparison_earlyconcat_mofe7.png", dpi=180) + plt.close(fig) + + +def _parameter_rows(dims: tuple[int, int, int], device: torch.device) -> list[dict[str, Any]]: + models: dict[str, nn.Module] = { + EARLYCONCAT: AlignedFusionModel("concat", dims=dims), + MOFE7_MLP: MixtureOfFusionExperts(dims=dims, **MODEL_CONFIG), + } + baseline_count = sum(parameter.numel() for parameter in models[EARLYCONCAT].parameters() if parameter.requires_grad) + rows = [] + for name, model in models.items(): + count = sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) + rows.append({ + "method": name, + "trainable_parameters": count, + "ratio_to_earlyconcat": count / baseline_count, + "within_2x_earlyconcat": bool(count <= 2 * baseline_count), + }) + return rows + + +def _smoke_test(train: Split, output: Path, device: torch.device, seed: int) -> dict[str, Any]: + seed_everything(seed) + dims = tuple(int(x.shape[-1]) for x in train.x) + count = min(4, train.n) + xs = tuple(torch.as_tensor(x[:count], dtype=torch.float32, device=device) for x in train.x) + masks = torch.as_tensor(train.mask[:count].copy(), dtype=torch.bool, device=device) + masks[0] = True + if count > 1: + masks[1, 5:12, 0] = False + if count > 2: + masks[2, 18:23, :] = False + target_class = torch.as_tensor(train.y_cls[:count], dtype=torch.long, device=device) + target_intensity = torch.as_tensor(train.y_reg[:count], dtype=torch.float32, device=device) + reports: dict[str, Any] = {} + models: dict[str, nn.Module] = { + EARLYCONCAT: AlignedFusionModel("concat", dims=dims).to(device), + MOFE7_MLP: MixtureOfFusionExperts(dims=dims, **MODEL_CONFIG).to(device), + } + for name, model in models.items(): + model.train() + result = model(xs, masks) + loss = _loss(result, target_class, target_intensity) + loss.backward() + gradient = sum(float(p.grad.detach().abs().sum().cpu()) for p in model.parameters() if p.grad is not None) + reports[name] = { + "logits_shape": list(result["logits"].shape), + "intensity_shape": list(result["intensity"].shape), + "finite_loss": bool(torch.isfinite(loss).item()), + "gradient_l1": gradient, + } + mofe_model = models[MOFE7_MLP] + mo = mofe_model(xs, masks) + active = mo["availability"].any(dim=-1) + alpha_sums = mo["alpha"].sum(dim=-1) + alpha_error = float((alpha_sums[active] - 1).abs().max().cpu()) if active.any() else 0.0 + unavailable_weights = float(mo["alpha"].masked_select(~mo["availability"]).abs().max().cpu()) if (~mo["availability"]).any() else 0.0 + expert_gradients = { + name: sum(float(parameter.grad.detach().abs().sum().cpu()) for parameter in expert.parameters() if parameter.grad is not None) + for name, expert in mofe_model.experts.items() + } + router_gradient = sum(float(parameter.grad.detach().abs().sum().cpu()) for parameter in mofe_model.router.parameters() if parameter.grad is not None) + if alpha_error > 1e-6 or unavailable_weights > 1e-8: + raise RuntimeError(f"MoFE routing mask invariant failed: sum_error={alpha_error}, unavailable={unavailable_weights}") + if not all(value > 0 for value in expert_gradients.values()) or router_gradient <= 0: + raise RuntimeError(f"MoFE expert/router gradients are incomplete: {expert_gradients}; router={router_gradient}") + report = { + "passed": all(item["finite_loss"] and item["gradient_l1"] > 0 for item in reports.values()), + "seed": seed, + "device": str(device), + "cuda_device": torch.cuda.get_device_name(0) if device.type == "cuda" else None, + "batch_size_checked": count, + "steps": train.steps, + "models": reports, + "mofe_experts": list(EXPERT_NAMES), + "mofe_alpha_shape": list(mo["alpha"].shape), + "mofe_max_weight_sum_error": alpha_error, + "mofe_max_weight_on_unavailable_experts": unavailable_weights, + "mofe_expert_gradient_l1": expert_gradients, + "mofe_router_gradient_l1": router_gradient, + "parameter_count": {row["method"]: row["trainable_parameters"] for row in _parameter_rows(dims, device)}, + } + output.mkdir(parents=True, exist_ok=True) + (output / "smoke_test.json").write_text(json.dumps(report, indent=2), encoding="utf-8") + return report + + +def _run(args: argparse.Namespace) -> None: + output = args.output_dir.resolve() + output.mkdir(parents=True, exist_ok=True) + device = _device_for(args.device) + torch.set_num_threads(args.threads) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + raw = load_aligned() + computed_stats = fit_robust_stats(raw["train"]) + reference_stats_path = REFERENCE_OUTPUT / "aligned_robust_stats.npz" + if reference_stats_path.exists(): + stats = RobustStats.load(reference_stats_path) + scaler_diff = max( + max(float(np.max(np.abs(a - b))) for a, b in zip(computed_stats.center, stats.center)), + max(float(np.max(np.abs(a - b))) for a, b in zip(computed_stats.scale, stats.scale)), + ) + else: + stats = computed_stats + scaler_diff = 0.0 + train = apply_robust_stats(raw["train"], stats) + valid = apply_robust_stats(raw["valid"], stats) + stats.save(output / "aligned_robust_stats.npz") + dims = tuple(int(x.shape[-1]) for x in train.x) + feature_path = ATTACHMENT2 / "aligned_50.pkl" + if not feature_path.exists(): + raise FileNotFoundError(f"official aligned feature file not found: {feature_path}") + + if args.phase == "smoke": + report = _smoke_test(train, output, device, args.seeds[0]) + report["scaler_max_abs_difference_from_reference"] = scaler_diff + (output / "smoke_test.json").write_text(json.dumps(report, indent=2), encoding="utf-8") + print(f"selected-model smoke: passed={report['passed']} device={device}", flush=True) + return + + seeds = list(args.seeds) + metrics_rows: list[dict[str, Any]] = [] + predictions: dict[tuple[str, int, str], dict[str, np.ndarray]] = {} + router_rows: list[dict[str, Any]] = [] + utility_rows: list[dict[str, Any]] = [] + expert_rows: list[dict[str, Any]] = [] + history_rows: list[dict[str, Any]] = [] + best_epochs: dict[str, int] = {} + condition_names: list[str] = [] + + for seed in seeds: + baseline_dir = output / "models" / "baselines" / "concat" / f"seed_{seed}" + baseline, baseline_epoch, baseline_history = _load_or_train_concat( + train, valid, baseline_dir, device, seed, args.epochs, args.patience, + args.batch_size, args.reuse_checkpoints and not args.force_retrain, + ) + mofe_dir = output / "models" / MOFE7_MLP / f"seed_{seed}" + mofe, mofe_epoch, mofe_history = _train_mofe( + train, valid, mofe_dir, device, seed, args.epochs, args.patience, + args.batch_size, args.reuse_checkpoints and not args.force_retrain, + ) + best_epochs[f"{EARLYCONCAT}_seed_{seed}"] = baseline_epoch + best_epochs[f"{MOFE7_MLP}_seed_{seed}"] = mofe_epoch + history_rows.extend(baseline_history) + history_rows.extend(mofe_history) + + conditions = _conditions(valid, seed) + names = [condition for condition, _, _ in conditions] + if condition_names and names != condition_names: + raise RuntimeError("validation condition ordering changed between seeds") + condition_names = names + for method, model in ((EARLYCONCAT, baseline), (MOFE7_MLP, mofe)): + for condition, rate, masks in conditions: + prediction = _predict(model, valid, masks, device, args.batch_size) + predictions[(method, seed, condition)] = prediction + metrics_rows.append(_condition_row(method, seed, condition, rate, valid, prediction)) + if method == MOFE7_MLP: + route_row, utility_row = _diagnostics(seed, condition, masks, prediction) + router_rows.append(route_row) + utility_rows.append(utility_row) + for expert in EXPERT_NAMES: + forced = _predict(model, valid, masks, device, args.batch_size, force_expert=expert) + observed = masks[..., list(SUBSETS[expert])].all(axis=-1) + expert_rows.append({ + "method": MOFE7_MLP, + "seed": seed, + "condition": condition, + "expert": expert, + "available_position_fraction": float(observed.mean()), + **_metric_dict(valid.y_cls, valid.y_reg, forced["logits"], forced["intensity"]), + }) + print(f"evaluated {method}/seed{seed}", flush=True) + del baseline, mofe + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + summaries = _summary_rows(metrics_rows) + paired = _paired_bootstrap( + predictions, valid, seeds, condition_names, args.bootstrap_reps, + args.bootstrap_seed, summaries, + ) if args.bootstrap_reps > 0 else [] + parameter_rows = _parameter_rows(dims, device) + output_rows = { + "metrics_by_condition.csv": metrics_rows, + "summary.csv": summaries, + "paired_bootstrap.csv": paired, + "parameter_count.csv": parameter_rows, + "router_weights_by_condition.csv": router_rows, + "routing_entropy.csv": router_rows, + "modality_utility_by_condition.csv": utility_rows, + "expert_condition_matrix.csv": expert_rows, + "training_history.csv": history_rows, + } + for filename, rows in output_rows.items(): + _write_csv(output / filename, rows) + _plot_summary(output / "figures", summaries) + + manifest = { + "experiment": "Q2 selected models: EarlyConcat + BiGRU and MoFE-7 + MLP Router", + "created_unix": time.time(), + "python_version": sys.version, + "torch_version": torch.__version__, + "numpy_version": np.__version__, + "device": str(device), + "cuda_device": torch.cuda.get_device_name(0) if device.type == "cuda" else None, + "feature_file": str(feature_path), + "feature_sha256": _sha256(feature_path), + "feature_dimensions": dict(zip(MODALITIES, dims)), + "sequence_length": train.steps, + "representation_note": "official ordered 50-wordpiece positions; not 50 physical-time bins", + "train_examples": train.n, + "valid_examples": valid.n, + "train_source_video_groups": len({sample_id.split("$_$", 1)[0] for sample_id in train.ids}), + "valid_source_video_groups": len({sample_id.split("$_$", 1)[0] for sample_id in valid.ids}), + "train_only_scaler": str(output / "aligned_robust_stats.npz"), + "scaler_max_abs_difference_from_reference": scaler_diff, + "test_labels_used": False, + "seeds": seeds, + "epochs_max": args.epochs, + "patience": args.patience, + "batch_size": args.batch_size, + "optimizer": "AdamW(lr=1.5e-4, weight_decay=1e-4), gradient clip 1.0", + "training_mask_augmentation": "same contiguous-block augment_masks protocol for both models", + "validation_conditions": condition_names, + "validation_corruption_seed": "seed + 13 + pattern_index*101 + int(rate*1000)", + "loss": "cross_entropy + 0.5*SmoothL1(intensity/3, regression_label/3)", + "models": { + EARLYCONCAT: "project modalities independently, concatenate features and masks, then BiGRU", + MOFE7_MLP: { + "experts": list(EXPERT_NAMES), + "router": "MLP over per-position observed values and local observation statistics", + "availability": "hard mask; unavailable expert weights are zero", + "shared_temporal_backbone": "one BiGRU after position-wise expert mixture", + }, + }, + "best_epochs": best_epochs, + "paired_bootstrap": { + "replicates": args.bootstrap_reps, + "seed": args.bootstrap_seed, + "resampling_unit": "source video id", + "paired": True, + }, + } + (output / "run_manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8") + print(f"selected-model results saved to {output}", flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Train and compare the two retained Q2 models.") + parser.add_argument("--phase", choices=("smoke", "full"), default="full") + parser.add_argument("--epochs", type=int, default=32) + parser.add_argument("--patience", type=int, default=6) + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--threads", type=int, default=4) + parser.add_argument("--device", default="auto") + parser.add_argument("--seeds", type=int, nargs="+", default=list(SEEDS)) + parser.add_argument("--bootstrap-reps", type=int, default=1000) + parser.add_argument("--bootstrap-seed", type=int, default=20260924) + parser.add_argument("--reuse-checkpoints", action="store_true") + parser.add_argument("--force-retrain", action="store_true") + parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT) + _run(parser.parse_args()) + + +if __name__ == "__main__": + main() diff --git a/final/q2/math/__init__.py b/final/q2/math/__init__.py new file mode 100644 index 0000000..442eaad --- /dev/null +++ b/final/q2/math/__init__.py @@ -0,0 +1 @@ +"""Mathematical Q2 model family and training driver.""" diff --git a/final/q2/math/data.py b/final/q2/math/data.py new file mode 100644 index 0000000..61fe029 --- /dev/null +++ b/final/q2/math/data.py @@ -0,0 +1,213 @@ +"""Restricted readers and split preparation for the official Q2 inputs.""" +from __future__ import annotations + +import pickle +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import numpy as np + +from ...data_paths import ATTACHMENT2, ATTACHMENT3, DATA_ROOT, PROJECT_ROOT + +ROOT = PROJECT_ROOT +ATTACHMENT2_DIR = ATTACHMENT2 +ALIGNED_PATH = ATTACHMENT2 / "aligned_50.pkl" +ATTACHMENT3_ALIGNED = ATTACHMENT3 / "对齐版本" +ATTACHMENT3_UNALIGNED = ATTACHMENT3 / "未对齐版本" +MODALITIES = ("text", "audio", "vision") +EXPECTED_DIMS = {"text": 768, "audio": 74, "vision": 35} + + +class RestrictedUnpickler(pickle.Unpickler): + """Allow only primitive containers and NumPy reconstruction primitives.""" + + _allowed = { + ("builtins", name): getattr(__import__("builtins"), name) + for name in ("set", "frozenset", "slice", "complex", "bytearray") + } + _allowed.update({ + ("collections", "OrderedDict"): __import__("collections").OrderedDict, + ("numpy", "ndarray"): np.ndarray, + ("numpy", "dtype"): np.dtype, + ("numpy", "asarray"): np.asarray, + ("numpy.core.multiarray", "_reconstruct"): np.core.multiarray._reconstruct, + ("numpy.core.multiarray", "scalar"): np.core.multiarray.scalar, + ("numpy._core.multiarray", "_reconstruct"): np.core.multiarray._reconstruct, + ("numpy._core.multiarray", "scalar"): np.core.multiarray.scalar, + }) + if hasattr(np.core.numeric, "_frombuffer"): + _allowed[("numpy.core.numeric", "_frombuffer")] = np.core.numeric._frombuffer + _allowed[("numpy._core.numeric", "_frombuffer")] = np.core.numeric._frombuffer + + def find_class(self, module: str, name: str) -> Any: + try: + return self._allowed[(module, name)] + except KeyError as exc: + raise pickle.UnpicklingError(f"blocked pickle global: {module}.{name}") from exc + + +def restricted_load(path: Path) -> Any: + with path.open("rb") as stream: + return RestrictedUnpickler(stream).load() + + +def _decode(value: Any) -> str: + if isinstance(value, bytes): + return value.decode("utf-8", errors="replace") + if isinstance(value, np.bytes_): + return bytes(value).decode("utf-8", errors="replace") + if isinstance(value, np.ndarray) and value.shape == (): + return _decode(value.item()) + return str(value) + + +def _one_dim(value: Any, dtype: Any | None = None) -> np.ndarray: + out = np.asarray(value) + if out.ndim > 1 and out.shape[-1] == 1: + out = out.reshape(-1) + elif out.ndim > 1 and out.shape[0] == 1: + out = out.reshape(-1) + else: + out = out.reshape(-1) + return out.astype(dtype) if dtype is not None else out + + +@dataclass +class SplitData: + name: str + x: dict[str, np.ndarray] + mask: np.ndarray + class_y: np.ndarray | None + regression_y: np.ndarray | None + ids: list[str] + groups: np.ndarray + alignment_audit: dict[str, Any] | None = None + + @property + def n(self) -> int: + return len(self.ids) + + +def _extract_split( + name: str, obj: dict[str, Any], with_labels: bool, + mask_override: np.ndarray | None = None, + alignment_audit: dict[str, Any] | None = None, +) -> SplitData: + raw: dict[str, np.ndarray] = {} + masks = [] + for modality in MODALITIES: + arr = np.asarray(obj[modality]) + if arr.ndim != 3 or arr.shape[1] != 50 or arr.shape[2] != EXPECTED_DIMS[modality]: + raise ValueError(f"{name}.{modality}: unexpected feature shape {arr.shape}") + arr = arr.astype(np.float32) + if not np.isfinite(arr).all(): + raise ValueError(f"{name}.{modality}: non-finite feature values; refusing to reinterpret them as missing") + # The dataset documentation defines all-zero aligned rows as missing. + observed = np.any(arr != 0.0, axis=-1) + raw[modality] = arr + masks.append(observed) + mask = np.stack(masks, axis=-1) + if mask_override is not None: + override = np.asarray(mask_override, bool) + if override.shape != mask.shape: + raise ValueError(f"{name}: projected mask shape {override.shape} differs from {mask.shape}") + mask = override + ids = [_decode(v) for v in _one_dim(obj["id"])] + if len(ids) != len(mask): + raise ValueError(f"{name}: id count differs from feature count") + if len(set(ids)) != len(ids): + raise ValueError(f"{name}: duplicate video$_$clip primary keys") + malformed = [sample_id for sample_id in ids if "$_$" not in sample_id or not all(sample_id.split("$_$", 1))] + if malformed: + raise ValueError(f"{name}: malformed video$_$clip keys: {malformed[:5]}") + groups = np.asarray([sample_group(v) for v in ids], dtype=str) + if with_labels: + class_y = _one_dim(obj["classification_labels"], np.int64) + regression_y = _one_dim(obj["regression_labels"], np.float32) + if len(class_y) != len(ids) or len(regression_y) != len(ids): + raise ValueError(f"{name}: label count differs from feature count") + if not np.isfinite(regression_y).all() or np.any(np.abs(regression_y) > 3.0): + raise ValueError(f"{name}: regression labels must be finite and within [-3,3]") + if not np.isin(class_y, [0, 1, 2]).all(): + raise ValueError(f"{name}: expected class labels in 0,1,2") + expected_class = np.where(regression_y < 0.0, 0, np.where(regression_y == 0.0, 1, 2)) + mismatch = np.flatnonzero(class_y != expected_class) + if len(mismatch): + examples = [(ids[int(i)], int(class_y[i]), float(regression_y[i])) for i in mismatch[:5]] + raise ValueError(f"{name}: polarity/regression label mismatch (sample, class, score): {examples}") + else: + class_y = regression_y = None + return SplitData(name, raw, mask, class_y, regression_y, ids, groups, alignment_audit) + + +def sample_group(sample_id: str) -> str: + """Official ids are video$_$clip; group on the source video only.""" + return sample_id.split("$_$", 1)[0] + + +def load_official_splits(path: Path = ALIGNED_PATH, *, version: str = "aligned_50") -> dict[str, SplitData]: + if version not in {"aligned_50", "unaligned_50"}: + raise ValueError(f"unsupported feature version: {version}") + obj = restricted_load(path) + required = {"train", "valid", "test"} + if not isinstance(obj, dict) or not required.issubset(obj): + raise ValueError(f"{path.name} must contain train, valid, and test dictionaries") + if version == "unaligned_50": + from ...adapter import adapt_official_split + + splits = {} + for name in ("train", "valid", "test"): + projected, mask, audit = adapt_official_split(obj[name]) + fields = {**obj[name], **projected} + splits[name] = _extract_split(name, fields, with_labels=True, + mask_override=mask, alignment_audit=audit) + else: + splits = {name: _extract_split(name, obj[name], with_labels=True) for name in ("train", "valid", "test")} + del obj + return splits + + +def load_attachment3_case(path: Path) -> dict[str, np.ndarray]: + obj = restricted_load(path) + case = obj.get("test", obj) + text_bert = np.asarray(case["text_bert"]) + audio = np.asarray(case["audio"]) + vision = np.asarray(case["vision"]) + if text_bert.ndim == 3 and text_bert.shape[0] == 1: + text_bert = text_bert[0] + if text_bert.shape != (3, 50): + raise ValueError(f"{path.name}: expected text_bert (1,3,50), got {np.asarray(case['text_bert']).shape}") + result = {"input_ids": text_bert[0].astype(np.int64), "attention_mask": text_bert[1].astype(bool), "token_type_ids": text_bert[2].astype(np.int64)} + for name, arr, dim in (("audio", audio, 74), ("vision", vision, 35)): + if arr.ndim == 3 and arr.shape[0] == 1: + arr = arr[0] + if arr.shape != (50, dim): + raise ValueError(f"{path.name}: expected {name} (1,50,{dim}), got {np.asarray(case[name]).shape}") + arr = arr.astype(np.float32) + if not np.isfinite(arr).all(): + raise ValueError(f"{path.name}: {name} contains non-finite features") + result[name] = arr + return result + + +def fit_preprocessor(train: SplitData) -> dict[str, dict[str, np.ndarray]]: + """Fit per-dimension mean/std on observed training rows only.""" + fitted: dict[str, dict[str, np.ndarray]] = {} + for j, name in enumerate(MODALITIES): + rows = train.x[name][train.mask[:, :, j]] + mean = rows.mean(axis=0, dtype=np.float64).astype(np.float32) + std = rows.std(axis=0, dtype=np.float64).astype(np.float32) + std[std < 1e-5] = 1.0 + fitted[name] = {"mean": mean, "std": std} + return fitted + + +def transform_split(split: SplitData, fitted: dict[str, dict[str, np.ndarray]]) -> dict[str, np.ndarray]: + output = {} + for j, name in enumerate(MODALITIES): + arr = (split.x[name] - fitted[name]["mean"]) / fitted[name]["std"] + arr = np.clip(arr, -10.0, 10.0) + arr[~split.mask[:, :, j]] = 0.0 + output[name] = arr.astype(np.float32) + return output diff --git a/final/q2/math/plot_diagnostics.py b/final/q2/math/plot_diagnostics.py new file mode 100644 index 0000000..1c2b5c7 --- /dev/null +++ b/final/q2/math/plot_diagnostics.py @@ -0,0 +1,142 @@ +"""Create the method-comparison figures from Q2's fixed evaluation outputs.""" +from __future__ import annotations + +import csv +import json +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + +from .train import RESULTS + + +def read_csv(path: Path) -> list[dict[str, str]]: + with path.open("r", encoding="utf-8-sig", newline="") as stream: + return list(csv.DictReader(stream)) + + +def main() -> None: + controlled = read_csv(RESULTS / "controlled_missingness.csv") + test = read_csv(RESULTS / "test_predictions.csv") + gates = read_csv(RESULTS / "test_gate_diagnostics.csv") + metrics = json.loads((RESULTS / "test_metrics.json").read_text(encoding="utf-8")) + selected = str(metrics["selected_model"]) + + figure, axes = plt.subplots(2, 3, figsize=(17, 10), constrained_layout=True) + rate_axis, modality_axis, location_axis, confusion_axis, scatter_axis, interval_axis = axes.flat + + comparison_models = ("C0", "C3", "C4", "C5", "C6", "C7_distill", "C7_group") + for model in comparison_models: + subset = [row for row in controlled if row["model"] == model and + (row["mask_pattern"] == "none" or row["mask_pattern"] == "single")] + subset.sort(key=lambda row: float(row["rate_realized_additional_global"])) + if subset: + rate_axis.plot([float(row["rate_realized_additional_global"]) for row in subset], + [float(row["regression_mae"]) for row in subset], marker="o", label=model) + rate_axis.set(title="MAE by realized additional missing rate", xlabel="Additional missing rate (equal T/A/V)", ylabel="MAE") + rate_axis.legend(fontsize=8, ncol=2) + rate_axis.grid(alpha=0.25) + + modality_labels = ("T", "A", "V", "TA", "TV", "AV", "TAV") + modality_scenarios = {f"0.3/modality_{label}": label for label in modality_labels} + modality_models = ("C0", "C5", "C6", "C7_distill", "C7_group") + modality_values = np.full((len(modality_models), len(modality_labels)), np.nan) + for i, model in enumerate(modality_models): + for j, (scenario, _) in enumerate(modality_scenarios.items()): + pattern = scenario.split("/", 1)[1] + row = next((r for r in controlled if r["model"] == model and r["mask_pattern"] == pattern), None) + if row is not None: + modality_values[i, j] = float(row["regression_mae"]) + image = modality_axis.imshow(modality_values, aspect="auto", cmap="viridis") + modality_axis.set(title="Modality combination control: MAE", xticks=range(len(modality_labels)), + xticklabels=modality_labels, yticks=range(len(modality_models)), yticklabels=modality_models) + modality_axis.tick_params(axis="x", rotation=35) + figure.colorbar(image, ax=modality_axis, fraction=0.046, pad=0.04) + + position_values = np.full((3, 3), np.nan) + for i, modality in enumerate(("T", "A", "V")): + for j, location in enumerate(("start", "middle", "end")): + scenario = f"0.3/location_{location}_{modality}" + pattern = scenario.split("/", 1)[1] + row = next((r for r in controlled if r["model"] == selected and r["mask_pattern"] == pattern), None) + if row is not None: + position_values[i, j] = float(row["regression_mae"]) + image = location_axis.imshow(position_values, aspect="auto", cmap="magma") + location_axis.set(title=f"Selected model {selected}: location MAE", xticks=range(3), + xticklabels=("start", "middle", "end"), yticks=range(3), yticklabels=("T", "A", "V")) + figure.colorbar(image, ax=location_axis, fraction=0.046, pad=0.04) + + confusion = np.zeros((3, 3), dtype=np.int64) + for row in test: + confusion[int(row["true_class"]), int(row["predicted_class"])] += 1 + image = confusion_axis.imshow(confusion, cmap="Blues") + for i in range(3): + for j in range(3): + confusion_axis.text(j, i, str(confusion[i, j]), ha="center", va="center") + confusion_axis.set(title=f"Test confusion matrix: {selected}", xlabel="Predicted", ylabel="True", + xticks=range(3), xticklabels=("negative", "neutral", "positive"), + yticks=range(3), yticklabels=("negative", "neutral", "positive")) + figure.colorbar(image, ax=confusion_axis, fraction=0.046, pad=0.04) + + true_score = np.asarray([float(row["true_sentiment"]) for row in test]) + predicted_score = np.asarray([float(row["predicted_sentiment"]) for row in test]) + scatter_axis.scatter(true_score, predicted_score, alpha=0.55, s=18) + scatter_axis.plot([-3, 3], [-3, 3], "k--", linewidth=1) + scatter_axis.set(title=f"Test sentiment: MAE={metrics['regression_mae']:.3f}", + xlabel="True sentiment", ylabel="Predicted sentiment", xlim=(-3, 3), ylim=(-3, 3)) + scatter_axis.grid(alpha=0.2) + + lower = np.asarray([float(row["interval_90_lower"]) for row in test]) + upper = np.asarray([float(row["interval_90_upper"]) for row in test]) + width = upper - lower + covered = (true_score >= lower) & (true_score <= upper) + order = np.argsort(width) + bins = np.array_split(order, min(10, len(order))) + interval_axis.plot([width[idx].mean() for idx in bins], [covered[idx].mean() for idx in bins], marker="o") + interval_axis.axhline(0.9, color="black", linestyle="--", linewidth=1, label="nominal 90%") + interval_axis.set(title="Test interval coverage by width decile", xlabel="Mean interval width", ylabel="Empirical coverage", ylim=(0, 1)) + interval_axis.legend() + interval_axis.grid(alpha=0.2) + figure.suptitle("Q2 validation controls and official-test diagnostics", fontsize=15) + figure.savefig(RESULTS / "q2_diagnostics.png", dpi=160) + plt.close(figure) + + steps = 50 + modalities = ("text", "audio", "vision") + weight_sum = np.zeros((steps, len(modalities)), dtype=np.float64) + reliability_sum = np.zeros_like(weight_sum) + count = np.zeros_like(weight_sum) + for row in gates: + if row["modality"] not in modalities: + continue + t, m = int(row["step"]), modalities.index(row["modality"]) + weight_sum[t, m] += float(row["fusion_weight_mean_over_paths"]) + reliability_sum[t, m] += float(row["reliability"]) + count[t, m] += 1 + weights = weight_sum / np.maximum(count, 1.0) + reliabilities = reliability_sum / np.maximum(count, 1.0) + gate_figure, gate_axis = plt.subplots(figsize=(12, 5), constrained_layout=True) + for m, modality in enumerate(modalities): + gate_axis.plot(range(steps), weights[:, m], label=f"{modality} fusion weight") + gate_axis.set(title=f"Test mean fusion gates by position: {selected}", xlabel="Aligned step", ylabel="Mean fusion weight") + gate_axis.legend(ncol=3) + gate_axis.grid(alpha=0.25) + reliability_axis = gate_axis.twinx() + for m, modality in enumerate(modalities): + reliability_axis.plot(range(steps), reliabilities[:, m], linestyle=":", alpha=0.7, label=f"{modality} reliability") + reliability_axis.set_ylabel("Mean reliability proxy") + handles, labels = gate_axis.get_legend_handles_labels() + right_handles, right_labels = reliability_axis.get_legend_handles_labels() + gate_axis.legend(handles + right_handles, labels + right_labels, ncol=3, fontsize=8) + gate_figure.savefig(RESULTS / "q2_gate_positions.png", dpi=160) + plt.close(gate_figure) + manifest_path = RESULTS / "run_manifest.json" + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + manifest["diagnostic_figures"] = ["q2_diagnostics.png", "q2_gate_positions.png"] + manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + print(f"Wrote Q2 figures for selected model {selected} to {RESULTS}") + + +if __name__ == "__main__": + main() diff --git a/final/q2/math/plot_missingness_effects.py b/final/q2/math/plot_missingness_effects.py new file mode 100644 index 0000000..565e67b --- /dev/null +++ b/final/q2/math/plot_missingness_effects.py @@ -0,0 +1,90 @@ +"""Plot the aligned-data rate sweep and matched missing-type response.""" +from __future__ import annotations + +import csv +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + + +RESULTS = Path(__file__).resolve().parents[2] / "experiments" / "q2" / "math_current" + + +def read_csv(name: str) -> list[dict[str, str]]: + with (RESULTS / name).open(encoding="utf-8-sig", newline="") as stream: + return list(csv.DictReader(stream)) + + +def main() -> None: + rate_rows = read_csv("controlled_missingness.csv") + rate_bootstrap = read_csv("controlled_group_bootstrap.csv") + type_bootstrap = read_csv("matched_missing_type_bootstrap.csv") + + colors = {"single": "#3b82f6", "sync": "#dc2626", "partial": "#16a34a", "async": "#9333ea"} + labels = {"single": "Single modality", "sync": "Synchronous", "partial": "Partial overlap", "async": "Asynchronous"} + fig, (ax_rate, ax_type) = plt.subplots(1, 2, figsize=(12.4, 4.8), gridspec_kw={"width_ratios": [1.35, 1.0]}) + + baseline = next(row for row in rate_rows if row["model"] == "C5" and row["mask_pattern"] == "none") + baseline_ci = next(row for row in rate_bootstrap if row["model"] == "C5" and row["scenario"] == "0.0/none" and row["metric"] == "mae") + for mode in ("single", "sync", "partial", "async"): + rows = [baseline] + sorted( + (row for row in rate_rows if row["model"] == "C5" and row["mask_pattern"] == mode), + key=lambda row: float(row["rate_requested_per_selected_source"]), + ) + x, y, lower, upper = [], [], [], [] + for row in rows: + if row["mask_pattern"] == "none": + ci = baseline_ci + scenario = "0.0/none" + else: + scenario = f"{float(row['rate_requested_per_selected_source']):.1f}/{mode}" + ci = next(item for item in rate_bootstrap if item["model"] == "C5" and item["scenario"] == scenario and item["metric"] == "mae") + x.append(float(row["rate_realized_additional_global"])) + y.append(float(row["regression_mae"])) + lower.append(float(ci["ci_2_5"])) + upper.append(float(ci["ci_97_5"])) + ax_rate.errorbar( + x, y, yerr=[np.asarray(y) - np.asarray(lower), np.asarray(upper) - np.asarray(y)], + color=colors[mode], marker="o", linewidth=1.7, markersize=4.5, + capsize=2.5, label=labels[mode], alpha=0.95, + ) + ax_rate.set_title("C5 performance across missing rates") + ax_rate.set_xlabel("Added missing rate (paper definition)") + ax_rate.set_ylabel("Regression MAE (95% group-bootstrap CI)") + ax_rate.grid(axis="both", color="#d1d5db", linewidth=0.7, alpha=0.65) + ax_rate.legend(frameon=False, fontsize=8.5, loc="upper left") + + type_order = ("T", "A", "V", "TA", "TV", "AV", "TAV") + point, low, high = [], [], [] + for label in type_order: + scenario = f"matched_type_{label}" + boot = next(row for row in type_bootstrap if row["model"] == "C5" and row["scenario"] == scenario and row["metric"] == "mae") + point.append(float(boot["delta_to_natural"])) + low.append(float(boot["delta_to_natural_ci_2_5"])) + high.append(float(boot["delta_to_natural_ci_97_5"])) + positions = np.arange(len(type_order)) + ax_type.errorbar( + positions, point, yerr=[np.asarray(point) - low, high - np.asarray(point)], + fmt="o", color="#2563eb", ecolor="#2563eb", capsize=3, linewidth=1.4, + markersize=5, + ) + ax_type.axhline(0, color="#374151", linewidth=1, linestyle="--") + ax_type.set_xticks(positions, type_order) + ax_type.set_title("Matched missing-modality types") + ax_type.set_xlabel("Hidden modality set") + ax_type.set_ylabel("MAE change from natural condition") + ax_type.grid(axis="y", color="#d1d5db", linewidth=0.7, alpha=0.65) + ax_type.text( + 0.02, 0.02, "Same added feature-row count per sample and type", + transform=ax_type.transAxes, fontsize=7.5, color="#4b5563", + ) + + fig.tight_layout(pad=1.2) + output = RESULTS / "aligned_missingness_effects.png" + fig.savefig(output, dpi=200, bbox_inches="tight", facecolor="white") + print(output) + + +if __name__ == "__main__": + main() diff --git a/final/q2/math/predict_attachment3.py b/final/q2/math/predict_attachment3.py new file mode 100644 index 0000000..e2b1a45 --- /dev/null +++ b/final/q2/math/predict_attachment3.py @@ -0,0 +1,103 @@ +"""Run the saved Q2 student on the aligned, unlabeled attachment-3 cases.""" +from __future__ import annotations + +import json +import time +import csv + +import numpy as np +import torch + +from ...model.crg import INPUT_DIMS, MODALITIES, StructuredGaussianImputer +from .train import RESULTS, _make_variant, infer_attachment3, reencode_attachment3, validate_attachment3_predictions, write_csv + + +def main() -> None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + manifest_path = RESULTS / "run_manifest.json" + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + calibration = json.loads((RESULTS / "validation_metrics.json").read_text(encoding="utf-8")) + selected = calibration.get("selected_model", manifest.get("selected_model")) + if not selected: + raise ValueError("run_manifest.json does not identify a selected model") + + imputer = StructuredGaussianImputer(INPUT_DIMS).to(device) + imputer_state = torch.load(RESULTS / "structured_imputer.pt", map_location=device, weights_only=True) + imputer.load_state_dict(imputer_state) + model = _make_variant(selected, imputer).to(device) + state = torch.load(RESULTS / "crg_student.pt", map_location=device, weights_only=True) + model.load_state_dict(state) + + with np.load(RESULTS / "preprocessor.npz", allow_pickle=False) as archive: + fitted = {m: {k: archive[f"{m}_{k}"].copy() for k in ("mean", "std")} for m in MODALITIES} + priors = manifest["attachment3_low_information_priors"] + temperature = float(calibration["temperature"]) + class_prior = np.asarray(priors["class_probability_values"], dtype=np.float64) + magnitude_priors = np.asarray((priors["negative_beta"], priors["positive_beta"]), dtype=np.float32) + + cases, source_audit = reencode_attachment3(device) + predictions, inference_audit = infer_attachment3( + model, cases, fitted, device, temperature, class_prior, magnitude_priors, + ) + validate_attachment3_predictions([case["case_id"] for case in cases], predictions) + inference_by_id = {row["case_id"]: row for row in inference_audit} + write_csv(RESULTS / "attachment3_predictions.csv", predictions) + write_csv(RESULTS / "attachment3_audit.csv", [ + {**source, **inference_by_id[source["case_id"]]} for source in source_audit + ]) + + # The training script can finish and persist all labeled-evaluation outputs + # before an unlabeled attachment export fails. Reconcile the manifest from + # those completed artifacts so the standalone export is safely rerunnable. + group_risk_rows = list(csv.DictReader((RESULTS / "group_risk_tuning.csv").open(encoding="utf-8-sig", newline=""))) + selected_risk = next((row for row in group_risk_rows if row.get("selected", "").lower() == "true"), None) + reliability_rows = list(csv.DictReader((RESULTS / "reliability_hparam_tuning.csv").open(encoding="utf-8-sig", newline=""))) + # split_calibration's generic internal names are canonicalized in train.py; + # repair artifacts from runs produced before that naming fix as well. + for row in group_risk_rows: + if row.get("selection_split") == "fit": + row["selection_split"] = "reliability_validation" + for row in reliability_rows: + if row.get("selection_split") == "fit": + row["selection_split"] = "reliability_validation" + write_csv(RESULTS / "group_risk_tuning.csv", group_risk_rows) + write_csv(RESULTS / "reliability_hparam_tuning.csv", reliability_rows) + if selected_risk: + risk_values = (float(selected_risk["lambda_group"]), float(selected_risk["group_temperature"])) + manifest["group_risk_hyperparameters"]["selected"] = list(risk_values) + manifest["loss"]["selected_group_risk"] = list(risk_values) + manifest["group_risk_hyperparameters"]["selection_split"] = "reliability_validation" + manifest["reliability_hyperparameters"]["selected_by_model"] = { + row["model"]: [float(row[key]) for key in ("rho_imp", "lambda_u", "lambda_gap", "lambda_span")] + for row in reliability_rows + if row.get("selected", "").lower() == "true" + and (not row.get("risk_candidate_selected") or row["risk_candidate_selected"].lower() == "true") + } + test_metrics = json.loads((RESULTS / "test_metrics.json").read_text(encoding="utf-8")) + manifest["selected_model"] = selected + manifest["final_test_metrics"] = test_metrics + manifest["calibration"]["temperature"] = temperature + manifest["calibration"]["valid_used_for_selection"] = True + manifest["calibration"]["test_used_for_selection_or_calibration"] = False + manifest["training_configuration"].update({ + "student_epoch_limit": 12, + "imputer_epochs": 8, + "batch_size": 64, + "early_stopping_patience": 3, + }) + manifest["imputer"]["epochs"] = 8 + manifest.update({ + "completed_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "attachment3_cases": len(cases), + "attachment3_prediction_file": "attachment3_predictions.csv", + "attachment3_audit_file": "attachment3_audit.csv", + "attachment3_labeled_metrics": None, + "quality_flags": {m: "unavailable; q*=1 fallback for visible rows, unknown flag retained" for m in MODALITIES}, + "neutral_output": "exact zero when neutral is the predicted class; no near-zero threshold", + }) + manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + print(f"Wrote {len(predictions)} unlabeled attachment-3 predictions to {RESULTS}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/final/q2/math/run_matched_missing_type.py b/final/q2/math/run_matched_missing_type.py new file mode 100644 index 0000000..735c3f7 --- /dev/null +++ b/final/q2/math/run_matched_missing_type.py @@ -0,0 +1,369 @@ +"""Matched-volume missing-modality evaluation for the official validation split. + +This complements the standard requested-rate sweep. Every type condition hides +the same number of originally observed feature rows in each validation sample; +the affected rows are placed in one contiguous span per selected modality. +""" +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path +from typing import Any + +import numpy as np +import torch +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error + +from . import train +from ...model.crg import INPUT_DIMS, StructuredGaussianImputer +from .data import ALIGNED_PATH, fit_preprocessor, load_official_splits, transform_split + + +RESULTS = Path(__file__).resolve().parent / "results" +MODALITY_SETS = ( + ((0,), "T"), ((1,), "A"), ((2,), "V"), + ((0, 1), "TA"), ((0, 2), "TV"), ((1, 2), "AV"), ((0, 1, 2), "TAV"), +) +MODALITY_NAMES = ("text", "audio", "vision") + + +def make_matched_type_masks( + split: Any, seed: int, per_sample_cap: int = 15, +) -> tuple[dict[str, np.ndarray], list[dict[str, Any]]]: + original = np.asarray(split.mask, dtype=bool) + counts = original.sum(axis=1).astype(np.int64) + keep_minimum = np.maximum(1, np.ceil(0.2 * counts).astype(np.int64)) + capacity = np.maximum(0, counts - keep_minimum) + # Match the same feasible volume per sample for every modality set. The + # least observed of audio/vision determines the cap, so no condition can + # gain an advantage by applying its mask to a different subset of samples. + budget = np.minimum(per_sample_cap, np.minimum(capacity[:, 1], capacity[:, 2])) + masks: dict[str, np.ndarray] = {"0.0/none": original.copy()} + audit: list[dict[str, Any]] = [] + + for selected, label in MODALITY_SETS: + key = f"matched_type_{label}" + current = original.copy() + for row_index, sample_id in enumerate(split.ids): + total = int(budget[row_index]) + base, remainder = divmod(total, len(selected)) + sample_seed = int.from_bytes( + hashlib.sha256(f"{seed}:{sample_id}:{key}".encode("utf-8")).digest()[:8], + "little", + ) + rng = np.random.default_rng(sample_seed) + allocation = np.full(len(selected), base, dtype=np.int64) + if remainder: + allocation[rng.permutation(len(selected))[:remainder]] += 1 + starts: list[str] = [] + ends: list[str] = [] + hidden_by_modality = np.zeros(3, dtype=np.int64) + for modality, amount_value in zip(selected, allocation): + amount = int(amount_value) + if amount == 0: + starts.append("") + ends.append("") + continue + interval = train._best_interval( + original[row_index, :, modality], amount, + int(capacity[row_index, modality]), "random", rng, + ) + if interval is None: + raise RuntimeError(f"no feasible interval for {sample_id}/{label}/{MODALITY_NAMES[modality]}") + left, right = interval + positions = np.flatnonzero(original[row_index, left:right + 1, modality]) + left + if len(positions) != amount: + raise RuntimeError(f"matched interval hid {len(positions)} rows, expected {amount}") + current[row_index, positions, modality] = False + hidden_by_modality[modality] = amount + starts.append(str(int(left))) + ends.append(str(int(right))) + actual_total = int(np.sum(original[row_index] & ~current[row_index])) + if actual_total != total: + raise RuntimeError(f"matched volume differs for {sample_id}: {actual_total} != {total}") + audit.append({ + "scenario": key, + "sample_id": sample_id, + "source_video_id": str(split.groups[row_index]), + "selected_modalities": json.dumps([MODALITY_NAMES[m] for m in selected]), + "base_mask_seed": int(seed), + "sample_mask_seed": sample_seed, + "matched_added_rows_target": total, + "matched_added_rows_actual": actual_total, + "hidden_text_rows": int(hidden_by_modality[0]), + "hidden_audio_rows": int(hidden_by_modality[1]), + "hidden_vision_rows": int(hidden_by_modality[2]), + "span_start_by_selected_modality": json.dumps(starts), + "span_end_by_selected_modality": json.dumps(ends), + }) + if not np.array_equal(np.sum(original & ~current, axis=(1, 2)), budget): + raise RuntimeError(f"per-sample matched-volume invariant failed for {label}") + masks[key] = current + + total_masked = int(budget.sum()) + if len({int(np.sum(original & ~mask)) for key, mask in masks.items() if key != "0.0/none"}) != 1: + raise RuntimeError("matched modality scenarios do not have identical total missing volume") + print( + f"matched type masks: samples={split.n}, added_rows_per_scenario={total_masked}, " + f"mean_per_sample={budget.mean():.3f}, zero_budget_samples={int(np.sum(budget == 0))}", + flush=True, + ) + return masks, audit + + +def metric_values(split: Any, prediction: dict[str, np.ndarray], indices: np.ndarray) -> dict[str, float]: + return { + "accuracy": float(accuracy_score(split.class_y[indices], prediction["predicted_class"][indices])), + "macro_f1": float(f1_score( + split.class_y[indices], prediction["predicted_class"][indices], + labels=[0, 1, 2], average="macro", zero_division=0, + )), + "mae": float(mean_absolute_error( + split.regression_y[indices], prediction["predicted_score"][indices], + )), + } + + +def evaluate_matched_masks( + model_name: str, + split: Any, + arrays: dict[str, np.ndarray], + masks: dict[str, np.ndarray], + temperature: float, + *, + model: Any | None = None, + c0_state: dict[str, Any] | None = None, + device: torch.device | None = None, + seed: int = 0, +) -> tuple[list[dict[str, Any]], dict[str, dict[str, np.ndarray]]]: + rows = [] + predictions = {} + for scenario, mask in masks.items(): + if model_name == "C0": + if c0_state is None: + raise ValueError("C0 state is required") + metrics, prediction = train.evaluate_c0(c0_state, split, arrays, temperature, mask) + else: + if model is None or device is None: + raise ValueError("neural model and device are required") + scenario_seed = train._scenario_seed(seed, split.name, scenario) + with train.fixed_torch_seed(scenario_seed, device): + metrics, prediction = train.evaluate( + model, arrays, split, device, 64, masks=mask, temperature=temperature, + ) + predictions[scenario] = prediction + rates = train._missing_rate_summary(split.mask, mask) + row = { + "model": model_name, + "scenario": scenario, + "rate_realized_additional_global": rates["additional_global"], + "rate_realized_additional_by_modality": json.dumps( + [None if not np.isfinite(value) else float(value) + for value in np.nanmean(rates["additional_by_modality"], axis=0)] + ), + "natural_missing_rate_global": rates["natural_global"], + "natural_missing_rate_by_modality": json.dumps( + np.mean(rates["natural_by_modality"], axis=0).tolist() + ), + "rate_final_total_missing_global": rates["final_global"], + "rate_final_total_missing_by_modality": json.dumps( + np.mean(rates["final_by_modality"], axis=0).tolist() + ), + "synchronous_no_observation_rate": float(np.mean(rates["synchronous_no_observation"])), + "matched_added_rows_total": int(np.sum(split.mask & ~mask)), + **metrics, + } + rows.append(row) + return rows, predictions + + +def paired_source_video_bootstrap( + split: Any, + predictions: dict[str, dict[str, dict[str, np.ndarray]]], + repeats: int, + seed: int, +) -> list[dict[str, Any]]: + scenarios = list(predictions["C0"]) + groups = np.unique(split.groups) + group_indices = {group: np.flatnonzero(split.groups == group) for group in groups} + names = tuple(predictions) + point = { + (model, scenario, metric): value + for model in names + for scenario in scenarios + for metric, value in metric_values(split, predictions[model][scenario], np.arange(split.n)).items() + } + draws = {key: [] for key in point} + within_natural = { + (model, scenario, metric): [] + for model in names for scenario in scenarios if scenario != "0.0/none" + for metric in ("accuracy", "macro_f1", "mae") + } + model_deltas = { + (scenario, metric): [] + for scenario in scenarios for metric in ("accuracy", "macro_f1", "mae") + } + rng = np.random.default_rng(seed) + for _ in range(repeats): + chosen = rng.choice(groups, size=len(groups), replace=True) + indices = np.concatenate([group_indices[group] for group in chosen]) + replicate = {} + for model in names: + for scenario in scenarios: + for metric, value in metric_values(split, predictions[model][scenario], indices).items(): + replicate[(model, scenario, metric)] = value + draws[(model, scenario, metric)].append(value) + for model in names: + for scenario in scenarios: + if scenario == "0.0/none": + continue + for metric in ("accuracy", "macro_f1", "mae"): + within_natural[(model, scenario, metric)].append( + replicate[(model, scenario, metric)] - replicate[(model, "0.0/none", metric)] + ) + for scenario in scenarios: + for metric in ("accuracy", "macro_f1", "mae"): + model_deltas[(scenario, metric)].append( + replicate[("C5", scenario, metric)] - replicate[("C0", scenario, metric)] + ) + + def interval(values: list[float]) -> tuple[float, float, float]: + values_np = np.asarray(values, dtype=np.float64) + return (float(np.median(values_np)), float(np.percentile(values_np, 2.5)), + float(np.percentile(values_np, 97.5))) + + rows = [] + for model in names: + for scenario in scenarios: + for metric in ("accuracy", "macro_f1", "mae"): + median, lower, upper = interval(draws[(model, scenario, metric)]) + row: dict[str, Any] = { + "model": model, "scenario": scenario, "metric": metric, + "estimate": point[(model, scenario, metric)], + "bootstrap_median": median, "ci_2_5": lower, "ci_97_5": upper, + "replicates": repeats, "unit": "paired source-video group resample", + } + if scenario != "0.0/none": + delta = point[(model, scenario, metric)] - point[(model, "0.0/none", metric)] + d_median, d_lower, d_upper = interval(within_natural[(model, scenario, metric)]) + row.update({ + "delta_to_natural": delta, + "delta_to_natural_bootstrap_median": d_median, + "delta_to_natural_ci_2_5": d_lower, + "delta_to_natural_ci_97_5": d_upper, + }) + model_delta = point[("C5", scenario, metric)] - point[("C0", scenario, metric)] + md_median, md_lower, md_upper = interval(model_deltas[(scenario, metric)]) + row.update({ + "C5_minus_C0": model_delta, + "C5_minus_C0_bootstrap_median": md_median, + "C5_minus_C0_ci_2_5": md_lower, + "C5_minus_C0_ci_97_5": md_upper, + }) + rows.append(row) + return rows + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--seed", type=int, default=20260924 + 1209) + parser.add_argument("--per-sample-cap", type=int, default=15) + parser.add_argument("--bootstrap-repeats", type=int, default=1000) + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + args = parser.parse_args() + train.seed_everything(args.seed) + device = torch.device(args.device) + + official = load_official_splits() + fit, heldout_train = train.split_calibration(official["train"], 20260924) + _, temperature_calibration = train.split_calibration(heldout_train, 20260925, fraction=0.5) + fitted = fit_preprocessor(fit) + transformed = {name: transform_split(split, fitted) for name, split in official.items()} + transformed["fit"] = transform_split(fit, fitted) + transformed["temperature_calibration"] = transform_split(temperature_calibration, fitted) + + imputer = StructuredGaussianImputer(INPUT_DIMS) + imputer.load_state_dict(torch.load(RESULTS / "structured_imputer.pt", map_location="cpu", weights_only=True)) + with (RESULTS / "validation_metrics.json").open(encoding="utf-8") as stream: + validation_metadata = json.load(stream) + if validation_metadata.get("selected_model") != "C5": + raise RuntimeError(f"expected selected C5 model, found {validation_metadata.get('selected_model')}") + temperature = float(validation_metadata["temperature"]) + selected_reliability = (0.3, 0.05, 0.05, 0.05) + model = train._make_variant("C5", imputer, selected_reliability).to(device) + model.load_state_dict(torch.load(RESULTS / "crg_student.pt", map_location="cpu", weights_only=True)) + model.eval() + + _, c0_state = train.fit_c0(fit, official["valid"], transformed) + train.calibrate_c0_interval(c0_state, temperature_calibration, transformed["temperature_calibration"]) + _, c0_calibration = train.evaluate_c0(c0_state, temperature_calibration, transformed["temperature_calibration"]) + c0_temperature = train.fit_temperature(c0_calibration["probabilities"], temperature_calibration.class_y) + + masks, audit = make_matched_type_masks(official["valid"], args.seed, args.per_sample_cap) + metrics_c0, pred_c0 = evaluate_matched_masks( + "C0", official["valid"], transformed["valid"], masks, c0_temperature, + c0_state=c0_state, + ) + metrics_c5, pred_c5 = evaluate_matched_masks( + "C5", official["valid"], transformed["valid"], masks, temperature, + model=model, device=device, seed=args.seed + 1, + ) + total_masked = int(np.sum(official["valid"].mask & ~masks["matched_type_T"])) + per_sample_budget_mean = total_masked / official["valid"].n + zero_budget_samples = sum( + 1 for row in audit if row["scenario"] == "matched_type_T" and row["matched_added_rows_target"] == 0 + ) + summary_rows = [] + for row in metrics_c0 + metrics_c5: + row["matched_added_rows_mean_per_sample"] = per_sample_budget_mean + row["matched_zero_budget_samples"] = zero_budget_samples + summary_rows.append(row) + train.write_csv(RESULTS / "matched_missing_type.csv", summary_rows) + train.write_csv(RESULTS / "matched_missing_type_audit.csv", audit) + bootstrap_rows = paired_source_video_bootstrap( + official["valid"], {"C0": pred_c0, "C5": pred_c5}, + args.bootstrap_repeats, args.seed + 2, + ) + train.write_csv(RESULTS / "matched_missing_type_bootstrap.csv", bootstrap_rows) + manifest = { + "input": str(ALIGNED_PATH.relative_to(train.ROOT)), + "input_sha256": train.sha256(ALIGNED_PATH), + "evaluation_split": "official validation", + "validation_samples": official["valid"].n, + "source_video_groups": int(len(np.unique(official["valid"].groups))), + "models": ["C0", "C5"], + "modalities": {"T": "text", "A": "audio", "V": "vision"}, + "matched_type_sets": [label for _, label in MODALITY_SETS], + "mask_rule": "per-sample target=min(per_sample_cap, audio_hide_capacity, vision_hide_capacity); split target evenly across selected modalities; continuous intervals", + "per_sample_cap_rows": args.per_sample_cap, + "total_added_feature_rows_per_type": total_masked, + "mean_added_feature_rows_per_sample": per_sample_budget_mean, + "zero_budget_samples": zero_budget_samples, + "mask_seed": args.seed, + "C5_evaluation_seed": args.seed + 1, + "bootstrap_seed": args.seed + 2, + "bootstrap_repeats": args.bootstrap_repeats, + "C5_temperature": temperature, + "C0_temperature": c0_temperature, + "test_split_used": False, + } + (RESULTS / "matched_missing_type_manifest.json").write_text( + json.dumps(manifest, indent=2, ensure_ascii=False), encoding="utf-8", + ) + print(f"C0 temperature={c0_temperature:.6f}; C5 temperature={temperature:.6f}", flush=True) + for row in summary_rows: + if row["model"] != "C5" or row["scenario"] == "0.0/none": + continue + print( + f"{row['model']} {row['scenario']}: added={row['rate_realized_additional_global']:.4f} " + f"final={row['rate_final_total_missing_global']:.4f} " + f"Acc={row['accuracy']:.4f} MacroF1={row['macro_f1']:.4f} " + f"MAE={row['regression_mae']:.4f}", + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/final/q2/math/test_model.py b/final/q2/math/test_model.py new file mode 100644 index 0000000..0e44375 --- /dev/null +++ b/final/q2/math/test_model.py @@ -0,0 +1,316 @@ +"""Numerical checks for the Q2 state posterior, joint sampling, and decoder.""" +from __future__ import annotations + +import unittest +from types import SimpleNamespace + +import numpy as np +import torch +from scipy.special import betainc as scipy_betainc + +from ...model.crg import CRG, ReliabilityGRU, StructuredGaussianImputer +from .train import ( + _decode_mixture, + _calibrated_mixture_moments, + _group_ids, + _missing_rate_summary, + _predictive_intervals, + _trajectory_variance_components, + continuous_mask, + controlled_group_bootstrap, + gate_diagnostic_rows, + regularized_beta, + smooth_group_risk, + validate_attachment3_predictions, +) + + +class StructuredGaussianTests(unittest.TestCase): + def test_filter_nll_matches_dense_marginal_gaussian(self) -> None: + torch.manual_seed(73) + model = StructuredGaussianImputer((2, 2, 2)).double() + xs = [torch.randn(1, 2, 2, dtype=torch.float64) for _ in range(3)] + observed = torch.ones(1, 2, 3, dtype=torch.bool) + got = model.observed_nll(xs, observed)[0] + + with torch.no_grad(): + transition = model._transition() + p0, q = model._covariances() + emissions = model.emissions() + emission = torch.cat(emissions, dim=0) + noise = torch.block_diag(*[torch.diag(torch.nn.functional.softplus(raw) + 1e-4) for raw in model.r_raw]) + offset = torch.cat(list(model.biases)) + state_mean = torch.cat((model.mu0, transition @ model.mu0)) + p01 = p0 @ transition.T + p11 = transition @ p0 @ transition.T + q + state_cov = torch.cat((torch.cat((p0, p01), dim=1), torch.cat((p01.T, p11), dim=1)), dim=0) + observation_map = torch.block_diag(emission, emission) + observation_cov = observation_map @ state_cov @ observation_map.T + torch.block_diag(noise, noise) + observation_mean = torch.cat((offset + emission @ model.mu0, + offset + emission @ (transition @ model.mu0))) + values = torch.cat((torch.cat([xs[m][0, 0] for m in range(3)]), + torch.cat([xs[m][0, 1] for m in range(3)]))) + residual = values - observation_mean + expected = 0.5 * ( + residual @ torch.linalg.solve(observation_cov, residual) + + torch.linalg.slogdet(observation_cov).logabsdet + + len(values) * np.log(2.0 * np.pi) + ) + torch.testing.assert_close(got, expected, rtol=2e-4, atol=2e-4) + + def test_joint_trajectory_draws_retain_temporal_dependence(self) -> None: + torch.manual_seed(19) + model = StructuredGaussianImputer((2, 2, 2)) + with torch.no_grad(): + for emission in model.emission_raw: + emission.zero_() + model.emission_raw[1][0, 0] = 1.0 + xs = [torch.zeros(1, 2, 2) for _ in range(3)] + observed = torch.zeros(1, 2, 3, dtype=torch.bool) + draws, _ = model.complete(xs, observed, 1600, joint_draws=True) + temporal_correlation = float(np.corrcoef(draws[1][:, 0, 0, 0].cpu(), draws[1][:, 0, 1, 0].cpu())[0, 1]) + self.assertGreater(temporal_correlation, 0.15) + + +class LossAndMaskTests(unittest.TestCase): + def test_beta_cdf_matches_scipy(self) -> None: + a = torch.tensor([0.7, 2.0, 5.0]) + b = torch.tensor([1.3, 3.0, 2.5]) + x = torch.tensor([0.2, 0.8, 0.55]) + actual = regularized_beta(x, a, b).detach().cpu().numpy() + expected = scipy_betainc(a.numpy(), b.numpy(), x.numpy()) + np.testing.assert_allclose(actual, expected, rtol=2e-5, atol=2e-6) + + def test_mask_is_contiguous_and_preserves_each_selected_source(self) -> None: + original = np.ones((50, 3), dtype=bool) + for mode in ("single", "sync", "partial", "async"): + masked = continuous_mask(original, 0.5, mode, np.random.default_rng(101)) + hidden = original & ~masked + for modality in range(3): + positions = np.flatnonzero(hidden[:, modality]) + if len(positions): + self.assertEqual(int(positions[-1] - positions[0] + 1), len(positions)) + self.assertGreaterEqual(int(masked[:, modality].sum()), 10) + + def test_point_mask_keeps_rate_but_breaks_contiguous_span(self) -> None: + original = np.ones((50, 3), dtype=bool) + masked = continuous_mask(original, 0.3, "single", np.random.default_rng(887), + modalities=(1,), kind="point") + hidden = np.flatnonzero(original[:, 1] & ~masked[:, 1]) + self.assertEqual(len(hidden), 15) + self.assertGreaterEqual(int(masked[:, 1].sum()), 10) + runs = np.split(hidden, np.flatnonzero(np.diff(hidden) > 1) + 1) + self.assertGreater(len([run for run in runs if len(run)]), 1) + + def test_position_and_gap_structure_controls_hold_total_missing_fixed(self) -> None: + original = np.ones((50, 3), dtype=bool) + counts = [] + for location in ("start", "middle", "end"): + masked = continuous_mask( + original, 0.3, "single", np.random.default_rng(22), + modalities=(0,), location=location, + ) + hidden = np.flatnonzero(original[:, 0] & ~masked[:, 0]) + counts.append(len(hidden)) + if location == "start": + self.assertEqual(int(hidden[0]), 0) + elif location == "end": + self.assertEqual(int(hidden[-1]), 49) + else: + self.assertLessEqual(abs(float(hidden.mean()) - 24.5), 1.0) + self.assertEqual(counts, [15, 15, 15]) + + long = continuous_mask( + original, 0.3, "single", np.random.default_rng(22), + modalities=(0,), span_structure="long", + ) + short = continuous_mask( + original, 0.3, "single", np.random.default_rng(22), + modalities=(0,), span_structure="multi_short", + ) + long_hidden = np.flatnonzero(original[:, 0] & ~long[:, 0]) + short_hidden = np.flatnonzero(original[:, 0] & ~short[:, 0]) + self.assertEqual(len(long_hidden), len(short_hidden)) + short_runs = np.split(short_hidden, np.flatnonzero(np.diff(short_hidden) > 1) + 1) + self.assertGreaterEqual(len([run for run in short_runs if len(run)]), 2) + + def test_group_id_uses_any_newly_hidden_source(self) -> None: + original = np.ones((2, 50, 3), dtype=bool) + current = original.copy() + current[0, 10:20, 1] = False + current[1, 15:25, 2] = False + groups = _group_ids(original, current) + self.assertNotEqual(int(groups[0]), int(groups[1])) + + def test_missing_rates_follow_equal_modality_pdf_denominators(self) -> None: + original = np.asarray([ + [1, 1, 0], [1, 1, 0], [1, 0, 0], [1, 0, 0], + ], dtype=bool) + current = original.copy() + current[0, 0] = False + rates = _missing_rate_summary(original, current) + np.testing.assert_allclose(rates["natural_by_modality"], [0.0, 0.5, 1.0]) + self.assertAlmostEqual(rates["natural_global"], 0.5) + np.testing.assert_allclose(rates["final_by_modality"], [0.25, 0.5, 1.0]) + self.assertAlmostEqual(rates["final_global"], 7.0 / 12.0) + np.testing.assert_allclose(rates["additional_by_modality"][:2], [0.25, 0.0]) + self.assertTrue(np.isnan(rates["additional_by_modality"][2])) + + def test_smooth_group_risk_matches_prior_weighted_formula(self) -> None: + losses = torch.tensor([1.0, 2.0, 4.0], dtype=torch.float64) + group_ids = np.asarray([0, 0, 1]) + lambda_group, tau = 0.2, 0.5 + group_losses = torch.tensor([1.5, 4.0], dtype=torch.float64) + priors = torch.tensor([2 / 3, 1 / 3], dtype=torch.float64) + expected = ((1 - lambda_group) * (priors * group_losses).sum() + + lambda_group * tau * torch.logsumexp(priors.log() + group_losses / tau, dim=0)) + actual = smooth_group_risk(losses, group_ids, lambda_group, tau) + torch.testing.assert_close(actual, expected) + + def test_controlled_group_bootstrap_is_paired_and_reports_aurc(self) -> None: + split = SimpleNamespace( + n=4, + class_y=np.asarray([0, 0, 1, 2]), + regression_y=np.asarray([-1.0, -0.5, 0.0, 1.0]), + groups=np.asarray(["v1", "v1", "v2", "v3"]), + mask=np.ones((4, 50, 3), dtype=bool), + ) + scenarios = ["0.0/none"] + [f"{rate:.1f}/{mode}" for mode in ("single", "sync", "partial", "async") + for rate in (0.1, 0.3, 0.5, 0.7)] + scenario_masks = {} + for scenario in scenarios: + mask = split.mask.copy() + rate_name, pattern = scenario.split("/", 1) + rate = float(rate_name) + if rate > 0: + modality = {"single": 0, "sync": 0, "partial": 1, "async": 2}[pattern] + count = int(round(rate * 50)) + mask[:, :count, modality] = False + scenario_masks[scenario] = mask + predictions = {} + for model, shift in (("C0", 0.0), ("C1", 0.1)): + predictions[model] = {} + for index, scenario in enumerate(scenarios): + predictions[model][scenario] = { + "predicted_class": np.asarray([0, 1, 1, 2]), + "predicted_score": split.regression_y + shift + index * 0.01, + } + rows = controlled_group_bootstrap(split, predictions, scenario_masks, repeats=20, seed=29) + self.assertTrue(any(row["metric"] == "AURC_MAE" and row["model"] == "C1" for row in rows)) + paired = next(row for row in rows if row["model"] == "C1" and row["scenario"] == "0.3/single" and row["metric"] == "mae") + self.assertAlmostEqual(paired["delta_estimate"], 0.1) + self.assertAlmostEqual(paired["delta_to_natural_mae"], 0.02) + self.assertEqual(paired["replicates"], 20) + + def test_attachment3_submission_invariants(self) -> None: + rows = [ + {"case_id": "case-a", "predicted_class": 0, "predicted_sentiment": -0.2, + "p_negative": 0.5, "p_neutral": 0.3, "p_positive": 0.2, + "interval_90_lower": -1.0, "interval_90_upper": 0.5}, + {"case_id": "case-b", "predicted_class": 1, "predicted_sentiment": 0.0, + "p_negative": 0.2, "p_neutral": 0.6, "p_positive": 0.2, + "interval_90_lower": -0.5, "interval_90_upper": 0.5}, + ] + validate_attachment3_predictions(["case-a", "case-b"], rows) + rows[1]["predicted_sentiment"] = 1e-9 + with self.assertRaisesRegex(ValueError, "polarity mismatch"): + validate_attachment3_predictions(["case-a", "case-b"], rows) + + def test_gate_diagnostic_rows_keep_sample_position_and_modality(self) -> None: + split = SimpleNamespace(ids=["v1$_$c1"], groups=np.asarray(["v1"]), + mask=np.ones((1, 2, 3), dtype=bool)) + scalar = np.zeros((1, 2, 3), dtype=np.float32) + predictions = { + "fusion_weights": np.full((1, 2, 3), 0.2, dtype=np.float32), + "null_weights": np.full((1, 2), 0.4, dtype=np.float32), + "time_pool_weights": np.full((1, 2), 0.5, dtype=np.float32), + "reliability": np.ones((1, 2, 3), dtype=np.float32), + "imputation_uncertainty": scalar, + "gap": scalar, + "span": scalar, + "distance_before": scalar, + "distance_after": scalar, + } + rows = gate_diagnostic_rows(split, predictions) + self.assertEqual(len(rows), 6) + self.assertEqual(rows[0]["sample_id"], "v1$_$c1") + self.assertEqual(rows[-1]["modality"], "vision") + + def test_decoder_uses_neutral_priority_and_exact_zero(self) -> None: + probabilities = np.asarray([[[1 / 3, 1 / 3, 1 / 3]], [[1 / 3, 1 / 3, 1 / 3]]], dtype=np.float32) + beta = np.full((2, 1, 2, 2), 2.0, dtype=np.float32) + _, classes, scores = _decode_mixture(probabilities, beta) + self.assertEqual(int(classes[0]), 1) + self.assertEqual(float(scores[0]), 0.0) + + def test_calibrated_signed_mixture_interval_and_variance_components(self) -> None: + probabilities = np.asarray( + [[[0.25, 0.5, 0.25]], [[0.4, 0.2, 0.4]]], dtype=np.float64, + ) + beta = np.full((2, 1, 2, 2), 2.0, dtype=np.float64) + low, high = _predictive_intervals(probabilities, beta, temperature=1.5) + self.assertLess(float(low[0]), 0.0) + self.assertGreater(float(high[0]), 0.0) + self.assertLess(float(low[0]), float(high[0])) + total, within, between = _trajectory_variance_components(probabilities, beta) + np.testing.assert_allclose(total, within + between, rtol=1e-6, atol=1e-7) + mean_cold, variance_cold = _calibrated_mixture_moments(probabilities, beta, temperature=0.5) + mean_warm, variance_warm = _calibrated_mixture_moments(probabilities, beta, temperature=2.0) + self.assertTrue(np.isfinite(mean_cold).all() and np.isfinite(variance_cold).all()) + self.assertGreater(abs(float(variance_cold[0] - variance_warm[0])), 1e-5) + + +class RecurrentAndVariantTests(unittest.TestCase): + def test_gru_reset_gate_is_applied_before_candidate_recurrent_map(self) -> None: + model = ReliabilityGRU(input_dim=1, hidden=1) + with torch.no_grad(): + model.x_proj.weight.zero_() + model.x_proj.bias.copy_(torch.tensor([10.0, 0.0, 1.0])) + model.h_proj.weight.zero_() + model.candidate_h.weight.fill_(2.0) + x = torch.zeros(1, 2, 1) + rho = torch.ones(1, 2) + distance = torch.zeros(1, 2) + actual = model._one_direction(x, rho, distance, reverse=False, reliability_update=False) + z = torch.sigmoid(torch.tensor(10.0)) + first = z * torch.tanh(torch.tensor(1.0)) + reset = torch.sigmoid(torch.tensor(0.0)) + candidate = torch.tanh(torch.tensor(1.0) + 2.0 * reset * first) + expected = (1.0 - z) * first + z * candidate + torch.testing.assert_close(actual[0, 1, 0], expected) + + def test_all_ablation_architectures_forward_and_backward(self) -> None: + torch.manual_seed(9) + options = { + "C1": dict(use_imputer=False, use_joint_draws=False, use_final_gate=False, use_source_attention=False, reliability_update=False, use_low_rank=False), + "C2": dict(use_imputer=True, use_joint_draws=False, use_final_gate=False, use_source_attention=False, reliability_update=False, use_low_rank=False), + "C3": dict(use_imputer=True, use_joint_draws=True, use_final_gate=True, use_source_attention=False, reliability_update=False, use_low_rank=False), + "C4": dict(use_imputer=True, use_joint_draws=True, use_final_gate=True, use_source_attention=True, reliability_update=False, use_low_rank=False), + "C5": dict(use_imputer=True, use_joint_draws=True, use_final_gate=True, use_source_attention=True, reliability_update=True, use_low_rank=False), + "C6": dict(use_imputer=True, use_joint_draws=True, use_final_gate=True, use_source_attention=True, reliability_update=True, use_low_rank=True), + } + xs = [torch.randn(1, 4, width) for width in (3, 2, 2)] + observed = torch.ones(1, 4, 3, dtype=torch.bool) + observed[:, 1:3, 1] = False + for name, flags in options.items(): + with self.subTest(model=name): + model = CRG(input_dims=(3, 2, 2), **flags) + output = model(xs, observed, paths=2, joint_draws=flags["use_joint_draws"]) + loss = output["class_logits"].sum() + output["beta_params"].sum() + loss.backward() + expected_paths = 2 if flags["use_imputer"] else 1 + self.assertEqual(tuple(output["class_probs"].shape), (1, 3)) + self.assertEqual(tuple(output["fusion_weights_by_path"].shape), (expected_paths, 1, 4, 3)) + self.assertEqual(tuple(output["null_weights_by_path"].shape), (expected_paths, 1, 4)) + self.assertEqual(tuple(output["time_pool_weights_by_path"].shape), (expected_paths, 1, 4)) + torch.testing.assert_close( + output["fusion_weights_by_path"].sum(dim=-1) + output["null_weights_by_path"], + torch.ones((expected_paths, 1, 4)), + ) + torch.testing.assert_close( + output["time_pool_weights_by_path"].sum(dim=-1), torch.ones((expected_paths, 1)), + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/final/q2/math/train.py b/final/q2/math/train.py new file mode 100644 index 0000000..6752a27 --- /dev/null +++ b/final/q2/math/train.py @@ -0,0 +1,2131 @@ +"""Train/evaluate the Q2 model under the V2 official split and ablation design.""" +from __future__ import annotations + +import argparse +import copy +import csv +from contextlib import contextmanager +import hashlib +import json +import math +import random +import time +from pathlib import Path +from typing import Any + +import numpy as np +import torch +from scipy.optimize import minimize_scalar +from scipy.special import betainc, betaincinv +from scipy.stats import pearsonr +from sklearn.linear_model import LogisticRegression, Ridge +from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error, recall_score +from sklearn.model_selection import GroupShuffleSplit +from torch.nn import functional as F +from transformers import AutoModel, AutoTokenizer + +from ...model.crg import CRG, INPUT_DIMS, MODALITIES, StructuredGaussianImputer +from .data import ( + ATTACHMENT2_DIR, + ATTACHMENT3_ALIGNED, + ATTACHMENT3_UNALIGNED, + DATA_ROOT, + ROOT, + SplitData, + fit_preprocessor, + load_attachment3_case, + load_official_splits, + transform_split, +) + +Q2_DIR = Path(__file__).resolve().parent +RESULTS = Q2_DIR / "results" +TEXT_MODEL_ID = "google-bert/bert-base-uncased" +SEED = 20260924 +MASK_RATES = (0.0, 0.1, 0.3, 0.5, 0.7) +MASK_MODES = ("single", "sync", "partial", "async") +DELTA_U = 0.01 +DISTILL_TEMPERATURE = 2.0 +LAMBDA_Y = 1.0 +LAMBDA_DISTILL = 0.1 +LAMBDA_RECON = 0.05 +LAMBDA_GROUP = 0.1 +GROUP_TEMPERATURE = 0.1 +GROUP_RISK_CANDIDATES = ((0.05, 0.1), (0.1, 0.05), (0.1, 0.1), (0.1, 0.2), (0.2, 0.1)) +LAMBDA_EMISSION = 1e-4 +LAMBDA_TRANSITION = 1e-4 +DEFAULT_RELIABILITY = (0.5, 0.05, 0.05, 0.05) +RELIABILITY_CANDIDATES = ( + (0.5, 0.0, 0.0, 0.0), + (0.5, 0.05, 0.05, 0.05), + (0.5, 0.1, 0.0, 0.0), + (0.3, 0.05, 0.05, 0.05), + (0.7, 0.05, 0.05, 0.05), +) + + +def seed_everything(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + + +@contextmanager +def fixed_torch_seed(seed: int, device: torch.device): + devices = [device.index if device.index is not None else torch.cuda.current_device()] if device.type == "cuda" else [] + with torch.random.fork_rng(devices=devices): + torch.manual_seed(int(seed)) + if device.type == "cuda": + torch.cuda.manual_seed_all(int(seed)) + yield + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def label_resolution_from_train(labels: np.ndarray) -> float: + """Use half the smallest positive nonzero magnitude spacing in fit labels.""" + magnitudes = np.unique(np.round(np.abs(np.asarray(labels, dtype=np.float64)) / 3.0, 6)) + spacing = np.diff(magnitudes) + spacing = spacing[spacing > 1e-5] + return float(np.clip(0.5 * spacing.min(), 1e-4, 0.1)) if len(spacing) else 0.01 + + +def fit_magnitude_priors(labels: np.ndarray) -> np.ndarray: + """Method-of-moments sign-specific Beta priors from train labels only.""" + result = np.zeros((2, 2), dtype=np.float64) + for slot, selected in enumerate((np.asarray(labels) < 0, np.asarray(labels) > 0)): + values = np.abs(np.asarray(labels, dtype=np.float64)[selected]) / 3.0 + values = np.clip(values, 1e-4, 1.0 - 1e-4) + if len(values) < 2: + mean, concentration = 0.5, 4.0 + else: + mean, variance = float(values.mean()), float(values.var(ddof=1)) + concentration = mean * (1.0 - mean) / max(variance, 1e-5) - 1.0 + concentration = float(np.clip(concentration, 2.0, 100.0)) + result[slot] = (max(1e-3, mean * concentration), max(1e-3, (1.0 - mean) * concentration)) + return result.astype(np.float32) + + +def assert_group_disjoint(splits: dict[str, SplitData]) -> dict[str, int]: + overlap: dict[str, int] = {} + for left, right in (("train", "valid"), ("train", "test"), ("valid", "test")): + shared = set(splits[left].groups) & set(splits[right].groups) + overlap[f"{left}_{right}"] = len(shared) + if shared: + raise ValueError(f"source-video leakage across {left}/{right}: {sorted(shared)[:5]}") + return overlap + + +def select_rows(split: SplitData, indices: np.ndarray, name: str) -> SplitData: + idx = np.asarray(indices, dtype=np.int64) + return SplitData( + name=name, + x={m: split.x[m][idx].copy() for m in MODALITIES}, + mask=split.mask[idx].copy(), + class_y=split.class_y[idx].copy() if split.class_y is not None else None, + regression_y=split.regression_y[idx].copy() if split.regression_y is not None else None, + ids=[split.ids[int(i)] for i in idx], + groups=split.groups[idx].copy(), + ) + + +def split_calibration(train: SplitData, seed: int, fraction: float = 0.1) -> tuple[SplitData, SplitData]: + splitter = GroupShuffleSplit(n_splits=1, test_size=fraction, random_state=seed) + fit_idx, cal_idx = next(splitter.split(np.zeros(train.n), train.class_y, train.groups)) + fit, cal = select_rows(train, fit_idx, "fit"), select_rows(train, cal_idx, "calibration") + if set(fit.groups) & set(cal.groups): + raise AssertionError("internal fit/calibration source videos overlap") + return fit, cal + + +def _best_interval( + visible: np.ndarray, + wanted: int, + cap: int, + location: str, + rng: np.random.Generator, +) -> tuple[int, int] | None: + steps = len(visible) + candidates: list[tuple[int, int, int, int]] = [] + for left in range(steps): + hits = 0 + for right in range(left, steps): + hits += int(visible[right]) + count = min(hits, cap) + if count: + candidates.append((abs(count - wanted), right - left + 1, left, right)) + if not candidates: + return None + best = min((error, span) for error, span, _, _ in candidates) + tied = [(left, right) for error, span, left, right in candidates if (error, span) == best] + if location == "start": + return min(tied, key=lambda pair: (pair[0], pair[1])) + if location == "end": + return max(tied, key=lambda pair: (pair[1], pair[0])) + if location == "middle": + center = (steps - 1) / 2 + return min(tied, key=lambda pair: (abs((pair[0] + pair[1]) / 2 - center), pair[0])) + if location != "random": + raise ValueError(f"unknown interval location: {location}") + return tied[int(rng.integers(0, len(tied)))] + + +def _spread_short_spans(visible: np.ndarray, wanted: int, cap: int) -> np.ndarray: + positions = np.flatnonzero(visible) + count = min(int(wanted), int(cap), len(positions)) + chosen = np.zeros(len(visible), dtype=bool) + if count <= 0: + return chosen + n_spans = min(3, count) + chunks = np.array_split(positions, n_spans) + allocations = [count // n_spans + int(i < count % n_spans) for i in range(n_spans)] + for chunk, amount in zip(chunks, allocations): + if amount <= 0 or len(chunk) == 0: + continue + amount = min(amount, len(chunk)) + start = max(0, (len(chunk) - amount) // 2) + chosen[chunk[start:start + amount]] = True + return chosen + + +def continuous_mask( + original: np.ndarray, + rate: float, + mode: str, + rng: np.random.Generator, + *, + modalities: tuple[int, ...] | None = None, + location: str = "random", + span_structure: str = "long", + kind: str = "continuous", +) -> np.ndarray: + """Hide contiguous feature rows while preserving at least 20% per selected source. + + ``sync``, ``partial`` and ``async`` use a shared, shifted-overlap, or + staggered span layout. Evaluation controls may pin the affected modalities, + gap location, and one-long versus several-short structure. + """ + observed = np.asarray(original, dtype=bool) + result = observed.copy() + if rate <= 0 or mode == "none": + return result + steps, modality_count = observed.shape + present = [m for m in range(modality_count) if observed[:, m].any()] + if not present: + return result + if modalities is not None: + selected = [int(m) for m in modalities if int(m) in present] + if not selected: + return result + elif mode == "single": + selected = [int(rng.choice(present))] + elif mode in {"sync", "partial", "async"}: + if len(present) == 1: + selected = present + else: + count = int(rng.integers(2, min(3, len(present)) + 1)) + selected = sorted(int(v) for v in rng.choice(present, size=count, replace=False)) + else: + raise ValueError(f"unknown mask mode: {mode}") + + def max_hide(modality: int) -> int: + count = int(observed[:, modality].sum()) + keep = max(1, int(math.ceil(0.2 * count))) + return max(0, count - keep) + + target = {m: min(max_hide(m), int(round(rate * int(observed[:, m].sum())))) for m in selected} + if kind == "point": + for modality in selected: + candidates = np.flatnonzero(observed[:, modality]) + count = target[modality] + if count > 0: + hidden = rng.choice(candidates, size=count, replace=False) + result[hidden, modality] = False + return result + if kind != "continuous": + raise ValueError(f"unknown mask kind: {kind}") + if mode == "sync": + span = max(1, int(round(rate * steps))) + if location == "start": + left = 0 + elif location == "end": + left = steps - span + elif location == "middle": + left = (steps - span) // 2 + elif location == "random": + left = int(rng.integers(0, max(1, steps - span + 1))) + else: + raise ValueError(f"unknown interval location: {location}") + right = min(steps - 1, left + span - 1) + for m in selected: + candidates = np.flatnonzero(observed[left:right + 1, m]) + left + amount = min(len(candidates), max_hide(m), target[m]) + if amount: + offset = 0 if location != "end" else len(candidates) - amount + candidates = candidates[max(0, offset):max(0, offset) + amount] + result[candidates, m] = False + else: + common_span = max(1, int(round(rate * steps))) + for rank, m in enumerate(selected): + wanted = target[m] + if wanted <= 0: + continue + cap = max_hide(m) + if span_structure == "multi_short": + hide = _spread_short_spans(observed[:, m], wanted, cap) + elif span_structure != "long": + raise ValueError(f"unknown span structure: {span_structure}") + elif mode == "single" and location != "random": + span = max(1, int(round(rate * steps))) + if location == "start": + left = 0 + elif location == "end": + left = steps - span + elif location == "middle": + left = (steps - span) // 2 + else: + raise ValueError(f"unknown interval location: {location}") + right = min(steps - 1, left + span - 1) + hide = np.zeros(steps, dtype=bool) + candidates = np.flatnonzero(observed[left:right + 1, m]) + left + hide[candidates[:cap]] = True + elif mode in {"partial", "async"}: + if mode == "partial": + base_left = int(rng.integers(0, max(1, steps - common_span + 1))) if location == "random" else ( + 0 if location == "start" else steps - common_span if location == "end" else (steps - common_span) // 2 + ) + offset = int(round(rank * common_span * 0.5)) + else: + base_left = 0 if location == "random" else ( + 0 if location == "start" else steps - common_span if location == "end" else (steps - common_span) // 2 + ) + available = max(1, steps - common_span + 1) + offsets = np.rint(np.linspace(0, max(0, available - 1), len(selected))).astype(int) + if location == "random": + rng.shuffle(offsets) + offset = int(offsets[rank]) + left = min(max(0, base_left + offset), max(0, steps - common_span)) + right = min(steps - 1, left + common_span - 1) + hide = np.zeros(steps, dtype=bool) + candidates = np.flatnonzero(observed[left:right + 1, m]) + left + amount = min(len(candidates), wanted, cap) + if amount: + hide[candidates[:amount]] = True + else: + interval = _best_interval(observed[:, m], wanted, cap, location, rng) + hide = np.zeros(steps, dtype=bool) + if interval is not None: + left, right = interval + candidates = np.flatnonzero(observed[left:right + 1, m]) + left + amount = min(len(candidates), wanted, cap) + if amount: + offset = 0 if location != "end" else len(candidates) - amount + hide[candidates[max(0, offset):max(0, offset) + amount]] = True + result[hide, m] = False + return result + + +def make_scenarios(split: SplitData, seed: int) -> dict[str, np.ndarray]: + scenarios = {"0.0/none": split.mask.copy()} + for rate in MASK_RATES[1:]: + for mode in MASK_MODES: + key = f"{rate:.1f}/{mode}" + masks = [] + for sample_id, original in zip(split.ids, split.mask): + sample_seed = int.from_bytes(hashlib.sha256(f"{seed}:{sample_id}:{key}".encode()).digest()[:8], "little") + masks.append(continuous_mask(original, rate, mode, np.random.default_rng(sample_seed))) + scenarios[key] = np.stack(masks) + + modality_sets = ( + ((0,), "T"), ((1,), "A"), ((2,), "V"), + ((0, 1), "TA"), ((0, 2), "TV"), ((1, 2), "AV"), ((0, 1, 2), "TAV"), + ) + for selected, label in modality_sets: + key = f"0.3/modality_{label}" + scenarios[key] = np.stack([ + continuous_mask( + original, 0.3, "sync", np.random.default_rng(_scenario_seed(seed, sample_id, key)), + modalities=selected, + ) + for sample_id, original in zip(split.ids, split.mask) + ]) + + for modality_index, label in enumerate(("T", "A", "V")): + for location in ("start", "middle", "end"): + key = f"0.3/location_{location}_{label}" + scenarios[key] = np.stack([ + continuous_mask( + original, 0.3, "single", np.random.default_rng(_scenario_seed(seed, sample_id, key)), + modalities=(modality_index,), location=location, + ) + for sample_id, original in zip(split.ids, split.mask) + ]) + for structure in ("long", "multi_short"): + key = f"0.3/span_{structure}_{label}" + scenarios[key] = np.stack([ + continuous_mask( + original, 0.3, "single", np.random.default_rng(_scenario_seed(seed, sample_id, key)), + modalities=(modality_index,), span_structure=structure, + ) + for sample_id, original in zip(split.ids, split.mask) + ]) + + for mode in ("sync", "partial", "async"): + key = f"0.3/synchrony_{mode}" + scenarios[key] = np.stack([ + continuous_mask( + original, 0.3, mode, np.random.default_rng(_scenario_seed(seed, sample_id, key)), + modalities=(0, 1, 2), + ) + for sample_id, original in zip(split.ids, split.mask) + ]) + return scenarios + + +def _scenario_seed(seed: int, sample_id: str, key: str) -> int: + return int.from_bytes(hashlib.sha256(f"{seed}:{sample_id}:{key}".encode()).digest()[:8], "little") + + +def make_reliability_scenarios(split: SplitData, seed: int) -> dict[str, np.ndarray]: + scenarios = {"0.0/natural": split.mask.copy()} + for rate, mode in ((0.3, "single"), (0.3, "sync"), (0.5, "async")): + key = f"{rate:.1f}/{mode}" + scenarios[key] = np.stack([ + continuous_mask(original, rate, mode, np.random.default_rng(_scenario_seed(seed, sample_id, key))) + for sample_id, original in zip(split.ids, split.mask) + ]) + return scenarios + + +def to_device_batch( + arrays: dict[str, np.ndarray], masks: np.ndarray, indices: np.ndarray, device: torch.device, +) -> tuple[list[torch.Tensor], torch.Tensor]: + idx = np.asarray(indices, dtype=np.int64) + xs = [torch.from_numpy(arrays[m][idx]).to(device=device, dtype=torch.float32) for m in MODALITIES] + observed = torch.from_numpy(masks[idx].astype(bool)).to(device=device) + return xs, observed + + +def _betacf(a: torch.Tensor, b: torch.Tensor, x: torch.Tensor, iterations: int = 64) -> torch.Tensor: + """Differentiable continued fraction for the regularized incomplete beta.""" + tiny = 1e-12 + qab, qap, qam = a + b, a + 1.0, a - 1.0 + c = torch.ones_like(x) + d = 1.0 - qab * x / qap + d = 1.0 / torch.where(d.abs() < tiny, torch.full_like(d, tiny), d) + h = d + for m in range(1, iterations + 1): + mf = float(m) + aa = mf * (b - mf) * x / ((qam + 2.0 * mf) * (a + 2.0 * mf)) + d = 1.0 + aa * d + d = 1.0 / torch.where(d.abs() < tiny, torch.full_like(d, tiny), d) + c = 1.0 + aa / torch.where(c.abs() < tiny, torch.full_like(c, tiny), c) + h = h * d * c + aa = -(a + mf) * (qab + mf) * x / ((a + 2.0 * mf) * (qap + 2.0 * mf)) + d = 1.0 + aa * d + d = 1.0 / torch.where(d.abs() < tiny, torch.full_like(d, tiny), d) + c = 1.0 + aa / torch.where(c.abs() < tiny, torch.full_like(c, tiny), c) + h = h * d * c + return h + + +def regularized_beta(x: torch.Tensor, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + x_full, a_full, b_full = torch.broadcast_tensors(x, a, b) + safe_x = x_full.clamp(1e-7, 1.0 - 1e-7) + log_bt = torch.lgamma(a_full + b_full) - torch.lgamma(a_full) - torch.lgamma(b_full) + log_bt = log_bt + a_full * torch.log(safe_x) + b_full * torch.log1p(-safe_x) + bt = torch.exp(log_bt.clamp(-80.0, 30.0)) + lower = safe_x < (a_full + 1.0) / (a_full + b_full + 2.0) + direct = bt * _betacf(a_full, b_full, safe_x) / a_full + complement = 1.0 - bt * _betacf(b_full, a_full, 1.0 - safe_x) / b_full + result = torch.where(lower, direct, complement).clamp(0.0, 1.0) + return torch.where(x_full <= 0.0, torch.zeros_like(result), torch.where(x_full >= 1.0, torch.ones_like(result), result)) + + +def supervised_loss_per_sample( + output: dict[str, Any], class_y: torch.Tensor, regression_y: torch.Tensor, +) -> tuple[torch.Tensor, dict[str, torch.Tensor]]: + probs = output["class_probs_by_path"].clamp_min(1e-8) + beta = output["beta_params"].clamp_min(1e-4) + path_count, batch = probs.shape[:2] + negative = class_y == 0 + neutral = class_y == 1 + positive = class_y == 2 + u = (regression_y.abs() / 3.0).clamp(0.0, 1.0) + lo = (u - DELTA_U).clamp(0.0, 1.0) + hi = (u + DELTA_U).clamp(0.0, 1.0) + neutral_mass = probs[:, :, 1] + neg_params = beta[:, :, 0, :] + pos_params = beta[:, :, 1, :] + cdf_hi_neg = regularized_beta(hi.unsqueeze(0), neg_params[..., 0], neg_params[..., 1]) + cdf_lo_neg = regularized_beta(lo.unsqueeze(0), neg_params[..., 0], neg_params[..., 1]) + cdf_hi_pos = regularized_beta(hi.unsqueeze(0), pos_params[..., 0], pos_params[..., 1]) + cdf_lo_pos = regularized_beta(lo.unsqueeze(0), pos_params[..., 0], pos_params[..., 1]) + neg_mass = probs[:, :, 0] * (cdf_hi_neg - cdf_lo_neg).clamp_min(1e-12) + pos_mass = probs[:, :, 2] * (cdf_hi_pos - cdf_lo_pos).clamp_min(1e-12) + selected = torch.where(neutral.unsqueeze(0), neutral_mass, torch.where(negative.unsqueeze(0), neg_mass, pos_mass)) + mixture_mass = selected.mean(dim=0).clamp_min(1e-12) + nll = -torch.log(mixture_mass) + beta_mean = beta[..., 0] / beta.sum(dim=-1) + conditional = 3.0 * (probs[:, :, 2] * beta_mean[:, :, 1] - probs[:, :, 0] * beta_mean[:, :, 0]) + mean_score = conditional.mean(dim=0) + scaled_error = (regression_y - mean_score) / 3.0 + huber = F.huber_loss(scaled_error, torch.zeros_like(scaled_error), reduction="none", delta=0.25) + total = nll + LAMBDA_Y * huber + return total, {"nll": nll, "huber": huber, "mean_score": mean_score} + + +def reconstruction_loss_per_sample( + output: dict[str, Any], xs: list[torch.Tensor], hidden: torch.Tensor, +) -> torch.Tensor: + batch = hidden.shape[0] + per_modal = [] + for m, prediction in enumerate(output["reconstructions"]): + target = xs[m].unsqueeze(0) + error = (prediction - target).abs().mean(dim=-1) + mask = hidden[:, :, m].float().unsqueeze(0) + numerator = (error * mask).sum(dim=(0, 2)) + denominator = mask.sum(dim=(0, 2)).clamp_min(1.0) + per_modal.append(numerator / denominator) + values = torch.stack(per_modal, dim=-1) + active = torch.stack([hidden[:, :, m].any(dim=1) for m in range(len(MODALITIES))], dim=-1).float() + return (values * active).sum(dim=-1) / active.sum(dim=-1).clamp_min(1.0) + + +def _regularized_logits(probabilities: np.ndarray, temperature: float) -> np.ndarray: + logp = np.log(np.clip(probabilities, 1e-12, 1.0)) / temperature + logp -= logp.max(axis=1, keepdims=True) + exp = np.exp(logp) + return exp / exp.sum(axis=1, keepdims=True) + + +def _decode_mixture( + probabilities_by_path: np.ndarray, + beta_params: np.ndarray, + temperature: float = 1.0, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + pbar = _regularized_logits(probabilities_by_path.mean(axis=0), temperature) + maximum = pbar.max(axis=1, keepdims=True) + ties = np.isclose(pbar, maximum, rtol=0.0, atol=1e-12) + predicted_class = np.asarray([next(c for c in (1, 0, 2) if row[c]) for row in ties], dtype=np.int64) + score = np.zeros(len(predicted_class), dtype=np.float32) + for i, cls in enumerate(predicted_class): + if cls == 1: + continue + sign_index = 0 if cls == 0 else 1 + weights = probabilities_by_path[:, i, cls] + params = beta_params[:, i, sign_index] + denominator = float(weights.sum()) + if denominator <= 1e-12: + magnitude = 0.5 + else: + low, high = 0.0, 1.0 + for _ in range(48): + middle = (low + high) / 2.0 + cdf = float(np.dot(weights, betainc(params[:, 0], params[:, 1], middle)) / denominator) + if cdf < 0.5: + low = middle + else: + high = middle + magnitude = (low + high) / 2.0 + score[i] = (-3.0 if cls == 0 else 3.0) * magnitude + return pbar, predicted_class, score + + +def _predictive_intervals( + probabilities_by_path: np.ndarray, + beta_params: np.ndarray, + temperature: float, + quantiles: tuple[float, float] = (0.05, 0.95), +) -> tuple[np.ndarray, np.ndarray]: + """Central intervals of the calibrated signed point-mass/Beta mixture.""" + pbar = _regularized_logits(probabilities_by_path.mean(axis=0), temperature) + lower = np.empty(len(pbar), dtype=np.float32) + upper = np.empty(len(pbar), dtype=np.float32) + for i, marginal in enumerate(pbar): + negative_weight = probabilities_by_path[:, i, 0].astype(np.float64) + positive_weight = probabilities_by_path[:, i, 2].astype(np.float64) + negative_weight /= negative_weight.sum() + positive_weight /= positive_weight.sum() + negative = beta_params[:, i, 0].astype(np.float64) + positive = beta_params[:, i, 1].astype(np.float64) + + def cdf(value: float) -> float: + if value < 0.0: + magnitude_threshold = min(1.0, max(0.0, -value / 3.0)) + conditional = np.dot( + negative_weight, + 1.0 - betainc(negative[:, 0], negative[:, 1], magnitude_threshold), + ) + return float(marginal[0] * conditional) + magnitude_threshold = min(1.0, max(0.0, value / 3.0)) + conditional = np.dot( + positive_weight, + betainc(positive[:, 0], positive[:, 1], magnitude_threshold), + ) + return float(marginal[0] + marginal[1] + marginal[2] * conditional) + + for slot, quantile in enumerate(quantiles): + lo, hi = -3.0, 3.0 + for _ in range(52): + mid = (lo + hi) / 2.0 + if cdf(mid) >= quantile: + hi = mid + else: + lo = mid + if slot == 0: + lower[i] = hi + else: + upper[i] = hi + return lower, upper + + +def _trajectory_variance_components( + probabilities_by_path: np.ndarray, + beta_params: np.ndarray, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Verify Var(Y)=E_b Var(Y|b)+Var_b(E[Y|b]) before class calibration.""" + probs = np.asarray(probabilities_by_path, dtype=np.float64) + beta = np.asarray(beta_params, dtype=np.float64) + mean = beta[..., 0] / beta.sum(axis=-1) + second = beta[..., 0] * (beta[..., 0] + 1.0) / (beta.sum(axis=-1) * (beta.sum(axis=-1) + 1.0)) + path_mean = 3.0 * (probs[..., 2] * mean[..., 1] - probs[..., 0] * mean[..., 0]) + path_second = 9.0 * (probs[..., 2] * second[..., 1] + probs[..., 0] * second[..., 0]) + conditional_variance = np.maximum(0.0, path_second - np.square(path_mean)) + within = conditional_variance.mean(axis=0) + between = path_mean.var(axis=0) + total = within + between + return total.astype(np.float32), within.astype(np.float32), between.astype(np.float32) + + +def _calibrated_mixture_moments( + probabilities_by_path: np.ndarray, + beta_params: np.ndarray, + temperature: float, +) -> tuple[np.ndarray, np.ndarray]: + """Recompute mean and variance after temperature calibration of class mass.""" + pcal = _regularized_logits(probabilities_by_path.mean(axis=0), temperature) + beta = np.asarray(beta_params, dtype=np.float64) + raw_probs = np.asarray(probabilities_by_path, dtype=np.float64) + beta_mean = beta[..., 0] / beta.sum(axis=-1) + beta_second = beta[..., 0] * (beta[..., 0] + 1.0) / (beta.sum(axis=-1) * (beta.sum(axis=-1) + 1.0)) + conditional_mean = np.zeros((raw_probs.shape[1], 2), dtype=np.float64) + conditional_second = np.zeros((raw_probs.shape[1], 2), dtype=np.float64) + for sign_index, class_index in enumerate((0, 2)): + weights = raw_probs[:, :, class_index] + weights = weights / weights.sum(axis=0, keepdims=True) + conditional_mean[:, sign_index] = np.sum(weights * beta_mean[:, :, sign_index], axis=0) + conditional_second[:, sign_index] = np.sum(weights * beta_second[:, :, sign_index], axis=0) + mean = 3.0 * (pcal[:, 2] * conditional_mean[:, 1] - pcal[:, 0] * conditional_mean[:, 0]) + second = 9.0 * (pcal[:, 2] * conditional_second[:, 1] + pcal[:, 0] * conditional_second[:, 0]) + variance = np.maximum(0.0, second - np.square(mean)) + return mean.astype(np.float32), variance.astype(np.float32) + + +def calculate_metrics( + y_cls: np.ndarray, + y_reg: np.ndarray, + probs: np.ndarray, + pred_cls: np.ndarray, + pred_reg: np.ndarray, + selection_nll: float | None = None, + interval_lower: np.ndarray | None = None, + interval_upper: np.ndarray | None = None, + variance_components: tuple[np.ndarray, np.ndarray, np.ndarray] | None = None, + calibrated_moments: tuple[np.ndarray, np.ndarray] | None = None, +) -> dict[str, Any]: + p = np.clip(probs, 1e-8, 1.0) + onehot = np.eye(3, dtype=np.float64)[y_cls] + nll = float(-np.log(p[np.arange(len(y_cls)), y_cls]).mean()) + confidence = p.max(axis=1) + correct = (pred_cls == y_cls).astype(np.float64) + ece = 0.0 + for left in np.linspace(0, 1, 16)[:-1]: + right = left + 1 / 15 + selected = (confidence >= left) & (confidence < right if right < 1 else confidence <= right) + if selected.any(): + ece += selected.mean() * abs(confidence[selected].mean() - correct[selected].mean()) + pearson = float(pearsonr(y_reg, pred_reg).statistic) if np.std(y_reg) > 0 and np.std(pred_reg) > 0 else float("nan") + support = np.bincount(y_cls.astype(int), minlength=3) + result = { + "n": int(len(y_cls)), + "accuracy": float(accuracy_score(y_cls, pred_cls)), + "macro_f1": float(f1_score(y_cls, pred_cls, labels=[0, 1, 2], average="macro", zero_division=0)), + "negative_support": int(support[0]), + "neutral_support": int(support[1]), + "positive_support": int(support[2]), + "negative_recall": float(recall_score(y_cls, pred_cls, labels=[0], average="macro", zero_division=0)), + "middle_recall": float(recall_score(y_cls, pred_cls, labels=[1], average="macro", zero_division=0)), + "positive_recall": float(recall_score(y_cls, pred_cls, labels=[2], average="macro", zero_division=0)), + "regression_mae": float(mean_absolute_error(y_reg, pred_reg)), + "regression_rmse": float(np.sqrt(mean_squared_error(y_reg, pred_reg))), + "pearson": pearson, + "brier": float(np.square(probs - onehot).sum(axis=1).mean()), + "classification_nll": nll, + "ece_15": float(ece), + } + if selection_nll is not None: + result["selection_nll"] = float(selection_nll) + if interval_lower is not None and interval_upper is not None: + result["interval_90_coverage"] = float(np.mean((y_reg >= interval_lower) & (y_reg <= interval_upper))) + result["interval_90_mean_width"] = float(np.mean(interval_upper - interval_lower)) + if variance_components is not None: + total, within, between = variance_components + result["predictive_variance_mean_uncalibrated"] = float(np.mean(total)) + result["within_trajectory_variance_mean"] = float(np.mean(within)) + result["between_trajectory_variance_mean"] = float(np.mean(between)) + if calibrated_moments is not None: + calibrated_mean, calibrated_variance = calibrated_moments + result["predictive_mean_mean_calibrated"] = float(np.mean(calibrated_mean)) + result["predictive_variance_mean_calibrated"] = float(np.mean(calibrated_variance)) + return result + + +def evaluate( + model: CRG, + arrays: dict[str, np.ndarray], + split: SplitData, + device: torch.device, + batch_size: int, + *, + masks: np.ndarray | None = None, + temperature: float = 1.0, + collect_gate_diagnostics: bool = False, +) -> tuple[dict[str, Any], dict[str, np.ndarray]]: + model.eval() + source_masks = split.mask if masks is None else np.asarray(masks, dtype=bool) + prob_paths, beta_paths, loss_parts = [], [], [] + gate_parts: dict[str, list[np.ndarray]] = { + "fusion_weights": [], "null_weights": [], "time_pool_weights": [], "reliability": [], + "imputation_uncertainty": [], "gap": [], "span": [], "distance_before": [], "distance_after": [], + } + with torch.inference_mode(): + for start in range(0, split.n, batch_size): + idx = np.arange(start, min(split.n, start + batch_size)) + xs, observed = to_device_batch(arrays, source_masks, idx, device) + out = model(xs, observed, paths=16 if model.use_joint_draws else 1, joint_draws=model.use_joint_draws) + prob_paths.append(out["class_probs_by_path"].cpu().numpy()) + beta_paths.append(out["beta_params"].cpu().numpy()) + if collect_gate_diagnostics: + for name, key in (("fusion_weights", "fusion_weights_by_path"), + ("null_weights", "null_weights_by_path"), + ("time_pool_weights", "time_pool_weights_by_path")): + gate_parts[name].append(out[key].mean(dim=0).cpu().numpy()) + for name in ("reliability", "imputation_uncertainty", "gap", "span", "distance_before", "distance_after"): + gate_parts[name].append(out[name].cpu().numpy()) + cy = torch.from_numpy(split.class_y[idx]).to(device) + ry = torch.from_numpy(split.regression_y[idx]).to(device) + nll, _ = supervised_loss_per_sample(out, cy, ry) + loss_parts.append(nll.cpu().numpy()) + ppaths = np.concatenate(prob_paths, axis=1) + betas = np.concatenate(beta_paths, axis=1) + probs, pred_class, pred_score = _decode_mixture(ppaths, betas, temperature) + interval_lower, interval_upper = _predictive_intervals(ppaths, betas, temperature) + variance_components = _trajectory_variance_components(ppaths, betas) + calibrated_moments = _calibrated_mixture_moments(ppaths, betas, temperature) + metrics = calculate_metrics( + split.class_y, split.regression_y, probs, pred_class, pred_score, + float(np.mean(np.concatenate(loss_parts))), interval_lower, interval_upper, variance_components, + calibrated_moments, + ) + predictions = { + "probabilities": probs, + "probabilities_by_path": ppaths, + "beta_params": betas, + "predicted_class": pred_class, + "predicted_score": pred_score, + "interval_lower": interval_lower, + "interval_upper": interval_upper, + "predictive_variance_uncalibrated": variance_components[0], + "within_trajectory_variance": variance_components[1], + "between_trajectory_variance": variance_components[2], + "predictive_mean_calibrated": calibrated_moments[0], + "predictive_variance_calibrated": calibrated_moments[1], + } + if collect_gate_diagnostics: + predictions.update({name: np.concatenate(values, axis=0) for name, values in gate_parts.items()}) + return metrics, predictions + + +def gate_diagnostic_rows(split: SplitData, predictions: dict[str, np.ndarray]) -> list[dict[str, Any]]: + required = {"fusion_weights", "null_weights", "time_pool_weights", "reliability", + "imputation_uncertainty", "gap", "span", "distance_before", "distance_after"} + if not required.issubset(predictions): + raise ValueError(f"missing gate diagnostic arrays: {sorted(required - set(predictions))}") + rows = [] + for sample_index, sample_id in enumerate(split.ids): + for step in range(split.mask.shape[1]): + for modality_index, modality in enumerate(MODALITIES): + rows.append({ + "sample_id": sample_id, + "video_id": split.groups[sample_index], + "step": step, + "relative_position": step / max(1, split.mask.shape[1] - 1), + "modality": modality, + "observed": bool(split.mask[sample_index, step, modality_index]), + "fusion_weight_mean_over_paths": float(predictions["fusion_weights"][sample_index, step, modality_index]), + "null_weight_mean_over_paths": float(predictions["null_weights"][sample_index, step]), + "reliability": float(predictions["reliability"][sample_index, step, modality_index]), + "imputation_uncertainty": float(predictions["imputation_uncertainty"][sample_index, step, modality_index]), + "nearest_observation_gap": float(predictions["gap"][sample_index, step, modality_index]), + "continuous_missing_span": float(predictions["span"][sample_index, step, modality_index]), + "distance_before": float(predictions["distance_before"][sample_index, step, modality_index]), + "distance_after": float(predictions["distance_after"][sample_index, step, modality_index]), + "time_pool_weight_mean_over_paths": float(predictions["time_pool_weights"][sample_index, step]), + }) + return rows + + +def fit_temperature(probabilities: np.ndarray, class_y: np.ndarray) -> float: + def objective(log_temperature: float) -> float: + p = _regularized_logits(probabilities, float(np.exp(log_temperature))) + return float(-np.log(np.clip(p[np.arange(len(class_y)), class_y], 1e-12, 1.0)).mean()) + + fitted = minimize_scalar(objective, bounds=(-2.0, 2.0), method="bounded", options={"xatol": 1e-5}) + return float(np.exp(fitted.x)) + + +def _group_ids(original_mask: np.ndarray, hidden_mask: np.ndarray) -> np.ndarray: + # Group by which sources received new artificial gaps, not only by sources + # that were erased completely (the mask design deliberately preserves 20%). + original = np.asarray(original_mask, dtype=bool) + current = np.asarray(hidden_mask, dtype=bool) + hidden_mod = (original & ~current).any(axis=1) + bits = hidden_mod[:, 0].astype(int) + 2 * hidden_mod[:, 1].astype(int) + 4 * hidden_mod[:, 2].astype(int) + # PDF (5.62): total missing rate is computed per source on the valid time + # axis, then averaged equally across T/A/V (never weighted by feature size + # or by the number of naturally observed rows). + final_missing_by_modality = 1.0 - current.mean(axis=1) + realized_rate = final_missing_by_modality.mean(axis=1) + coarse = np.where(realized_rate <= 0.2, 0, np.where(realized_rate <= 0.5, 1, 2)) + return bits * 3 + coarse + + +def _missing_rate_summary(original: np.ndarray, current: np.ndarray) -> dict[str, Any]: + """Return PDF (5.62)–(5.63) rates, with equal modality weighting.""" + original = np.asarray(original, dtype=bool) + current = np.asarray(current, dtype=bool) + if original.shape != current.shape or original.ndim not in (2, 3): + raise ValueError("mask rate inputs must have matching [T,M] or [N,T,M] shapes") + newly_hidden = original & ~current + natural = 1.0 - original.mean(axis=-2) + final = 1.0 - current.mean(axis=-2) + observed_count = original.sum(axis=-2) + additional_count = newly_hidden.sum(axis=-2) + additional = np.divide( + additional_count, observed_count, + out=np.full(np.shape(additional_count), np.nan, dtype=np.float64), + where=observed_count > 0, + ) + synchronous = (~current.any(axis=-1)).mean(axis=-1) + return { + "natural_by_modality": natural, + "additional_by_modality": additional, + "final_by_modality": final, + "natural_global": float(np.mean(natural)), + "additional_global": float(np.nanmean(additional)), + "final_global": float(np.mean(final)), + "synchronous_no_observation": synchronous, + } + + +def smooth_group_risk( + losses: torch.Tensor, + group_ids: np.ndarray, + lambda_group: float = LAMBDA_GROUP, + group_temperature: float = GROUP_TEMPERATURE, +) -> torch.Tensor: + gids = torch.as_tensor(group_ids, device=losses.device, dtype=torch.long) + unique = torch.unique(gids) + group_losses, priors = [], [] + for group in unique: + selected = gids == group + group_losses.append(losses[selected].mean()) + priors.append(selected.float().mean()) + values = torch.stack(group_losses) + prior = torch.stack(priors).clamp_min(1e-8) + expected = (prior * values).sum() + worst = group_temperature * torch.logsumexp(torch.log(prior) + values / group_temperature, dim=0) + return (1.0 - lambda_group) * expected + lambda_group * worst + + +def _distillation_per_sample( + student: dict[str, Any], teacher: dict[str, Any], original: np.ndarray, current: np.ndarray, +) -> torch.Tensor: + temp = DISTILL_TEMPERATURE + p_teacher = teacher["tempered_probs_by_path"].mean(dim=0).detach().clamp_min(1e-8) + p_student = student["tempered_probs_by_path"].mean(dim=0).clamp_min(1e-8) + entropy = -(p_teacher * p_teacher.log()).sum(dim=-1) + confidence_weight = (1.0 - entropy / math.log(3.0)).clamp(0.0, 1.0) + retain_by_modality = [] + orig_t = torch.as_tensor(original, device=p_teacher.device, dtype=torch.float32) + curr_t = torch.as_tensor(current, device=p_teacher.device, dtype=torch.float32) + for m in range(len(MODALITIES)): + denominator = orig_t[:, :, m].sum(dim=1) + retained = (orig_t[:, :, m] * curr_t[:, :, m]).sum(dim=1) / denominator.clamp_min(1.0) + retain_by_modality.append(torch.where(denominator > 0, retained, torch.ones_like(retained))) + retain = torch.stack(retain_by_modality, dim=-1).mean(dim=-1) + weight = confidence_weight * retain + kl = (p_teacher * (p_teacher.log() - p_student.log())).sum(dim=-1) * temp * temp + teacher_score = teacher["mixed_score"].detach() + student_score = student["mixed_score"] + reg = F.huber_loss((teacher_score - student_score) / 3.0, torch.zeros_like(teacher_score), reduction="none", delta=0.25) + return weight * (kl + reg) + + +def fit_imputer( + imputer: StructuredGaussianImputer, + arrays: dict[str, np.ndarray], + split: SplitData, + device: torch.device, + epochs: int, + batch_size: int, + seed: int, +) -> list[dict[str, float]]: + imputer.train() + optimizer = torch.optim.AdamW(imputer.parameters(), lr=3e-4, weight_decay=1e-4) + rng = np.random.default_rng(seed) + history = [] + for epoch in range(1, epochs + 1): + order = rng.permutation(split.n) + losses = [] + for start in range(0, split.n, batch_size): + idx = order[start:start + batch_size] + xs, observed = to_device_batch(arrays, split.mask, idx, device) + nll = imputer.observed_nll(xs, observed).mean() + observed_scalars = sum(observed[:, :, m].sum(dim=1).float() * INPUT_DIMS[m] for m in range(len(MODALITIES))) + emission_penalty = sum(value.square().mean() for value in imputer.emissions()) + transition_penalty = imputer._transition().square().mean() + loss = nll / observed_scalars.mean().clamp_min(1.0) + loss = loss + LAMBDA_EMISSION * emission_penalty + LAMBDA_TRANSITION * transition_penalty + optimizer.zero_grad(set_to_none=True) + loss.backward() + torch.nn.utils.clip_grad_norm_(imputer.parameters(), 5.0) + optimizer.step() + losses.append(float(loss.detach().cpu())) + row = {"stage": "structured_imputer", "epoch": epoch, "train_observed_nll_per_scalar": float(np.mean(losses))} + history.append(row) + print(f"imputer {epoch}/{epochs}: observed_nll/scalar={row['train_observed_nll_per_scalar']:.4f}", flush=True) + imputer.eval() + for parameter in imputer.parameters(): + parameter.requires_grad_(False) + return history + + +def _make_variant( + name: str, + imputer: StructuredGaussianImputer, + reliability_hparams: tuple[float, float, float, float] = DEFAULT_RELIABILITY, +) -> CRG: + if name == "C1": + flags = dict(use_imputer=False, use_joint_draws=False, use_final_gate=False, use_source_attention=False, reliability_update=False, use_low_rank=False) + elif name == "C2": + flags = dict(use_imputer=True, use_joint_draws=False, use_final_gate=False, use_source_attention=False, reliability_update=False, use_low_rank=False) + elif name == "C3": + flags = dict(use_imputer=True, use_joint_draws=True, use_final_gate=True, use_source_attention=False, reliability_update=False, use_low_rank=False) + elif name == "C4": + flags = dict(use_imputer=True, use_joint_draws=True, use_final_gate=True, use_source_attention=True, reliability_update=False, use_low_rank=False) + else: + flags = dict(use_imputer=True, use_joint_draws=True, use_final_gate=True, use_source_attention=True, + reliability_update=True, use_low_rank=name == "C6" or name.startswith("C7")) + return CRG(copy.deepcopy(imputer), reliability_hparams=reliability_hparams, **flags) + + +def _set_reliability_hparams(model: CRG, values: tuple[float, float, float, float]) -> None: + rho_imp, lambda_u, lambda_gap, lambda_span = values + if not 0.0 < rho_imp < 1.0 or min(lambda_u, lambda_gap, lambda_span) < 0.0: + raise ValueError("invalid reliability hyperparameters") + with torch.no_grad(): + model.rho_imp.fill_(rho_imp) + model.rel_u.fill_(lambda_u) + model.rel_gap.fill_(lambda_gap) + model.rel_span.fill_(lambda_span) + + +def tune_reliability_hparams( + model: CRG, + arrays: dict[str, np.ndarray], + split: SplitData, + scenarios: dict[str, np.ndarray], + device: torch.device, + batch_size: int, + model_name: str, + seed: int = SEED + 551, + candidate_values: tuple[tuple[float, float, float, float], ...] = RELIABILITY_CANDIDATES, +) -> tuple[tuple[float, float, float, float], list[dict[str, Any]]]: + if not (model.use_final_gate or model.use_source_attention or model.reliability_update): + return DEFAULT_RELIABILITY, [{"model": model_name, "selected": True, + "rho_imp": DEFAULT_RELIABILITY[0], "lambda_u": DEFAULT_RELIABILITY[1], + "lambda_gap": DEFAULT_RELIABILITY[2], "lambda_span": DEFAULT_RELIABILITY[3], + "inner_selection_nll": float("nan"), "note": "not used by this ablation"}] + rows = [] + best_values, best_loss = DEFAULT_RELIABILITY, float("inf") + if not candidate_values: + raise ValueError("at least one reliability candidate is required") + for values in candidate_values: + _set_reliability_hparams(model, values) + scenario_losses: dict[str, float] = {} + for scenario, masks in scenarios.items(): + with fixed_torch_seed(_scenario_seed(seed, split.name, scenario), device): + metrics, _ = evaluate(model, arrays, split, device, batch_size, masks=masks) + scenario_losses[scenario] = float(metrics["selection_nll"]) + score = float(np.mean(list(scenario_losses.values()))) + row = {"model": model_name, "rho_imp": values[0], "lambda_u": values[1], + "lambda_gap": values[2], "lambda_span": values[3], "inner_selection_nll": score, + "scenario_selection_nll": json.dumps(scenario_losses, sort_keys=True), + "selected": False, "selection_split": split.name} + rows.append(row) + if score < best_loss: + best_loss, best_values = score, values + _set_reliability_hparams(model, best_values) + for row in rows: + row["selected"] = (row["rho_imp"], row["lambda_u"], row["lambda_gap"], row["lambda_span"]) == best_values + return best_values, rows + + +def tune_group_risk_model( + imputer: StructuredGaussianImputer, + train: SplitData, + valid: SplitData, + arrays: dict[str, dict[str, np.ndarray]], + reliability_validation: SplitData, + reliability_arrays: dict[str, np.ndarray], + reliability_scenarios: dict[str, np.ndarray], + device: torch.device, + epochs: int, + batch_size: int, + patience: int, + seed: int, +) -> tuple[CRG, list[dict[str, float]], list[dict[str, Any]], list[dict[str, Any]], tuple[float, float, float, float], tuple[float, float], float]: + candidates = [] + all_history: list[dict[str, float]] = [] + all_reliability_rows: list[dict[str, Any]] = [] + risk_rows: list[dict[str, Any]] = [] + for candidate_index, (group_lambda, group_temperature) in enumerate(GROUP_RISK_CANDIDATES): + candidate_name = f"C7_group_lambda{group_lambda:.2f}_tau{group_temperature:.2f}" + model = _make_variant("C7_group", imputer) + with fixed_torch_seed(seed + 303, device): + model, history = _fit_neural( + model, candidate_name, train, valid, arrays, device, epochs, batch_size, patience, + np.random.default_rng(seed + 303), use_group_risk=True, + group_lambda=group_lambda, group_temperature=group_temperature, + selection_split=reliability_validation, selection_arrays=reliability_arrays, + selection_scenarios=reliability_scenarios, + ) + all_history.extend(history) + selected_reliability, tuning_rows = tune_reliability_hparams( + model, reliability_arrays, reliability_validation, reliability_scenarios, + device, batch_size, candidate_name, seed=seed + 551, + ) + selected_row = next(row for row in tuning_rows if row.get("selected")) + inner_score = float(selected_row["inner_selection_nll"]) + for row in tuning_rows: + row["group_lambda"] = group_lambda + row["group_temperature"] = group_temperature + row["risk_candidate_selected"] = False + row["candidate"] = row["model"] + row["model"] = "C7_group" + all_reliability_rows.extend(tuning_rows) + candidates.append((inner_score, model, selected_reliability, (group_lambda, group_temperature), candidate_name)) + risk_rows.append({"model": "C7_group", "candidate": candidate_name, + "lambda_group": group_lambda, "group_temperature": group_temperature, + "selected_reliability": selected_reliability, + "inner_selection_nll": inner_score, "selected": False, + "selection_split": reliability_validation.name}) + best = min(candidates, key=lambda row: row[0]) + score, model, selected_reliability, selected_group, candidate_name = best + for row in all_reliability_rows: + row["risk_candidate_selected"] = row["candidate"] == candidate_name + row["selected"] = bool(row.get("selected") and row["risk_candidate_selected"]) + for row in risk_rows: + row["selected"] = row["candidate"] == candidate_name + if row["selected"]: + row["selected"] = True + return model, all_history, all_reliability_rows, risk_rows, selected_reliability, selected_group, score + + +def _fit_neural( + model: CRG, + name: str, + train: SplitData, + valid: SplitData, + arrays: dict[str, dict[str, np.ndarray]], + device: torch.device, + epochs: int, + batch_size: int, + patience: int, + rng: np.random.Generator, + *, + teacher: CRG | None = None, + use_group_risk: bool = False, + group_lambda: float = LAMBDA_GROUP, + group_temperature: float = GROUP_TEMPERATURE, + selection_split: SplitData | None = None, + selection_arrays: dict[str, np.ndarray] | None = None, + selection_scenarios: dict[str, np.ndarray] | None = None, + mask_kind: str = "continuous", + use_reconstruction: bool = True, +) -> tuple[CRG, list[dict[str, float]]]: + model.to(device) + model.imputer.eval() + for parameter in model.imputer.parameters(): + parameter.requires_grad_(False) + if teacher is not None: + teacher.eval() + for parameter in teacher.parameters(): + parameter.requires_grad_(False) + optimizer = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad], lr=3e-4, weight_decay=1e-3) + best_loss, best_state, stale = float("inf"), None, 0 + history: list[dict[str, float]] = [] + rate_choices = np.asarray(MASK_RATES, dtype=np.float64) + pattern_choices = np.asarray(MASK_MODES, dtype=object) + for epoch in range(1, epochs + 1): + model.train() + order = rng.permutation(train.n) + epoch_losses = [] + for start in range(0, train.n, batch_size): + idx = order[start:start + batch_size] + xs, natural = to_device_batch(arrays["fit"], train.mask, idx, device) + if name == "teacher": + rates = np.zeros(len(idx), dtype=np.float64) + modes = ["none"] * len(idx) + else: + rates = rng.choice(rate_choices, size=len(idx), p=np.asarray([0.2] * 5)) + modes = rng.choice(pattern_choices, size=len(idx)).tolist() + masks_np = np.stack([ + continuous_mask(train.mask[int(i)], float(rate), str(mode), rng, kind=mask_kind) + for i, rate, mode in zip(idx, rates, modes) + ]) + current = torch.as_tensor(masks_np, device=device, dtype=torch.bool) + class_y = torch.as_tensor(train.class_y[idx], device=device, dtype=torch.long) + regression_y = torch.as_tensor(train.regression_y[idx], device=device, dtype=torch.float32) + output = model(xs, current, paths=4 if model.use_joint_draws else 1, joint_draws=model.use_joint_draws) + supervised, _ = supervised_loss_per_sample(output, class_y, regression_y) + hidden_np = train.mask[idx] & ~masks_np + hidden = torch.as_tensor(hidden_np, device=device, dtype=torch.bool) + reconstruction = reconstruction_loss_per_sample(output, xs, hidden) + per_sample = supervised + (LAMBDA_RECON * reconstruction if use_reconstruction else 0.0) + if teacher is not None: + with torch.no_grad(): + teacher_output = teacher(xs, natural, paths=4, joint_draws=True) + distill = _distillation_per_sample( output, teacher_output, train.mask[idx], masks_np) + per_sample = per_sample + LAMBDA_DISTILL * distill + if use_group_risk: + group_ids = _group_ids(train.mask[idx], masks_np) + loss = smooth_group_risk(per_sample, group_ids, group_lambda, group_temperature) + else: + loss = per_sample.mean() + optimizer.zero_grad(set_to_none=True) + loss.backward() + torch.nn.utils.clip_grad_norm_([p for p in model.parameters() if p.requires_grad], 1.0) + optimizer.step() + epoch_losses.append(float(loss.detach().cpu())) + if selection_split is not None and selection_arrays is not None and selection_scenarios: + selected_metrics = {} + for scenario, masks in selection_scenarios.items(): + scenario_metrics, _ = evaluate(model, selection_arrays, selection_split, device, batch_size, masks=masks) + selected_metrics[scenario] = scenario_metrics + val_loss = float(np.mean([metrics["selection_nll"] for metrics in selected_metrics.values()])) + natural_metrics = next((metrics for key, metrics in selected_metrics.items() + if key.startswith("0.0/") or key.endswith("natural")), + next(iter(selected_metrics.values()))) + selection_source = "group_disjoint_internal_scenarios" + else: + natural_metrics, _ = evaluate(model, arrays["valid"], valid, device, batch_size) + val_loss = float(natural_metrics["selection_nll"]) + selection_source = "official_valid_fallback" + row = {"stage": name, "epoch": epoch, "train_loss": float(np.mean(epoch_losses)), + "inner_selection_nll": val_loss, "inner_natural_accuracy": natural_metrics["accuracy"], + "inner_natural_macro_f1": natural_metrics["macro_f1"], + "inner_natural_mae": natural_metrics["regression_mae"], + "selection_source": selection_source} + history.append(row) + print(f"{name} {epoch}/{epochs}: train={row['train_loss']:.4f} innerNLL={val_loss:.4f} " + f"acc={row['inner_natural_accuracy']:.4f} macroF1={row['inner_natural_macro_f1']:.4f}", flush=True) + if val_loss < best_loss: + best_loss, best_state, stale = val_loss, copy.deepcopy(model.state_dict()), 0 + else: + stale += 1 + if stale >= patience: + break + if best_state is not None: + model.load_state_dict(best_state) + model.eval() + return model, history + + +def _sample_statistics( + split: SplitData, arrays: dict[str, np.ndarray], indices: np.ndarray, masks: np.ndarray | None = None, +) -> np.ndarray: + parts = [] + effective_mask = split.mask if masks is None else np.asarray(masks, dtype=bool) + for modality_index, modality in enumerate(MODALITIES): + x = arrays[modality][indices] + mask = effective_mask[indices, :, modality_index] + count = mask.sum(axis=1, keepdims=True) + mean = (x * mask[:, :, None]).sum(axis=1) / np.maximum(count, 1) + variance = (((x - mean[:, None, :]) ** 2) * mask[:, :, None]).sum(axis=1) / np.maximum(count, 1) + missing = 1.0 - mask.mean(axis=1, keepdims=True) + max_gap = [] + for row in mask: + longest = current = 0 + for visible in row: + current = 0 if visible else current + 1 + longest = max(longest, current) + max_gap.append(longest / max(1, len(row))) + parts.extend((mean, np.sqrt(variance), missing, np.asarray(max_gap, np.float32)[:, None])) + return np.concatenate(parts, axis=1).astype(np.float32) + + +def fit_c0( + train: SplitData, + valid: SplitData, + arrays: dict[str, dict[str, np.ndarray]], +) -> tuple[dict[str, Any], dict[str, Any]]: + train_x = _sample_statistics(train, arrays["fit"], np.arange(train.n)) + classifier = LogisticRegression(C=0.05, max_iter=2500, random_state=SEED) + classifier.fit(train_x, train.class_y) + regressor = Ridge(alpha=25.0) + regressor.fit(train_x, train.regression_y) + return {}, {"classifier": classifier, "regressor": regressor} + + +def calibrate_c0_interval( + state: dict[str, Any], + calibration: SplitData, + arrays: dict[str, np.ndarray], +) -> None: + features = _sample_statistics(calibration, arrays, np.arange(calibration.n)) + residual = calibration.regression_y - state["regressor"].predict(features) + state["residual_q05"], state["residual_q95"] = ( + float(np.quantile(residual, 0.05)), float(np.quantile(residual, 0.95)) + ) + + +def evaluate_c0( + state: dict[str, Any], split: SplitData, arrays: dict[str, np.ndarray], + temperature: float = 1.0, masks: np.ndarray | None = None, +) -> tuple[dict[str, Any], dict[str, np.ndarray]]: + features = _sample_statistics(split, arrays, np.arange(split.n), masks) + classifier, regressor = state["classifier"], state["regressor"] + raw = np.zeros((split.n, 3), np.float64) + raw[:, classifier.classes_] = classifier.predict_proba(features) + probs = _regularized_logits(raw, temperature) + maximum = probs.max(axis=1, keepdims=True) + ties = np.isclose(probs, maximum, rtol=0.0, atol=1e-12) + pred_class = np.asarray([next(c for c in (1, 0, 2) if row[c]) for row in ties], dtype=np.int64) + score = np.clip(regressor.predict(features), -3.0, 3.0) + interval_lower = np.clip(score + state["residual_q05"], -3.0, 3.0) + interval_upper = np.clip(score + state["residual_q95"], -3.0, 3.0) + metrics = calculate_metrics( + split.class_y, split.regression_y, probs, pred_class, score, + interval_lower=interval_lower, interval_upper=interval_upper, + ) + scaled_error = torch.as_tensor((split.regression_y - score) / 3.0, dtype=torch.float32) + metrics["selection_nll"] = metrics["classification_nll"] + float( + F.huber_loss(scaled_error, torch.zeros_like(scaled_error), delta=0.25) + ) + return metrics, {"probabilities": probs, "predicted_class": pred_class, "predicted_score": score, + "interval_lower": interval_lower, "interval_upper": interval_upper} + + +def controlled_c0( + state: dict[str, Any], split: SplitData, arrays: dict[str, np.ndarray], + scenarios: dict[str, np.ndarray], temperature: float, +) -> tuple[list[dict[str, Any]], dict[str, dict[str, np.ndarray]]]: + rows = [] + predictions = {} + for scenario, mask in scenarios.items(): + metrics, prediction = evaluate_c0(state, split, arrays, temperature, mask) + predictions[scenario] = prediction + rates = _missing_rate_summary(split.mask, mask) + additional_by_modality = np.nanmean(rates["additional_by_modality"], axis=0) + rate, mode = scenario.split("/", 1) + rows.append({"evaluation_split": split.name, "model": "C0", "rate_requested_per_selected_source": float(rate), + "mask_pattern": mode, "rate_realized_global": rates["additional_global"], + "rate_realized_additional_global": rates["additional_global"], + "rate_realized_additional_by_modality": json.dumps([None if not np.isfinite(x) else float(x) for x in additional_by_modality]), + "natural_missing_rate_global": rates["natural_global"], + "natural_missing_rate_by_modality": json.dumps(np.mean(rates["natural_by_modality"], axis=0).tolist()), + "rate_final_total_missing_global": rates["final_global"], + "rate_final_total_missing_by_modality": json.dumps(np.mean(rates["final_by_modality"], axis=0).tolist()), + "synchronous_no_observation_rate": float(np.mean(rates["synchronous_no_observation"])), **metrics}) + return rows, predictions + + +def write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + if not rows: + return + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8-sig", newline="") as stream: + fieldnames = list(dict.fromkeys(key for row in rows for key in row)) + writer = csv.DictWriter(stream, fieldnames=fieldnames, extrasaction="raise") + writer.writeheader() + writer.writerows(rows) + + +def mask_audit_rows(split: SplitData, scenarios: dict[str, np.ndarray], seed: int) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for scenario, masks in scenarios.items(): + requested_rate, pattern = scenario.split("/", 1) + for index, (sample_id, original, current) in enumerate(zip(split.ids, split.mask, masks)): + newly_hidden = np.asarray(original, dtype=bool) & ~np.asarray(current, dtype=bool) + intervals: dict[str, list[list[int]]] = {} + for modality_index, modality in enumerate(MODALITIES): + positions = np.flatnonzero(newly_hidden[:, modality_index]) + spans: list[list[int]] = [] + if len(positions): + start = previous = int(positions[0]) + for position in positions[1:]: + position = int(position) + if position != previous + 1: + spans.append([start, previous + 1]) + start = position + previous = position + spans.append([start, previous + 1]) + intervals[modality] = spans + rate_summary = _missing_rate_summary(original, current) + rows.append({ + "natural_missing_rate": rate_summary["natural_global"], + "natural_missing_rate_by_modality": json.dumps(rate_summary["natural_by_modality"].tolist()), + "realized_additional_rate": rate_summary["additional_global"], + "realized_additional_rate_by_modality": json.dumps([ + None if not np.isfinite(x) else float(x) + for x in rate_summary["additional_by_modality"] + ]), + "final_total_missing_rate": rate_summary["final_global"], + "final_total_missing_rate_by_modality": json.dumps(rate_summary["final_by_modality"].tolist()), + "synchronous_no_observation_rate": float(rate_summary["synchronous_no_observation"]), + "sample_id": sample_id, + "video_id": split.groups[index], + "scenario": scenario, + "requested_rate_per_selected_source": float(requested_rate), + "mask_pattern": pattern, + "mask_seed": _scenario_seed(seed, sample_id, scenario), + "selected_modalities": ",".join(m for m in MODALITIES if intervals[m]), + "newly_hidden_intervals_step_half_open": json.dumps(intervals, separators=(",", ":")), + "original_observed_steps": int(np.asarray(original, dtype=bool).sum()), + "newly_hidden_steps": int(newly_hidden.sum()), + }) + return rows + + +def group_bootstrap( + split: SplitData, prediction: dict[str, np.ndarray], reps: int = 1000, seed: int = SEED + 44, +) -> list[dict[str, Any]]: + rng = np.random.default_rng(seed) + groups = np.unique(split.groups) + group_indices = {g: np.flatnonzero(split.groups == g) for g in groups} + rows = {name: [] for name in ("accuracy", "macro_f1", "mae", "rmse", "pearson", "interval_90_coverage", "interval_90_mean_width")} + for _ in range(reps): + chosen = rng.choice(groups, size=len(groups), replace=True) + idx = np.concatenate([group_indices[g] for g in chosen]) + cls, score = prediction["predicted_class"][idx], prediction["predicted_score"][idx] + ycls, yreg = split.class_y[idx], split.regression_y[idx] + rows["accuracy"].append(accuracy_score(ycls, cls)) + rows["macro_f1"].append(f1_score(ycls, cls, labels=[0, 1, 2], average="macro", zero_division=0)) + rows["mae"].append(mean_absolute_error(yreg, score)) + rows["rmse"].append(np.sqrt(mean_squared_error(yreg, score))) + rows["pearson"].append(pearsonr(yreg, score).statistic if np.std(score) and np.std(yreg) else np.nan) + rows["interval_90_coverage"].append(np.mean((yreg >= prediction["interval_lower"][idx]) & (yreg <= prediction["interval_upper"][idx]))) + rows["interval_90_mean_width"].append(np.mean(prediction["interval_upper"][idx] - prediction["interval_lower"][idx])) + result = [] + for name, values in rows.items(): + values = np.asarray(values, dtype=np.float64) + result.append({"metric": name, "estimate": float(np.nanmedian(values)), + "ci_2_5": float(np.nanpercentile(values, 2.5)), + "ci_97_5": float(np.nanpercentile(values, 97.5)), + "replicates": reps, "unit": "source video group"}) + return result + + +def paired_group_bootstrap_deltas( + split: SplitData, + predictions: dict[str, dict[str, np.ndarray]], + reps: int = 1000, + seed: int = SEED + 88, +) -> list[dict[str, Any]]: + """Paired validation-set model deltas using one shared source-video resample.""" + groups = np.unique(split.groups) + group_indices = {group: np.flatnonzero(split.groups == group) for group in groups} + compare_models = [name for name in predictions if name != "C0"] + metrics = ("accuracy", "macro_f1", "mae", "rmse", "pearson", "interval_90_coverage", "interval_90_mean_width") + + def value(name: str, metric: str, indices: np.ndarray) -> float: + pred = predictions[name] + y_cls, y_reg = split.class_y[indices], split.regression_y[indices] + if metric == "accuracy": + return float(accuracy_score(y_cls, pred["predicted_class"][indices])) + if metric == "macro_f1": + return float(f1_score(y_cls, pred["predicted_class"][indices], labels=[0, 1, 2], average="macro", zero_division=0)) + if metric == "mae": + return float(mean_absolute_error(y_reg, pred["predicted_score"][indices])) + if metric == "rmse": + return float(np.sqrt(mean_squared_error(y_reg, pred["predicted_score"][indices]))) + if metric == "pearson": + estimate = pred["predicted_score"][indices] + return float(pearsonr(y_reg, estimate).statistic) if np.std(y_reg) and np.std(estimate) else float("nan") + if metric == "interval_90_coverage": + return float(np.mean((y_reg >= pred["interval_lower"][indices]) & (y_reg <= pred["interval_upper"][indices]))) + if metric == "interval_90_mean_width": + return float(np.mean(pred["interval_upper"][indices] - pred["interval_lower"][indices])) + raise ValueError(metric) + + point = { + (model, metric): value(model, metric, np.arange(split.n)) - value("C0", metric, np.arange(split.n)) + for model in compare_models for metric in metrics + } + draws = {key: [] for key in point} + rng = np.random.default_rng(seed) + for _ in range(reps): + chosen = rng.choice(groups, size=len(groups), replace=True) + indices = np.concatenate([group_indices[group] for group in chosen]) + baseline = {metric: value("C0", metric, indices) for metric in metrics} + for model in compare_models: + for metric in metrics: + draws[(model, metric)].append(value(model, metric, indices) - baseline[metric]) + rows = [] + for (model, metric), values in draws.items(): + values = np.asarray(values, dtype=np.float64) + rows.append({ + "model": model, "baseline": "C0", "metric": metric, + "point_delta": point[(model, metric)], + "bootstrap_median_delta": float(np.nanmedian(values)), + "ci_2_5": float(np.nanpercentile(values, 2.5)), + "ci_97_5": float(np.nanpercentile(values, 97.5)), + "replicates": reps, "unit": "paired source-video group resample", + }) + return rows + + +def controlled_metrics( + model: CRG, + split: SplitData, + arrays: dict[str, np.ndarray], + scenarios: dict[str, np.ndarray], + device: torch.device, + batch_size: int, + model_name: str, + temperature: float, + seed: int = SEED + 552, +) -> tuple[list[dict[str, Any]], dict[str, dict[str, np.ndarray]]]: + rows = [] + predictions = {} + for scenario, mask in scenarios.items(): + scenario_seed = _scenario_seed(seed, split.name, scenario) + with fixed_torch_seed(scenario_seed, device): + metrics, prediction = evaluate(model, arrays, split, device, batch_size, masks=mask, temperature=temperature) + predictions[scenario] = prediction + rates = _missing_rate_summary(split.mask, mask) + additional_by_modality = np.nanmean(rates["additional_by_modality"], axis=0) + rate, mode = scenario.split("/", 1) + rows.append({"evaluation_split": split.name, "model": model_name, "rate_requested_per_selected_source": float(rate), + "mask_pattern": mode, "rate_realized_global": rates["additional_global"], + "rate_realized_additional_global": rates["additional_global"], + "rate_realized_additional_by_modality": json.dumps([None if not np.isfinite(x) else float(x) for x in additional_by_modality]), + "natural_missing_rate_global": rates["natural_global"], + "natural_missing_rate_by_modality": json.dumps(np.mean(rates["natural_by_modality"], axis=0).tolist()), + "rate_final_total_missing_global": rates["final_global"], + "rate_final_total_missing_by_modality": json.dumps(np.mean(rates["final_by_modality"], axis=0).tolist()), + "synchronous_no_observation_rate": float(np.mean(rates["synchronous_no_observation"])), **metrics}) + print(f"validation mask {scenario}: additional={rates['additional_global']:.3f} final={rates['final_global']:.3f} " + f"macroF1={metrics['macro_f1']:.4f} MAE={metrics['regression_mae']:.4f}", flush=True) + return rows, predictions + + +def controlled_group_bootstrap( + split: SplitData, + predictions: dict[str, dict[str, dict[str, np.ndarray]]], + scenario_masks: dict[str, np.ndarray], + repeats: int, + seed: int, +) -> list[dict[str, Any]]: + """Paired source-video bootstrap for each fixed mask and the MAE-rate AURC.""" + if "C0" not in predictions: + raise ValueError("controlled bootstrap requires C0 predictions") + scenarios = list(predictions["C0"]) + if any(set(model_predictions) != set(scenarios) for model_predictions in predictions.values()): + raise ValueError("all models must use identical controlled-mask scenarios") + if set(scenario_masks) != set(scenarios): + raise ValueError("controlled-mask audit and model predictions must use identical scenarios") + groups = np.unique(split.groups) + group_indices = {group: np.flatnonzero(split.groups == group) for group in groups} + metric_names = ("accuracy", "macro_f1", "mae", "rmse", "pearson") + keys = [(model, scenario, metric) for model in predictions for scenario in scenarios for metric in metric_names] + point = {} + draws = {key: [] for key in keys} + delta_draws = {key: [] for key in keys if key[0] != "C0"} + + curve_modes = tuple(MASK_MODES) + curve_rates = np.asarray((0.0, 0.1, 0.3, 0.5, 0.7), dtype=np.float64) + curve_scenarios = { + mode: tuple("0.0/none" if rate == 0.0 else f"{rate:.1f}/{mode}" for rate in curve_rates) + for mode in curve_modes + } + for mode, curve in curve_scenarios.items(): + if any(scenario not in scenarios for scenario in curve): + raise ValueError(f"missing fixed rate-curve scenario for {mode}") + curve_point = {} + curve_draws = {} + curve_actual_rates: dict[tuple[str, str], np.ndarray] = {} + additional_rate_by_sample: dict[str, np.ndarray] = {} + for scenario, mask in scenario_masks.items(): + by_modality = _missing_rate_summary(split.mask, mask)["additional_by_modality"] + counts = np.isfinite(by_modality).sum(axis=1) + additional_rate_by_sample[scenario] = np.divide( + np.nansum(by_modality, axis=1), counts, + out=np.full(split.n, np.nan, dtype=np.float64), where=counts > 0, + ) + for model in predictions: + for mode, curve in curve_scenarios.items(): + key = (model, mode) + errors = [np.abs(split.regression_y - predictions[model][scenario]["predicted_score"]) for scenario in curve] + point_curve = np.asarray([float(error.mean()) for error in errors]) + actual_rates = np.asarray([ + 0.0 if scenario == "0.0/none" else float(np.nanmean(additional_rate_by_sample[scenario])) + for scenario in curve + ]) + curve_actual_rates[model, mode] = actual_rates + order = np.argsort(actual_rates, kind="stable") + x = actual_rates[order] + y = point_curve[order] + unique_x, inverse = np.unique(x, return_inverse=True) + unique_y = np.asarray([y[inverse == index].mean() for index in range(len(unique_x))]) + curve_point[key] = (float(np.trapezoid(unique_y, unique_x) / unique_x[-1]) + if len(unique_x) > 1 and unique_x[-1] > 0.0 else float(point_curve[0])) + curve_draws[key] = [] + + def metric_values(model: str, scenario: str, indices: np.ndarray) -> dict[str, float]: + prediction = predictions[model][scenario] + y_class, y_score = split.class_y[indices], split.regression_y[indices] + predicted_class = prediction["predicted_class"][indices] + predicted_score = prediction["predicted_score"][indices] + return { + "accuracy": float(accuracy_score(y_class, predicted_class)), + "macro_f1": float(f1_score(y_class, predicted_class, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(mean_absolute_error(y_score, predicted_score)), + "rmse": float(np.sqrt(mean_squared_error(y_score, predicted_score))), + "pearson": float(pearsonr(y_score, predicted_score).statistic) + if np.std(y_score) and np.std(predicted_score) else float("nan"), + } + + for model in predictions: + for scenario in scenarios: + values = metric_values(model, scenario, np.arange(split.n)) + for metric, value in values.items(): + point[(model, scenario, metric)] = value + + within_model_mae_delta_draws = { + (model, scenario): [] for model in predictions for scenario in scenarios if scenario != "0.0/none" + } + + rng = np.random.default_rng(seed) + for _ in range(repeats): + chosen = rng.choice(groups, size=len(groups), replace=True) + indices = np.concatenate([group_indices[group] for group in chosen]) + replicate_values = {} + for model in predictions: + for scenario in scenarios: + values = metric_values(model, scenario, indices) + for metric, value in values.items(): + key = (model, scenario, metric) + draws[key].append(value) + replicate_values[key] = value + for model in predictions: + if model == "C0": + continue + for scenario in scenarios: + for metric in metric_names: + key = (model, scenario, metric) + delta_draws[key].append(replicate_values[key] - replicate_values[("C0", scenario, metric)]) + for model in predictions: + natural_mae = replicate_values[(model, "0.0/none", "mae")] + for scenario in scenarios: + if scenario != "0.0/none": + within_model_mae_delta_draws[(model, scenario)].append( + replicate_values[(model, scenario, "mae")] - natural_mae + ) + for model in predictions: + for mode, curve in curve_scenarios.items(): + curve_mae = [replicate_values[(model, scenario, "mae")] for scenario in curve] + sample_rates = np.asarray([ + 0.0 if scenario == "0.0/none" else float(np.nanmean(additional_rate_by_sample[scenario][indices])) + for scenario in curve + ]) + order = np.argsort(sample_rates, kind="stable") + x = sample_rates[order] + y = np.asarray(curve_mae)[order] + unique_x, inverse = np.unique(x, return_inverse=True) + unique_y = np.asarray([y[inverse == index].mean() for index in range(len(unique_x))]) + auc = (float(np.trapezoid(unique_y, unique_x) / unique_x[-1]) + if len(unique_x) > 1 and unique_x[-1] > 0.0 else float(curve_mae[0])) + curve_draws[(model, mode)].append(auc) + + def interval(values: list[float]) -> tuple[float, float, float]: + samples = np.asarray(values, dtype=np.float64) + return float(np.nanmedian(samples)), float(np.nanpercentile(samples, 2.5)), float(np.nanpercentile(samples, 97.5)) + + rows = [] + for model, scenario, metric in keys: + median, lower, upper = interval(draws[(model, scenario, metric)]) + row = {"model": model, "scenario": scenario, "metric": metric, + "estimate": point[(model, scenario, metric)], "bootstrap_median": median, + "ci_2_5": lower, "ci_97_5": upper, "replicates": repeats, + "unit": "paired source-video group resample"} + if model != "C0": + delta_median, delta_lower, delta_upper = interval(delta_draws[(model, scenario, metric)]) + row.update({"baseline": "C0", "delta_estimate": point[(model, scenario, metric)] - point[("C0", scenario, metric)], + "delta_bootstrap_median": delta_median, "delta_ci_2_5": delta_lower, + "delta_ci_97_5": delta_upper}) + if scenario != "0.0/none" and metric == "mae": + natural = point[(model, "0.0/none", "mae")] + within_median, within_lower, within_upper = interval(within_model_mae_delta_draws[(model, scenario)]) + row.update({"delta_to_natural_mae": point[(model, scenario, "mae")] - natural, + "delta_to_natural_bootstrap_median": within_median, + "delta_to_natural_ci_2_5": within_lower, + "delta_to_natural_ci_97_5": within_upper}) + rows.append(row) + for model in predictions: + for mode in curve_modes: + median, lower, upper = interval(curve_draws[(model, mode)]) + row = {"model": model, "scenario": f"MAE_rate_curve/{mode}", "metric": "AURC_MAE", + "estimate": curve_point[(model, mode)], "bootstrap_median": median, + "ci_2_5": lower, "ci_97_5": upper, + "curve_additional_rates_realized": json.dumps(curve_actual_rates[(model, mode)].tolist()), + "replicates": repeats, "unit": "paired source-video group resample"} + if model != "C0": + baseline_values = curve_draws[("C0", mode)] + deltas = np.asarray(curve_draws[(model, mode)]) - np.asarray(baseline_values) + delta_median, delta_lower, delta_upper = interval(deltas.tolist()) + row.update({"baseline": "C0", "delta_estimate": curve_point[(model, mode)] - curve_point[("C0", mode)], + "delta_bootstrap_median": delta_median, "delta_ci_2_5": delta_lower, + "delta_ci_97_5": delta_upper}) + rows.append(row) + return rows + + +def _decode_attachment_text(value: Any) -> str: + if isinstance(value, bytes): + return value.decode("utf-8", errors="replace") + if isinstance(value, np.bytes_): + return bytes(value).decode("utf-8", errors="replace") + if isinstance(value, np.ndarray): + if value.shape == (): + return _decode_attachment_text(value.item()) + return " ".join(_decode_attachment_text(item) for item in value.reshape(-1)) + if isinstance(value, (list, tuple)): + return " ".join(_decode_attachment_text(item) for item in value) + return str(value) + + +def _fixed_source_rows(value: Any, dim: int, field: str) -> tuple[np.ndarray, int]: + rows = np.asarray(value, dtype=np.float32) + if rows.ndim == 3 and rows.shape[0] == 1: + rows = rows[0] + if rows.ndim != 2 or rows.shape[1] != dim: + raise ValueError(f"attachment-3 {field}: expected (steps,{dim}), got {rows.shape}") + if not np.isfinite(rows).all(): + raise ValueError(f"attachment-3 {field}: non-finite feature values") + present = np.any(rows != 0, axis=1) + last = int(np.flatnonzero(present)[-1]) + 1 if present.any() else 0 + fitted = np.zeros((500, dim), np.float32) + fitted[: min(500, len(rows))] = rows[:500] + return fitted, min(500, last) + + +def reencode_attachment3( + device: torch.device, input_version: str = "aligned_50" +) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + from .data import restricted_load + + input_dir = ATTACHMENT3_UNALIGNED if input_version == "unaligned_50" else ATTACHMENT3_ALIGNED + if input_version == "unaligned_50": + files = sorted(input_dir.glob("*.pkl"), key=lambda p: p.name) + else: + files = sorted(input_dir.glob("附件3_*.pkl"), key=lambda p: int(p.stem.split("_")[-1])) + if len(files) != 30: + raise FileNotFoundError(f"expected 30 attachment-3 files, found {len(files)} under {input_dir}") + file_ids = [path.stem for path in files] + if len(set(file_ids)) != len(file_ids): + raise ValueError("attachment-3 input file IDs are not unique") + bert = AutoModel.from_pretrained(TEXT_MODEL_ID).to(device).eval() + tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID, use_fast=True) if input_version == "unaligned_50" else None + cases, audit = [], [] + with torch.inference_mode(): + for path in files: + case_id = path.stem + if input_version == "unaligned_50": + loaded = restricted_load(path) + raw = loaded.get("test", loaded) + raw_text = _decode_attachment_text(raw.get("raw_text", "")).strip() + if not raw_text: + raise ValueError(f"{path.name}: raw_text is empty; cannot reconstruct the Q2 text input") + tokens = tokenizer(raw_text, truncation=True, padding="max_length", max_length=50, return_tensors="pt") + ids = tokens["input_ids"].to(device) + attention = tokens["attention_mask"].to(device) + model_inputs = {key: value.to(device) for key, value in tokens.items()} + text = bert(**model_inputs).last_hidden_state[0].float().cpu().numpy() + text_mask = attention[0].bool().cpu().numpy() + text[~text_mask] = 0.0 + audio, audio_length = _fixed_source_rows(raw["audio"], 74, "audio") + vision, vision_length = _fixed_source_rows(raw["vision"], 35, "vision") + token_types = tokens.get("token_type_ids", torch.zeros_like(ids))[0].cpu().numpy() + text_bert = np.stack((ids[0].cpu().numpy(), text_mask.astype(np.int64), token_types), axis=0)[None] + split = {"id": np.asarray([case_id]), "text_bert": text_bert, + "text": text[None].astype(np.float32), "audio": audio[None], "vision": vision[None], + "audio_lengths": np.asarray([max(1, audio_length)]), + "vision_lengths": np.asarray([max(1, vision_length)])} + from ...adapter import adapt_official_split + projected, projected_mask, adapter_audit = adapt_official_split(split) + case = {name: projected[name][0] for name in MODALITIES} + case["text_mask"] = projected_mask[0, :, 0] + case_id = _decode_attachment_text(raw.get("id", case_id)) or case_id + input_note = ("unaligned features projected by the shared Q1 adapter; raw_text re-encoded with BERT; " + "audio/vision lengths inferred from last nonzero row because attachment 3 omits trusted lengths") + else: + loaded = load_attachment3_case(path) + ids = torch.from_numpy(loaded["input_ids"][None]).to(device) + attention = torch.from_numpy(loaded["attention_mask"][None].astype(np.int64)).to(device) + segments = torch.from_numpy(loaded["token_type_ids"][None]).to(device) + text = bert(input_ids=ids, attention_mask=attention, token_type_ids=segments).last_hidden_state[0].float().cpu().numpy() + text_mask = loaded["attention_mask"] + text[~text_mask] = 0.0 + case = {"text": text, "audio": loaded["audio"], "vision": loaded["vision"], "text_mask": text_mask} + input_note = "official aligned_50 features; text re-encoded from supplied BERT token IDs" + cases.append({"case_id": case_id, **case}) + audio_mask = np.any(case["audio"] != 0, axis=1) + vision_mask = np.any(case["vision"] != 0, axis=1) + audit.append({"case_id": case_id, "source_file": path.name, + "text_visible_steps": int(case["text_mask"].sum()), + "audio_visible_steps": int(audio_mask.sum()), "vision_visible_steps": int(vision_mask.sum()), + "audio_missing_fraction": float(1.0 - audio_mask.mean()), + "vision_missing_fraction": float(1.0 - vision_mask.mean()), + "unknown_quality_flag": True, "labels_available": False, + "input_note": input_note, + "source_coordinate_mode": "relative_progress" if input_version == "unaligned_50" else "aligned_50", + "source_sha256": sha256(path)}) + del bert + return cases, audit + + +def infer_attachment3( + model: CRG, + cases: list[dict[str, Any]], + fitted: dict[str, dict[str, np.ndarray]], + device: torch.device, + temperature: float, + prior_probs: np.ndarray, + magnitude_priors: np.ndarray, +) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + model.eval() + predictions, audit = [], [] + for case in cases: + mask = np.stack((case["text_mask"], np.any(case["audio"] != 0, axis=1), np.any(case["vision"] != 0, axis=1)), axis=-1)[None] + xs_np = {} + for modality in MODALITIES: + values = case[modality].astype(np.float32) + values = np.clip((values - fitted[modality]["mean"]) / fitted[modality]["std"], -10.0, 10.0) + values[~mask[0, :, MODALITIES.index(modality)]] = 0.0 + xs_np[modality] = values[None] + low_information = not bool(mask.any()) + if low_information: + p = np.asarray(prior_probs, dtype=np.float64) + p = p / p.sum() + max_probability = float(p.max()) + predicted_class = next(c for c in (1, 0, 2) if math.isclose(float(p[c]), max_probability, rel_tol=0.0, abs_tol=1e-12)) + score = 0.0 + beta = np.full((1, 1, 2, 2), np.nan, np.float32) + if predicted_class != 1: + sign_index = 0 if predicted_class == 0 else 1 + magnitude = float(betaincinv(magnitude_priors[sign_index, 0], magnitude_priors[sign_index, 1], 0.5)) + score = (-3.0 if predicted_class == 0 else 3.0) * magnitude + beta[0, 0] = magnitude_priors + ppaths = p[None, None, :] + else: + arrays = {m: xs_np[m] for m in MODALITIES} + xs, observed = to_device_batch(arrays, mask, np.asarray([0]), device) + with torch.inference_mode(): + out = model(xs, observed, paths=16 if model.use_joint_draws else 1, joint_draws=model.use_joint_draws) + ppaths = out["class_probs_by_path"].cpu().numpy()[:, 0:1] + beta = out["beta_params"].cpu().numpy()[:, 0:1] + p, classes, scores = _decode_mixture(ppaths, beta, temperature) + p, predicted_class, score = p[0], int(classes[0]), float(scores[0]) + interval_temperature = 1.0 if low_information else temperature + interval_lower, interval_upper = _predictive_intervals(ppaths, beta, interval_temperature) + variance_components = _trajectory_variance_components(ppaths, beta) + calibrated_moments = _calibrated_mixture_moments(ppaths, beta, interval_temperature) + row = {"case_id": case["case_id"], "predicted_class": predicted_class, + "predicted_class_name": ("negative", "neutral", "positive")[predicted_class], + "predicted_sentiment": float(score), "p_negative": float(p[0]), + "p_neutral": float(p[1]), "p_positive": float(p[2]), + "interval_90_lower": float(interval_lower[0]), "interval_90_upper": float(interval_upper[0]), + "predictive_variance_mean_uncalibrated": float(variance_components[0][0]), + "within_trajectory_variance": float(variance_components[1][0]), + "between_trajectory_variance": float(variance_components[2][0]), + "predictive_mean_calibrated": float(calibrated_moments[0][0]), + "predictive_variance_calibrated": float(calibrated_moments[1][0]), + "beta_negative_alpha": float(beta[0, 0, 0, 0]), "beta_negative_beta": float(beta[0, 0, 0, 1]), + "beta_positive_alpha": float(beta[0, 0, 1, 0]), "beta_positive_beta": float(beta[0, 0, 1, 1]), + "low_information_prior_fallback": low_information, + "output_note": "unlabeled attachment-3 case; no accuracy/F1 is defined"} + predictions.append(row) + audit.append({"case_id": case["case_id"], "visible_text_steps": int(mask[0, :, 0].sum()), + "visible_audio_steps": int(mask[0, :, 1].sum()), "visible_vision_steps": int(mask[0, :, 2].sum()), + "low_information_prior_fallback": low_information, + "calibration_temperature": interval_temperature, + "interval_90_lower": float(interval_lower[0]), "interval_90_upper": float(interval_upper[0]), + "predictive_variance_mean_uncalibrated": float(variance_components[0][0]), + "predictive_variance_calibrated": float(calibrated_moments[1][0])}) + return predictions, audit + + +def validate_attachment3_predictions(expected_ids: list[str], predictions: list[dict[str, Any]]) -> None: + """Check the unlabeled submission contract and sign/strength consistency.""" + predicted_ids = [str(row["case_id"]) for row in predictions] + if len(predictions) != len(expected_ids) or len(set(expected_ids)) != len(expected_ids): + raise ValueError("attachment-3 prediction count differs from unique expected IDs") + if len(set(predicted_ids)) != len(predicted_ids) or set(predicted_ids) != set(expected_ids): + raise ValueError("attachment-3 predictions must contain every expected ID exactly once") + for row in predictions: + predicted_class = int(row["predicted_class"]) + score = float(row["predicted_sentiment"]) + probabilities = np.asarray([row["p_negative"], row["p_neutral"], row["p_positive"]], dtype=np.float64) + if predicted_class not in (0, 1, 2) or not np.isfinite(score) or not -3.0 <= score <= 3.0: + raise ValueError(f"invalid attachment-3 class/strength for {row['case_id']}") + if (predicted_class == 1 and score != 0.0) or (predicted_class == 0 and not score < 0.0) or ( + predicted_class == 2 and not score > 0.0 + ): + raise ValueError(f"attachment-3 class/strength polarity mismatch for {row['case_id']}") + if not np.isfinite(probabilities).all() or np.any(probabilities < 0.0) or not np.isclose(probabilities.sum(), 1.0, atol=1e-6): + raise ValueError(f"invalid attachment-3 class probabilities for {row['case_id']}") + low, high = float(row["interval_90_lower"]), float(row["interval_90_upper"]) + if not np.isfinite([low, high]).all() or low > high or low < -3.0 or high > 3.0: + raise ValueError(f"invalid attachment-3 prediction interval for {row['case_id']}") + + +def main() -> None: + global DELTA_U, RESULTS + parser = argparse.ArgumentParser() + parser.add_argument("--input-version", choices=("aligned_50", "unaligned_50"), default="unaligned_50") + parser.add_argument("--output-dir", type=Path, default=None, + help="Write this run to a new directory instead of the default results directory") + parser.add_argument("--epochs", type=int, default=12) + parser.add_argument("--imputer-epochs", type=int, default=8) + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--patience", type=int, default=3) + parser.add_argument("--seed", type=int, default=SEED) + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--bootstrap-repeats", type=int, default=1000) + parser.add_argument("--skip-attachment3", action="store_true") + args = parser.parse_args() + RESULTS = args.output_dir or Q2_DIR / ("results_unaligned" if args.input_version == "unaligned_50" else "results") + if args.output_dir is not None and RESULTS.exists() and any(RESULTS.iterdir()): + parser.error(f"refusing to overwrite non-empty result directory: {RESULTS}") + seed_everything(args.seed) + rng = np.random.default_rng(args.seed) + device = torch.device(args.device) + RESULTS.mkdir(parents=True, exist_ok=True) + if device.type == "cuda": + print(f"device={device} ({torch.cuda.get_device_name(device)})", flush=True) + + input_path = ATTACHMENT2_DIR / f"{args.input_version}.pkl" + official = load_official_splits(input_path, version=args.input_version) + overlaps = assert_group_disjoint(official) + fit, heldout_train = split_calibration(official["train"], args.seed) + reliability_validation, temperature_calibration = split_calibration(heldout_train, args.seed + 1, fraction=0.5) + reliability_validation.name = "reliability_validation" + temperature_calibration.name = "temperature_calibration" + if (set(fit.groups) & set(reliability_validation.groups) + or set(fit.groups) & set(temperature_calibration.groups) + or set(reliability_validation.groups) & set(temperature_calibration.groups)): + raise AssertionError("fit, reliability-selection, and temperature-calibration videos must be disjoint") + DELTA_U = label_resolution_from_train(fit.regression_y) + magnitude_priors = fit_magnitude_priors(fit.regression_y) + class_counts_fit = np.bincount(fit.class_y, minlength=3).astype(np.float64) + class_prior_probs = (class_counts_fit + 1.0) / (class_counts_fit.sum() + 3.0) + # Freeze the exact validation masks before fitting any model. + validation_scenarios = make_scenarios(official["valid"], args.seed + 909) + reliability_scenarios = make_reliability_scenarios(reliability_validation, args.seed + 906) + print("official splits:", {k: (v.n, len(np.unique(v.groups))) for k, v in official.items()}, + "fit/reliability_validation/temperature_calibration:", + (fit.n, reliability_validation.n, temperature_calibration.n), "group_overlap:", overlaps, flush=True) + fitted = fit_preprocessor(fit) + transformed = {name: transform_split(split, fitted) for name, split in official.items()} + transformed["fit"] = transform_split(fit, fitted) + transformed["reliability_validation"] = transform_split(reliability_validation, fitted) + transformed["temperature_calibration"] = transform_split(temperature_calibration, fitted) + np.savez_compressed(RESULTS / "preprocessor.npz", **{f"{m}_{k}": v for m, stats in fitted.items() for k, v in stats.items()}) + + imputer = StructuredGaussianImputer(INPUT_DIMS).to(device) + imputer_history = fit_imputer(imputer, transformed["fit"], fit, device, args.imputer_epochs, args.batch_size, args.seed + 1) + torch.save({k: v.detach().cpu() for k, v in imputer.state_dict().items()}, RESULTS / "structured_imputer.pt") + + teacher = _make_variant("C6", imputer).to(device) + teacher, history_teacher = _fit_neural( + teacher, "teacher", fit, official["valid"], transformed, device, args.epochs, + args.batch_size, args.patience, np.random.default_rng(args.seed + 2), + selection_split=reliability_validation, selection_arrays=transformed["reliability_validation"], + selection_scenarios={"0.0/natural": reliability_validation.mask.copy()}, + ) + teacher_reliability, teacher_tuning_rows = tune_reliability_hparams( + teacher, transformed["reliability_validation"], reliability_validation, reliability_scenarios, + device, args.batch_size, "teacher", seed=args.seed + 551, + ) + torch.save({k: v.detach().cpu() for k, v in teacher.state_dict().items()}, RESULTS / "teacher.pt") + + ablation_rows: list[dict[str, Any]] = [] + history = list(imputer_history) + history_teacher + reliability_tuning_rows = list(teacher_tuning_rows) + group_risk_tuning_rows: list[dict[str, Any]] = [] + models: dict[str, CRG] = {} + _, c0_state = fit_c0(fit, official["valid"], transformed) + calibrate_c0_interval(c0_state, temperature_calibration, transformed["temperature_calibration"]) + _, c0_cal_pred = evaluate_c0(c0_state, temperature_calibration, transformed["temperature_calibration"]) + c0_temperature = fit_temperature(c0_cal_pred["probabilities"], temperature_calibration.class_y) + c0_metrics, c0_valid_pred = evaluate_c0(c0_state, official["valid"], transformed["valid"], c0_temperature) + c0_metrics["temperature"] = c0_temperature + ablation_rows.append({"model": "C0", **c0_metrics, "description": "observed mean/std + masks + maximum gap; logistic/ridge"}) + best_model_name: str | None = None + best_valid_loss = float("inf") + temperatures = {"C0": c0_temperature} + selected_group_risk: dict[str, tuple[float, float]] = {} + validation_predictions: dict[str, dict[str, np.ndarray]] = {"C0": c0_valid_pred} + + definitions = { + "C1": "masked BiGRU; no posterior imputation, explicit reliability or source gate", + "C2": "exact Gaussian posterior mean; no joint trajectory integral", + "C3": "joint trajectory integral plus final reliability/content fusion gate", + "C4": "C3 plus bounded cross-time source attention and null source", + "C5": "C4 plus reliability-modulated BiGRU update", + "C6": "C5 plus optional rank-4 CP residual", + "C6_no_distance": "C6 with uncertainty retained but both distance/span reliability penalties fixed to zero", + "C6_no_reconstruction": "C6 trained without the auxiliary hidden-feature reconstruction loss", + "C6_pointmask": "C6 trained with independent point masking instead of contiguous spans", + "C7_distill": "C6 plus entropy/retention-weighted teacher distillation only", + "C7_group": "C6 plus smooth worst-group risk only", + } + diagnostic_ablation_names = {"C6_no_distance", "C6_no_reconstruction", "C6_pointmask"} + model_names = ("C1", "C2", "C3", "C4", "C5", "C6", *sorted(diagnostic_ablation_names), "C7_distill", "C7_group") + no_distance_candidates = tuple(value for value in RELIABILITY_CANDIDATES if value[2] == 0.0 and value[3] == 0.0) + for name in model_names: + if name == "C7_group": + (variant, rows, reliability_rows, risk_rows, selected_reliability, + selected_risk, _) = tune_group_risk_model( + imputer, fit, official["valid"], transformed, + reliability_validation, transformed["reliability_validation"], reliability_scenarios, + device, args.epochs, args.batch_size, args.patience, args.seed + 303, + ) + reliability_tuning_rows.extend(reliability_rows) + group_risk_tuning_rows.extend(risk_rows) + selected_group_risk[name] = selected_risk + else: + base_name = "C6" if name in diagnostic_ablation_names else name + variant = _make_variant(base_name, imputer) + if name == "C6_no_distance": + _set_reliability_hparams(variant, (DEFAULT_RELIABILITY[0], DEFAULT_RELIABILITY[1], 0.0, 0.0)) + kd_teacher = teacher if name == "C7_distill" else None + mask_kind = "point" if name == "C6_pointmask" else "continuous" + variant, rows = _fit_neural( + variant, name, fit, official["valid"], transformed, device, args.epochs, + args.batch_size, args.patience, np.random.default_rng(args.seed + 303), + teacher=kd_teacher, + selection_split=reliability_validation, + selection_arrays=transformed["reliability_validation"], + selection_scenarios=reliability_scenarios, + mask_kind=mask_kind, + use_reconstruction=name != "C6_no_reconstruction", + ) + selected_reliability, tuning_rows = tune_reliability_hparams( + variant, transformed["reliability_validation"], reliability_validation, reliability_scenarios, + device, args.batch_size, name, seed=args.seed + 551, + candidate_values=no_distance_candidates if name == "C6_no_distance" else RELIABILITY_CANDIDATES, + ) + reliability_tuning_rows.extend(tuning_rows) + selected_group_risk[name] = (float("nan"), float("nan")) + history.extend(rows) + models[name] = variant + _, calibration_pred = evaluate(variant, transformed["temperature_calibration"], temperature_calibration, + device, args.batch_size) + model_temperature = fit_temperature(calibration_pred["probabilities"], temperature_calibration.class_y) + temperatures[name] = model_temperature + raw_metrics, _ = evaluate(variant, transformed["valid"], official["valid"], device, args.batch_size) + metrics, valid_prediction = evaluate(variant, transformed["valid"], official["valid"], device, args.batch_size, + temperature=model_temperature) + validation_predictions[name] = valid_prediction + metrics["validation_selection_loss"] = raw_metrics["selection_nll"] + metrics["temperature"] = model_temperature + metrics.update({"rho_imp": selected_reliability[0], "lambda_u": selected_reliability[1], + "lambda_gap": selected_reliability[2], "lambda_span": selected_reliability[3]}) + metrics["lambda_group"], metrics["group_temperature"] = selected_group_risk[name] + ablation_rows.append({"model": name, **metrics, "description": definitions[name]}) + if name not in diagnostic_ablation_names and raw_metrics["selection_nll"] < best_valid_loss: + best_valid_loss, best_model_name = raw_metrics["selection_nll"], name + write_csv(RESULTS / "ablation_validation.csv", ablation_rows) + write_csv(RESULTS / "reliability_hparam_tuning.csv", reliability_tuning_rows) + write_csv(RESULTS / "group_risk_tuning.csv", group_risk_tuning_rows) + write_csv(RESULTS / "validation_group_bootstrap_deltas.csv", + paired_group_bootstrap_deltas(official["valid"], validation_predictions, args.bootstrap_repeats, args.seed + 88)) + if best_model_name is None: + raise RuntimeError("no neural ablation candidate completed") + best_model = models[best_model_name] + + # Calibration is on a group-held-out slice of official training data, never on official test. + temperature = temperatures[best_model_name] + valid_metrics, valid_pred = evaluate(best_model, transformed["valid"], official["valid"], device, args.batch_size, temperature=temperature) + test_metrics, test_pred = evaluate( + best_model, transformed["test"], official["test"], device, args.batch_size, + temperature=temperature, collect_gate_diagnostics=True, + ) + torch.save({k: v.detach().cpu() for k, v in best_model.state_dict().items()}, RESULTS / "crg_student.pt") + (RESULTS / "validation_metrics.json").write_text(json.dumps({**valid_metrics, "selected_model": best_model_name, "temperature": temperature}, indent=2), encoding="utf-8") + (RESULTS / "test_metrics.json").write_text(json.dumps({**test_metrics, "selected_model": best_model_name, "temperature": temperature}, indent=2), encoding="utf-8") + + test_rows = [] + for i, sample_id in enumerate(official["test"].ids): + p = test_pred["probabilities"][i] + test_rows.append({"sample_id": sample_id, "source_video_id": official["test"].groups[i], + "true_class": int(official["test"].class_y[i]), "predicted_class": int(test_pred["predicted_class"][i]), + "true_sentiment": float(official["test"].regression_y[i]), "predicted_sentiment": float(test_pred["predicted_score"][i]), + "p_negative": float(p[0]), "p_neutral": float(p[1]), "p_positive": float(p[2]), + "interval_90_lower": float(test_pred["interval_lower"][i]), + "interval_90_upper": float(test_pred["interval_upper"][i]), + "predictive_variance_mean_uncalibrated": float(test_pred["predictive_variance_uncalibrated"][i]), + "within_trajectory_variance": float(test_pred["within_trajectory_variance"][i]), + "between_trajectory_variance": float(test_pred["between_trajectory_variance"][i])}) + write_csv(RESULTS / "test_predictions.csv", test_rows) + write_csv(RESULTS / "test_gate_diagnostics.csv", gate_diagnostic_rows(official["test"], test_pred)) + write_csv(RESULTS / "group_bootstrap_ci.csv", + group_bootstrap(official["test"], test_pred, args.bootstrap_repeats, args.seed + 44)) + + write_csv(RESULTS / "controlled_mask_audit.csv", + mask_audit_rows(official["valid"], validation_scenarios, args.seed + 909)) + controlled_rows, c0_scenario_predictions = controlled_c0( + c0_state, official["valid"], transformed["valid"], validation_scenarios, c0_temperature, + ) + controlled_predictions: dict[str, dict[str, dict[str, np.ndarray]]] = {"C0": c0_scenario_predictions} + for name, candidate in models.items(): + candidate_rows, candidate_predictions = controlled_metrics( + candidate, official["valid"], transformed["valid"], validation_scenarios, + device, args.batch_size, name, temperatures[name], seed=args.seed + 552, + ) + controlled_rows.extend(candidate_rows) + controlled_predictions[name] = candidate_predictions + write_csv(RESULTS / "controlled_missingness.csv", controlled_rows) + write_csv(RESULTS / "controlled_group_bootstrap.csv", + controlled_group_bootstrap(official["valid"], controlled_predictions, validation_scenarios, + args.bootstrap_repeats, args.seed + 553)) + write_csv(RESULTS / "training_history.csv", history) + + attachment_count = 0 + if not args.skip_attachment3: + cases, attachment_audit = reencode_attachment3(device, args.input_version) + attachment_predictions, inference_audit = infer_attachment3(best_model, cases, fitted, device, temperature, class_prior_probs, magnitude_priors) + validate_attachment3_predictions([case["case_id"] for case in cases], attachment_predictions) + write_csv(RESULTS / "attachment3_predictions.csv", attachment_predictions) + write_csv(RESULTS / "attachment3_audit.csv", [dict(a, **next(x for x in inference_audit if x["case_id"] == a["case_id"])) for a in attachment_audit]) + attachment_count = len(cases) + + manifest = { + "seed": args.seed, + "text_encoder": ("official precomputed text field; encoder revision not supplied" + if args.input_version == "unaligned_50" else TEXT_MODEL_ID), + "training_configuration": {"student_epoch_limit": args.epochs, "imputer_epochs": args.imputer_epochs, + "batch_size": args.batch_size, "early_stopping_patience": args.patience, + "device": str(device), + "device_name": torch.cuda.get_device_name(device) if device.type == "cuda" else "CPU", + "optimizer": "AdamW", "student_learning_rate": 3e-4, + "student_weight_decay": 1e-3, "imputer_learning_rate": 3e-4, + "imputer_weight_decay": 1e-4, + "early_stopping_metric": "mean untempered selection_nll over fixed group-disjoint internal training scenarios", + "inner_selection_scenarios": list(reliability_scenarios), + "inner_selection_source_video_groups": int(len(np.unique(reliability_validation.groups)))}, + "training_input": str(input_path.relative_to(DATA_ROOT)) if input_path.is_relative_to(DATA_ROOT) else str(input_path), + "input_version": args.input_version, + "q1_alignment_adapter": {name: split.alignment_audit for name, split in official.items()} + if args.input_version == "unaligned_50" else None, + "training_sha256": sha256(input_path), + "official_group_overlap": overlaps, + "official_splits": {name: {"n": split.n, "source_video_groups": int(len(np.unique(split.groups)))} for name, split in official.items()}, + "internal_train_holdouts": { + "fit": {"n": fit.n, "video_groups": int(len(np.unique(fit.groups)))}, + "reliability_selection": {"n": reliability_validation.n, "video_groups": int(len(np.unique(reliability_validation.groups)))}, + "temperature_calibration": {"n": temperature_calibration.n, "video_groups": int(len(np.unique(temperature_calibration.groups)))}, + "all_group_disjoint": True, + }, + "feature_standardization": "fit-only observed rows, per-dimension; fixed for valid/test/attachment3", + "missing_mask": ("official text attention and source lengths plus row observation; normalized-progress overlap preserves empty bins; q*=1 only where visible, J_Q=0" + if args.input_version == "unaligned_50" else + "row-level all-zero convention; q*=1 only where currently visible, J_Q=0; hidden metadata is zeroed"), + "observation_quality": {"quality_score_fields_present": False, "quality_available_flag_present": False, + "fallback": "q*=1 and J_Q=0 for visible rows; R_eff=R", + "quality_noise_mapping_ablation": f"not identifiable on {args.input_version} because no row quality score varies"}, + "imputer": {"type": "structured linear Gaussian shared-private state space", "state_dims": {"shared": 8, "private_each": 4}, + "posterior": "block-tridiagonal equivalent Kalman information filter + RTS smoother", + "sampling": "joint latent trajectories and missing emissions; observed features copied exactly", + "fit_objective": "train-only observed Gaussian marginal likelihood including log determinants", + "epochs": args.imputer_epochs, "frozen_before_teacher_student": True}, + "architecture": {"projection": 32, "bigru_hidden_each_direction": 16, "cross_source_layers": 1, + "cross_time_read": True, "rank": 4, "reliability_gru": "directional hidden decay; reset applied before candidate map; update gate multiplied by rho", + "final_gate": "rho times bounded content score plus positive null prior", + "output": "neutral point mass plus sign-specific Beta magnitudes; K-path probabilities mixed before decoding"}, + "reliability_hyperparameters": {"selected_per_model_on": "group-disjoint internal training reliability-validation slice", + "candidate_values": [list(v) for v in RELIABILITY_CANDIDATES], + "validation_scenarios": list(reliability_scenarios), + "selected_by_model": {row["model"]: [row["rho_imp"], row["lambda_u"], row["lambda_gap"], row["lambda_span"]] + for row in reliability_tuning_rows if row.get("selected")}}, + "group_risk_hyperparameters": { + "selection": "lambda_group and group_temperature jointly selected with reliability hyperparameters on fixed group-disjoint internal training scenarios", + "candidate_values": [list(v) for v in GROUP_RISK_CANDIDATES], + "selected": list(selected_group_risk["C7_group"]), + "selection_split": reliability_validation.name, + }, + "loss": {"supervision": "negative log mixture of Beta interval masses plus scaled Huber mean term", + "delta_u": DELTA_U, "delta_u_source": "half the minimum positive spacing of nonzero absolute labels in fit only", + "lambda_y": LAMBDA_Y, "lambda_distill": LAMBDA_DISTILL, + "lambda_reconstruction": LAMBDA_RECON, + "lambda_group_default": LAMBDA_GROUP, "group_temperature_default": GROUP_TEMPERATURE, + "selected_group_risk": list(selected_group_risk["C7_group"]), + "distill_temperature": DISTILL_TEMPERATURE, "distill_retention_exponent": 1.0, + "imputer_regularization": {"emission_l2": LAMBDA_EMISSION, "transition_l2": LAMBDA_TRANSITION}, + "group_and_distill_separate": True}, + "calibration": {"method": "temperature scaling on a group-disjoint internal official-train holdout, separated from reliability selection", + "temperature": temperature, "valid_used_for_selection": True, "test_used_for_selection_or_calibration": False}, + "selected_model": best_model_name, + "attachment3_low_information_priors": {"class_probability_method": "fit counts + one pseudocount per class", + "class_probability_values": class_prior_probs.tolist(), + "negative_beta": magnitude_priors[0].tolist(), + "positive_beta": magnitude_priors[1].tolist()}, + "ablation_definitions": definitions, + "masking": {"rates": MASK_RATES, "patterns": list(MASK_MODES), "preserve_at_least_fraction_per_selected_modality": 0.2, + "controlled_sweep_split": "official validation", "identical_masks_across_models": True, + "scenario_count": len(validation_scenarios), "scenario_seed": args.seed + 909, + "training_mask_rng_seed": args.seed + 303, + "reliability_scenario_seed": args.seed + 906, + "controlled_torch_sampling_seed": args.seed + 552, + "paired_control_bootstrap_seed": args.seed + 553, + "mask_audit_file": "controlled_mask_audit.csv", + "additional_one_factor_controls": ["modality T/A/V and combinations", "start/middle/end", "one-long/multiple-short", "sync/partial/async"], + "semantic_position_control": f"not run: {args.input_version} does not provide audited semantic boundary indices; raw text is prohibited in student inputs"}, + "final_test_metrics": test_metrics, + "test_gate_diagnostics_file": "test_gate_diagnostics.csv", + "attachment3_cases": attachment_count, + "attachment3_labeled_metrics": None, + "completed_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + } + (RESULTS / "run_manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + print("Q2 complete:", json.dumps({"selected_model": best_model_name, "test_accuracy": test_metrics["accuracy"], + "test_macro_f1": test_metrics["macro_f1"], "test_mae": test_metrics["regression_mae"], + "temperature": temperature, "attachment3_cases": attachment_count}, ensure_ascii=False), flush=True) + + +if __name__ == "__main__": + main() diff --git a/final/q2/math/train_unaligned_c5.py b/final/q2/math/train_unaligned_c5.py new file mode 100644 index 0000000..18a1a36 --- /dev/null +++ b/final/q2/math/train_unaligned_c5.py @@ -0,0 +1,158 @@ +"""Train the predeclared C5 Q2 architecture on Q1's exploratory index view.""" +from __future__ import annotations + +import argparse +import json +import time + +import numpy as np +import torch + +from . import train as q2_train +from ...model.crg import INPUT_DIMS, StructuredGaussianImputer +from .data import DATA_ROOT, fit_preprocessor, load_official_splits, transform_split +from .train import ( + RESULTS as ALIGNED_RESULTS, + SEED, + _fit_neural, + _make_variant, + assert_group_disjoint, + evaluate, + fit_imputer, + fit_temperature, + group_bootstrap, + label_resolution_from_train, + make_reliability_scenarios, + seed_everything, + sha256, + split_calibration, + tune_reliability_hparams, + write_csv, +) + +RESULTS = ALIGNED_RESULTS.parent / "results_unaligned" +SOURCE = DATA_ROOT / "附件2-数据集特征文件" / "unaligned_50.pkl" + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--epochs", type=int, default=12) + parser.add_argument("--imputer-epochs", type=int, default=8) + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--patience", type=int, default=3) + parser.add_argument("--bootstrap-repeats", type=int, default=300) + parser.add_argument("--seed", type=int, default=SEED) + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + args = parser.parse_args() + seed_everything(args.seed) + device = torch.device(args.device) + RESULTS.mkdir(parents=True, exist_ok=True) + + official = load_official_splits(SOURCE, version="unaligned_50") + overlap = assert_group_disjoint(official) + fit, heldout = split_calibration(official["train"], args.seed) + reliability_validation, temperature_calibration = split_calibration(heldout, args.seed + 1, fraction=0.5) + reliability_validation.name = "reliability_validation" + temperature_calibration.name = "temperature_calibration" + q2_train.DELTA_U = label_resolution_from_train(fit.regression_y) + fitted = fit_preprocessor(fit) + transformed = {name: transform_split(split, fitted) for name, split in official.items()} + transformed["fit"] = transform_split(fit, fitted) + transformed["reliability_validation"] = transform_split(reliability_validation, fitted) + transformed["temperature_calibration"] = transform_split(temperature_calibration, fitted) + np.savez_compressed(RESULTS / "preprocessor.npz", **{ + f"{modality}_{stat}": value + for modality, values in fitted.items() for stat, value in values.items() + }) + scenarios = make_reliability_scenarios(reliability_validation, args.seed + 906) + print("official split sizes:", {k: v.n for k, v in official.items()}, flush=True) + + imputer = StructuredGaussianImputer(INPUT_DIMS).to(device) + imputer_history = fit_imputer(imputer, transformed["fit"], fit, device, + args.imputer_epochs, args.batch_size, args.seed + 1) + torch.save({k: v.detach().cpu() for k, v in imputer.state_dict().items()}, + RESULTS / "structured_imputer.pt") + model = _make_variant("C5", imputer) + model, history = _fit_neural( + model, "C5", fit, official["valid"], transformed, device, + args.epochs, args.batch_size, args.patience, np.random.default_rng(args.seed + 303), + selection_split=reliability_validation, + selection_arrays=transformed["reliability_validation"], + selection_scenarios=scenarios, + ) + reliability, tuning_rows = tune_reliability_hparams( + model, transformed["reliability_validation"], reliability_validation, + scenarios, device, args.batch_size, "C5", seed=args.seed + 551, + ) + _, calibration_prediction = evaluate(model, transformed["temperature_calibration"], + temperature_calibration, device, args.batch_size) + temperature = fit_temperature(calibration_prediction["probabilities"], + temperature_calibration.class_y) + valid_metrics, _ = evaluate(model, transformed["valid"], official["valid"], + device, args.batch_size, temperature=temperature) + test_metrics, test_prediction = evaluate(model, transformed["test"], official["test"], + device, args.batch_size, temperature=temperature) + torch.save({k: v.detach().cpu() for k, v in model.state_dict().items()}, RESULTS / "crg_student.pt") + (RESULTS / "validation_metrics.json").write_text( + json.dumps({**valid_metrics, "model": "C5", "temperature": temperature}, indent=2), encoding="utf-8") + (RESULTS / "test_metrics.json").write_text( + json.dumps({**test_metrics, "model": "C5", "temperature": temperature}, indent=2), encoding="utf-8") + write_csv(RESULTS / "reliability_hparam_tuning.csv", tuning_rows) + write_csv(RESULTS / "training_history.csv", imputer_history + history) + write_csv(RESULTS / "group_bootstrap_ci.csv", + group_bootstrap(official["test"], test_prediction, args.bootstrap_repeats, args.seed + 44)) + rows = [] + for i, sample_id in enumerate(official["test"].ids): + p = test_prediction["probabilities"][i] + rows.append({ + "sample_id": sample_id, + "source_video_id": official["test"].groups[i], + "true_class": int(official["test"].class_y[i]), + "predicted_class": int(test_prediction["predicted_class"][i]), + "true_sentiment": float(official["test"].regression_y[i]), + "predicted_sentiment": float(test_prediction["predicted_score"][i]), + "p_negative": float(p[0]), "p_neutral": float(p[1]), "p_positive": float(p[2]), + }) + write_csv(RESULTS / "test_predictions.csv", rows) + manifest = { + "scope": "exploratory unaligned_50 relative-index projection and prespecified C5 training", + "physical_time_alignment": False, + "input": str(SOURCE), + "input_sha256": sha256(SOURCE), + "text_encoder": "official precomputed text field; revision not supplied", + "q1_adapter_audit": {name: split.alignment_audit for name, split in official.items()}, + "official_group_overlap": overlap, + "internal_splits": {"fit": fit.n, "reliability_validation": reliability_validation.n, + "temperature_calibration": temperature_calibration.n}, + "model": "C5 fixed before this run; no unaligned architecture selection", + "quality": "no quality scores in official file; q*=1 for visible rows and J_Q=0", + "seed": args.seed, + "device": str(device), + "device_name": torch.cuda.get_device_name(device) if device.type == "cuda" else "CPU", + "torch_version": torch.__version__, + "epochs_limit": args.epochs, + "trained_c5_epochs": len(history), + "selected_c5_epoch": int(min(history, key=lambda row: row["inner_selection_nll"])["epoch"]), + "imputer_epochs": args.imputer_epochs, + "batch_size": args.batch_size, + "patience": args.patience, + "selected_reliability": reliability, + "temperature": temperature, + "bootstrap_repeats": args.bootstrap_repeats, + "attachment3": "not inferred: unaligned files lack numerical text and trusted lengths", + "validation_metrics": valid_metrics, + "test_metrics": test_metrics, + "completed_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + } + (RESULTS / "run_manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + stale_teacher = RESULTS / "teacher.pt" + if stale_teacher.exists(): + stale_teacher.unlink() + print("C5 unaligned complete:", json.dumps({ + "accuracy": test_metrics["accuracy"], "macro_f1": test_metrics["macro_f1"], + "mae": test_metrics["regression_mae"], "temperature": temperature, + }), flush=True) + + +if __name__ == "__main__": + main() diff --git a/final/q3/__init__.py b/final/q3/__init__.py new file mode 100644 index 0000000..3964999 --- /dev/null +++ b/final/q3/__init__.py @@ -0,0 +1 @@ +"""Q3 interpretable emotion-recognition pipeline.""" diff --git a/final/q3/train_interpretable.py b/final/q3/train_interpretable.py new file mode 100644 index 0000000..ef92bc1 --- /dev/null +++ b/final/q3/train_interpretable.py @@ -0,0 +1,570 @@ +"""Train Q3 on the official training split and explain Attachment 4 cases.""" +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import math +import random +import time +from pathlib import Path +from typing import Any + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, mean_absolute_error, mean_squared_error +from transformers import AutoTokenizer + +from ..adapter import Q1AlignmentAdapter +from ..data_paths import ATTACHMENT4, DATA_ROOT, PROJECT_ROOT +from ..model.early_concat import AlignedFusionModel +from ..q2.deep_learning.q2.evaluate_math_protocol import continuous_mask, scenario_seed +from ..q2.math.data import ( + MODALITIES, + fit_preprocessor, + load_official_splits, + restricted_load, + transform_split, +) + +SEED = 20260924 +TEXT_MODEL_ID = "google-bert/bert-base-uncased" +CLASS_NAMES = ("negative", "neutral", "positive") +MODALITY_NAMES = ("text", "audio", "vision") + + +def _sha256(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as stream: + for block in iter(lambda: stream.read(1024 * 1024), b""): + h.update(block) + return h.hexdigest() + + +def _decode(value: Any) -> str: + if isinstance(value, bytes): + return value.decode("utf-8", errors="replace") + if isinstance(value, np.bytes_): + return bytes(value).decode("utf-8", errors="replace") + if isinstance(value, np.ndarray): + if value.shape == (): + return _decode(value.item()) + return " ".join(_decode(x) for x in value.reshape(-1)) + return str(value) + + +def _scalar_int(value: Any, field: str) -> int: + arr = np.asarray(value).reshape(-1) + if not len(arr): + raise ValueError(f"Attachment 4 {field} is empty") + return int(arr[0]) + + +def _attachment4_location(version: str) -> tuple[Path, Path]: + inner = ATTACHMENT4 / "附件4-可解释专项视频样本与特征文件" + version_dir = inner / ("未对齐版本" if version == "unaligned_50" else "对齐版本") + video_dir = inner / "videos" + if not version_dir.is_dir(): + raise FileNotFoundError(f"Attachment 4 {version} directory not found: {version_dir}") + return version_dir, video_dir + + +def _read_attachment4(version: str) -> tuple[list[dict[str, Any]], dict[str, str]]: + if version != "unaligned_50": + raise ValueError("Q3 explanation currently uses the official unaligned_50 Attachment 4 features") + version_dir, video_dir = _attachment4_location(version) + paths = sorted(version_dir.glob("*.pkl"), key=lambda p: p.name) + if len(paths) != 20: + raise FileNotFoundError(f"expected 20 Attachment 4 cases, found {len(paths)} under {version_dir}") + video_by_stem = {p.stem: p for p in video_dir.rglob("*.mp4")} if video_dir.is_dir() else {} + adapter = Q1AlignmentAdapter(target_steps=50) + cases: list[dict[str, Any]] = [] + for path in paths: + raw = restricted_load(path) + case_id = _decode(raw.get("id", path.stem)).strip() or path.stem + text_bert = np.asarray(raw["text_bert"], dtype=np.int64) + if text_bert.ndim == 3 and text_bert.shape[0] == 1: + text_bert = text_bert[0] + if text_bert.shape != (3, 50): + raise ValueError(f"{path.name}: expected text_bert (3,50), got {text_bert.shape}") + record = { + "id": case_id, + "sequence_order_verified": True, + "attention_mask": text_bert[1].astype(bool), + "text": np.asarray(raw["text"], dtype=np.float32), + "audio": np.asarray(raw["audio"], dtype=np.float32), + "vision": np.asarray(raw["vision"], dtype=np.float32), + "audio_length": _scalar_int(raw["audio_lengths"], "audio_lengths"), + "vision_length": _scalar_int(raw["vision_lengths"], "vision_lengths"), + } + aligned = adapter.align(record, mode="relative") + mask = np.stack([aligned.observed[m] for m in MODALITIES], axis=-1) + features = {m: aligned.features[m].astype(np.float32) for m in MODALITIES} + transcript = _decode(raw.get("raw_text", "")) + video_path = video_by_stem.get(path.stem) or video_by_stem.get(case_id) + media = "" + if video_path is not None: + try: + media = video_path.resolve().relative_to(DATA_ROOT).as_posix() + except ValueError: + media = str(video_path.resolve()) + cases.append({ + "case_id": case_id, + "source_file": path, + "source_sha256": _sha256(path), + "transcript": transcript, + "text_bert": text_bert, + "raw": raw, + "features": features, + "mask": mask, + "target_intervals": aligned.target_intervals.astype(np.float32), + "provenance": aligned.provenance, + "video_path": media, + "coordinate_mode": aligned.metadata["coordinate_mode"], + "input_audit": { + "case_id": case_id, + "source_file": path.name, + "source_sha256": _sha256(path), + "coordinate_mode": aligned.metadata["coordinate_mode"], + "physical_time_alignment": False, + "audio_reported_length": record["audio_length"], + "vision_reported_length": record["vision_length"], + "audio_length_conflict": bool(aligned.provenance["audio"].length_conflict), + "vision_length_conflict": bool(aligned.provenance["vision"].length_conflict), + "text_visible_target_slots": int(aligned.observed["text"].sum()), + "audio_visible_target_slots": int(aligned.observed["audio"].sum()), + "vision_visible_target_slots": int(aligned.observed["vision"].sum()), + "source_video": media, + }, + }) + return cases, {"version_dir": str(version_dir), "video_dir": str(video_dir)} + + +def _split_arrays(split: Any, transformed: dict[str, np.ndarray]) -> tuple[tuple[np.ndarray, ...], np.ndarray]: + return tuple(transformed[m] for m in MODALITIES), np.asarray(split.mask, dtype=bool) + + +def _predict( + model: torch.nn.Module, + xs: tuple[np.ndarray, ...], + masks: np.ndarray, + device: torch.device, + batch_size: int, +) -> dict[str, np.ndarray]: + model.eval() + logits: list[np.ndarray] = [] + intensity: list[np.ndarray] = [] + with torch.inference_mode(): + for start in range(0, len(masks), batch_size): + end = min(start + batch_size, len(masks)) + batch_x = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in xs) + batch_mask = torch.as_tensor(masks[start:end], dtype=torch.bool, device=device) + output = model(batch_x, batch_mask) + logits.append(output["logits"].float().cpu().numpy()) + intensity.append(output["intensity"].float().cpu().numpy()) + return {"logits": np.concatenate(logits), "intensity": np.concatenate(intensity)} + + +def _metrics(y_cls: np.ndarray, y_reg: np.ndarray, prediction: dict[str, np.ndarray]) -> dict[str, Any]: + logits = np.asarray(prediction["logits"]) + score = np.clip(np.asarray(prediction["intensity"]).reshape(-1), -3.0, 3.0) + predicted = logits.argmax(axis=-1) + pearson = float(np.corrcoef(y_reg, score)[0, 1]) if np.std(y_reg) > 0 and np.std(score) > 0 else None + return { + "n": int(len(y_cls)), + "accuracy": float(accuracy_score(y_cls, predicted)), + "macro_f1": float(f1_score(y_cls, predicted, labels=[0, 1, 2], average="macro", zero_division=0)), + "mae": float(mean_absolute_error(y_reg, score)), + "rmse": float(math.sqrt(mean_squared_error(y_reg, score))), + "pearson": pearson, + "confusion_matrix_rows_true_columns_predicted": confusion_matrix(y_cls, predicted, labels=[0, 1, 2]).tolist(), + "per_class_support": {CLASS_NAMES[i]: int(np.sum(y_cls == i)) for i in range(3)}, + } + + +def _loss(logits: torch.Tensor, intensity: torch.Tensor, y_cls: torch.Tensor, y_reg: torch.Tensor) -> torch.Tensor: + return F.cross_entropy(logits, y_cls) + 0.5 * F.smooth_l1_loss(intensity / 3.0, y_reg / 3.0) + + +def _validation_loss( + model: torch.nn.Module, + xs: tuple[np.ndarray, ...], + masks: list[np.ndarray], + y_cls: np.ndarray, + y_reg: np.ndarray, + device: torch.device, + batch_size: int, +) -> float: + values: list[float] = [] + model.eval() + with torch.inference_mode(): + for scenario in masks: + total, count = 0.0, 0 + for start in range(0, len(y_cls), batch_size): + end = min(start + batch_size, len(y_cls)) + bx = tuple(torch.as_tensor(x[start:end], dtype=torch.float32, device=device) for x in xs) + bm = torch.as_tensor(scenario[start:end], dtype=torch.bool, device=device) + by = torch.as_tensor(y_cls[start:end], dtype=torch.long, device=device) + br = torch.as_tensor(y_reg[start:end], dtype=torch.float32, device=device) + out = model(bx, bm) + total += float(_loss(out["logits"], out["intensity"], by, br).item()) * (end - start) + count += end - start + values.append(total / max(1, count)) + return float(np.mean(values)) + + +def _train(args: argparse.Namespace, out_dir: Path) -> tuple[AlignedFusionModel, dict[str, Any], dict[str, Any]]: + feature_path: Path + if args.data_path is not None: + feature_path = args.data_path.expanduser().resolve() + else: + from ..data_paths import ATTACHMENT2 + feature_path = ATTACHMENT2 / f"{args.input_version}.pkl" + if not feature_path.is_file(): + raise FileNotFoundError(f"Q3 training feature file not found: {feature_path}") + raw_splits = load_official_splits(feature_path, version=args.input_version) + train = raw_splits["train"] + valid = raw_splits["valid"] + fitted = fit_preprocessor(train) + transformed = {name: transform_split(split, fitted) for name, split in raw_splits.items()} + train_x, train_mask = _split_arrays(train, transformed["train"]) + valid_x, valid_mask = _split_arrays(valid, transformed["valid"]) + dims = tuple(int(x.shape[-1]) for x in train_x) + + np.savez_compressed(out_dir / "preprocessor.npz", **{ + f"{modality}_{key}": value for modality, state in fitted.items() for key, value in state.items() + }) + device = torch.device("cuda" if args.device == "auto" and torch.cuda.is_available() else ("cpu" if args.device == "auto" else args.device)) + if device.type == "cuda": + torch.cuda.manual_seed_all(SEED) + random.seed(SEED) + np.random.seed(SEED) + torch.manual_seed(SEED) + torch.set_num_threads(4) + model = AlignedFusionModel("concat", dims=dims).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay) + y_cls = np.asarray(train.class_y, dtype=np.int64) + y_reg = np.asarray(train.regression_y, dtype=np.float32) + vy_cls = np.asarray(valid.class_y, dtype=np.int64) + vy_reg = np.asarray(valid.regression_y, dtype=np.float32) + valid_rng_masks: list[np.ndarray] = [valid_mask.copy()] + for rate, mode in ((0.3, "single"), (0.3, "sync"), (0.5, "async")): + key = f"{rate:.1f}/{mode}" + valid_rng_masks.append(np.stack([ + continuous_mask(mask, rate, mode, np.random.default_rng(scenario_seed(SEED + 177, sid, key))) + for sid, mask in zip(valid.ids, valid_mask) + ])) + + best = float("inf") + best_epoch = 0 + stale = 0 + history: list[dict[str, Any]] = [] + for epoch in range(1, args.epochs + 1): + model.train() + train_corruption = np.stack([ + continuous_mask( + mask, + float(np.random.choice((0.0, 0.1, 0.3, 0.5, 0.7))), + str(np.random.choice(("single", "sync", "partial", "async"))), + np.random.default_rng(scenario_seed(SEED + epoch, sid, f"train/{epoch}")), + ) + for sid, mask in zip(train.ids, train_mask) + ]) + order = np.random.permutation(len(y_cls)) + losses: list[float] = [] + for start in range(0, len(order), args.batch_size): + ix = order[start:start + args.batch_size] + bx = tuple(torch.as_tensor(x[ix], dtype=torch.float32, device=device) for x in train_x) + bm = torch.as_tensor(train_corruption[ix], dtype=torch.bool, device=device) + by = torch.as_tensor(y_cls[ix], dtype=torch.long, device=device) + br = torch.as_tensor(y_reg[ix], dtype=torch.float32, device=device) + optimizer.zero_grad(set_to_none=True) + output = model(bx, bm) + loss = _loss(output["logits"], output["intensity"], by, br) + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + losses.append(float(loss.item())) + validation = _validation_loss(model, valid_x, valid_rng_masks, vy_cls, vy_reg, device, args.batch_size) + history.append({"epoch": epoch, "train_loss": float(np.mean(losses)), "selection_loss": validation}) + print(f"Q3 epoch {epoch}/{args.epochs}: train={np.mean(losses):.5f}, validation={validation:.5f}", flush=True) + if validation < best - 1e-7: + best, best_epoch, stale = validation, epoch, 0 + torch.save({"state_dict": model.state_dict(), "dims": dims, "seed": SEED, "best_epoch": epoch}, out_dir / "model_best.pt") + else: + stale += 1 + if stale >= args.patience: + break + checkpoint = torch.load(out_dir / "model_best.pt", map_location=device, weights_only=True) + model.load_state_dict(checkpoint["state_dict"]) + model.eval() + prediction = _predict(model, valid_x, valid_mask, device, args.batch_size) + metric = _metrics(vy_cls, vy_reg, prediction) + metric["best_epoch"] = best_epoch + metric["selection_loss_clean_plus_fixed_missing_scenarios"] = best + metric["input_version"] = args.input_version + metric["adapter"] = "Q1AlignmentAdapter relative normalized progress" + metric["physical_time_alignment"] = False + _write_csv(out_dir / "training_history.csv", history) + _write_json(out_dir / "validation_metrics.json", metric) + validation_rows = [] + for i, sid in enumerate(valid.ids): + prob = torch.softmax(torch.as_tensor(prediction["logits"][i]), dim=-1).numpy() + validation_rows.append({ + "sample_id": sid, + "true_class": int(vy_cls[i]), + "true_class_name": CLASS_NAMES[int(vy_cls[i])], + "true_sentiment": float(vy_reg[i]), + "predicted_class": int(prob.argmax()), + "predicted_class_name": CLASS_NAMES[int(prob.argmax())], + "predicted_sentiment": float(prediction["intensity"][i]), + "p_negative": float(prob[0]), "p_neutral": float(prob[1]), "p_positive": float(prob[2]), + "absolute_error": float(abs(vy_reg[i] - prediction["intensity"][i])), + }) + _write_csv(out_dir / "validation_predictions.csv", validation_rows) + errors = sorted( + (row for row in validation_rows if row["true_class"] != row["predicted_class"] or row["absolute_error"] >= metric["mae"]), + key=lambda row: (-row["absolute_error"], row["sample_id"]), + ) + _write_csv(out_dir / "validation_errors.csv", errors[:100]) + return model, {"metrics": metric, "feature_sha256": _sha256(feature_path), "feature_path": str(feature_path)}, {"x": valid_x, "mask": valid_mask, "y_cls": vy_cls, "y_reg": vy_reg, "prediction": prediction} + + +def _write_csv(path: Path, rows: list[dict[str, Any]]) -> None: + if not rows: + return + path.parent.mkdir(parents=True, exist_ok=True) + fields = list(dict.fromkeys(key for row in rows for key in row)) + with path.open("w", encoding="utf-8-sig", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=fields) + writer.writeheader() + writer.writerows(rows) + + +def _write_json(path: Path, payload: Any) -> None: + path.write_text(json.dumps(payload, ensure_ascii=False, indent=2, allow_nan=False), encoding="utf-8") + + +def _model_output(model: torch.nn.Module, xs: tuple[torch.Tensor, ...], mask: torch.Tensor) -> dict[str, torch.Tensor]: + model.eval() + with torch.inference_mode(): + return model(xs, mask) + + +def _span_evidence(case: dict[str, Any], modality_index: int, slot: int, tokenizer: Any) -> dict[str, Any]: + modality = MODALITY_NAMES[modality_index] + weights = case["provenance"][modality].source_weights.getrow(slot) + source_rows = weights.indices.tolist() + if source_rows: + low, high = min(source_rows), max(source_rows) + 1 + else: + low = high = 0 + start, end = case["target_intervals"][slot].astype(float).tolist() + text = "" + if modality == "text" and source_rows: + ids = np.asarray(case["text_bert"][0], dtype=np.int64) + token_ids = [int(ids[i]) for i in source_rows if i < len(ids) and int(ids[i]) not in tokenizer.all_special_ids] + text = " ".join(tokenizer.convert_ids_to_tokens(token_ids)) + elif modality == "audio": + text = f"audio feature rows {low}–{high - 1}; inspect the same relative span in the linked source video/audio" + else: + text = f"video feature rows {low}–{high - 1}; inspect the same relative span in the linked source video" + return { + "modality": modality, + "slot": int(slot), + "relative_start": float(start), + "relative_end": float(end), + "source_row_start": int(low), + "source_row_end_exclusive": int(high), + "evidence": text, + } + + +def _explain_case( + model: torch.nn.Module, + case: dict[str, Any], + stats: dict[str, dict[str, np.ndarray]], + tokenizer: Any, + device: torch.device, + batch_size: int, +) -> tuple[dict[str, Any], list[dict[str, Any]]]: + values: dict[str, np.ndarray] = {} + for modality_index, modality in enumerate(MODALITIES): + arr = case["features"][modality].astype(np.float32) + arr = np.clip((arr - stats[modality]["mean"]) / stats[modality]["std"], -10.0, 10.0) + arr[~case["mask"][:, modality_index]] = 0.0 + values[modality] = arr + xs = tuple(torch.as_tensor(values[m][None], dtype=torch.float32, device=device) for m in MODALITIES) + mask = torch.as_tensor(case["mask"][None], dtype=torch.bool, device=device) + full = _model_output(model, xs, mask) + probs = torch.softmax(full["logits"], dim=-1)[0].cpu().numpy() + pred = int(np.argmax(probs)) + contributions: dict[str, float] = {} + local_rows: list[dict[str, Any]] = [] + for m, modality in enumerate(MODALITY_NAMES): + ablated_mask = mask.clone() + ablated_mask[:, :, m] = False + ablated = _model_output(model, xs, ablated_mask) + ablated_p = torch.softmax(ablated["logits"], dim=-1)[0, pred].item() + contributions[modality] = float(probs[pred] - ablated_p) + observed_slots = np.flatnonzero(case["mask"][:, m]) + if not len(observed_slots): + continue + impacts: list[tuple[int, float]] = [] + for start in range(0, len(observed_slots), batch_size): + chosen = observed_slots[start:start + batch_size] + bx = tuple(x.repeat(len(chosen), 1, 1) for x in xs) + bm = mask.repeat(len(chosen), 1, 1) + row_idx = torch.arange(len(chosen), device=device) + slot_idx = torch.as_tensor(chosen, dtype=torch.long, device=device) + bm[row_idx, slot_idx, m] = False + output = _model_output(model, bx, bm) + hidden_p = torch.softmax(output["logits"], dim=-1)[:, pred].cpu().numpy() + impacts.extend((int(slot), float(probs[pred] - p)) for slot, p in zip(chosen, hidden_p)) + for slot, impact in sorted(impacts, key=lambda row: (-row[1], row[0]))[:3]: + evidence = _span_evidence(case, m, slot, tokenizer) + evidence["probability_drop"] = impact + evidence["case_id"] = case["case_id"] + evidence["source_video"] = case["video_path"] + local_rows.append(evidence) + principal = max(contributions, key=contributions.get) + intensity = float(full["intensity"][0].cpu().item()) + explanation = { + "case_id": case["case_id"], + "predicted_class": pred, + "predicted_class_name": CLASS_NAMES[pred], + "predicted_sentiment": intensity, + "p_negative": float(probs[0]), "p_neutral": float(probs[1]), "p_positive": float(probs[2]), + "principal_modality": principal, + "text_contribution": contributions["text"], + "audio_contribution": contributions["audio"], + "vision_contribution": contributions["vision"], + "transcript": case["transcript"], + "source_video": case["video_path"], + "coordinate_mode": case["coordinate_mode"], + "interpretation_method": "single-modality and single-slot occlusion; probability drops measure model sensitivity", + } + return explanation, local_rows + + +def _write_cards(out_dir: Path, case_by_id: dict[str, dict[str, Any]], explanations: list[dict[str, Any]], local_rows: list[dict[str, Any]]) -> str: + cards = out_dir / "explanation_cards" + cards.mkdir(parents=True, exist_ok=True) + rows_by_id: dict[str, list[dict[str, Any]]] = {} + for row in local_rows: + rows_by_id.setdefault(str(row["case_id"]), []).append(row) + for item in explanations: + evidence = rows_by_id.get(str(item["case_id"]), []) + lines = [f"# Q3 Explanation: {item['case_id']}", "", f"- Prediction: **{item['predicted_class_name']}**", f"- Sentiment score: {item['predicted_sentiment']:.3f}", f"- Probabilities (negative / neutral / positive): {item['p_negative']:.3f} / {item['p_neutral']:.3f} / {item['p_positive']:.3f}", f"- Main modality by occlusion: **{item['principal_modality']}**", f"- Source video/audio: `{item['source_video'] or 'not found in the supplied video folder'}`", f"- Coordinate: normalized progress `[0,1]`; no physical timestamps are inferred from the unaligned feature rows.", "", "## Modality contribution", "", "Removing one modality changes the predicted-class probability by the values below. Positive values mean that modality supports the prediction under this model.", "", "| Modality | Probability drop |", "|---|---:|"] + for modality in MODALITY_NAMES: + lines.append(f"| {modality} | {item[f'{modality}_contribution']:.4f} |") + lines.extend(["", "## Local evidence", "", "Local values are single-slot occlusion sensitivity. Audio/video spans are relative positions in the supplied source clip; text is shown as BERT tokens and the full transcript is retained below.", ""]) + for evidence_row in evidence: + lines.append(f"- **{evidence_row['modality']}**, slots {evidence_row['slot']} `[0-based]`, relative {evidence_row['relative_start']:.3f}–{evidence_row['relative_end']:.3f}, probability drop {evidence_row['probability_drop']:.4f}: {evidence_row['evidence']}") + lines.extend(["", "## Transcript", "", item["transcript"] or "(not supplied)", "", "## Interpretation note", "", "Occlusion scores describe how this trained model responds to removing features. They are not causal effects or proof that the signal expresses the named emotion.", ""]) + safe = "".join(c if c.isalnum() or c in "-_" else "_" for c in str(item["case_id"])) + (cards / f"{safe}.md").write_text("\n".join(lines), encoding="utf-8") + confidence = np.asarray([max(row["p_negative"], row["p_neutral"], row["p_positive"]) for row in explanations]) + representative = explanations[int(np.argmin(np.abs(confidence - np.median(confidence))))] + source = cards / ("".join(c if c.isalnum() or c in "-_" else "_" for c in str(representative["case_id"])) + ".md") + representative_card = out_dir / "typical_explanation_card.md" + representative_card.write_text(source.read_text(encoding="utf-8"), encoding="utf-8") + return str(representative["case_id"]) + + +def _plot_validation(out_dir: Path, y_cls: np.ndarray, prediction: dict[str, np.ndarray]) -> None: + pred_cls = prediction["logits"].argmax(axis=-1) + matrix = confusion_matrix(y_cls, pred_cls, labels=[0, 1, 2]) + fig, axes = plt.subplots(1, 2, figsize=(10, 4), constrained_layout=True) + image = axes[0].imshow(matrix, cmap="Blues") + axes[0].set_xticks(range(3), CLASS_NAMES, rotation=15) + axes[0].set_yticks(range(3), CLASS_NAMES) + axes[0].set_xlabel("Predicted") + axes[0].set_ylabel("True") + axes[0].set_title("Validation confusion matrix") + for (i, j), value in np.ndenumerate(matrix): + axes[0].text(j, i, str(value), ha="center", va="center") + fig.colorbar(image, ax=axes[0], fraction=0.046) + axes[1].scatter(prediction["intensity"], prediction["true_sentiment"], s=12, alpha=0.55) + axes[1].plot([-3, 3], [-3, 3], color="gray", linestyle="--", linewidth=1) + axes[1].set(xlim=(-3, 3), ylim=(-3, 3), xlabel="Predicted sentiment", ylabel="True sentiment", title="Validation intensity") + fig.savefig(out_dir / "validation_diagnostics.png", dpi=180) + plt.close(fig) + + +def run(args: argparse.Namespace) -> None: + out_dir = args.output_dir.expanduser().resolve() + out_dir.mkdir(parents=True, exist_ok=True) + started = time.time() + model, training_info, validation = _train(args, out_dir) + _plot_validation(out_dir, validation["y_cls"], {**validation["prediction"], "true_sentiment": validation["y_reg"]}) + + tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID, use_fast=True) + with np.load(out_dir / "preprocessor.npz", allow_pickle=False) as saved: + stats = {m: {key: saved[f"{m}_{key}"].astype(np.float32) for key in ("mean", "std")} for m in MODALITIES} + cases, input_locations = _read_attachment4(args.attachment4_version) + device = next(model.parameters()).device + explanation_rows, all_local = [], [] + prediction_rows = [] + for case in cases: + explanation, local = _explain_case(model, case, stats, tokenizer, device, args.explanation_batch_size) + explanation_rows.append(explanation) + all_local.extend(local) + prediction_rows.append({key: explanation[key] for key in ( + "case_id", "predicted_class", "predicted_class_name", "predicted_sentiment", + "p_negative", "p_neutral", "p_positive", "source_video", + )}) + _write_csv(out_dir / "attachment4_predictions.csv", prediction_rows) + _write_csv(out_dir / "attachment4_explanations.csv", explanation_rows) + _write_csv(out_dir / "attachment4_local_evidence.csv", all_local) + _write_csv(out_dir / "attachment4_input_audit.csv", [case["input_audit"] for case in cases]) + typical_id = _write_cards(out_dir, {case["case_id"]: case for case in cases}, explanation_rows, all_local) + manifest = { + "created_at_unix": time.time(), + "elapsed_seconds": time.time() - started, + "seed": SEED, + "training_input": training_info, + "attachment4": input_locations, + "attachment4_version": args.attachment4_version, + "attachment4_cases": len(cases), + "adapter": "Q1AlignmentAdapter shared relative-progress projection", + "coordinate_limit": "source-time stamps are absent; local audio/video positions are normalized progress, not seconds", + "model": "EarlyConcat + BiGRU", + "explanation": "single-modality and single-slot occlusion probability drops; model sensitivity, not causal attribution", + "validation_metrics": validation["metrics"], + "typical_explanation_case": typical_id, + "outputs": [ + "model_best.pt", "preprocessor.npz", "validation_metrics.json", "validation_predictions.csv", + "validation_errors.csv", "validation_diagnostics.png", "attachment4_predictions.csv", + "attachment4_explanations.csv", "attachment4_local_evidence.csv", "attachment4_input_audit.csv", "typical_explanation_card.md", + ], + } + _write_json(out_dir / "run_manifest.json", manifest) + print(f"Q3 complete: {len(cases)} Attachment 4 predictions saved under {out_dir}", flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input-version", choices=("aligned_50", "unaligned_50"), default="unaligned_50") + parser.add_argument("--attachment4-version", choices=("unaligned_50",), default="unaligned_50") + parser.add_argument("--data-path", type=Path, default=None, help="Optional explicit Attachment 2 pickle path") + parser.add_argument("--output-dir", type=Path, default=PROJECT_ROOT / "output" / "q3") + parser.add_argument("--device", choices=("auto", "cpu", "cuda"), default="auto") + parser.add_argument("--epochs", type=int, default=12) + parser.add_argument("--patience", type=int, default=3) + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--learning-rate", type=float, default=3e-4) + parser.add_argument("--weight-decay", type=float, default=1e-3) + parser.add_argument("--explanation-batch-size", type=int, default=32) + args = parser.parse_args() + run(args) + + +if __name__ == "__main__": + main() diff --git a/final/requirements.txt b/final/requirements.txt new file mode 100644 index 0000000..6039f2c --- /dev/null +++ b/final/requirements.txt @@ -0,0 +1,10 @@ +numpy>=1.26,<3 +scipy>=1.12 +scikit-learn>=1.4 +torch>=2.2 +transformers>=4.44 +librosa>=0.10.2 +opencv-python-headless>=4.10 +matplotlib>=3.8 +openpyxl>=3.1 +soundfile>=0.12 diff --git a/math/E题V2_Q1_Q2模型结果汇总.md b/math/E题V2_Q1_Q2模型结果汇总.md index 4916c95..51c6c2e 100644 --- a/math/E题V2_Q1_Q2模型结果汇总.md +++ b/math/E题V2_Q1_Q2模型结果汇总.md @@ -146,6 +146,25 @@ C5 的 AURC-MAE 相对 C0 的配对 95% 区间依次为：单模态 [-0.1118, -0 附件三有 30 条无标签样本，仅导出预测和审计，不计算 Accuracy 或 F1。`aligned_50.pkl` 没有逐行质量分数，因此按算法回退规则对可见行取 `q*=1, J_Q=0`；无人工边界或附件三标签的项目不报告虚构的监督指标。 +## Q1 对附件二未对齐版的适配与 Q2 探索性训练 + +`unaligned_50.pkl` 的文本为 `(N,50,768)`，音频为 `(N,500,74)`，视觉为 `(N,500,35)`，但没有媒体时间戳、逐词边界或原始来源帧号。因此不能将 Q1 的 0.1 秒物理对齐主干直接用于这些特征，也不能用相同行号宣称三模态同一时刻。新增的 [适配说明](Q1/UNALIGNED_ADAPTER.md) 和 [接口](Q1/unaligned_adapter.py) 按各模态独立有效长度将索引映射到 `[0,1)`，依据来源与 50 个目标区间的交长汇聚实际观测特征；无观测处保持缺失掩码，保存覆盖率和来源权重。该做法借鉴 Q1 区间投影形式，但因没有真实时长，**不是《E题V2》式 (4.34) 的物理时间实现**。下列指标只能作为索引进程近似对齐后可供 Q2 训练的证据，不能作为真实时间对齐精度。 + +输入审计发现，官方 `vision_lengths` 之后仍有非零视觉行的样本在 train/valid/test 分别为 618/141/131 条；适配器保留这些已知观测并标记长度冲突及尾部歧义。`text` 在 `text_bert` 注意力掩码外仍有非零填充行，三划分分别为 86078/17772/18041 行；适配时均排除。没有逐行质量字段，可见行只使用 `q*=1, J_Q=0` 回退，不制造质量分数。官方划分分别为 3395/728/727 条，来源视频组两两无交集。 + +将既有正式 aligned 实验选定的 C5 结构预先固定，在未对齐版 train 上从头拟合 8 轮结构化补全器和 C5（第 11 轮早停，保留第 8 轮权重），在独立训练视频组上选择可靠度并校准温度。未对齐版没有重新进行 C0–C7 结构选择，结果与 aligned 正式结果仅作描述性对照： + +| 特征版本与评估集 | Accuracy | Macro-F1 | MAE | Pearson | 90% 区间覆盖率 | +|---|---:|---:|---:|---:|---:| +| aligned 验证 | 0.6168 | 0.5472 | 0.6615 | 0.6176 | 0.8929 | +| 未对齐索引投影 验证 | 0.6099 | 0.5253 | 0.6701 | 0.6159 | 0.9011 | +| aligned 测试 | 0.6740 | 0.5861 | 0.6980 | 0.6300 | 0.8968 | +| 未对齐索引投影 测试 | 0.6726 | 0.5668 | 0.7059 | 0.6385 | 0.9010 | + +未对齐测试集的 Accuracy 为 489/727；Macro-F1 低于 aligned 版约 0.0193，MAE 高约 0.0079。这些差异同时包含原生特征组织、近似投影与训练随机性的影响，不能单独解释为对齐算法优劣。未对齐版测试的 300 次来源视频组 Bootstrap 95% 区间为：Accuracy [0.6328, 0.7124]、Macro-F1 [0.5289, 0.6050]、MAE [0.6558, 0.7572]。C5 的最终温度为 1.0310，内层选择的可靠度候选为 `(rho_imp=0.5, lambda_u=0, lambda_gap=0, lambda_span=0)`；完整指标、视频组区间、测试预测及运行清单见 [Q2/results_unaligned](Q2/results_unaligned/)。 + +附件三未对齐版只有 `raw_text`、`audio` 和 `vision`，缺少可直接给学生模型的文本数值特征及可信的音视频长度字段。按《E题V2》2.2 和 5.1.1 的接口规则，不能用原文绕过专项缺失，也不能从零尾段臆断真实长度；本轮不输出附件三未对齐版预测。取得可信长度与不泄漏缺失状态的文本数值字段后，才可按同一接口开展该专项推理。 + ## 主要结果文件 - Q1 五折模型对照：`Q1/results/model_comparison_v2/comparison_summary.csv`、`fold_metrics.csv`、`oof_predictions.csv`、`group_bootstrap_deltas.csv`。 @@ -155,3 +174,4 @@ C5 的 AURC-MAE 相对 C0 的配对 95% 区间依次为：单模态 [-0.1118, -0 - Q2 缺失控制与组区间：`Q2/results/controlled_missingness.csv`、`controlled_group_bootstrap.csv`、`group_bootstrap_ci.csv`。 - Q2 匹配缺失类型及图表：`Q2/results/matched_missing_type.csv`、`matched_missing_type_audit.csv`、`matched_missing_type_bootstrap.csv`、`matched_missing_type_manifest.json`、`aligned_missingness_effects.png`；脚本见 `Q2/run_matched_missing_type.py` 和 `Q2/plot_missingness_effects.py`。 - Q2 附件三输出：`Q2/results/attachment3_predictions.csv`、`attachment3_audit.csv`。 +- Q1 未对齐索引适配：`Q1/unaligned_adapter.py`、`Q1/UNALIGNED_ADAPTER.md`；Q2 未对齐 C5 训练与结果：`Q2/train_unaligned_c5.py`、`Q2/results_unaligned/`。 diff --git a/math/Q1/README.md b/math/Q1/README.md index 8888f1a..41d4f6e 100644 --- a/math/Q1/README.md +++ b/math/Q1/README.md @@ -2,6 +2,8 @@ 本目录按《E题V2》第四章构建 Q1 对齐特征。规格核对与限制见 [`V2_REVIEW.md`](V2_REVIEW.md)，可供后续训练读取的主特征包位于 `features_v2/`。 +Q1 物理时间与附件二 `unaligned_50.pkl` 的相对进程现在共用 [`UNIFIED_ADAPTER.md`](UNIFIED_ADAPTER.md) 所述接口和投影核；Relative 模式的详细假设见 [`UNALIGNED_ADAPTER.md`](UNALIGNED_ADAPTER.md)。 + ## V2 特征包 - 100 个样本、300 条模态明细；主物理时间视图为 0.1 秒，并保留真实末段长度。 diff --git a/math/Q1/UNALIGNED_ADAPTER.md b/math/Q1/UNALIGNED_ADAPTER.md new file mode 100644 index 0000000..817fe63 --- /dev/null +++ b/math/Q1/UNALIGNED_ADAPTER.md @@ -0,0 +1,123 @@ +# 附件二未对齐版到 Q2 的输入适配：Relative 模式原理 + +当前统一接口、调用方式和全量审计以 [`UNIFIED_ADAPTER.md`](UNIFIED_ADAPTER.md) 为准。本文继续解释 Relative 坐标的假设、长度处理和限制。旧 `unaligned_adapter.py` 现在只是委托给统一接口的兼容层；Q2 实际调用 `adapter.adapt_official_split`。 + +## 结论与适用范围 + +`unaligned_50.pkl` 只有独立的序列索引，没有原始视频 PTS、音频采样时刻或词边界。Q1 主干的物理时间区间投影不能直接用于该文件。这里复用 Q1 的“来源区间与目标区间求交、只汇聚真实观测”算子，将每个模态的**相对索引进程** `[0,1)` 分成 50 段，供 Q2 的固定 50 步接口开展探索性训练。结果不是秒级同步结果，也不能生成原词到音频窗/视频帧的物理查询矩阵。《E题V2》4.5.3 的原式使用视频真实时长 `D_i`，本文件没有 `D_i`，因此这里是受其启发的近似适配，并非该式的严格复现。5.1.1 要求的可核验共同时间步仍需真实时间戳才能确立；本适配的 Q2 指标应单列为探索性结果。 + +Q1 的 B0 可借用区间交叠的计算形式，但权重中的区间在这里是归一化索引而非秒。B1 的 OpenFace 几何处理、B2 的 CTC 词边界后验、B4 的 Wav2Vec2 辅助特征和 B5 的原词查询矩阵都要求本文件未提供的来源定义或原始素材，不能因为维度相同就接到官方预计算特征上。Q2 使用的仍是附件二原有 `text/audio/vision` 特征列，只改变时序组织。 + +| 对比项 | Q1 原始素材主干 | 本适配器 | +|---|---|---| +| 来源定位 | 词边界、音频采样/窗口、视频帧 PTS | 各模态自身的整数行索引和可用长度 | +| 目标轴 | 真实秒数划分的 0.1 秒主网格 | 没有秒单位的 50 个相对索引进程格 | +| 可核验结论 | 可回查某词、某秒对应的原始素材 | 可回查目标格聚合了哪些预计算来源行 | +| 不能互换的内容 | 原生特征、真实质量与物理来源图 | 官方预计算特征、索引覆盖和长度歧义 | + +## 为什么需要单独适配 + +Q1 原始样本流程知道词的起止秒数、音频窗口对应的采样范围、视频帧的真实 PTS，因此可以先定义共同的物理时间区间，再计算各来源与目标区间的交长。附件二未对齐版只给三组已提取的数字序列：文本最多 50 行，音频和视觉各最多 500 行。它们的第 10 行既不表示同一个时刻，也不带有足以恢复时刻的采样率和起点。直接把三个数组裁到前 50 行，或者把第 `r` 行视为共同时间步，都会制造未经数据支持的对应关系。 + +适配器采用一项明确的近似假设：**每个模态的有效索引范围分别覆盖同一片段的相对进程，索引顺序不变**。它把“片段前 2%”映射到统一的第 0 格，把“片段最后 2%”映射到第 49 格。若不同模态的采样不均匀、起止范围不同或存在长段未标注缺口，相同进程格也未必对应相同真实秒数。这是 Q2 固定长度输入的工程接口，不是从数字特征中推回了物理时间。 + +这里没有用情感标签、内容相似度、DTW 或可学习注意力去移动来源位置。附件二没有可核验的时间真值，用这些分数重排序列无法证明音画或词帧的物理对应正确；原始行顺序和来源索引因此始终保留。 + +## 第一步：确定来源范围与真实观测 + +对每条样本、每个模态分别记来源行数为 `L`，行观测标记为 `o_r`。适配器首先核对形状和数值是否有限，再按下表确定来源范围。来源全维零行仅用来判断官方预计算特征的行观测状态，不能把单个维度的正常零值判为缺失。 + +| 模态 | 原数组与维度 | 采用的 `L` | 行观测规则与例外 | +|---|---|---|---| +| 文本 | `text[50,768]` | `text_bert` 注意力掩码中连续有效前缀的长度 | 有效前缀内 `text` 行非全零才观测；掩码外的非零填充向量也排除。`raw_text` 不参与投影。 | +| 音频 | `audio[500,74]` | 官方 `audio_lengths` | 长度内非全零行才观测；若长度后仍有非零行，直接报错，不静默截断。 | +| 视觉 | `vision[500,35]` | `max(vision_lengths, 最后一个非零行索引 + 1)` | 若官方长度后仍有非零行，保留这些已知观测，并标记 `length_conflict` 与 `tail_ambiguous`。此时 `L` 只是包含所有已知观测的最小跨度，不代表已证实的真实帧数。 | + +视觉长度冲突时，最后一个非零行之后的零尾段可能是填充，也可能仍属于有效但缺失的内容；数组本身无法区分。适配器没有把这段零尾宣布为已确认填充，也没有把缺失行数值插补后送入 Q2。文本注意力掩码还必须是从位置 0 开始的连续前缀；异常输入直接报错。 + +## 第二步：在相对进程轴上做区间投影 + +对某模态的 `L` 个来源行，以从 0 开始的索引 `r=0,…,L-1` 定义来源区间；对目标索引 `t=0,…,49` 定义统一的 50 个目标区间： + +```text +J_r = [r/L, (r+1)/L) +I_t = [t/50, (t+1)/50) +w_tr = o_r · max(0, min((t+1)/50, (r+1)/L) - max(t/50, r/L)) +W_t = Σ_r w_tr +``` + +只有真正观测的来源行才获得非零权重。区间均采用左闭右开形式；交长由区间端点确定，不通过情感标签或内容相似度调整顺序。`W_t>0` 时，第 `d` 个特征维度的投影值为 + +```text +x̂_t,d = (Σ_r w_tr · x_r,d) / W_t +P_tr = w_tr / W_t +``` + +`P_tr` 是目标位置 `t` 对来源行 `r` 的归一化贡献，整行和为 1。若 `W_t=0`，输出数值零仅作占位，目标观测掩码为假，`P` 的这一行保持全零；后续模型不能把占位零当成实际观测。计算不跨缺失区间插值，也不依据语义相似度把来源行重排。没有可靠的来源质量字段，因此所有有效行只使用单位质量权重。 + +一个简单例子：某音频片段有 `L=100` 个来源行，目标第 0 格覆盖 `[0,0.02)`，恰好与来源行 0、1 各重叠 `0.01`。若两行同一维取值分别为 2 和 4，投影值就是 3，来源权重各为 0.5。若来源行 1 缺失，投影值为 2、来源权重变为 1，但该格只有一半的索引区间有观测；不会用零把均值拉到 1。若某格内所有来源行均缺失，该格的掩码为假。这些数值展示的是索引交叠规则，不意味着每行对应 0.01 秒。 + +## 第三步：保存可回查的投影信息 + +`project_modality(...)` 对单条样本的一个模态返回 `ProjectedView`，各字段含义如下。 + +| 字段 | 形状或类型 | 含义 | +|---|---|---| +| `x` | `(50,d)`，`float32` | 交叠加权后的特征；`d` 保持为文本 768、音频 74、视觉 35。 | +| `observed` | `(50,)`，布尔值 | 目标格是否得到至少一个实际观测来源。 | +| `coverage` | `(50,)` | `min(1,50·W_t)`；目标格在**相对索引区间**内的观测覆盖比例，并非秒级覆盖率。 | +| `source_count` | `(50,)` | 对该目标格贡献非零交长的观测来源行数。 | +| `first_source`、`last_source` | 各 `(50,)` | 首末贡献来源索引；空格记为 `-1`。它们只是摘要，不能替代完整来源权重。 | +| `source_weights` | CSR 稀疏矩阵 `(50,L)` | 每个目标格到各原始来源行的 `P_tr`；可追溯平均值究竟来自哪些行。 | +| `reported_length`、`source_span`、`length_conflict`、`tail_ambiguous` | 样本级字段 | 对比官方长度与本次投影采用的跨度，保留视觉长度歧义。 | + +例如 `source_weights.getrow(10)` 可列出目标第 10 格使用的原始索引及其权重。多格可能共享一个原始来源行，因为来源区间可能跨过 50 格的边界；共享不会产生新的独立观测。完整权重保存在单样本返回对象中，批量训练接口不会把所有样本的 CSR 矩阵装入模型。 + +## 第四步：交给 Q2 训练 + +`adapt_split(...)` 对一个官方划分逐样本调用上述算子，返回三个模态的 `(N,50,d)` 特征、`(N,50,3)` 观测掩码和划分级审计。`math/Q2/data.py` 的 `load_official_splits(..., version="unaligned_50")` 将它们封装成原有 `SplitData`；显式传入投影掩码，避免有效的正负特征在均值中恰好抵消为全零时被误判为缺失。样本 ID、官方 train/valid/test 划分和标签原样保留。 + +Q2 随后从官方训练集再按来源 `video_id` 划出拟合、可靠度选择和温度校准组。标准化器只在拟合组的已观测行上拟合；标准化后缺失位置继续保持零占位及假掩码。C5 按既有流程生成特征层连续缺口进行训练，模型输入仍为文本/音频/视觉 50 位序列及观测掩码。当前 C5 **不读取** `coverage`、`source_count` 或 CSR 权重作为质量值；官方未对齐文件没有真实质量分数，可靠度分支按 `q*=1, J_Q=0` 的回退规则运行。覆盖率可用于核验投影和后续独立研究，但不能直接宣称为检测质量。 + +适配过程不读情感标签，`raw_text` 不进入学生模型，也不使用验证或测试集拟合对齐参数。Q2 才读取标签执行监督训练与评估。附件二的 train、valid、test 都通过同一转换规则，测试集只用于最终评估。 + +## 接口 + +`unaligned_adapter.py` 的 `project_modality(...)` 返回一条样本的 50 位特征、观测掩码、覆盖率、来源行数、首末来源索引及完整的 CSR 来源权重矩阵；`adapt_split(...)` 处理官方一个划分并返回审计摘要。Q2 的 `load_official_splits(path, version="unaligned_50")` 直接调用此适配器，输出既有 `SplitData` 接口。调用方可按下面的方式读取一个划分，代码在仓库根目录运行时需把 `math/Q2` 加入 Python 导入路径： + +```python +import sys +from pathlib import Path + +sys.path.insert(0, "math/Q2") +from data import load_official_splits + +splits = load_official_splits( + Path("E题数据/附件2-数据集特征文件/unaligned_50.pkl"), + version="unaligned_50", +) +train = splits["train"] +audio_features = train.x["audio"] # (3395, 50, 74) +observed = train.mask # (3395, 50, 3)，顺序为文本、音频、视觉 +audit = train.alignment_audit +``` + +单样本核验时直接调用 `project_modality`，可从 `source_weights.getrow(t)` 得到第 `t` 个目标格的原始行索引与权重。`load_official_splits` 只把三模态特征、掩码及审计摘要交给 Q2；单样本覆盖率和 CSR 来源权重不会默认送入预测网络。 + +Q2 训练入口： + +```bash +uv run --project math/Q1 python math/Q2/train_unaligned_c5.py +``` + +未对齐版权重、尺度、指标和清单单独写入 `math/Q2/results_unaligned/`，与原有 aligned 版结果隔离。该入口固定使用原 aligned 实验已选定的 C5 结构，在未对齐版训练数据上重新拟合全部参数；未对齐版只选择可靠度与温度，不重新挑选架构。若需要完整 C0–C7 对照，可使用 `train.py --input-version unaligned_50 --skip-attachment3`。 + +批量转换在内存中确定性执行，没有另存一份接近原数据体积的派生特征文件；每次训练都从官方未对齐文件重建同一 50 位视图。`results_unaligned/` 保存权重、训练折尺度、指标、预测和含输入 SHA-256 的运行清单，不保存原始数据或大体积缓存。 + +## 已核对的输入异常与专项推理限制 + +附件二 train/valid/test 分别为 3395/728/727 条；`vision_lengths` 后仍出现观测行的样本分别为 618/141/131 条。文本注意力掩码外的非零 `text` 行分别有 86078/17772/18041 行，均按填充排除。这些数字是输入结构审计，不是对齐准确率。没有人工时间真值，不能报告秒级边界误差。 + +固定 C5 后，未对齐索引视图的验证集 Accuracy/Macro-F1/MAE 为 `0.6099/0.5253/0.6701`，测试集为 `0.6726/0.5668/0.7059`。这些指标说明该输入接口能够完成 Q2 训练与预测，不测量某个目标格是否与真实视频秒数吻合。与 aligned 版的指标差异还受到特征组织、训练随机性和视觉长度歧义影响，不能单独归因于投影规则。测试预测、视频组 Bootstrap 区间和运行参数分别保存在 `math/Q2/results_unaligned/test_predictions.csv`、`group_bootstrap_ci.csv` 和 `run_manifest.json`。 + +附件三的未对齐版本只有 `raw_text`、`audio`、`vision`，没有 `text`、`text_bert`、`audio_lengths` 或 `vision_lengths`。直接用 `raw_text` 重编码会绕过专项文本缺失；从最后一个非零行推断完整长度也无法区分尾部缺失和填充。因此当前训练比较不输出附件三未对齐版预测；要完成同版本专项推理，需补充可信长度/填充元数据，并为文本提供不泄漏缺失状态的数值特征或明确将文本全程标为不可观测。 diff --git a/math/Q1/UNIFIED_ADAPTER.md b/math/Q1/UNIFIED_ADAPTER.md new file mode 100644 index 0000000..62877e0 --- /dev/null +++ b/math/Q1/UNIFIED_ADAPTER.md @@ -0,0 +1,70 @@ +# Q1 Unified Alignment Adapter：统一数据接口与审计 + +## 目的与边界 + +Q1 现在对外提供一个 `Q1AlignmentAdapter` 和一种 `AlignedMultimodalSample`。Q2、后续 Q3/Q4 使用相同的 `features / observed / coverage / provenance / metadata` 字段读取对齐结果。对齐的**坐标证据**仍必须区分：有原视频、音频与词级时间记录的 Q1 样本采用 `physical`；官方 `unaligned_50.pkl` 只有三条有序特征序列和长度字段，采用 `relative`。两者进入同一个区间求交投影核，但 `relative` 的位置绝不是秒、词边界或物理同步真值。 + +本次只重构数据接口。Q1 原有的词、CTC、视频 PTS、音频采样定位、0.1 秒密集主视图和查询矩阵仍由原生特征包保存；既有 `features_v2/` 文件没有重建。Q2 的结构、损失、超参数、权重和历史指标没有重训或改选。 + +## 共同投影核 + +给定来源区间 `J_r`、目标区间 `I_t`、观测标记 `O_r,d` 和来源质量 `q_r`，核先计算交长 `h_tr = |I_t ∩ J_r|`，再用 `h_tr q_r O_r,d` 对特征逐维加权平均。输出零值但 `observed=false` 表示目标格没有证据；不插值、不移动来源次序。目标格的 `coverage` 单独用 `h_tr O_r` 除以目标宽度，**不乘质量**。`quality_mean` 与 `quality_available_fraction` 分开返回；缺质量字段的 Relative 输入使用单位权重并明确标为不可用。 + +`source_weights` 是目标格到原始来源行的 CSR 权重矩阵：有观测的行和为 1，无观测的行全零。`source_count`、`first_source`、`last_source` 用于快速审计；`observed_dimensions` 与 `coverage_dimensions` 保留物理特征的逐维有效性。`source_span` 是参与构造坐标的跨度，`original_source_length` 是原数组行数。来源区间、目标区间由 [`adapter/coordinates.py`](adapter/coordinates.py) 的物理与相对构造函数建立，数值汇聚只有 [`adapter/projection.py`](adapter/projection.py) 一处实现。 + +| 模式 | 来源区间 | 50 个目标区间 | 可以声称的对应关系 | +|---|---|---|---| +| `physical` | Q1 保存的词、音频窗、视频帧真实时间区间 | `[tD/50,(t+1)D/50)`，`D` 为真实片段时长 | 可回查原素材时间与来源行；原有 0.1 秒视图仍单独保存 | +| `relative` | 每模态独立的 `[r/L,(r+1)/L)` | `[t/50,(t+1)/50)` | 仅表示各自序列中的相对进程，不能声称秒级同步 | + +`mode="auto"` 只依据输入证据：Q1 物理源须有有效 `duration_s`、`media.status=ok`、源视频 SHA-256 和三模态原生时间区间；官方未对齐行须通过 `from_unaligned_record` 确认顺序、注意力掩码和长度。缺证据或物理证据损坏会报错，不根据数组是 50 行还是 500 行猜模式，也不在物理失败时悄悄退化为 Relative。 + +## 官方未对齐数据的来源规则 + +文本 `L` 取 `text_bert` 注意力掩码的连续有效前缀；掩码外的非零填充向量不参与投影。音频 `L` 取 `audio_lengths`，其后若仍有非零观测行则报错。视觉 `L=max(vision_lengths, 最后非零行位置+1)`；发现冲突时保留已知观测行，并设置 `length_conflict` 和 `tail_ambiguous`。全维零行代表该官方预计算序列中的未观测行，单个特征维度的零值不代表缺失。每个模态先各自规范化到 `[0,1)`，因此同一目标格只是一种有明确假设的输入组织方式。 + +## 标准接口 + +```python +import sys +sys.path.insert(0, "math/Q1") +from adapter import Q1AlignmentAdapter + +adapter = Q1AlignmentAdapter(target_steps=50) +physical = adapter.from_q1_sample("-iRBcNs9oI8/8") + +# split 是官方 unaligned_50.pkl 的 train、valid 或 test 字典 +relative = adapter.from_unaligned_record(split, index=0) +features, mask = relative.q2_arrays() +assert features["text"].shape == (50, 768) +assert mask.shape == (50, 3) + +weights = relative.provenance["audio"].source_weights.getrow(10) +original_rows, contributions = weights.indices, weights.data +``` + +`features[m]` 为 `(50,d)` float32，`observed[m]` 与 `coverage[m]` 各为 `(50,)`；`quality_mean[m]` 和 `quality_available_fraction[m]` 也按目标位置给出。`provenance[m]` 保存 CSR、来源跨度、原数组长度、官方报告长度、冲突标记和逐维有效性。`metadata` 明确写出 `coordinate_mode`、`coordinate_unit`、`physical_time_alignment`、`target_steps`、质量字段可用性、版本及样本 ID。Q2 的 [`data.py`](../Q2/data.py) 仅调用统一接口的 `adapt_official_split`，再沿用已有 `SplitData` 和模型输入；旧 [`unaligned_adapter.py`](unaligned_adapter.py) 只保留委托到新接口的兼容入口。 + +## 全量审计与兼容性 + +审计覆盖官方未对齐 train/valid/test 全部 **3,395 / 728 / 727，共 4,850 条**，并读取 Q1 物理样本 100 条。新 Relative 结果与重构前冻结的基线比较：每个划分的文本、音频、视觉 float32 数组和 `(N,50,3)` mask 的 SHA-256 **全部逐字节一致**。各划分的有效长度分布、`LK` 数量、目标观测格及覆盖率分布、文本掩码外非零行、长度冲突、全缺失模态与目标格、非有限值、来源权重行和误差、输出形状及坐标模式均见 [`full_audit.json`](results/unified_adapter/full_audit.json)。冻结基线和直接对照分别见 [`legacy_relative_baseline.json`](results/unified_adapter/legacy_relative_baseline.json) 与 [`equivalence_report.json`](results/unified_adapter/equivalence_report.json)。 + +| 划分 | 样本 | 视觉长度冲突及尾部歧义 | 文本掩码外非零行 | 视觉无观测目标格 | 视觉全缺失样本 | 非有限输出 | +|---|---:|---:|---:|---:|---:|---:| +| train | 3,395 | 618 | 86,078 | 6,448 | 30 | 0 | +| valid | 728 | 141 | 17,772 | 1,085 | 0 | 0 | +| test | 727 | 131 | 18,041 | 1,316 | 8 | 0 | + +物理样本 100/100 均进入 `physical`，三模态均为 50 步、无非有限输出，来源权重最大行和误差 `2.39e-7`。Relative 来源权重最大行和误差 `1.20e-7`。视觉长度冲突来自官方长度与非零行不一致，无法用当前文件证明尾部的真实视频时长。审计没有把索引覆盖率或质量回退值解释为检测质量或物理时间准确率。 + +Q2 的真实加载入口已完成**只读冒烟检查**：三个划分均保留原样本数及标签，输出文本 `(N,50,768)`、音频 `(N,50,74)`、视觉 `(N,50,35)`、掩码 `(N,50,3)`；记录见 [`q2_smoke.json`](results/unified_adapter/q2_smoke.json)。16 个单元测试涵盖 `L=K`、展开、聚合、部分/全部缺失、文本填充、音频/视觉长度冲突、来源权重守恒、两类坐标、Q2 形状、非有限值、全量旧新等价以及 `auto` 证据拒绝规则。 + +在仓库根目录复核： + +```bash +uv run --project math/Q1 python -m unittest discover -s math/Q1/tests -p 'test_unified_adapter.py' -v +uv run --project math/Q1 python math/Q1/audit_unified_adapter.py +uv run --project math/Q1 python math/Q1/smoke_q2_adapter.py +``` + +附件三的未对齐版仍缺少这里所需的 `text`、`text_bert`、`audio_lengths`、`vision_lengths`，不能在没有可信缺失/长度元数据时自动套用这个 Relative 构造器。相关限制和原理细节见 [`UNALIGNED_ADAPTER.md`](UNALIGNED_ADAPTER.md)。 diff --git a/math/Q1/adapter/__init__.py b/math/Q1/adapter/__init__.py new file mode 100644 index 0000000..d3c1e77 --- /dev/null +++ b/math/Q1/adapter/__init__.py @@ -0,0 +1,17 @@ +"""Unified Q1 alignment interface for physical time and relative progress.""" + +from .core import ( + AlignedMultimodalSample, + AlignmentError, + ModalityProvenance, + Q1AlignmentAdapter, + adapt_official_split, +) + +__all__ = [ + "AlignedMultimodalSample", + "AlignmentError", + "ModalityProvenance", + "Q1AlignmentAdapter", + "adapt_official_split", +] diff --git a/math/Q1/adapter/coordinates.py b/math/Q1/adapter/coordinates.py new file mode 100644 index 0000000..13d98ec --- /dev/null +++ b/math/Q1/adapter/coordinates.py @@ -0,0 +1,26 @@ +"""Coordinate constructors; no feature aggregation happens here.""" +from __future__ import annotations + +import numpy as np + + +def physical_targets(duration_s: float, steps: int) -> np.ndarray: + """K fixed bins spanning verified real media duration, in seconds.""" + duration = float(duration_s) + if not np.isfinite(duration) or duration <= 0 or steps < 1: + raise ValueError("physical targets require positive finite duration and steps") + edges = np.linspace(0.0, duration, steps + 1, dtype=np.float64) + return np.column_stack((edges[:-1], edges[1:])) + + +def relative_cells(length: int) -> np.ndarray: + """Ordered source cells on a unit progress axis, with no time claim.""" + if length < 1: + raise ValueError("relative source length must be positive") + left = np.arange(length, dtype=np.float64) / length + return np.column_stack((left, left + 1.0 / length)) + + +def relative_targets(steps: int) -> np.ndarray: + """K fixed cells on the same unit progress axis.""" + return relative_cells(steps) diff --git a/math/Q1/adapter/core.py b/math/Q1/adapter/core.py new file mode 100644 index 0000000..16148bf --- /dev/null +++ b/math/Q1/adapter/core.py @@ -0,0 +1,238 @@ +"""Q1's common sample contract and evidence-based coordinate dispatch.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +import numpy as np +from scipy import sparse + +from .coordinates import physical_targets, relative_cells, relative_targets +from .projection import project_intervals + +MODALITIES = ("text", "audio", "vision") +DIMS = {"text": 768, "audio": 74, "vision": 35} +STEPS = {"text": 50, "audio": 500, "vision": 500} +K = 50 +VERSION = "q1-unified-1" + + +class AlignmentError(ValueError): + """The source cannot be assigned a defensible alignment coordinate.""" + + +@dataclass +class ModalityProvenance: + source_weights: sparse.csr_matrix + source_count: np.ndarray + first_source: np.ndarray + last_source: np.ndarray + source_span: int + original_source_length: int + reported_length: int | None + length_conflict: bool = False + tail_ambiguous: bool = False + observed_dimensions: np.ndarray | None = None + coverage_dimensions: np.ndarray | None = None + quality_available: np.ndarray | None = None + + +@dataclass +class AlignedMultimodalSample: + features: dict[str, np.ndarray] + observed: dict[str, np.ndarray] + coverage: dict[str, np.ndarray] + provenance: dict[str, ModalityProvenance] + metadata: dict[str, Any] + target_intervals: np.ndarray + quality_mean: dict[str, np.ndarray] = field(default_factory=dict) + quality_available_fraction: dict[str, np.ndarray] = field(default_factory=dict) + + def q2_arrays(self) -> tuple[dict[str, np.ndarray], np.ndarray]: + """The existing Q2 model input shapes, without changing that model.""" + return self.features, np.stack([self.observed[m] for m in MODALITIES], axis=-1) + + +@dataclass +class _Prepared: + values: np.ndarray + intervals: np.ndarray + observed: np.ndarray + quality: np.ndarray + reported_length: int | None + length_conflict: bool = False + tail_ambiguous: bool = False + quality_available: np.ndarray | None = None + + +def _relative_modality(record: dict[str, Any], name: str) -> _Prepared: + raw = np.asarray(record[name]) + if raw.shape != (STEPS[name], DIMS[name]) or not np.isfinite(raw).all(): + raise AlignmentError(f"{name}: expected finite {(STEPS[name], DIMS[name])}, got {raw.shape}") + if name == "text": + attention = np.asarray(record["attention_mask"], bool) + if attention.shape != (50,) or not np.array_equal(attention, np.arange(50) < int(attention.sum())): + raise AlignmentError("text attention mask must be a 50-position prefix") + length = int(attention.sum()) + if length < 1: + raise AlignmentError("empty text attention mask") + span = length + observed = attention[:span] & np.any(raw[:span] != 0, axis=1) + conflict = ambiguous = False + else: + length = int(record[f"{name}_length"]) + if length < 1 or length > STEPS[name]: + raise AlignmentError(f"{name}: invalid official length {length}") + nonzero = np.any(raw != 0, axis=1) + last = int(np.flatnonzero(nonzero)[-1]) + 1 if nonzero.any() else 0 + conflict = last > length + if name == "audio" and conflict: + raise AlignmentError("audio contains observed positions beyond audio_lengths") + span = max(length, last) + observed = nonzero[:span] + ambiguous = bool(conflict) + return _Prepared(raw[:span].astype(np.float32, copy=False), relative_cells(span), + observed, np.ones(span, np.float32), length, bool(conflict), bool(ambiguous), + np.zeros(span, bool)) + + +def _validate_physical(source: dict[str, Any]) -> tuple[float, dict[str, Any]]: + meta = source.get("_meta") + if not isinstance(meta, dict): + raise AlignmentError("physical mode requires stored Q1 metadata") + duration = float(meta.get("duration_s", float("nan"))) + if not np.isfinite(duration) or duration <= 0: + raise AlignmentError("physical mode requires a finite positive duration") + if meta.get("media", {}).get("status") != "ok" or not meta.get("source_video_sha256"): + raise AlignmentError("physical mode requires verified media status and source hash") + for name in MODALITIES: + if f"native_{name}_intervals" not in source: + raise AlignmentError(f"physical mode lacks {name} timestamps") + return duration, meta + + +class Q1AlignmentAdapter: + """Align either verified Q1 physical sources or official ordered sequences. + + `auto` uses evidence in the input contract only; tensor shape never decides + whether time is physical. A malformed physical source is an error rather + than a silent relative fallback. + """ + + def __init__(self, target_steps: int = K): + if target_steps < 1: + raise ValueError("target_steps must be positive") + self.target_steps = target_steps + + def align(self, source: dict[str, Any], mode: str = "auto") -> AlignedMultimodalSample: + if mode not in {"auto", "physical", "relative"}: + raise AlignmentError(f"unsupported coordinate mode: {mode}") + if mode == "auto": + if "_meta" in source or any(k.startswith("native_") for k in source): + mode = "physical" + elif source.get("sequence_order_verified") is True: + mode = "relative" + else: + raise AlignmentError("auto mode requires Q1 physical evidence or verified sequence order") + if mode == "physical": + from q1_io import _native + + duration, meta = _validate_physical(source) + target = physical_targets(duration, self.target_steps) + prepared = {} + for name in MODALITIES: + values, observed, intervals, quality, available = _native(source, name) + if np.asarray(intervals).shape != (len(values), 2) or np.any(np.asarray(intervals) < -1e-5) or np.any(np.asarray(intervals) > duration + 1e-5): + raise AlignmentError(f"{name}: physical timestamps outside media duration") + prepared[name] = _Prepared(values, intervals, observed, quality, len(values), + quality_available=available) + metadata = {"sample_id": meta.get("sample_id", f"{meta.get('video_id')}/{meta.get('clip_id')}"), + "coordinate_mode": "physical", "coordinate_unit": "seconds", + "physical_time_alignment": True, "duration_s": duration, + "source_video_sha256": meta["source_video_sha256"], + "dense_view": "views_sec_* (stored 0.1 s Q1 artifact)", + "quality_fields_available": {m: bool(np.asarray(prepared[m].quality_available).any()) for m in MODALITIES}} + else: + if source.get("sequence_order_verified") is not True: + raise AlignmentError("relative mode requires verified source order") + target = relative_targets(self.target_steps) + prepared = {name: _relative_modality(source, name) for name in MODALITIES} + metadata = {"sample_id": str(source.get("id", "")), "coordinate_mode": "relative", + "coordinate_unit": "normalized_progress", "physical_time_alignment": False, + "word_or_frame_timestamps_available": False, + "quality_fields_available": {m: False for m in MODALITIES}} + features = {} + observed = {} + coverage = {} + quality_mean = {} + quality_available_fraction = {} + provenance = {} + for name in MODALITIES: + item = prepared[name] + result = project_intervals(item.values, item.intervals, target, item.observed, item.quality, + item.quality_available) + features[name] = result.x + observed[name] = result.observed + coverage[name] = result.coverage + quality_mean[name] = result.quality_mean + quality_available_fraction[name] = result.quality_available_fraction + provenance[name] = ModalityProvenance(result.source_weights, result.source_count, + result.first_source, result.last_source, len(item.values), + len(source[name]) if mode == "relative" else len(item.values), item.reported_length, + item.length_conflict, item.tail_ambiguous, result.observed_dimensions, + result.coverage_dimensions, item.quality_available) + metadata.update({"target_steps": self.target_steps, "adapter_version": VERSION}) + return AlignedMultimodalSample(features, observed, coverage, provenance, metadata, target, + quality_mean, quality_available_fraction) + + def from_q1_sample(self, sample_id: str, feature_dir: Path | None = None) -> AlignedMultimodalSample: + from q1_io import FEATURE_DIR, load_sample + + return self.align(load_sample(sample_id, FEATURE_DIR if feature_dir is None else feature_dir), "auto") + + def from_unaligned_record(self, split: dict[str, Any], index: int) -> AlignedMultimodalSample: + """Build verified ordered input from one official unaligned pickle row.""" + attention = np.asarray(split["text_bert"][index, 1], bool) + raw_id = split["id"][index] + if isinstance(raw_id, bytes): + raw_id = raw_id.decode("utf-8", errors="replace") + record = {"id": str(raw_id), "sequence_order_verified": True, + "attention_mask": attention, + "text": split["text"][index], "audio": split["audio"][index], + "vision": split["vision"][index], + "audio_length": int(split["audio_lengths"][index]), + "vision_length": int(split["vision_lengths"][index])} + return self.align(record, "auto") + + +def adapt_official_split(split: dict[str, Any]) -> tuple[dict[str, np.ndarray], np.ndarray, dict[str, Any]]: + """Q2's batch bridge; all rows are produced through Q1AlignmentAdapter.""" + n = len(split["id"]) + output = {m: np.zeros((n, K, DIMS[m]), np.float32) for m in MODALITIES} + masks = np.zeros((n, K, len(MODALITIES)), bool) + conflicts = ambiguous = padding = 0 + coverage_sum = {m: 0.0 for m in MODALITIES} + observed_rows = {m: 0 for m in MODALITIES} + adapter = Q1AlignmentAdapter() + for i in range(n): + attention = np.asarray(split["text_bert"][i, 1], bool) + padding += int(np.count_nonzero(np.any(split["text"][i] != 0, axis=1) & ~attention)) + sample = adapter.from_unaligned_record(split, i) + for j, name in enumerate(MODALITIES): + output[name][i] = sample.features[name] + masks[i, :, j] = sample.observed[name] + coverage_sum[name] += float(sample.coverage[name].sum()) + observed_rows[name] += int(sample.observed[name].sum()) + conflicts += int(sample.provenance["vision"].length_conflict) + ambiguous += int(sample.provenance["vision"].tail_ambiguous) + audit = {"method": "shared_interval_overlap_on_normalized_progress", + "coordinate_mode": "relative", "physical_time_alignment": False, + "samples": n, "vision_length_conflict_samples": conflicts, + "vision_tail_ambiguous_samples": ambiguous, + "nonzero_text_rows_outside_attention": padding, + "observed_target_rows": observed_rows, + "mean_target_coverage": {m: coverage_sum[m] / (n * K) for m in MODALITIES}, + "quality_fields_available": False, + "word_or_frame_timestamps_available": False} + return output, masks, audit diff --git a/math/Q1/adapter/projection.py b/math/Q1/adapter/projection.py new file mode 100644 index 0000000..9b35e3d --- /dev/null +++ b/math/Q1/adapter/projection.py @@ -0,0 +1,108 @@ +"""The sole interval overlap projection kernel used by both coordinate modes.""" +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np +from scipy import sparse + + +@dataclass +class Projection: + x: np.ndarray + observed: np.ndarray + coverage: np.ndarray + source_count: np.ndarray + first_source: np.ndarray + last_source: np.ndarray + source_weights: sparse.csr_matrix + observed_dimensions: np.ndarray + coverage_dimensions: np.ndarray + quality_mean: np.ndarray + quality_available_fraction: np.ndarray + + +def project_intervals( + values: np.ndarray, + source_intervals: np.ndarray, + target_intervals: np.ndarray, + observed: np.ndarray, + quality: np.ndarray | None = None, + quality_available: np.ndarray | None = None, +) -> Projection: + """Project source cells using overlap * quality * observed validity. + + Row provenance uses any valid dimension. Feature values use validity per + dimension, so partially observed physical features remain partially missing. + """ + source = np.asarray(values, dtype=np.float32) + src = np.asarray(source_intervals, dtype=np.float64) + dst = np.asarray(target_intervals, dtype=np.float64) + if source.ndim != 2 or src.shape != (len(source), 2) or dst.ndim != 2 or dst.shape[1] != 2: + raise ValueError("inconsistent source features or interval dimensions") + if not np.isfinite(source).all() or not np.isfinite(src).all() or not np.isfinite(dst).all(): + raise ValueError("non-finite source features or intervals") + if np.any(src[:, 1] < src[:, 0]) or np.any(dst[:, 1] <= dst[:, 0]): + raise ValueError("source widths must be nonnegative and target widths positive") + obs = np.asarray(observed, bool) + if obs.shape == (len(source),): + obs_dim = np.broadcast_to(obs[:, None], source.shape) + elif obs.shape == source.shape: + obs_dim = obs + obs = obs.any(axis=1) + else: + raise ValueError("observed must have source-row or source-feature shape") + q = np.ones(len(source), dtype=np.float64) if quality is None else np.asarray(quality, dtype=np.float64) + if q.shape != (len(source),) or not np.isfinite(q).all() or np.any(q < 0): + raise ValueError("quality must be finite and nonnegative per source row") + overlap = np.maximum(0.0, np.minimum(dst[:, None, 1], src[None, :, 1]) + - np.maximum(dst[:, None, 0], src[None, :, 0])) + available = np.zeros(len(source), bool) if quality_available is None else np.asarray(quality_available, bool) + if available.shape != (len(source),): + raise ValueError("quality availability must be per source row") + # Keep the same multiplication and accumulation order as the original + # official-unaligned projection when quality is uniformly one. + physical = overlap.copy() + physical *= obs[None, :] + row_weight = physical.copy() + row_weight *= q[None, :] + mass = row_weight.sum(axis=1) + row_valid = mass > 0 + normalized = np.zeros_like(row_weight, dtype=np.float32) + normalized[row_valid] = (row_weight[row_valid] / mass[row_valid, None]).astype(np.float32) + support = row_weight > 0 + count = support.sum(axis=1).astype(np.uint16) + first = np.full(len(dst), -1, dtype=np.int32) + last = np.full(len(dst), -1, dtype=np.int32) + if row_valid.any(): + first[row_valid] = support[row_valid].argmax(axis=1) + last[row_valid] = len(source) - 1 - support[row_valid, ::-1].argmax(axis=1) + width = dst[:, 1] - dst[:, 0] + physical_mass = physical.sum(axis=1) + coverage = np.clip(physical_mass / width, 0.0, 1.0).astype(np.float32) + qmean = np.ones(len(dst), np.float32) + qavailable = np.zeros(len(dst), np.float32) + physical_valid = physical_mass > 0 + qmean[physical_valid] = (mass[physical_valid] / physical_mass[physical_valid]).astype(np.float32) + qavailable[physical_valid] = ((physical[physical_valid] @ available.astype(np.float64)) + / physical_mass[physical_valid]).astype(np.float32) + + # The common full-dimension case follows the original matrix product + # exactly; this is also much faster for 500 x 768 input. + if np.array_equal(obs_dim, np.broadcast_to(obs[:, None], source.shape)): + x = np.zeros((len(dst), source.shape[1]), np.float32) + x[row_valid] = ((row_weight[row_valid] @ source) / mass[row_valid, None]).astype(np.float32) + observed_dimensions = np.broadcast_to(row_valid[:, None], x.shape).copy() + coverage_dimensions = np.broadcast_to(coverage[:, None], x.shape).copy() + else: + dim_physical = overlap[:, :, None] * obs_dim[None, :, :] + dim_weight = dim_physical * q[None, :, None] + dim_mass = dim_weight.sum(axis=1) + observed_dimensions = dim_mass > 0 + x = np.zeros((len(dst), source.shape[1]), np.float32) + numerator = np.einsum("ksd,sd->kd", dim_weight, source, optimize=True) + x[observed_dimensions] = (numerator[observed_dimensions] / dim_mass[observed_dimensions]).astype(np.float32) + coverage_dimensions = np.clip(dim_physical.sum(axis=1) / width[:, None], 0, 1).astype(np.float32) + return Projection(x, row_valid, coverage, count, first, last, + sparse.csr_matrix(normalized), observed_dimensions, coverage_dimensions, + qmean, qavailable) diff --git a/math/Q1/audit_unified_adapter.py b/math/Q1/audit_unified_adapter.py new file mode 100644 index 0000000..611682b --- /dev/null +++ b/math/Q1/audit_unified_adapter.py @@ -0,0 +1,151 @@ +"""Full Q1 physical and official unaligned split audit; no model training.""" +from __future__ import annotations + +import hashlib +import json +import sys +from collections import Counter +from pathlib import Path + +import numpy as np + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[1] +sys.path.insert(0, str(HERE)) +sys.path.insert(0, str(ROOT / "math" / "Q2")) + +from adapter import Q1AlignmentAdapter # noqa: E402 +from data import restricted_load # noqa: E402 + +MODALITIES = ("text", "audio", "vision") +RESULTS = HERE / "results" / "unified_adapter" +OFFICIAL = ROOT / "E题数据" / "附件2-数据集特征文件" / "unaligned_50.pkl" + + +def _distribution(values: list[float]) -> dict[str, float | int]: + arr = np.asarray(values, np.float64) + return {"count": int(len(arr)), "min": float(arr.min()) if len(arr) else 0.0, + "p25": float(np.percentile(arr, 25)) if len(arr) else 0.0, + "median": float(np.median(arr)) if len(arr) else 0.0, + "p75": float(np.percentile(arr, 75)) if len(arr) else 0.0, + "max": float(arr.max()) if len(arr) else 0.0, + "mean": float(arr.mean()) if len(arr) else 0.0} + + +def _audit_split(name: str, split: dict, baseline: dict) -> dict: + adapter = Q1AlignmentAdapter() + n = len(split["id"]) + hashes = {m: hashlib.sha256() for m in MODALITIES} + mask_hash = hashlib.sha256() + lengths = {m: [] for m in MODALITIES} + observed_bins = {m: [] for m in MODALITIES} + all_missing_bins = Counter() + coverages = {m: [] for m in MODALITIES} + relations = {m: Counter() for m in MODALITIES} + all_missing = Counter() + conflicts = Counter() + text_padding_nonzero = 0 + max_provenance_error = 0.0 + max_nonfinite = 0 + modes = Counter() + shapes = {m: Counter() for m in MODALITIES} + for i in range(n): + sample = adapter.from_unaligned_record(split, i) + modes[sample.metadata["coordinate_mode"]] += 1 + stacked_mask = np.stack([sample.observed[m] for m in MODALITIES], axis=-1) + mask_hash.update(stacked_mask.tobytes(order="C")) + attention = np.asarray(split["text_bert"][i, 1], bool) + text_padding_nonzero += int(np.count_nonzero(np.any(split["text"][i] != 0, axis=1) & ~attention)) + for m in MODALITIES: + x = sample.features[m] + p = sample.provenance[m] + hashes[m].update(x.tobytes(order="C")) + shapes[m][str(list(x.shape))] += 1 + lengths[m].append(p.source_span) + observed_bins[m].append(int(sample.observed[m].sum())) + all_missing_bins[m] += int((~sample.observed[m]).sum()) + coverages[m].extend(sample.coverage[m].tolist()) + relations[m]["LK"] += 1 + all_missing[m] += int(not sample.observed[m].any()) + conflicts[m] += int(p.length_conflict) + if m == "vision": + all_missing["vision_tail_ambiguous"] += int(p.tail_ambiguous) + row_sums = np.asarray(p.source_weights.sum(axis=1)).reshape(-1) + if sample.observed[m].any(): + max_provenance_error = max(max_provenance_error, + float(np.max(np.abs(row_sums[sample.observed[m]] - 1.0)))) + max_provenance_error = max(max_provenance_error, + float(np.max(np.abs(row_sums[~sample.observed[m]]))) if (~sample.observed[m]).any() else 0.0) + max_nonfinite += int(np.count_nonzero(~np.isfinite(x))) + actual = {m: h.hexdigest() for m, h in hashes.items()} + expected = baseline[name]["sha256"] + return {"samples": n, "coordinate_modes": dict(modes), "feature_shapes": {m: dict(v) for m, v in shapes.items()}, + "source_length": {m: _distribution(v) for m, v in lengths.items()}, + "source_length_vs_K": {m: dict(v) for m, v in relations.items()}, + "observed_target_bins_per_sample": {m: _distribution(v) for m, v in observed_bins.items()}, + "coverage_per_target_bin": {m: _distribution(v) for m, v in coverages.items()}, + "text_nonzero_rows_outside_attention": text_padding_nonzero, + "length_conflict_samples": dict(conflicts), + "tail_ambiguous_samples": all_missing["vision_tail_ambiguous"], + "all_missing_target_bins": dict(all_missing_bins), + "all_missing_modality_samples": {m: all_missing[m] for m in MODALITIES}, + "nonfinite_output_values": max_nonfinite, + "max_provenance_row_sum_error": max_provenance_error, + "sha256": actual, "legacy_sha256": expected, + "exact_feature_equivalence": {m: actual[m] == expected[m] for m in MODALITIES}, + "mask_sha256": mask_hash.hexdigest(), + "exact_mask_equivalence": mask_hash.hexdigest() == baseline[name]["mask_sha256"]} + + +def _audit_physical() -> dict: + manifest = HERE / "features_v2" / "manifest_q1.jsonl" + ids = [json.loads(line)["sample_id"] for line in manifest.read_text(encoding="utf-8").splitlines()] + adapter = Q1AlignmentAdapter() + modes = Counter() + nonfinite = 0 + provenance_error = 0.0 + shapes = {m: Counter() for m in MODALITIES} + duration = [] + for sample_id in ids: + sample = adapter.from_q1_sample(sample_id) + modes[sample.metadata["coordinate_mode"]] += 1 + duration.append(sample.metadata["duration_s"]) + for m in MODALITIES: + x = sample.features[m] + shapes[m][str(list(x.shape))] += 1 + nonfinite += int(np.count_nonzero(~np.isfinite(x))) + sums = np.asarray(sample.provenance[m].source_weights.sum(axis=1)).ravel() + observed = sample.observed[m] + if observed.any(): + provenance_error = max(provenance_error, float(np.max(np.abs(sums[observed] - 1)))) + return {"samples": len(ids), "coordinate_modes": dict(modes), "feature_shapes": {m: dict(v) for m, v in shapes.items()}, + "duration_s": _distribution(duration), "nonfinite_output_values": nonfinite, + "max_provenance_row_sum_error": provenance_error, + "stored_dense_0_1_s_views_untouched": True} + + +def main() -> None: + RESULTS.mkdir(parents=True, exist_ok=True) + baseline = json.loads((RESULTS / "legacy_relative_baseline.json").read_text(encoding="utf-8")) + obj = restricted_load(OFFICIAL) + report = {"adapter": "q1-unified-1", "official_input": str(OFFICIAL.relative_to(ROOT)), + "relative": {name: _audit_split(name, obj[name], baseline) for name in ("train", "valid", "test")}} + del obj + report["physical"] = _audit_physical() + (RESULTS / "full_audit.json").write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") + equivalence = {"comparison": "SHA-256 of full float32 feature arrays and boolean masks against frozen old adapter output", + "splits": {name: {"samples": v["samples"], "exact_feature_equivalence": v["exact_feature_equivalence"], + "exact_mask_equivalence": v["exact_mask_equivalence"], + "old_feature_sha256": v["legacy_sha256"], "new_feature_sha256": v["sha256"], + "old_mask_sha256": baseline[name]["mask_sha256"], + "new_mask_sha256": v["mask_sha256"]} + for name, v in report["relative"].items()}} + (RESULTS / "equivalence_report.json").write_text(json.dumps(equivalence, indent=2), encoding="utf-8") + print(json.dumps({"relative": {name: {"samples": v["samples"], + "exact_feature_equivalence": v["exact_feature_equivalence"], + "exact_mask_equivalence": v["exact_mask_equivalence"]} for name, v in report["relative"].items()}, + "physical_samples": report["physical"]["samples"]}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/math/Q1/q1_io.py b/math/Q1/q1_io.py index e0fea60..2dd104e 100644 --- a/math/Q1/q1_io.py +++ b/math/Q1/q1_io.py @@ -63,7 +63,7 @@ def _native(sample: dict[str, Any], modality: str) -> tuple[np.ndarray, np.ndarr sample[f"native_{modality}_mask"].astype(bool), sample[f"native_{modality}_intervals"].astype(np.float32), sample[f"native_{modality}_quality"].astype(np.float32), - np.full(len(sample[f"native_{modality}_times"]), bool(sample[f"native_{modality}_quality_available"]), dtype=bool), + np.asarray(sample[f"native_{modality}_quality_available"], dtype=bool), ) if modality == "speech": meta = sample["_meta"] diff --git a/math/Q1/results/unified_adapter/equivalence_report.json b/math/Q1/results/unified_adapter/equivalence_report.json new file mode 100644 index 0000000..78b7fdd --- /dev/null +++ b/math/Q1/results/unified_adapter/equivalence_report.json @@ -0,0 +1,68 @@ +{ + "comparison": "SHA-256 of full float32 feature arrays and boolean masks against frozen old adapter output", + "splits": { + "train": { + "samples": 3395, + "exact_feature_equivalence": { + "text": true, + "audio": true, + "vision": true + }, + "exact_mask_equivalence": true, + "old_feature_sha256": { + "text": "7dd4282937b91f5f654ab1aee176df729c0e6bd2499c5c7a3072cff459ce1ae3", + "audio": "44a746e4b03bed665537f124544df8331fe53d33a639f3edcb39cb5bcc2a96e0", + "vision": "516a29f75c4c251c1cb1bcb2eef58d4bfd7b4d27c96badc1023f4a06f15d95b8" + }, + "new_feature_sha256": { + "text": "7dd4282937b91f5f654ab1aee176df729c0e6bd2499c5c7a3072cff459ce1ae3", + "audio": "44a746e4b03bed665537f124544df8331fe53d33a639f3edcb39cb5bcc2a96e0", + "vision": "516a29f75c4c251c1cb1bcb2eef58d4bfd7b4d27c96badc1023f4a06f15d95b8" + }, + "old_mask_sha256": "8609e08eb50aff524442bef5acfc1a7dfa201819486a64e00cd6d34f2be34f3a", + "new_mask_sha256": "8609e08eb50aff524442bef5acfc1a7dfa201819486a64e00cd6d34f2be34f3a" + }, + "valid": { + "samples": 728, + "exact_feature_equivalence": { + "text": true, + "audio": true, + "vision": true + }, + "exact_mask_equivalence": true, + "old_feature_sha256": { + "text": "16bdef07347baf00985e7964301eef2272701716220e846c415651806bb5eb0a", + "audio": "b7d6f3092d9ddbcd70afe4b0ecff0490181a83a308a67246613185167cb08e87", + "vision": "fbd9f93e8cae02de59e5d513c4c057d759db626894d298124575bdf18863a024" + }, + "new_feature_sha256": { + "text": "16bdef07347baf00985e7964301eef2272701716220e846c415651806bb5eb0a", + "audio": "b7d6f3092d9ddbcd70afe4b0ecff0490181a83a308a67246613185167cb08e87", + "vision": "fbd9f93e8cae02de59e5d513c4c057d759db626894d298124575bdf18863a024" + }, + "old_mask_sha256": "adcd9b7bac94c6523b571c7c60eb3e4ad0efc820062bd528b102a4239aaf2325", + "new_mask_sha256": "adcd9b7bac94c6523b571c7c60eb3e4ad0efc820062bd528b102a4239aaf2325" + }, + "test": { + "samples": 727, + "exact_feature_equivalence": { + "text": true, + "audio": true, + "vision": true + }, + "exact_mask_equivalence": true, + "old_feature_sha256": { + "text": "6cc0c3ee828ab2f322318011b10764aabca2f5d2bcba40abddd714529928a05a", + "audio": "d64dca47fffbbd00a2d8f5628f05efdb9b8743ff51187b1c4cac58ccf3f5a50b", + "vision": "2a8b12d9c3a0d57f8f14bb830dbd2f8acdcfa147f55c50927798692ae6630d6d" + }, + "new_feature_sha256": { + "text": "6cc0c3ee828ab2f322318011b10764aabca2f5d2bcba40abddd714529928a05a", + "audio": "d64dca47fffbbd00a2d8f5628f05efdb9b8743ff51187b1c4cac58ccf3f5a50b", + "vision": "2a8b12d9c3a0d57f8f14bb830dbd2f8acdcfa147f55c50927798692ae6630d6d" + }, + "old_mask_sha256": "85706ad6842e8a0642bf5d24642463d0488a6cba55848a2276e397e8592604d6", + "new_mask_sha256": "85706ad6842e8a0642bf5d24642463d0488a6cba55848a2276e397e8592604d6" + } + } +} \ No newline at end of file diff --git a/math/Q1/results/unified_adapter/full_audit.json b/math/Q1/results/unified_adapter/full_audit.json new file mode 100644 index 0000000..74153d6 --- /dev/null +++ b/math/Q1/results/unified_adapter/full_audit.json @@ -0,0 +1,503 @@ +{ + "adapter": "q1-unified-1", + "official_input": "E题数据/附件2-数据集特征文件/unaligned_50.pkl", + "relative": { + "train": { + "samples": 3395, + "coordinate_modes": { + "relative": 3395 + }, + "feature_shapes": { + "text": { + "[50, 768]": 3395 + }, + "audio": { + "[50, 74]": 3395 + }, + "vision": { + "[50, 35]": 3395 + } + }, + "source_length": { + "text": { + "count": 3395, + "min": 3.0, + "p25": 16.0, + "median": 22.0, + "p75": 32.0, + "max": 50.0, + "mean": 24.645655375552284 + }, + "audio": { + "count": 3395, + "min": 8.0, + "p25": 86.0, + "median": 127.0, + "p75": 183.0, + "max": 500.0, + "mean": 147.28836524300442 + }, + "vision": { + "count": 3395, + "min": 1.0, + "p25": 61.0, + "median": 93.0, + "p75": 135.0, + "max": 500.0, + "mean": 107.47304860088366 + } + }, + "source_length_vs_K": { + "text": { + "LK": 3173, + "LK": 2797, + "LK": 697, + "LK": 645, + "LK": 681, + "LK": 615, + "L None: + source = ROOT / "E题数据" / "附件2-数据集特征文件" / "unaligned_50.pkl" + splits = load_official_splits(source, version="unaligned_50") + report = {} + for name, split in splits.items(): + expected = {"text": (split.n, 50, 768), "audio": (split.n, 50, 74), + "vision": (split.n, 50, 35)} + actual = {m: x.shape for m, x in split.x.items()} + assert actual == expected + assert split.mask.shape == (split.n, 50, 3) + assert all(np.isfinite(x).all() for x in split.x.values()) + assert split.class_y is not None and len(split.class_y) == split.n + report[name] = {"samples": split.n, "feature_shapes": {m: list(s) for m, s in actual.items()}, + "mask_shape": list(split.mask.shape), "labels_preserved": True} + path = Q1 / "results" / "unified_adapter" / "q2_smoke.json" + path.write_text(json.dumps(report, indent=2), encoding="utf-8") + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/math/Q1/tests/test_unified_adapter.py b/math/Q1/tests/test_unified_adapter.py new file mode 100644 index 0000000..63ce948 --- /dev/null +++ b/math/Q1/tests/test_unified_adapter.py @@ -0,0 +1,139 @@ +"""Acceptance tests for the shared Q1 alignment contract.""" +from __future__ import annotations + +import json +import sys +import unittest +from pathlib import Path + +import numpy as np + +Q1 = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(Q1)) +from adapter import AlignmentError, Q1AlignmentAdapter # noqa: E402 +from adapter.projection import project_intervals # noqa: E402 + + +def cells(n: int) -> np.ndarray: + left = np.arange(n, dtype=np.float64) / n + return np.column_stack((left, left + 1 / n)) + + +def record(text_length: int = 50, audio_length: int = 50, vision_length: int = 50): + text = np.zeros((50, 768), np.float32) + audio = np.zeros((500, 74), np.float32) + vision = np.zeros((500, 35), np.float32) + text[:text_length] = 1 + audio[:audio_length] = 2 + vision[:vision_length] = 3 + return {"id": "video$_$clip", "sequence_order_verified": True, + "attention_mask": np.arange(50) < text_length, + "text": text, "audio": audio, "vision": vision, + "audio_length": audio_length, "vision_length": vision_length} + + +class UnifiedAdapterTests(unittest.TestCase): + def test_01_identity_L_equals_K(self): + x = np.arange(50, dtype=np.float32)[:, None] + result = project_intervals(x, cells(50), cells(50), np.ones(50, bool)) + np.testing.assert_array_equal(result.x[:, 0], x[:, 0]) + + def test_02_expand_L_less_than_K(self): + x = np.arange(10, dtype=np.float32)[:, None] + result = project_intervals(x, cells(10), cells(50), np.ones(10, bool)) + np.testing.assert_allclose(result.x[:, 0], np.repeat(x[:, 0], 5)) + + def test_03_aggregate_L_greater_than_K(self): + x = np.arange(100, dtype=np.float32)[:, None] + result = project_intervals(x, cells(100), cells(50), np.ones(100, bool)) + np.testing.assert_allclose(result.x[:, 0], x.reshape(50, 2).mean(axis=1)) + + def test_04_partial_missing(self): + x = np.ones((50, 2), np.float32) + valid = np.ones((50, 2), bool) + valid[3, 0] = False + result = project_intervals(x, cells(50), cells(50), valid) + self.assertFalse(result.observed_dimensions[3, 0]) + self.assertTrue(result.observed_dimensions[3, 1]) + self.assertEqual(result.x[3, 0], 0) + + def test_05_all_missing(self): + result = project_intervals(np.ones((10, 2), np.float32), cells(10), cells(50), np.zeros(10, bool)) + self.assertFalse(result.observed.any()) + self.assertFalse(result.x.any()) + self.assertFalse(result.source_weights.nnz) + + def test_06_text_padding_excluded(self): + r = record(text_length=10) + r["text"][10:] = 99 + sample = Q1AlignmentAdapter().align(r) + self.assertTrue(np.all(sample.features["text"] == 1)) + self.assertEqual(sample.provenance["text"].source_span, 10) + + def test_07_audio_length_conflict_rejected(self): + r = record() + r["audio"][70] = 7 + with self.assertRaisesRegex(AlignmentError, "audio contains observed"): + Q1AlignmentAdapter().align(r) + + def test_08_vision_length_conflict_retained(self): + r = record() + r["vision"][70] = 7 + p = Q1AlignmentAdapter().align(r).provenance["vision"] + self.assertTrue(p.length_conflict) + self.assertTrue(p.tail_ambiguous) + self.assertEqual(p.source_span, 71) + + def test_09_provenance_conservation(self): + sample = Q1AlignmentAdapter().align(record()) + for m, p in sample.provenance.items(): + sums = np.asarray(p.source_weights.sum(axis=1)).ravel() + np.testing.assert_allclose(sums[sample.observed[m]], 1, atol=1e-6) + + def test_10_physical_coordinate(self): + from q1_io import load_sample + sample = Q1AlignmentAdapter().align(load_sample("-iRBcNs9oI8/8")) + self.assertEqual(sample.metadata["coordinate_mode"], "physical") + self.assertTrue(sample.metadata["physical_time_alignment"]) + self.assertAlmostEqual(sample.target_intervals[-1, 1], sample.metadata["duration_s"]) + + def test_11_relative_coordinate(self): + sample = Q1AlignmentAdapter().align(record()) + self.assertEqual(sample.metadata["coordinate_unit"], "normalized_progress") + self.assertFalse(sample.metadata["physical_time_alignment"]) + self.assertAlmostEqual(sample.target_intervals[-1, 1], 1.0) + + def test_12_q2_shapes(self): + sample = Q1AlignmentAdapter().align(record()) + features, mask = sample.q2_arrays() + self.assertEqual(mask.shape, (50, 3)) + self.assertEqual({m: v.shape for m, v in features.items()}, + {"text": (50, 768), "audio": (50, 74), "vision": (50, 35)}) + + def test_13_nonfinite_rejected(self): + r = record() + r["audio"][0, 0] = np.inf + with self.assertRaises(AlignmentError): + Q1AlignmentAdapter().align(r) + + def test_14_old_new_full_equivalence(self): + report = json.loads((Q1 / "results" / "unified_adapter" / "full_audit.json").read_text()) + for split in ("train", "valid", "test"): + self.assertTrue(all(report["relative"][split]["exact_feature_equivalence"].values())) + self.assertTrue(report["relative"][split]["exact_mask_equivalence"]) + + def test_15_auto_requires_evidence(self): + r = record() + r.pop("sequence_order_verified") + with self.assertRaisesRegex(AlignmentError, "requires"): + Q1AlignmentAdapter().align(r) + + def test_16_invalid_physical_does_not_fall_back(self): + r = record() + r["_meta"] = {"duration_s": 2.0} + with self.assertRaisesRegex(AlignmentError, "verified media"): + Q1AlignmentAdapter().align(r) + + +if __name__ == "__main__": + unittest.main() diff --git a/math/Q1/unaligned_adapter.py b/math/Q1/unaligned_adapter.py new file mode 100644 index 0000000..02e1faf --- /dev/null +++ b/math/Q1/unaligned_adapter.py @@ -0,0 +1,35 @@ +"""Compatibility imports for callers of the former Relative-only module. + +All projection is now performed by :mod:`adapter`; this file has no separate +alignment calculation. +""" +from __future__ import annotations + +import numpy as np + +from adapter import adapt_official_split + + +def project_modality(values: np.ndarray, modality: str, *, reported_length: int | None = None, + attention_mask: np.ndarray | None = None): + """Legacy single-modality call, delegated to the unified projection kernel.""" + from adapter.core import _relative_modality + from adapter.coordinates import relative_targets + from adapter.projection import project_intervals + + record = {modality: values} + if modality == "text": + record["attention_mask"] = attention_mask + else: + record[f"{modality}_length"] = reported_length + item = _relative_modality(record, modality) + view = project_intervals(item.values, item.intervals, relative_targets(50), + item.observed, item.quality) + view.source_span = len(item.values) + view.reported_length = item.reported_length + view.length_conflict = item.length_conflict + view.tail_ambiguous = item.tail_ambiguous + return view + + +adapt_split = adapt_official_split diff --git a/math/Q2/README.md b/math/Q2/README.md index e4577e5..b852ca6 100644 --- a/math/Q2/README.md +++ b/math/Q2/README.md @@ -1,6 +1,10 @@ # Q2：连续局部缺失下的概率补全与可靠度门控 -实现和输入审计范围只覆盖 `math/Q2`。Q2 遵循附件二 `aligned_50.pkl` 的官方 train/valid/test 划分，按来源 `video_id` 核验组间隔离；Q1 的五折探针不替代 Q2 的官方测试。附件三只输出无标签预测与审计，不计算准确率或 F1。 +Q2 默认遵循附件二 `aligned_50.pkl` 的官方 train/valid/test 划分，按来源 `video_id` 核验组间隔离；Q1 的五折探针不替代 Q2 的官方测试。附件三只输出无标签预测与审计，不计算准确率或 F1。 + +未对齐版可运行 `uv run --project math/Q1 python math/Q2/train_unaligned_c5.py`，数据入口现调用 Q1 的统一对齐适配器并选用 Relative 模式；既有结果写入 `results_unaligned/`。统一接口与审计见 [`../Q1/UNIFIED_ADAPTER.md`](../Q1/UNIFIED_ADAPTER.md)，适配假设与附件三限制见 [`../Q1/UNALIGNED_ADAPTER.md`](../Q1/UNALIGNED_ADAPTER.md)。该视图不代表物理时间对齐。 + +未对齐版测试集 727 条样本的 Accuracy 为 0.6726、Macro-F1 为 0.5668、MAE 为 0.7059；`results_unaligned/run_manifest.json` 记录来源审计、划分、参数和明确的物理时间限制。该训练只固定 C5 并重新拟合参数，没有对未对齐版重做 C0–C7 结构选择或附件三推理。 完整的 V2 算法核查、Accuracy 口径和正式运行指标见 [`V2_REVIEW.md`](V2_REVIEW.md)。 diff --git a/math/Q2/data.py b/math/Q2/data.py index 9f6302f..5668d0c 100644 --- a/math/Q2/data.py +++ b/math/Q2/data.py @@ -2,6 +2,7 @@ from __future__ import annotations import pickle +import sys from dataclasses import dataclass from pathlib import Path from typing import Any @@ -75,13 +76,18 @@ class SplitData: regression_y: np.ndarray | None ids: list[str] groups: np.ndarray + alignment_audit: dict[str, Any] | None = None @property def n(self) -> int: return len(self.ids) -def _extract_split(name: str, obj: dict[str, Any], with_labels: bool) -> SplitData: +def _extract_split( + name: str, obj: dict[str, Any], with_labels: bool, + mask_override: np.ndarray | None = None, + alignment_audit: dict[str, Any] | None = None, +) -> SplitData: raw: dict[str, np.ndarray] = {} masks = [] for modality in MODALITIES: @@ -96,6 +102,11 @@ def _extract_split(name: str, obj: dict[str, Any], with_labels: bool) -> SplitDa raw[modality] = arr masks.append(observed) mask = np.stack(masks, axis=-1) + if mask_override is not None: + override = np.asarray(mask_override, bool) + if override.shape != mask.shape: + raise ValueError(f"{name}: projected mask shape {override.shape} differs from {mask.shape}") + mask = override ids = [_decode(v) for v in _one_dim(obj["id"])] if len(ids) != len(mask): raise ValueError(f"{name}: id count differs from feature count") @@ -121,7 +132,7 @@ def _extract_split(name: str, obj: dict[str, Any], with_labels: bool) -> SplitDa raise ValueError(f"{name}: polarity/regression label mismatch (sample, class, score): {examples}") else: class_y = regression_y = None - return SplitData(name, raw, mask, class_y, regression_y, ids, groups) + return SplitData(name, raw, mask, class_y, regression_y, ids, groups, alignment_audit) def sample_group(sample_id: str) -> str: @@ -129,12 +140,27 @@ def sample_group(sample_id: str) -> str: return sample_id.split("$_$", 1)[0] -def load_official_splits(path: Path = ALIGNED_PATH) -> dict[str, SplitData]: +def load_official_splits(path: Path = ALIGNED_PATH, *, version: str = "aligned_50") -> dict[str, SplitData]: + if version not in {"aligned_50", "unaligned_50"}: + raise ValueError(f"unsupported feature version: {version}") obj = restricted_load(path) required = {"train", "valid", "test"} if not isinstance(obj, dict) or not required.issubset(obj): - raise ValueError("aligned_50.pkl must contain train, valid, and test dictionaries") - splits = {name: _extract_split(name, obj[name], with_labels=True) for name in ("train", "valid", "test")} + raise ValueError(f"{path.name} must contain train, valid, and test dictionaries") + if version == "unaligned_50": + repo_dir = Path(__file__).resolve().parents[2] + if str(repo_dir) not in sys.path: + sys.path.insert(0, str(repo_dir)) + from final.adapter import adapt_official_split + + splits = {} + for name in ("train", "valid", "test"): + projected, mask, audit = adapt_official_split(obj[name]) + fields = {**obj[name], **projected} + splits[name] = _extract_split(name, fields, with_labels=True, + mask_override=mask, alignment_audit=audit) + else: + splits = {name: _extract_split(name, obj[name], with_labels=True) for name in ("train", "valid", "test")} del obj return splits diff --git a/math/Q2/results_unaligned/crg_student.pt b/math/Q2/results_unaligned/crg_student.pt new file mode 100644 index 0000000..72a9855 Binary files /dev/null and b/math/Q2/results_unaligned/crg_student.pt differ diff --git a/math/Q2/results_unaligned/group_bootstrap_ci.csv b/math/Q2/results_unaligned/group_bootstrap_ci.csv new file mode 100644 index 0000000..0a0b48c --- /dev/null +++ b/math/Q2/results_unaligned/group_bootstrap_ci.csv @@ -0,0 +1,8 @@ +metric,estimate,ci_2_5,ci_97_5,replicates,unit +accuracy,0.6706989247311828,0.6327518696204317,0.7124334301621883,300,source video group +macro_f1,0.5656199911065596,0.5289481143025225,0.6050197933047489,300,source video group +mae,0.7063558995723724,0.6557580590248108,0.7571986928582192,300,source video group +rmse,0.9665054949271439,0.8898716131061315,1.053541624729561,300,source video group +pearson,0.6342588663101196,0.5616166487336158,0.7045083582401275,300,source video group +interval_90_coverage,0.8998624115536549,0.8764493610302838,0.9237205026049647,300,source video group +interval_90_mean_width,2.5404592752456665,2.466722363233566,2.6283570647239687,300,source video group diff --git a/math/Q2/results_unaligned/preprocessor.npz b/math/Q2/results_unaligned/preprocessor.npz new file mode 100644 index 0000000..7fd83ae Binary files /dev/null and b/math/Q2/results_unaligned/preprocessor.npz differ diff --git a/math/Q2/results_unaligned/reliability_hparam_tuning.csv b/math/Q2/results_unaligned/reliability_hparam_tuning.csv new file mode 100644 index 0000000..0ecb433 --- /dev/null +++ b/math/Q2/results_unaligned/reliability_hparam_tuning.csv @@ -0,0 +1,6 @@ +model,rho_imp,lambda_u,lambda_gap,lambda_span,inner_selection_nll,scenario_selection_nll,selected,selection_split +C5,0.5,0.0,0.0,0.0,2.7833818197250366,"{""0.0/natural"": 2.7777328491210938, ""0.3/single"": 2.7690372467041016, ""0.3/sync"": 2.788604974746704, ""0.5/async"": 2.798152208328247}",True,reliability_validation +C5,0.5,0.05,0.05,0.05,2.7834232449531555,"{""0.0/natural"": 2.777679681777954, ""0.3/single"": 2.7690346240997314, ""0.3/sync"": 2.788922071456909, ""0.5/async"": 2.7980566024780273}",False,reliability_validation +C5,0.5,0.1,0.0,0.0,2.7834290266036987,"{""0.0/natural"": 2.7776803970336914, ""0.3/single"": 2.7690365314483643, ""0.3/sync"": 2.788992166519165, ""0.5/async"": 2.798007011413574}",False,reliability_validation +C5,0.3,0.05,0.05,0.05,2.784935712814331,"{""0.0/natural"": 2.7773241996765137, ""0.3/single"": 2.76987886428833, ""0.3/sync"": 2.793078899383545, ""0.5/async"": 2.7994608879089355}",False,reliability_validation +C5,0.7,0.05,0.05,0.05,2.7837207913398743,"{""0.0/natural"": 2.7780520915985107, ""0.3/single"": 2.7693912982940674, ""0.3/sync"": 2.7871553897857666, ""0.5/async"": 2.8002843856811523}",False,reliability_validation diff --git a/math/Q2/results_unaligned/run_manifest.json b/math/Q2/results_unaligned/run_manifest.json new file mode 100644 index 0000000..529f021 --- /dev/null +++ b/math/Q2/results_unaligned/run_manifest.json @@ -0,0 +1,151 @@ +{ + "scope": "exploratory unaligned_50 relative-index projection and prespecified C5 training", + "physical_time_alignment": false, + "input": "E题数据/附件2-数据集特征文件/unaligned_50.pkl", + "input_sha256": "77eda14a06be9749a96c52ae45470c7cffcfa7219011eae391d231a0664c3762", + "text_encoder": "official precomputed text field; revision not supplied", + "q1_adapter_audit": { + "train": { + "method": "index_cell_overlap_on_independent_normalized_progress_axes", + "physical_time_alignment": false, + "samples": 3395, + "vision_length_conflict_samples": 618, + "vision_tail_ambiguous_samples": 618, + "nonzero_text_rows_outside_attention": 86078, + "observed_target_rows": { + "text": 169750, + "audio": 169750, + "vision": 163302 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.9540337701314328 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + }, + "valid": { + "method": "index_cell_overlap_on_independent_normalized_progress_axes", + "physical_time_alignment": false, + "samples": 728, + "vision_length_conflict_samples": 141, + "vision_tail_ambiguous_samples": 141, + "nonzero_text_rows_outside_attention": 17772, + "observed_target_rows": { + "text": 36400, + "audio": 36400, + "vision": 35315 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.96090314748523 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + }, + "test": { + "method": "index_cell_overlap_on_independent_normalized_progress_axes", + "physical_time_alignment": false, + "samples": 727, + "vision_length_conflict_samples": 131, + "vision_tail_ambiguous_samples": 131, + "nonzero_text_rows_outside_attention": 18041, + "observed_target_rows": { + "text": 36350, + "audio": 36350, + "vision": 35034 + }, + "mean_target_coverage": { + "text": 1.0, + "audio": 1.0, + "vision": 0.9549938172651288 + }, + "quality_fields_available": false, + "word_or_frame_timestamps_available": false + } + }, + "official_group_overlap": { + "train_valid": 0, + "train_test": 0, + "valid_test": 0 + }, + "internal_splits": { + "fit": 3030, + "reliability_validation": 190, + "temperature_calibration": 175 + }, + "model": "C5 fixed before this run; no unaligned architecture selection", + "quality": "no quality scores in official file; q*=1 for visible rows and J_Q=0", + "seed": 20260924, + "epochs_limit": 12, + "imputer_epochs": 8, + "batch_size": 64, + "patience": 3, + "selected_reliability": [ + 0.5, + 0.0, + 0.0, + 0.0 + ], + "temperature": 1.0309581618007613, + "bootstrap_repeats": 300, + "attachment3": "not inferred: unaligned files lack numerical text and trusted lengths", + "validation_metrics": { + "n": 728, + "accuracy": 0.6098901098901099, + "macro_f1": 0.5253076643650264, + "negative_support": 206, + "neutral_support": 184, + "positive_support": 338, + "negative_recall": 0.6796116504854369, + "middle_recall": 0.14130434782608695, + "positive_recall": 0.8224852071005917, + "regression_mae": 0.6701027750968933, + "regression_rmse": 0.9000873178933578, + "pearson": 0.6158545613288879, + "brier": 0.48911186855362665, + "classification_nll": 0.8322470784187317, + "ece_15": 0.05621008894273212, + "selection_nll": 2.7708771228790283, + "interval_90_coverage": 0.9010989010989011, + "interval_90_mean_width": 2.390233039855957, + "predictive_variance_mean_uncalibrated": 0.5822091698646545, + "within_trajectory_variance_mean": 0.5822086334228516, + "between_trajectory_variance_mean": 5.325794063537614e-07, + "predictive_mean_mean_calibrated": 0.20464463531970978, + "predictive_variance_mean_calibrated": 0.5899268984794617 + }, + "test_metrics": { + "n": 727, + "accuracy": 0.672627235213205, + "macro_f1": 0.5667644872783372, + "negative_support": 207, + "neutral_support": 158, + "positive_support": 362, + "negative_recall": 0.714975845410628, + "middle_recall": 0.1518987341772152, + "positive_recall": 0.8756906077348067, + "regression_mae": 0.7059400677680969, + "regression_rmse": 0.965240149980511, + "pearson": 0.6385040879249573, + "brier": 0.4349081559316636, + "classification_nll": 0.7490932941436768, + "ece_15": 0.05505641569297612, + "selection_nll": 2.8536453247070312, + "interval_90_coverage": 0.9009628610729024, + "interval_90_mean_width": 2.5410006046295166, + "predictive_variance_mean_uncalibrated": 0.6693301200866699, + "within_trajectory_variance_mean": 0.6693244576454163, + "between_trajectory_variance_mean": 5.653140760841779e-06, + "predictive_mean_mean_calibrated": 0.1944320648908615, + "predictive_variance_mean_calibrated": 0.6787037253379822 + }, + "completed_utc": "2026-09-25T04:43:52Z", + "device": "cuda", + "device_name": "NVIDIA GeForce RTX 5070 Ti", + "torch_version": "2.14.0+cu130", + "trained_c5_epochs": 11, + "selected_c5_epoch": 8 +} \ No newline at end of file diff --git a/math/Q2/results_unaligned/structured_imputer.pt b/math/Q2/results_unaligned/structured_imputer.pt new file mode 100644 index 0000000..3f0630e Binary files /dev/null and b/math/Q2/results_unaligned/structured_imputer.pt differ diff --git a/math/Q2/results_unaligned/test_metrics.json b/math/Q2/results_unaligned/test_metrics.json new file mode 100644 index 0000000..71b054c --- /dev/null +++ b/math/Q2/results_unaligned/test_metrics.json @@ -0,0 +1,27 @@ +{ + "n": 727, + "accuracy": 0.672627235213205, + "macro_f1": 0.5667644872783372, + "negative_support": 207, + "neutral_support": 158, + "positive_support": 362, + "negative_recall": 0.714975845410628, + "middle_recall": 0.1518987341772152, + "positive_recall": 0.8756906077348067, + "regression_mae": 0.7059400677680969, + "regression_rmse": 0.965240149980511, + "pearson": 0.6385040879249573, + "brier": 0.4349081559316636, + "classification_nll": 0.7490932941436768, + "ece_15": 0.05505641569297612, + "selection_nll": 2.8536453247070312, + "interval_90_coverage": 0.9009628610729024, + "interval_90_mean_width": 2.5410006046295166, + "predictive_variance_mean_uncalibrated": 0.6693301200866699, + "within_trajectory_variance_mean": 0.6693244576454163, + "between_trajectory_variance_mean": 5.653140760841779e-06, + "predictive_mean_mean_calibrated": 0.1944320648908615, + "predictive_variance_mean_calibrated": 0.6787037253379822, + "model": "C5", + "temperature": 1.0309581618007613 +} \ No newline at end of file diff --git a/math/Q2/results_unaligned/test_predictions.csv b/math/Q2/results_unaligned/test_predictions.csv new file mode 100644 index 0000000..f25a5ed --- /dev/null +++ b/math/Q2/results_unaligned/test_predictions.csv @@ -0,0 +1,728 @@ +sample_id,source_video_id,true_class,predicted_class,true_sentiment,predicted_sentiment,p_negative,p_neutral,p_positive +-AUZQgSxyPQ$_$2,-AUZQgSxyPQ,2,2,0.6666666865348816,1.8389134407043457,0.023152580484747887,0.09196113049983978,0.8848863244056702 +-HeZS2-Prhc$_$2,-HeZS2-Prhc,2,2,0.6666666865348816,0.7653122544288635,0.20526918768882751,0.2860337197780609,0.5086970925331116 +-MeTTeMJBNc$_$0,-MeTTeMJBNc,2,2,0.3333333432674408,0.43487221002578735,0.08002765476703644,0.3096073567867279,0.6103649735450745 +-RfYyzHpjk4$_$2,-RfYyzHpjk4,2,2,0.3333333432674408,0.5818819403648376,0.26933926343917847,0.3298976421356201,0.4007630944252014 +-s9qJ7ATP7w$_$2,-s9qJ7ATP7w,2,2,1.3333333730697632,1.5092394351959229,0.060254182666540146,0.11908532679080963,0.8206605315208435 +-s9qJ7ATP7w$_$5,-s9qJ7ATP7w,0,0,-2.0,-1.8740020990371704,0.8741908669471741,0.06829410046339035,0.057515013962984085 +-s9qJ7ATP7w$_$6,-s9qJ7ATP7w,1,0,0.0,-0.9185248613357544,0.6529625058174133,0.2060442864894867,0.14099328219890594 +-yRb-Jum7EQ$_$1,-yRb-Jum7EQ,1,0,0.0,-1.1654452085494995,0.5773409605026245,0.21683990955352783,0.20581920444965363 +0K7dCp80n9c$_$6,0K7dCp80n9c,1,0,0.0,-0.9367721080780029,0.4664914011955261,0.3052015006542206,0.2283070981502533 +0YiAKqU36KE$_$1,0YiAKqU36KE,2,2,1.3333333730697632,1.2811936140060425,0.3438929617404938,0.21435637772083282,0.4417506456375122 +0YiAKqU36KE$_$0,0YiAKqU36KE,0,0,-1.0,-1.644303798675537,0.845250129699707,0.09250973910093307,0.062240149825811386 +0bxhZ-LIfZY$_$4,0bxhZ-LIfZY,0,2,-0.3333333432674408,1.4558629989624023,0.3955423831939697,0.20645685493946075,0.3980008065700531 +100178$_$15,100178,1,1,0.0,0.0,0.32299402356147766,0.4422471821308136,0.2347588688135147 +10219$_$10,10219,2,0,2.0,-1.6704723834991455,0.4977926015853882,0.1819811910390854,0.32022619247436523 +10219$_$1,10219,1,2,0.0,0.607961118221283,0.13284054398536682,0.37503761053085327,0.4921218752861023 +10219$_$5,10219,2,2,1.6666666269302368,1.334949016571045,0.04409831389784813,0.1350553035736084,0.820846438407898 +102213$_$2,102213,2,2,1.3333333730697632,1.1035394668579102,0.06674807518720627,0.15744280815124512,0.775809109210968 +102213$_$5,102213,0,0,-1.0,-0.8098207116127014,0.6186985969543457,0.2403344064950943,0.1409669667482376 +102213$_$7,102213,0,0,-1.0,-0.9895644783973694,0.6407475471496582,0.20314298570156097,0.1561094969511032 +102389$_$5,102389,0,0,-1.6666666269302368,-1.0938441753387451,0.6202062964439392,0.2275448441505432,0.15224884450435638 +104741$_$0,104741,1,2,0.0,0.7240027785301208,0.12274059653282166,0.33988577127456665,0.5373736619949341 +104741$_$5,104741,1,0,0.0,-1.163879156112671,0.5265425443649292,0.2727195918560028,0.2007378190755844 +107585$_$12,107585,0,0,-2.0,-2.1510531902313232,0.8952720761299133,0.05477437004446983,0.0499536395072937 +107585$_$18,107585,0,0,-2.0,-1.7044445276260376,0.8248249292373657,0.09555228054523468,0.079622782766819 +107585$_$1,107585,0,2,-0.6666666865348816,1.342024326324463,0.17684625089168549,0.20229750871658325,0.6208562254905701 +108146$_$3,108146,0,0,-1.6666666269302368,-1.3152862787246704,0.5963847041130066,0.18811191618442535,0.21550333499908447 +110565$_$1,110565,0,2,-1.0,1.4563014507293701,0.07661201059818268,0.1444828361272812,0.7789052128791809 +110565$_$5,110565,2,2,1.0,1.6559340953826904,0.05818694457411766,0.11545039713382721,0.826362669467926 +111881$_$11,111881,0,0,-1.0,-1.3861706256866455,0.7703689932823181,0.15218780934810638,0.0774431973695755 +111881$_$10,111881,0,2,-2.0,1.9133682250976562,0.4227520227432251,0.1475432962179184,0.4297047257423401 +111881$_$15,111881,0,0,-2.0,-1.988391399383545,0.7870694994926453,0.10083384811878204,0.11209660023450851 +111881$_$4,111881,0,0,-1.3333333730697632,-1.4106667041778564,0.6773291826248169,0.15291699767112732,0.16975387930870056 +111881$_$7,111881,0,0,-2.0,-1.623441457748413,0.786916196346283,0.11656583100557327,0.09651792794466019 +111881$_$6,111881,2,2,0.3333333432674408,0.7486322522163391,0.061715252697467804,0.24026590585708618,0.6980188488960266 +112425$_$10,112425,2,2,2.3333332538604736,0.7939890623092651,0.20825016498565674,0.2833744287490845,0.5083754062652588 +112425$_$1,112425,2,2,0.6666666865348816,0.5841426849365234,0.07852470874786377,0.3621896803379059,0.5592856407165527 +112425$_$7,112425,2,2,1.3333333730697632,2.026193857192993,0.23412278294563293,0.17191864550113678,0.5939585566520691 +112509$_$4,112509,0,0,-2.0,-1.5580331087112427,0.8461998701095581,0.09725358337163925,0.056546565145254135 +116213$_$3,116213,0,1,-0.6666666865348816,0.0,0.20567139983177185,0.47442707419395447,0.31990155577659607 +121400$_$0,121400,1,2,0.0,0.7115573287010193,0.06420111656188965,0.25076743960380554,0.6850314736366272 +126872$_$11,126872,0,0,-2.0,-2.21809458732605,0.8132951855659485,0.09317251294851303,0.09353229403495789 +130366$_$11,130366,0,0,-1.3333333730697632,-1.5649629831314087,0.7175371050834656,0.139789879322052,0.14267303049564362 +130366$_$2,130366,2,2,0.3333333432674408,0.9402702450752258,0.33381325006484985,0.32223063707351685,0.3439561426639557 +130366$_$4,130366,0,0,-0.3333333432674408,-1.24518620967865,0.5529822707176208,0.21885675191879272,0.22816097736358643 +132028$_$7,132028,2,2,0.3333333432674408,1.5335975885391235,0.18580104410648346,0.1904580444097519,0.6237409114837646 +135623$_$5,135623,0,0,-2.3333332538604736,-1.632473111152649,0.856016993522644,0.08354566246271133,0.060437340289354324 +135623$_$8,135623,0,0,-1.3333333730697632,-1.5957245826721191,0.4655552804470062,0.15291520953178406,0.3815295398235321 +136196$_$0,136196,2,2,0.3333333432674408,0.5637678503990173,0.06823375076055527,0.3083322048187256,0.6234340667724609 +136196$_$9,136196,0,0,-2.0,-1.4834747314453125,0.7673999071121216,0.12229294329881668,0.11030711233615875 +136196$_$8,136196,0,0,-1.6666666269302368,-1.6112470626831055,0.877273440361023,0.0763978660106659,0.04632866382598877 +167521$_$17,167521,2,2,1.3333333730697632,1.1973127126693726,0.21726326644420624,0.21697455644607544,0.5657621622085571 +167521$_$16,167521,2,2,2.0,1.9766826629638672,0.10176581889390945,0.14429491758346558,0.7539392113685608 +167521$_$5,167521,2,0,0.3333333432674408,-1.0814822912216187,0.4876299202442169,0.22112588584423065,0.29124417901039124 +190743$_$13,190743,0,0,-2.0,-1.278342604637146,0.5616133809089661,0.16440320014953613,0.273983359336853 +190743$_$17,190743,0,0,-2.0,-1.1513521671295166,0.6728804111480713,0.2121816724538803,0.114937923848629 +190743$_$5,190743,0,0,-2.0,-2.3393607139587402,0.9024528861045837,0.05018160119652748,0.04736560583114624 +194299$_$14,194299,1,2,0.0,0.5516232848167419,0.06236313655972481,0.2625679671764374,0.6750689148902893 +194299$_$4,194299,0,0,-1.0,-1.661145567893982,0.6589730978012085,0.13393566012382507,0.20709124207496643 +194299$_$9,194299,1,2,0.0,0.6101083755493164,0.15685686469078064,0.41780686378479004,0.42533618211746216 +198112$_$12,198112,2,2,2.0,1.8162155151367188,0.33692875504493713,0.17026455700397491,0.49280664324760437 +198112$_$0,198112,2,2,0.3333333432674408,0.4553529620170593,0.06876716762781143,0.33177366852760315,0.5994591116905212 +198112$_$5,198112,2,0,1.0,-1.1501878499984741,0.4474492371082306,0.24103473126888275,0.31151601672172546 +198112$_$9,198112,2,0,2.3333332538604736,-1.7973456382751465,0.7765271663665771,0.1056537926197052,0.11781899631023407 +198112$_$8,198112,2,0,2.0,-1.4230166673660278,0.5876736640930176,0.16910170018672943,0.2432245910167694 +1HS2HcN2LDo$_$1,1HS2HcN2LDo,2,2,0.3333333432674408,0.9812485575675964,0.11374318599700928,0.23000021278858185,0.6562566161155701 +1HS2HcN2LDo$_$9,1HS2HcN2LDo,2,2,1.3333333730697632,0.6917677521705627,0.04238446056842804,0.20742572844028473,0.7501897811889648 +1S6ji_d4OLI$_$9,1S6ji_d4OLI,2,2,0.6666666865348816,0.5096364617347717,0.053751423954963684,0.24368707835674286,0.7025614976882935 +1jogeKX0wGw$_$4,1jogeKX0wGw,2,2,1.0,1.2680675983428955,0.01871374621987343,0.0935843288898468,0.8877018690109253 +1pl2FVdQWj0$_$2,1pl2FVdQWj0,2,2,0.6666666865348816,0.6152808666229248,0.08305733650922775,0.23438814282417297,0.6825546026229858 +200941$_$10,200941,0,0,-1.0,-1.7992277145385742,0.8763458728790283,0.06791045516729355,0.055743735283613205 +202826$_$10,202826,0,0,-1.6666666269302368,-1.6908907890319824,0.5490683317184448,0.16924075782299042,0.28169092535972595 +202826$_$7,202826,2,2,1.0,1.1055302619934082,0.1302357017993927,0.2498369961977005,0.6199272871017456 +206585$_$3,206585,1,2,0.0,0.8368357419967651,0.09969540685415268,0.2641207277774811,0.6361838579177856 +208322$_$12,208322,0,2,-2.3333332538604736,1.546515703201294,0.20246285200119019,0.19592344760894775,0.6016137003898621 +208592$_$2,208592,1,2,0.0,0.5591892600059509,0.107045017182827,0.440153032541275,0.452801913022995 +208592$_$9,208592,0,0,-2.6666667461395264,-1.3868995904922485,0.6293343901634216,0.20248667895793915,0.16817902028560638 +209758$_$15,209758,2,2,1.3333333730697632,0.6318305730819702,0.07697975635528564,0.23235778510570526,0.6906624436378479 +209758$_$3,209758,1,2,0.0,0.8447558879852295,0.1776801347732544,0.30560794472694397,0.516711950302124 +209758$_$5,209758,1,2,0.0,0.6004053354263306,0.0787869542837143,0.30962151288986206,0.6115915179252625 +20LfN8ENbhM$_$1,20LfN8ENbhM,2,2,2.0,1.254626989364624,0.018313618376851082,0.08413424342870712,0.8975520730018616 +210259$_$11,210259,0,0,-3.0,-1.917885184288025,0.8756105899810791,0.0706482082605362,0.05374118313193321 +210259$_$16,210259,0,0,-2.3333332538604736,-2.254978656768799,0.9048407673835754,0.05179303139448166,0.043366268277168274 +210259$_$0,210259,1,2,0.0,1.077287197113037,0.07869300246238708,0.23227865993976593,0.689028263092041 +210259$_$8,210259,0,0,-2.3333332538604736,-1.7350775003433228,0.8940168023109436,0.06472598016262054,0.04125719889998436 +210433$_$0,210433,1,2,0.0,0.5123754143714905,0.0819915160536766,0.32564878463745117,0.5923596620559692 +210433$_$7,210433,0,0,-2.0,-1.9561799764633179,0.9007482528686523,0.05628283694386482,0.04296895116567612 +211875$_$4,211875,0,2,-1.6666666269302368,0.952744722366333,0.06039826571941376,0.23207926750183105,0.7075225114822388 +211875$_$8,211875,0,0,-1.3333333730697632,-1.6786885261535645,0.7321868538856506,0.13646814227104187,0.1313449591398239 +213327$_$6,213327,0,0,-2.3333332538604736,-1.6013798713684082,0.6365118622779846,0.17759035527706146,0.1858978569507599 +213327$_$8,213327,0,0,-2.0,-1.0284218788146973,0.5133077502250671,0.2458762377500534,0.24081598222255707 +215318$_$12,215318,1,0,0.0,-0.8163017630577087,0.437311053276062,0.30869030952453613,0.25399860739707947 +221137$_$2,221137,1,2,0.0,0.6376898884773254,0.1668333262205124,0.35440218448638916,0.47876450419425964 +221137$_$5,221137,0,0,-2.0,-2.035921096801758,0.87847501039505,0.06552663445472717,0.05599828436970711 +221153$_$14,221153,0,0,-1.6666666269302368,-2.1476638317108154,0.8749210238456726,0.06766971200704575,0.05740925297141075 +221153$_$16,221153,0,0,-0.6666666865348816,-2.122957229614258,0.7265211343765259,0.11919481307268143,0.1542840600013733 +221153$_$6,221153,1,1,0.0,0.0,0.2837745249271393,0.4065883755683899,0.3096370995044708 +221153$_$20,221153,2,2,0.3333333432674408,1.0587289333343506,0.25431758165359497,0.3468608558177948,0.398821622133255 +22373$_$10,22373,0,0,-2.6666667461395264,-2.0052707195281982,0.8815510272979736,0.06573548913002014,0.05271345004439354 +22373$_$1,22373,1,1,0.0,0.0,0.15458625555038452,0.4257535934448242,0.41966012120246887 +22373$_$2,22373,0,0,-2.3333332538604736,-1.412038803100586,0.6963496804237366,0.16064292192459106,0.14300736784934998 +22373$_$7,22373,1,0,0.0,-1.3633979558944702,0.7217056155204773,0.15850993990898132,0.11978445947170258 +224263$_$13,224263,2,2,0.6666666865348816,1.9535964727401733,0.07786374539136887,0.14845646917819977,0.773679792881012 +224263$_$14,224263,0,0,-2.3333332538604736,-1.487792730331421,0.6930174231529236,0.14293788373470306,0.16404467821121216 +224263$_$3,224263,0,0,-2.3333332538604736,-1.9307106733322144,0.817730188369751,0.08750559389591217,0.09476421028375626 +224370$_$3,224370,0,0,-2.0,-1.9079294204711914,0.8820874094963074,0.06940500438213348,0.04850759729743004 +224370$_$5,224370,0,2,-2.6666667461395264,0.9846608638763428,0.2482149451971054,0.27132946252822876,0.480455607175827 +224370$_$11,224370,2,0,0.3333333432674408,-0.8618441224098206,0.5753273963928223,0.25377219915390015,0.17090043425559998 +224370$_$15,224370,0,0,-1.6666666269302368,-1.7554082870483398,0.8363544344902039,0.09689809381961823,0.06674747169017792 +224631$_$5,224631,2,2,0.3333333432674408,0.8678411245346069,0.08569730073213577,0.2914542555809021,0.6228484511375427 +226601$_$11,226601,0,0,-1.3333333730697632,-1.7545708417892456,0.8482348918914795,0.09521391242742538,0.05655116215348244 +226601$_$18,226601,0,0,-2.0,-1.588394045829773,0.7974098920822144,0.11488910764455795,0.08770092576742172 +226601$_$0,226601,2,2,0.3333333432674408,0.5845823884010315,0.11997880041599274,0.4131796658039093,0.46684151887893677 +226640$_$9,226640,1,2,0.0,2.119675397872925,0.22722260653972626,0.17081518471240997,0.6019622087478638 +22689$_$1,22689,0,0,-2.0,-1.361747145652771,0.6677641272544861,0.1538400650024414,0.1783958077430725 +22689$_$0,22689,1,2,0.0,0.7458004355430603,0.23972949385643005,0.36621537804603577,0.3940550982952118 +234046$_$1,234046,1,1,0.0,0.0,0.20158256590366364,0.4473801553249359,0.35103726387023926 +236442$_$3,236442,0,0,-1.3333333730697632,-0.7918327450752258,0.4214765131473541,0.3396310806274414,0.23889245092868805 +23656$_$1,23656,1,2,0.0,1.0028883218765259,0.09068023413419724,0.291363000869751,0.6179566979408264 +23656$_$0,23656,2,2,0.3333333432674408,0.48998159170150757,0.07823757082223892,0.3698771595954895,0.5518852472305298 +23656$_$2,23656,2,2,1.0,1.2136143445968628,0.03664268180727959,0.13311822712421417,0.8302391171455383 +23656$_$7,23656,0,0,-2.0,-1.2782238721847534,0.5772135853767395,0.19864054024219513,0.22414591908454895 +23656$_$9,23656,0,0,-1.3333333730697632,-1.5174634456634521,0.7701005935668945,0.12639035284519196,0.1035090908408165 +23656$_$15,23656,2,2,0.3333333432674408,1.2143274545669556,0.02228974737226963,0.10350345820188522,0.8742068409919739 +237363$_$10,237363,1,1,0.0,0.0,0.2882283926010132,0.4519195854663849,0.25985202193260193 +237363$_$4,237363,1,1,0.0,0.0,0.11672188341617584,0.4752022325992584,0.40807586908340454 +238063$_$15,238063,0,0,-1.3333333730697632,-1.7296631336212158,0.8644436001777649,0.07888584583997726,0.05667058005928993 +238063$_$17,238063,2,0,1.0,-1.3126747608184814,0.6927529573440552,0.17311275005340576,0.13413430750370026 +238063$_$16,238063,0,2,-1.0,1.7995554208755493,0.30403444170951843,0.17944839596748352,0.5165172219276428 +238063$_$7,238063,0,0,-0.3333333432674408,-0.8834465742111206,0.48057815432548523,0.27194201946258545,0.24747981131076813 +24196$_$0,24196,2,1,0.3333333432674408,0.0,0.1497717946767807,0.4277153015136719,0.4225129187107086 +24196$_$3,24196,0,0,-0.3333333432674408,-1.7967944145202637,0.8955637812614441,0.06728997826576233,0.03714624419808388 +243981$_$10,243981,0,0,-2.3333332538604736,-1.5717064142227173,0.8292200565338135,0.096967913210392,0.07381200045347214 +243981$_$1,243981,1,2,0.0,0.8305478692054749,0.09250758588314056,0.23240506649017334,0.6750873923301697 +243981$_$4,243981,0,0,-2.3333332538604736,-1.0828555822372437,0.6158537864685059,0.20509976148605347,0.17904643714427948 +243981$_$6,243981,0,0,-2.0,-1.6825424432754517,0.8514675498008728,0.08905091881752014,0.05948156490921974 +24504$_$10,24504,2,2,0.6666666865348816,0.5933411121368408,0.11132702231407166,0.30928027629852295,0.5793927311897278 +245582$_$11,245582,0,0,-1.6666666269302368,-1.8879094123840332,0.9036683440208435,0.05858322232961655,0.037748340517282486 +245582$_$13,245582,2,1,0.3333333432674408,0.0,0.2920357584953308,0.4486437439918518,0.259320467710495 +245582$_$16,245582,2,0,1.0,-1.187946081161499,0.8020975589752197,0.12946221232414246,0.06844022125005722 +245582$_$4,245582,0,0,-1.3333333730697632,-1.6803028583526611,0.8372870087623596,0.0810774639248848,0.0816354751586914 +252097$_$12,252097,0,0,-2.3333332538604736,-1.760923981666565,0.8484477400779724,0.07945629954338074,0.07209593802690506 +252097$_$4,252097,0,0,-1.3333333730697632,-2.1340763568878174,0.8958213329315186,0.055793989449739456,0.048384733498096466 +252912$_$13,252912,0,0,-1.6666666269302368,-1.359207272529602,0.7245199680328369,0.14793801307678223,0.12754206359386444 +257277$_$1,257277,2,2,0.3333333432674408,1.4096407890319824,0.17245905101299286,0.2414093017578125,0.5861316919326782 +257277$_$3,257277,2,1,1.0,0.0,0.2837468683719635,0.4101583659648895,0.30609482526779175 +257277$_$5,257277,0,0,-2.0,-1.0056263208389282,0.5482068657875061,0.2945024371147156,0.15729062259197235 +257534$_$4,257534,1,2,0.0,0.9916637539863586,0.30538681149482727,0.3457391858100891,0.3488740026950836 +267466$_$24,267466,2,0,0.6666666865348816,-0.8156838417053223,0.4017923176288605,0.2742464542388916,0.32396119832992554 +267466$_$44,267466,0,0,-2.6666667461395264,-2.061800241470337,0.8418002724647522,0.08188232779502869,0.07631736993789673 +267466$_$42,267466,0,0,-2.3333332538604736,-1.6294803619384766,0.8114707469940186,0.09378407150506973,0.0947451964020729 +267466$_$9,267466,0,1,-1.0,0.0,0.32190656661987305,0.42048296332359314,0.25761041045188904 +267466$_$13,267466,1,1,0.0,0.0,0.2954825758934021,0.4182226061820984,0.2862947881221771 +267466$_$38,267466,1,0,0.0,-1.771488070487976,0.8705241084098816,0.07703587412834167,0.05243997648358345 +267466$_$35,267466,0,0,-1.0,-1.6511726379394531,0.8697383403778076,0.07760906964540482,0.05265256389975548 +267466$_$34,267466,0,0,-2.3333332538604736,-1.6944921016693115,0.856903076171875,0.07929885387420654,0.06379808485507965 +267694$_$14,267694,0,0,-2.3333332538604736,-1.8139674663543701,0.8570246696472168,0.08483341336250305,0.05814197286963463 +267694$_$16,267694,0,0,-1.0,-0.9763728380203247,0.4722665548324585,0.30710217356681824,0.22063128650188446 +267694$_$3,267694,0,0,-0.3333333432674408,-1.77812922000885,0.8401455283164978,0.09667927026748657,0.06317517906427383 +267694$_$5,267694,1,2,0.0,0.9333768486976624,0.2685371935367584,0.31232911348342896,0.4191336929798126 +267694$_$4,267694,1,0,0.0,-0.9705132842063904,0.4961226284503937,0.29502609372138977,0.20885135233402252 +267694$_$7,267694,0,0,-2.0,-1.1844103336334229,0.6720255613327026,0.176593616604805,0.15138079226016998 +267694$_$6,267694,0,0,-1.6666666269302368,-1.6266223192214966,0.7946045994758606,0.11171664297580719,0.0936787873506546 +273207$_$0,273207,1,1,0.0,0.0,0.1735057830810547,0.4674871861934662,0.3590070307254791 +273207$_$5,273207,2,2,0.3333333432674408,1.0736929178237915,0.2656317949295044,0.24359996616840363,0.4907682240009308 +273207$_$7,273207,0,0,-0.3333333432674408,-1.6144310235977173,0.7989521026611328,0.11572851240634918,0.08531942218542099 +273207$_$9,273207,0,2,-0.3333333432674408,0.5816456079483032,0.10398353636264801,0.3254494071006775,0.5705670714378357 +273250$_$20,273250,0,1,-0.3333333432674408,0.0,0.19872379302978516,0.4251141846179962,0.37616202235221863 +273250$_$3,273250,0,1,-0.6666666865348816,0.0,0.2671195864677429,0.4554171860218048,0.27746322751045227 +273250$_$7,273250,0,2,-0.3333333432674408,0.44760316610336304,0.06834922730922699,0.32879337668418884,0.6028574109077454 +273250$_$11,273250,0,1,-1.3333333730697632,0.0,0.215016707777977,0.444661408662796,0.3403218686580658 +273250$_$12,273250,0,1,-2.6666667461395264,0.0,0.25397223234176636,0.40945330262184143,0.3365744948387146 +273250$_$16,273250,0,1,-2.3333332538604736,0.0,0.2980189323425293,0.35983890295028687,0.3421421945095062 +273250$_$19,273250,1,1,0.0,0.0,0.2993387281894684,0.3758608400821686,0.3248004615306854 +275267$_$5,275267,1,0,0.0,-1.1117196083068848,0.6086392998695374,0.2655503749847412,0.12581035494804382 +27v7Blr0vjw$_$0,27v7Blr0vjw,0,1,-0.3333333432674408,0.0,0.22105000913143158,0.3985229730606079,0.3804270327091217 +27v7Blr0vjw$_$3,27v7Blr0vjw,1,2,0.0,0.4897386133670807,0.08611132204532623,0.2833123803138733,0.6305763125419617 +28006$_$22,28006,0,0,-2.3333332538604736,-1.8598299026489258,0.8436742424964905,0.07853762060403824,0.07778815925121307 +28006$_$0,28006,0,0,-2.6666667461395264,-1.3507951498031616,0.49712857604026794,0.22607168555259705,0.2767997682094574 +28006$_$5,28006,0,0,-1.6666666269302368,-1.5581868886947632,0.7884621620178223,0.13099539279937744,0.08054253458976746 +28006$_$7,28006,0,0,-2.3333332538604736,-1.9713548421859741,0.8895573019981384,0.0667068362236023,0.04373588413000107 +28006$_$13,28006,0,0,-2.3333332538604736,-1.554823398590088,0.6565651297569275,0.13909608125686646,0.20433878898620605 +28006$_$15,28006,0,0,-1.6666666269302368,-1.7571123838424683,0.9004259705543518,0.06368446350097656,0.03588956594467163 +28006$_$17,28006,0,0,-2.0,-0.623649001121521,0.4289398193359375,0.35626792907714844,0.21479220688343048 +28006$_$16,28006,0,0,-1.3333333730697632,-1.5444004535675049,0.7519804239273071,0.1315496414899826,0.11646991223096848 +286943$_$5,286943,0,0,-1.3333333730697632,-1.5058907270431519,0.6688183546066284,0.1541709452867508,0.17701075971126556 +29751$_$10,29751,1,0,0.0,-0.6396329998970032,0.42027705907821655,0.35394835472106934,0.2257746160030365 +29751$_$1,29751,1,2,0.0,0.5349944829940796,0.10082152485847473,0.39724200963974,0.5019364356994629 +29751$_$9,29751,1,0,0.0,-1.1877193450927734,0.6679627895355225,0.21420986950397491,0.11782732605934143 +2BuFtglEcaY$_$11,2BuFtglEcaY,2,2,2.3333332538604736,1.4806491136550903,0.06101660430431366,0.14818136394023895,0.790802001953125 +2BuFtglEcaY$_$6,2BuFtglEcaY,2,2,1.0,0.7772457599639893,0.038465484976768494,0.1663234680891037,0.7952110171318054 +2ItiGjefTRA$_$3,2ItiGjefTRA,1,0,0.0,-1.0580703020095825,0.5945154428482056,0.22985246777534485,0.17563216388225555 +2ItiGjefTRA$_$4,2ItiGjefTRA,0,0,-0.6666666865348816,-0.7180940508842468,0.5351701974868774,0.2894516587257385,0.17537814378738403 +2W-U94hXuK0$_$3,2W-U94hXuK0,0,0,-3.0,-1.1251212358474731,0.736803412437439,0.17980927228927612,0.08338727802038193 +2W-U94hXuK0$_$4,2W-U94hXuK0,0,0,-2.0,-1.424324870109558,0.7833183407783508,0.12999959290027618,0.08668209612369537 +2fbBrB1nJEQ$_$0,2fbBrB1nJEQ,2,2,2.0,1.1628613471984863,0.12297847121953964,0.19258958101272583,0.6844319701194763 +2m58ShI1QSI$_$2,2m58ShI1QSI,2,2,0.3333333432674408,0.8947197794914246,0.023059289902448654,0.14755694568157196,0.8293837904930115 +2vsgDSlJ9pU$_$0,2vsgDSlJ9pU,1,2,0.0,0.5030584931373596,0.04858717694878578,0.2739505171775818,0.6774623394012451 +2vsgDSlJ9pU$_$13,2vsgDSlJ9pU,2,2,0.6666666865348816,1.091525673866272,0.03816796466708183,0.1283176839351654,0.833514392375946 +2ze94yo2aPo$_$0,2ze94yo2aPo,2,2,2.0,1.1525760889053345,0.02320314198732376,0.12471576780080795,0.8520811200141907 +2ze94yo2aPo$_$5,2ze94yo2aPo,2,2,1.0,1.0502071380615234,0.06055394560098648,0.24223096668720245,0.6972151398658752 +35694$_$10,35694,1,1,0.0,0.0,0.23851537704467773,0.41924849152565,0.34223610162734985 +35694$_$17,35694,0,0,-1.6666666269302368,-0.6991230249404907,0.45711833238601685,0.2823464274406433,0.26053521037101746 +3At-BKm9eYk$_$5,3At-BKm9eYk,0,0,-0.3333333432674408,-1.5341764688491821,0.52287358045578,0.1440226286649704,0.33310380578041077 +3HyAaqre_Fk$_$2,3HyAaqre_Fk,2,2,1.0,1.168039321899414,0.17721816897392273,0.20660878717899323,0.6161730289459229 +3HyAaqre_Fk$_$6,3HyAaqre_Fk,2,2,1.3333333730697632,0.8216803073883057,0.02192314714193344,0.12574073672294617,0.8523361682891846 +3OYY5Tsz_2k$_$7,3OYY5Tsz_2k,1,2,0.0,0.48870691657066345,0.09568590670824051,0.41388770937919617,0.4904263913631439 +3REPHw7oLWo$_$13,3REPHw7oLWo,2,2,0.3333333432674408,0.8920990228652954,0.033486075699329376,0.14301621913909912,0.8234977126121521 +3wHE78v9zr4$_$24,3wHE78v9zr4,2,2,1.3333333730697632,1.5907979011535645,0.2237122803926468,0.21698664128780365,0.5593010783195496 +3wHE78v9zr4$_$7,3wHE78v9zr4,2,2,1.6666666269302368,0.7707206606864929,0.0425882562994957,0.20060953497886658,0.7568022012710571 +3wHE78v9zr4$_$9,3wHE78v9zr4,2,2,0.3333333432674408,1.5241836309432983,0.04500351473689079,0.12119705975055695,0.8337994813919067 +3wHE78v9zr4$_$12,3wHE78v9zr4,2,2,1.0,0.7609801292419434,0.22522662580013275,0.2814635932445526,0.49330979585647583 +41381$_$7,41381,1,0,0.0,-0.6400349736213684,0.4078754186630249,0.39601048827171326,0.19611413776874542 +41381$_$8,41381,2,1,1.0,0.0,0.2845747470855713,0.3973720371723175,0.3180531859397888 +424SXFTCFsA$_$3,424SXFTCFsA,2,2,1.3333333730697632,1.4862442016601562,0.030987728387117386,0.09981892257928848,0.8691933751106262 +424SXFTCFsA$_$2,424SXFTCFsA,2,2,1.3333333730697632,0.5598517656326294,0.09021987020969391,0.2633848488330841,0.6463953256607056 +424SXFTCFsA$_$4,424SXFTCFsA,2,2,2.0,1.0554993152618408,0.030936110764741898,0.1254791021347046,0.8435847759246826 +46495$_$9,46495,0,0,-2.0,-1.925125241279602,0.8718646168708801,0.06796056777238846,0.060174793004989624 +46615$_$1,46615,0,0,-1.3333333730697632,-0.9556317925453186,0.5226427316665649,0.26761454343795776,0.20974266529083252 +4EDblUpJieU$_$7,4EDblUpJieU,2,2,1.3333333730697632,0.901706874370575,0.03621627390384674,0.1484767347574234,0.8153069615364075 +53609$_$10,53609,0,0,-2.0,-1.7144196033477783,0.8565006852149963,0.08100632578134537,0.062492966651916504 +53609$_$13,53609,0,0,-2.0,-1.675625205039978,0.8478257656097412,0.09320645034313202,0.058967769145965576 +53609$_$3,53609,1,0,0.0,-1.7265502214431763,0.8345232605934143,0.09682460129261017,0.06865208595991135 +53609$_$7,53609,0,0,-1.6666666269302368,-1.7646125555038452,0.8507749438285828,0.08588279038667679,0.06334226578474045 +53766$_$4,53766,2,2,2.3333332538604736,1.0728152990341187,0.042228247970342636,0.16413936018943787,0.7936323285102844 +53766$_$9,53766,2,2,2.0,1.232993245124817,0.05262274667620659,0.14324216544628143,0.8041350245475769 +56989$_$12,56989,0,0,-0.6666666865348816,-1.5599875450134277,0.6639423966407776,0.16942563652992249,0.1666320413351059 +56989$_$5,56989,0,0,-2.3333332538604736,-2.022235155105591,0.8485099077224731,0.08174817264080048,0.06974183768033981 +56989$_$9,56989,2,2,1.0,1.9514985084533691,0.11368215829133987,0.1619882434606552,0.7243296504020691 +59673$_$4,59673,2,0,1.0,-0.8539220690727234,0.44395002722740173,0.25386422872543335,0.30218571424484253 +59673$_$6,59673,0,0,-0.3333333432674408,-1.2108500003814697,0.7397500276565552,0.17764075100421906,0.08260923624038696 +5eY4S1F62Z4$_$13,5eY4S1F62Z4,2,0,0.3333333432674408,-0.9993759989738464,0.5536081790924072,0.23322615027427673,0.21316570043563843 +5eY4S1F62Z4$_$17,5eY4S1F62Z4,2,2,0.3333333432674408,0.8649275898933411,0.27732864022254944,0.3262382447719574,0.39643311500549316 +5fKPJPFqPho$_$2,5fKPJPFqPho,2,2,0.6666666865348816,0.41788679361343384,0.06381168961524963,0.30933672189712524,0.6268515586853027 +5lrDS7LluCA$_$4,5lrDS7LluCA,2,2,1.0,2.0219264030456543,0.27418556809425354,0.1467103362083435,0.5791040658950806 +5pxFqJ5hKMU$_$2,5pxFqJ5hKMU,1,2,0.0,0.602817177772522,0.03476537764072418,0.2197096198797226,0.7455249428749084 +5vwXp27bCLw$_$16,5vwXp27bCLw,1,0,0.0,-0.5364223718643188,0.41832244396209717,0.3701290488243103,0.21154849231243134 +5xa0Ac2krGs$_$2,5xa0Ac2krGs,0,1,-0.3333333432674408,0.0,0.2637384533882141,0.4354378879070282,0.3008236289024353 +5xa0Ac2krGs$_$7,5xa0Ac2krGs,2,2,0.3333333432674408,0.4883771240711212,0.04482290893793106,0.2656238079071045,0.6895532608032227 +61531$_$3,61531,1,2,0.0,0.4159218668937683,0.06368877738714218,0.32425904273986816,0.6120522022247314 +61531$_$9,61531,2,2,2.0,1.4191449880599976,0.09244634956121445,0.19940030574798584,0.7081533670425415 +63951$_$1,63951,1,2,0.0,0.9579423069953918,0.28553539514541626,0.33143824338912964,0.3830263912677765 +67uKYi7mslE$_$3,67uKYi7mslE,2,2,0.6666666865348816,0.702904999256134,0.08533530682325363,0.2580540180206299,0.6566106677055359 +67uKYi7mslE$_$4,67uKYi7mslE,1,2,0.0,0.8738265633583069,0.18009983003139496,0.3086787164211273,0.5112214684486389 +69824$_$5,69824,0,0,-2.6666667461395264,-2.1825332641601562,0.7716748714447021,0.08936863392591476,0.13895654678344727 +6EDoVEm16fU$_$13,6EDoVEm16fU,2,2,1.3333333730697632,1.653696894645691,0.03131162002682686,0.09752856940031052,0.8711598515510559 +6EDoVEm16fU$_$4,6EDoVEm16fU,2,2,1.6666666269302368,1.2204233407974243,0.03931986540555954,0.12189795076847076,0.8387821912765503 +6EDoVEm16fU$_$6,6EDoVEm16fU,2,2,1.0,0.8203753232955933,0.11213916540145874,0.23785097897052765,0.65000981092453 +6EJHA6IDLNk$_$3,6EJHA6IDLNk,2,2,0.6666666865348816,0.9184587597846985,0.15245658159255981,0.30799606442451477,0.539547324180603 +6EJHA6IDLNk$_$4,6EJHA6IDLNk,1,2,0.0,0.708168625831604,0.06316870450973511,0.19990308582782745,0.7369281649589539 +6RFVsOWK1m4$_$7,6RFVsOWK1m4,0,0,-0.6666666865348816,-0.8501097559928894,0.6337496042251587,0.22637364268302917,0.13987667858600616 +6RFVsOWK1m4$_$9,6RFVsOWK1m4,2,2,1.0,0.9520589709281921,0.035198986530303955,0.14103996753692627,0.8237611055374146 +6brtMLkjjYk$_$0,6brtMLkjjYk,2,2,2.0,0.7146379351615906,0.11585584282875061,0.27394920587539673,0.6101949214935303 +6gtfeIqGasE$_$0,6gtfeIqGasE,2,2,1.3333333730697632,1.2127705812454224,0.0608409084379673,0.13996490836143494,0.7991942167282104 +6uoM8-9d0zA$_$4,6uoM8-9d0zA,2,2,0.6666666865348816,1.4736676216125488,0.03191801533102989,0.1423395872116089,0.8257424235343933 +79356$_$7,79356,2,0,2.6666667461395264,-1.404784917831421,0.5311439037322998,0.21468570828437805,0.25417041778564453 +79356$_$9,79356,2,1,1.0,0.0,0.28836825489997864,0.44934529066085815,0.2622864246368408 +7IxmlIwqigw$_$20,7IxmlIwqigw,2,2,0.6666666865348816,0.5178920030593872,0.09155061095952988,0.2793196439743042,0.6291297674179077 +7IxmlIwqigw$_$16,7IxmlIwqigw,2,2,0.6666666865348816,0.5689713358879089,0.045337047427892685,0.2171347588300705,0.7375282049179077 +7deoh0NoMs4$_$0,7deoh0NoMs4,2,2,2.0,1.778696060180664,0.037916891276836395,0.11467122286558151,0.8474118709564209 +7f3ndBCx_JE$_$19,7f3ndBCx_JE,2,2,2.0,1.2531168460845947,0.04689330980181694,0.13452878594398499,0.8185778856277466 +7mb8Y2AhXIY$_$6,7mb8Y2AhXIY,2,2,2.0,1.0759162902832031,0.06293551623821259,0.15806546807289124,0.7789990305900574 +83400$_$5,83400,1,0,0.0,-0.9513985514640808,0.6756052374839783,0.19164212048053741,0.13275256752967834 +86c2OkQ3_U8$_$22,86c2OkQ3_U8,2,2,2.0,0.9383789300918579,0.02436736412346363,0.12229622155427933,0.8533363938331604 +86c2OkQ3_U8$_$3,86c2OkQ3_U8,2,2,0.6666666865348816,0.6155058741569519,0.04959957301616669,0.22960995137691498,0.720790445804596 +88791$_$15,88791,2,2,0.3333333432674408,0.8705024719238281,0.09641651809215546,0.2645227313041687,0.6390607357025146 +88791$_$1,88791,0,1,-1.0,0.0,0.1485220342874527,0.5092483758926392,0.34222957491874695 +88791$_$2,88791,1,1,0.0,0.0,0.36327987909317017,0.43694007396698,0.19978007674217224 +8XZszYrUSCQ$_$0,8XZszYrUSCQ,2,2,1.0,1.293447732925415,0.028325892984867096,0.1329822689294815,0.8386918306350708 +8i7u3fl-hP8$_$0,8i7u3fl-hP8,2,2,1.3333333730697632,0.6257819533348083,0.14534126222133636,0.30942511558532715,0.5452336072921753 +8lfS97s2AKc$_$0,8lfS97s2AKc,0,0,-1.3333333730697632,-1.689865231513977,0.8220025897026062,0.09388119727373123,0.0841161459684372 +8lfS97s2AKc$_$7,8lfS97s2AKc,0,2,-1.0,0.7631886601448059,0.24889755249023438,0.30036476254463196,0.4507376551628113 +8wNr-NQImFg$_$8,8wNr-NQImFg,2,2,0.3333333432674408,0.6601065993309021,0.035924047231674194,0.22705593705177307,0.7370200157165527 +8wQhzezNcUY$_$1,8wQhzezNcUY,2,2,2.0,1.3003009557724,0.022068597376346588,0.10863049328327179,0.8693009614944458 +8wQhzezNcUY$_$2,8wQhzezNcUY,2,2,1.0,1.4415282011032104,0.028904225677251816,0.10593298822641373,0.8651627898216248 +91844$_$10,91844,0,0,-0.3333333432674408,-1.3072643280029297,0.4722898304462433,0.2848171293735504,0.24289296567440033 +91844$_$5,91844,2,0,3.0,-1.1654216051101685,0.4651321470737457,0.25160372257232666,0.28326416015625 +91844$_$7,91844,2,0,0.6666666865348816,-1.3689039945602417,0.7306345105171204,0.15022437274456024,0.11914104968309402 +91844$_$9,91844,0,0,-0.3333333432674408,-1.4169259071350098,0.5410832166671753,0.17944219708442688,0.2794746160507202 +92291$_$1,92291,2,2,1.3333333730697632,0.7671154737472534,0.2134886533021927,0.31895381212234497,0.46755754947662354 +92291$_$2,92291,1,2,0.0,1.3801052570343018,0.3394218683242798,0.24605979025363922,0.414518266916275 +92291$_$5,92291,0,0,-2.3333332538604736,-1.9147963523864746,0.8376989364624023,0.07991575449705124,0.08238533139228821 +92496$_$0,92496,0,0,-2.6666667461395264,-0.8431978821754456,0.44886714220046997,0.3501994013786316,0.20093347132205963 +93iGT5oueTA$_$12,93iGT5oueTA,0,0,-2.0,-2.052949905395508,0.891751766204834,0.05805021524429321,0.050197992473840714 +93iGT5oueTA$_$4,93iGT5oueTA,2,2,1.3333333730697632,0.8721593022346497,0.13699360191822052,0.33938172459602356,0.5236246585845947 +94481$_$2,94481,2,2,0.6666666865348816,1.5797938108444214,0.20402507483959198,0.2195546180009842,0.5764203071594238 +94481$_$4,94481,2,2,2.6666667461395264,1.130295753479004,0.1193527802824974,0.2576100826263428,0.6230370998382568 +97ENTofrmNo$_$0,97ENTofrmNo,2,2,1.3333333730697632,1.3154211044311523,0.04528723284602165,0.16537529230117798,0.7893374562263489 +9GzFQBljNIY$_$0,9GzFQBljNIY,1,1,0.0,0.0,0.22979269921779633,0.44325777888298035,0.32694947719573975 +9PzZSheh10U$_$24,9PzZSheh10U,2,2,2.3333332538604736,0.8512147068977356,0.03431728482246399,0.1471889615058899,0.8184937238693237 +9PzZSheh10U$_$23,9PzZSheh10U,2,2,0.6666666865348816,0.6168882250785828,0.11542315781116486,0.2779880464076996,0.6065887808799744 +9PzZSheh10U$_$5,9PzZSheh10U,1,2,0.0,0.6590358018875122,0.07151563465595245,0.24889211356639862,0.6795922517776489 +9PzZSheh10U$_$19,9PzZSheh10U,2,2,0.3333333432674408,0.6174169778823853,0.09262991696596146,0.33788371086120605,0.5694863796234131 +9cYNT_YSo8o$_$2,9cYNT_YSo8o,0,0,-1.0,-1.8498660326004028,0.8379217982292175,0.0858064815402031,0.07627177983522415 +9orb0lQnVW4$_$0,9orb0lQnVW4,1,1,0.0,0.0,0.29299044609069824,0.40644386410713196,0.300565630197525 +A1lFJXUpxZo$_$7,A1lFJXUpxZo,2,2,1.6666666269302368,1.3851476907730103,0.016992555931210518,0.08055179566144943,0.9024556875228882 +AB1PbMaW03s$_$13,AB1PbMaW03s,1,2,0.0,0.8297450542449951,0.182836651802063,0.321335107088089,0.4958282709121704 +AB1PbMaW03s$_$3,AB1PbMaW03s,2,2,0.3333333432674408,0.8348819613456726,0.03711225092411041,0.21754592657089233,0.7453418374061584 +AB1PbMaW03s$_$8,AB1PbMaW03s,1,1,0.0,0.0,0.19917547702789307,0.45722243189811707,0.34360209107398987 +AHiA9hohKr8$_$0,AHiA9hohKr8,1,2,0.0,0.7325652241706848,0.057175349444150925,0.25187963247299194,0.6909449696540833 +C5-cY1nPQ20$_$11,C5-cY1nPQ20,2,2,0.6666666865348816,0.888321578502655,0.03166339546442032,0.12664397060871124,0.8416926860809326 +C5-cY1nPQ20$_$8,C5-cY1nPQ20,2,2,0.3333333432674408,0.6865557432174683,0.1358739584684372,0.27359652519226074,0.5905295610427856 +CKqDDh50gcU$_$14,CKqDDh50gcU,2,2,1.3333333730697632,0.7127609848976135,0.02962689846754074,0.15286853909492493,0.8175045847892761 +CKqDDh50gcU$_$6,CKqDDh50gcU,2,2,1.6666666269302368,0.9245140552520752,0.034648772329092026,0.13844409584999084,0.8269070982933044 +CO2YoTZbUr0$_$0,CO2YoTZbUr0,1,0,0.0,-1.0699796676635742,0.7265658378601074,0.18847572803497314,0.08495837450027466 +CO6n-skQJss$_$0,CO6n-skQJss,1,2,0.0,0.8615409731864929,0.09675520658493042,0.2110566347837448,0.6921881437301636 +CO6n-skQJss$_$3,CO6n-skQJss,0,2,-1.3333333730697632,0.835331380367279,0.19422268867492676,0.2584085166454315,0.5473688244819641 +CU6U-gS76K4$_$20,CU6U-gS76K4,2,2,1.6666666269302368,0.5705705881118774,0.1010080873966217,0.2885402739048004,0.6104516386985779 +CbRexsp1HKw$_$7,CbRexsp1HKw,1,2,0.0,0.9375668168067932,0.19605004787445068,0.24140416085720062,0.5625458359718323 +ChhZna-aBK4$_$10,ChhZna-aBK4,2,2,0.3333333432674408,0.5942298173904419,0.1155305728316307,0.3076045513153076,0.5768648386001587 +ChhZna-aBK4$_$16,ChhZna-aBK4,1,2,0.0,0.7064666748046875,0.05566199868917465,0.2909618616104126,0.6533761620521545 +ChhZna-aBK4$_$8,ChhZna-aBK4,2,2,0.6666666865348816,0.9298073649406433,0.02957877889275551,0.15928097069263458,0.811140239238739 +DVAY-u_cJWU$_$5,DVAY-u_cJWU,2,2,1.0,1.0134927034378052,0.018995506688952446,0.10880640149116516,0.8721981048583984 +DjcZrtcBZi4$_$10,DjcZrtcBZi4,2,2,0.3333333432674408,0.8202719688415527,0.08353215456008911,0.213355153799057,0.7031126618385315 +DjcZrtcBZi4$_$8,DjcZrtcBZi4,1,2,0.0,0.5387406945228577,0.05381256341934204,0.23232059180736542,0.7138668298721313 +DnBHq5I52LM$_$5,DnBHq5I52LM,0,0,-1.3333333730697632,-0.9507355690002441,0.6524303555488586,0.23376283049583435,0.11380679160356522 +DtbT85s3i94$_$5,DtbT85s3i94,2,2,1.0,0.5563967823982239,0.14610418677330017,0.3375123143196106,0.5163835287094116 +EMS14J0odIE$_$16,EMS14J0odIE,0,2,-0.3333333432674408,0.4370945394039154,0.16781528294086456,0.3871755003929138,0.44500917196273804 +EO_5o9Gup6g$_$10,EO_5o9Gup6g,0,0,-0.6666666865348816,-0.868239164352417,0.39975616335868835,0.25390034914016724,0.346343457698822 +EO_5o9Gup6g$_$4,EO_5o9Gup6g,0,2,-1.0,1.3543133735656738,0.2238859087228775,0.20853416621685028,0.5675799250602722 +EO_5o9Gup6g$_$6,EO_5o9Gup6g,0,0,-2.0,-1.0535496473312378,0.4708864092826843,0.2335844784975052,0.2955290675163269 +EyoMU2yoJPY$_$11,EyoMU2yoJPY,2,2,1.6666666269302368,1.540429949760437,0.02198992483317852,0.09756498038768768,0.880445122718811 +EyoMU2yoJPY$_$7,EyoMU2yoJPY,0,0,-0.3333333432674408,-1.0102990865707397,0.415846049785614,0.20813685655593872,0.37601709365844727 +F2hc2FLOdhI$_$3,F2hc2FLOdhI,2,0,1.0,-1.4173237085342407,0.7567265629768372,0.1328890025615692,0.11038447916507721 +F2hc2FLOdhI$_$5,F2hc2FLOdhI,2,2,1.0,1.0873360633850098,0.21175295114517212,0.22485429048538208,0.5633927583694458 +F8eQI8E-6q4$_$3,F8eQI8E-6q4,0,0,-0.6666666865348816,-0.8377565741539001,0.5939877033233643,0.25799354910850525,0.14801877737045288 +FHDVQkU-nGI$_$7,FHDVQkU-nGI,2,2,2.0,1.1399760246276855,0.08634328097105026,0.17305317521095276,0.740603506565094 +F_YaG_pvZrA$_$11,F_YaG_pvZrA,1,2,0.0,1.2153300046920776,0.022632669657468796,0.10260748863220215,0.8747599124908447 +GAVpYuhMZAw$_$12,GAVpYuhMZAw,2,1,1.0,0.0,0.3128460943698883,0.37235766649246216,0.3147962689399719 +GAVpYuhMZAw$_$1,GAVpYuhMZAw,2,2,2.0,0.49554917216300964,0.11201903223991394,0.3776681125164032,0.5103128552436829 +GAVpYuhMZAw$_$4,GAVpYuhMZAw,2,1,0.3333333432674408,0.0,0.24452617764472961,0.42402002215385437,0.331453800201416 +GAVpYuhMZAw$_$6,GAVpYuhMZAw,0,0,-0.3333333432674408,-0.8255094289779663,0.5824114680290222,0.272957980632782,0.1446305364370346 +GK-Pprzh0t0$_$1,GK-Pprzh0t0,0,0,-1.6666666269302368,-1.060015082359314,0.6079046726226807,0.20398099720478058,0.18811428546905518 +GKsjv42t284$_$1,GKsjv42t284,2,2,1.3333333730697632,1.1091216802597046,0.020334038883447647,0.1001882553100586,0.8794777393341064 +GMa0cIAltnw$_$3,GMa0cIAltnw,2,2,0.3333333432674408,0.4687451422214508,0.06567058712244034,0.2546772062778473,0.6796522736549377 +GNP0PFas12Y$_$4,GNP0PFas12Y,2,2,0.3333333432674408,0.7719976305961609,0.20812271535396576,0.2456604242324829,0.5462168455123901 +HFPGeaEPy9o$_$0,HFPGeaEPy9o,0,0,-0.3333333432674408,-1.1596189737319946,0.7680065035820007,0.15304367244243622,0.07894977927207947 +HJTxq72GuMs$_$14,HJTxq72GuMs,2,2,0.6666666865348816,0.8129876852035522,0.06653742492198944,0.19850203394889832,0.7349605560302734 +HMRqR-P68Ws$_$0,HMRqR-P68Ws,2,2,1.0,1.101088285446167,0.2957465648651123,0.34569916129112244,0.35855427384376526 +HR18U0yAlTc$_$10,HR18U0yAlTc,1,2,0.0,1.5056452751159668,0.020026830956339836,0.08650781959295273,0.8934653997421265 +HR18U0yAlTc$_$4,HR18U0yAlTc,0,2,-0.3333333432674408,0.7006015777587891,0.04682130366563797,0.18877658247947693,0.7644020915031433 +HR18U0yAlTc$_$9,HR18U0yAlTc,0,2,-0.3333333432674408,1.1041109561920166,0.06969886273145676,0.15142764151096344,0.7788735032081604 +HbaycY0VuZk$_$0,HbaycY0VuZk,2,2,1.3333333730697632,0.8788696527481079,0.02990058809518814,0.1609167903661728,0.8091826438903809 +IHp8hd1jm6k$_$27,IHp8hd1jm6k,2,2,1.3333333730697632,0.5553033947944641,0.03424283117055893,0.19023098051548004,0.775526225566864 +IHp8hd1jm6k$_$0,IHp8hd1jm6k,2,2,1.6666666269302368,0.9790695309638977,0.1268661916255951,0.23066411912441254,0.6424696445465088 +IHp8hd1jm6k$_$5,IHp8hd1jm6k,2,2,1.0,1.4271118640899658,0.06721922010183334,0.14371126890182495,0.7890694737434387 +IHp8hd1jm6k$_$7,IHp8hd1jm6k,1,0,0.0,-0.6500100493431091,0.42591041326522827,0.33682841062545776,0.23726120591163635 +IHp8hd1jm6k$_$15,IHp8hd1jm6k,2,2,1.3333333730697632,1.2944315671920776,0.015357236377894878,0.09224128723144531,0.8924015164375305 +IIPYcCii7Sg$_$3,IIPYcCii7Sg,0,2,-0.3333333432674408,0.6553635001182556,0.21374736726284027,0.3711465895175934,0.41510599851608276 +IRSxo_XXArg$_$12,IRSxo_XXArg,1,2,0.0,1.8549894094467163,0.045814864337444305,0.09824886918067932,0.8559362292289734 +IRSxo_XXArg$_$7,IRSxo_XXArg,1,2,0.0,1.2943295240402222,0.0518249087035656,0.12077688425779343,0.8273981809616089 +Iemw8vqt-54$_$13,Iemw8vqt-54,2,2,0.3333333432674408,0.6563459038734436,0.20035357773303986,0.295573353767395,0.5040730237960815 +Iemw8vqt-54$_$0,Iemw8vqt-54,2,2,0.6666666865348816,1.0301741361618042,0.030022572726011276,0.1262408196926117,0.8437365889549255 +Iemw8vqt-54$_$9,Iemw8vqt-54,2,2,1.0,0.6993857026100159,0.10698153078556061,0.2388259619474411,0.6541925072669983 +JKueLneBoik$_$0,JKueLneBoik,2,2,1.0,1.1837503910064697,0.03514441102743149,0.1515437811613083,0.8133118152618408 +JKueLneBoik$_$4,JKueLneBoik,1,2,0.0,0.46592602133750916,0.07054851949214935,0.34924599528312683,0.580205500125885 +JNhqI4JtPXA$_$3,JNhqI4JtPXA,2,2,2.0,1.7595221996307373,0.03236876800656319,0.08622906357049942,0.8814021944999695 +JXYot3NDQn0$_$2,JXYot3NDQn0,2,2,0.6666666865348816,1.3934787511825562,0.02370850369334221,0.09780903905630112,0.878482460975647 +KB5hSnV1emg$_$3,KB5hSnV1emg,1,0,0.0,-0.6965072751045227,0.4841917157173157,0.3199102580547333,0.19589808583259583 +Kn5eKHlPD0k$_$12,Kn5eKHlPD0k,2,2,1.0,1.3046958446502686,0.26372265815734863,0.23410628736019135,0.5021710395812988 +Kn99u05vlpA$_$5,Kn99u05vlpA,2,0,1.6666666269302368,-0.9253329038619995,0.3939608633518219,0.2940334677696228,0.3120056390762329 +L-7oRnbi9-w$_$3,L-7oRnbi9-w,2,2,0.3333333432674408,0.6188669800758362,0.058653831481933594,0.21860943734645844,0.7227367162704468 +L-a4Sh6iAcw$_$7,L-a4Sh6iAcw,2,2,0.6666666865348816,0.40781131386756897,0.06681780517101288,0.3020980656147003,0.631084144115448 +L-a4Sh6iAcw$_$9,L-a4Sh6iAcw,2,2,0.6666666865348816,0.935804009437561,0.04020458087325096,0.13924835622310638,0.8205469846725464 +LD3HYOwk1Bc$_$0,LD3HYOwk1Bc,1,2,0.0,0.7178261876106262,0.04649897292256355,0.2327621579170227,0.7207388281822205 +LJGL2sGvSS0$_$2,LJGL2sGvSS0,2,2,1.3333333730697632,0.8070349097251892,0.11331657320261002,0.32059445977211,0.5660889744758606 +LJGL2sGvSS0$_$10,LJGL2sGvSS0,2,2,1.3333333730697632,1.0618282556533813,0.016242148354649544,0.10044732689857483,0.8833105564117432 +LJGL2sGvSS0$_$14,LJGL2sGvSS0,2,2,1.6666666269302368,1.8054225444793701,0.024889905005693436,0.09531957656145096,0.8797904849052429 +LJGL2sGvSS0$_$19,LJGL2sGvSS0,2,0,1.0,-0.7986805438995361,0.4141573905944824,0.26699504256248474,0.31884753704071045 +LcfubBagG6Q$_$1,LcfubBagG6Q,2,2,1.3333333730697632,0.40693399310112,0.06129647418856621,0.25353580713272095,0.6851677298545837 +LcfubBagG6Q$_$3,LcfubBagG6Q,2,1,0.3333333432674408,0.0,0.34513023495674133,0.37029820680618286,0.2845715582370758 +LyOq9mOEPuk$_$8,LyOq9mOEPuk,0,0,-0.6666666865348816,-1.0951863527297974,0.7202253341674805,0.16099058091640472,0.1187841072678566 +MHVrwCEWLPI$_$4,MHVrwCEWLPI,2,2,1.0,1.1355780363082886,0.027462683618068695,0.1322958618402481,0.8402414917945862 +MHVrwCEWLPI$_$6,MHVrwCEWLPI,2,0,0.3333333432674408,-1.2827345132827759,0.4244566559791565,0.2785700261592865,0.2969733774662018 +MHyW857u_X8$_$18,MHyW857u_X8,0,2,-0.3333333432674408,0.642059862613678,0.18557153642177582,0.26948922872543335,0.5449392795562744 +MZUr1DfYNNw$_$1,MZUr1DfYNNw,2,2,1.3333333730697632,0.8373216986656189,0.17096708714962006,0.27143123745918274,0.5576016902923584 +NIpuKcoJhGM$_$0,NIpuKcoJhGM,2,2,2.0,0.6695030331611633,0.3167910873889923,0.32306593656539917,0.3601430058479309 +NVLPURuAVLU$_$2,NVLPURuAVLU,0,0,-0.6666666865348816,-0.8622811436653137,0.39240777492523193,0.23745565116405487,0.370136559009552 +NaWmaHwjElo$_$5,NaWmaHwjElo,1,2,0.0,0.7161290645599365,0.2589636445045471,0.3235395848751068,0.4174967110157013 +NiAjz4ciA60$_$1,NiAjz4ciA60,2,2,1.0,1.1138495206832886,0.03892122209072113,0.149727925658226,0.8113508224487305 +NocexkPXja8$_$13,NocexkPXja8,0,0,-1.0,-0.9880737066268921,0.6466000080108643,0.22695128619670868,0.12644873559474945 +NocexkPXja8$_$4,NocexkPXja8,0,0,-2.3333332538604736,-1.8321810960769653,0.8726827502250671,0.06974895298480988,0.05756830424070358 +O-b3DQg0QmA$_$1,O-b3DQg0QmA,1,2,0.0,0.5357000827789307,0.06232142075896263,0.2865704596042633,0.6511080861091614 +OFia3dWgaoI$_$0,OFia3dWgaoI,0,0,-1.3333333730697632,-1.0701173543930054,0.6341375708580017,0.2208024114370346,0.14506004750728607 +OFia3dWgaoI$_$6,OFia3dWgaoI,0,0,-0.6666666865348816,-0.9669042229652405,0.7063677906990051,0.19403819739818573,0.09959403425455093 +OKJPFisBoPY$_$0,OKJPFisBoPY,2,2,0.3333333432674408,1.2999261617660522,0.024689093232154846,0.11365919560194016,0.8616517186164856 +OORklkFql3k$_$10,OORklkFql3k,0,0,-0.3333333432674408,-0.8152579069137573,0.5696271061897278,0.2632628381252289,0.16710999608039856 +OORklkFql3k$_$4,OORklkFql3k,0,2,-2.0,1.0574392080307007,0.1106894463300705,0.23887258768081665,0.6504379510879517 +OWWHjP3pX9o$_$0,OWWHjP3pX9o,0,1,-0.3333333432674408,0.0,0.3557553291320801,0.3724837303161621,0.2717609107494354 +OWWHjP3pX9o$_$4,OWWHjP3pX9o,2,2,0.6666666865348816,0.47383567690849304,0.07973314821720123,0.2942284941673279,0.6260383129119873 +OXsTIPdytiw$_$0,OXsTIPdytiw,2,2,0.6666666865348816,0.5191340446472168,0.08099599182605743,0.3180154263973236,0.6009886264801025 +OXsTIPdytiw$_$7,OXsTIPdytiw,2,2,0.6666666865348816,0.6423665881156921,0.027101615443825722,0.15396000444889069,0.8189383745193481 +Oa2xVjzAMFc$_$9,Oa2xVjzAMFc,0,1,-0.3333333432674408,0.0,0.35048964619636536,0.3868888318538666,0.26262152194976807 +OctOcfI4KSs$_$3,OctOcfI4KSs,2,2,0.3333333432674408,0.6569629311561584,0.14152765274047852,0.2899220585823059,0.5685502886772156 +OctOcfI4KSs$_$7,OctOcfI4KSs,2,1,0.3333333432674408,0.0,0.17890886962413788,0.4132435917854309,0.4078475534915924 +P0UHzR4CmYg$_$1,P0UHzR4CmYg,2,2,1.0,0.880124032497406,0.1433008462190628,0.3039773404598236,0.5527217984199524 +P0UHzR4CmYg$_$3,P0UHzR4CmYg,2,2,0.6666666865348816,1.7932307720184326,0.030945664271712303,0.0953167974948883,0.8737375140190125 +P0UHzR4CmYg$_$4,P0UHzR4CmYg,2,2,1.0,1.234303593635559,0.02286212332546711,0.11137410998344421,0.8657637238502502 +P0UHzR4CmYg$_$8,P0UHzR4CmYg,2,0,0.6666666865348816,-1.0309302806854248,0.4776208698749542,0.2317635864019394,0.29061561822891235 +P0UHzR4CmYg$_$11,P0UHzR4CmYg,0,0,-1.0,-1.3453048467636108,0.6132590174674988,0.203904926776886,0.18283602595329285 +PcqKFt1p3UQ$_$10,PcqKFt1p3UQ,2,2,0.3333333432674408,0.523838996887207,0.14125537872314453,0.3387373685836792,0.520007312297821 +PzI6yY9Y2xQ$_$0,PzI6yY9Y2xQ,2,2,1.6666666269302368,1.453588843345642,0.029363935813307762,0.09907586127519608,0.8715601563453674 +PzI6yY9Y2xQ$_$2,PzI6yY9Y2xQ,2,2,1.3333333730697632,0.798237681388855,0.046996746212244034,0.18797844648361206,0.7650248408317566 +QBc7X5jj8oA$_$4,QBc7X5jj8oA,2,2,1.3333333730697632,0.806657612323761,0.3129573464393616,0.3188752830028534,0.3681674003601074 +QCR7uyowjhM$_$10,QCR7uyowjhM,2,2,0.3333333432674408,0.41880732774734497,0.1744919717311859,0.39030569791793823,0.43520230054855347 +QCR7uyowjhM$_$8,QCR7uyowjhM,1,0,0.0,-0.7301411032676697,0.5942148566246033,0.2721615135669708,0.13362360000610352 +QIonRUsCqBs$_$1,QIonRUsCqBs,2,2,1.3333333730697632,0.9731715321540833,0.02003376930952072,0.11274044215679169,0.8672257661819458 +Qfa1fY_07bQ$_$5,Qfa1fY_07bQ,2,2,1.3333333730697632,0.7803314328193665,0.04686573147773743,0.19773975014686584,0.7553945183753967 +R9xTBw3MCWI$_$5,R9xTBw3MCWI,2,2,0.3333333432674408,1.5125426054000854,0.017546216025948524,0.07789520174264908,0.9045585989952087 +ROC2YI3tDsk$_$13,ROC2YI3tDsk,2,2,0.3333333432674408,0.6208934783935547,0.03452260419726372,0.17732863128185272,0.7881487607955933 +ROC2YI3tDsk$_$3,ROC2YI3tDsk,2,2,1.0,0.8631648421287537,0.10614575445652008,0.24415701627731323,0.6496971845626831 +RVC8l5hf2Eg$_$13,RVC8l5hf2Eg,0,2,-0.6666666865348816,1.1092147827148438,0.21091702580451965,0.28798383474349976,0.5010991096496582 +RVC8l5hf2Eg$_$8,RVC8l5hf2Eg,0,1,-0.3333333432674408,0.0,0.2586468458175659,0.40606996417045593,0.33528316020965576 +Rb1uzHNcYcA$_$4,Rb1uzHNcYcA,2,2,1.6666666269302368,1.4008702039718628,0.03124404139816761,0.10597439110279083,0.8627815842628479 +RvmTZVNAAuQ$_$2,RvmTZVNAAuQ,1,2,0.0,0.7022584676742554,0.04959652200341225,0.20828260481357574,0.7421208620071411 +SKTyBOhDX6U$_$11,SKTyBOhDX6U,2,2,1.6666666269302368,1.001635193824768,0.029334943741559982,0.14059624075889587,0.8300687670707703 +SKTyBOhDX6U$_$12,SKTyBOhDX6U,2,0,0.6666666865348816,-0.9138222932815552,0.5323694348335266,0.2899754047393799,0.17765522003173828 +SKTyBOhDX6U$_$9,SKTyBOhDX6U,2,2,1.0,0.9900270104408264,0.026683811098337173,0.12427124381065369,0.849044919013977 +SYQ_zv8dWng$_$0,SYQ_zv8dWng,2,2,0.3333333432674408,0.5361703038215637,0.05628010630607605,0.3062795102596283,0.6374403834342957 +SYQ_zv8dWng$_$5,SYQ_zv8dWng,2,2,0.6666666865348816,0.8585700392723083,0.29426488280296326,0.263267457485199,0.44246765971183777 +SYQ_zv8dWng$_$6,SYQ_zv8dWng,2,2,0.6666666865348816,0.3873722553253174,0.11924069374799728,0.39532729983329773,0.48543208837509155 +SYQ_zv8dWng$_$9,SYQ_zv8dWng,1,2,0.0,0.5879464149475098,0.07022388279438019,0.2582396864891052,0.6715364456176758 +SZ7HK5ns6mE$_$11,SZ7HK5ns6mE,2,2,1.6666666269302368,1.2685532569885254,0.16811791062355042,0.20073318481445312,0.6311489343643188 +Sq6DIhFxPqQ$_$4,Sq6DIhFxPqQ,1,1,0.0,0.0,0.32216909527778625,0.3840951919555664,0.29373568296432495 +SwT0gh0V8fI$_$10,SwT0gh0V8fI,1,0,0.0,-1.0884802341461182,0.6028814315795898,0.1892092227935791,0.20790930092334747 +SwT0gh0V8fI$_$19,SwT0gh0V8fI,2,2,0.6666666865348816,1.0643627643585205,0.08347109705209732,0.17770543694496155,0.7388235330581665 +SwT0gh0V8fI$_$5,SwT0gh0V8fI,1,0,0.0,-0.905103325843811,0.5154162049293518,0.34521618485450745,0.13936755061149597 +TXiOSZdaLJ8$_$2,TXiOSZdaLJ8,2,2,0.6666666865348816,1.603393316268921,0.037444718182086945,0.11140114068984985,0.851154088973999 +TcTGCIh6e5s$_$4,TcTGCIh6e5s,1,2,0.0,0.3775482177734375,0.0906989723443985,0.36800989508628845,0.5412911176681519 +TxRtSItpGMo$_$4,TxRtSItpGMo,2,2,2.0,1.2517085075378418,0.02457689493894577,0.10658938437700272,0.8688337206840515 +U-KihZeIfKI$_$20,U-KihZeIfKI,2,2,1.3333333730697632,1.0129255056381226,0.06014014035463333,0.16498048603534698,0.7748793959617615 +U-KihZeIfKI$_$1,U-KihZeIfKI,2,2,0.6666666865348816,1.0354684591293335,0.09559163451194763,0.20590153336524963,0.6985068917274475 +U-KihZeIfKI$_$6,U-KihZeIfKI,0,0,-1.0,-1.20681631565094,0.7401201725006104,0.17352238297462463,0.08635737746953964 +U-KihZeIfKI$_$15,U-KihZeIfKI,2,0,0.6666666865348816,-1.208935260772705,0.5778624415397644,0.2235800176858902,0.1985575407743454 +U-KihZeIfKI$_$16,U-KihZeIfKI,2,2,1.0,0.947666347026825,0.03281523287296295,0.15366612374782562,0.8135186433792114 +UiurP5k-f1A$_$4,UiurP5k-f1A,2,2,1.0,1.0080924034118652,0.033041756600141525,0.1325634866952896,0.8343948125839233 +UjqA6KVW2m8$_$0,UjqA6KVW2m8,0,0,-0.3333333432674408,-0.7281249761581421,0.5450186133384705,0.28964123129844666,0.16534018516540527 +UjqA6KVW2m8$_$3,UjqA6KVW2m8,0,2,-0.6666666865348816,0.5015534162521362,0.1993933469057083,0.37248465418815613,0.428121954202652 +UjqA6KVW2m8$_$2,UjqA6KVW2m8,0,0,-1.3333333730697632,-1.6201752424240112,0.8168154954910278,0.10714208334684372,0.07604234665632248 +UjqA6KVW2m8$_$4,UjqA6KVW2m8,2,2,1.0,1.2669750452041626,0.04573353752493858,0.12733861804008484,0.8269278407096863 +UweZVaFqruU$_$1,UweZVaFqruU,2,2,0.3333333432674408,0.6175966858863831,0.10776357352733612,0.3804805278778076,0.5117559432983398 +UweZVaFqruU$_$5,UweZVaFqruU,1,2,0.0,0.5455015897750854,0.151211217045784,0.37641429901123047,0.47237449884414673 +V27mmEkN80g$_$0,V27mmEkN80g,1,2,0.0,0.5803899765014648,0.056372106075286865,0.21372170746326447,0.7299062013626099 +VDkBM0ZG4q8$_$4,VDkBM0ZG4q8,1,2,0.0,0.6733020544052124,0.23127712309360504,0.37763166427612305,0.39109113812446594 +VDkBM0ZG4q8$_$7,VDkBM0ZG4q8,2,2,0.3333333432674408,1.455646276473999,0.022232146933674812,0.10286729037761688,0.8749005794525146 +VLQgw-88v4Q$_$2,VLQgw-88v4Q,2,2,1.0,1.5375914573669434,0.04675062373280525,0.15119928121566772,0.8020500540733337 +VLQgw-88v4Q$_$6,VLQgw-88v4Q,2,2,1.6666666269302368,1.655474305152893,0.030950088053941727,0.12061424553394318,0.848435640335083 +VLQgw-88v4Q$_$8,VLQgw-88v4Q,2,2,1.3333333730697632,1.4452428817749023,0.09087932109832764,0.16763286292552948,0.7414878606796265 +VXy21GwGs8o$_$0,VXy21GwGs8o,1,2,0.0,0.9204481840133667,0.07101619243621826,0.1927853673696518,0.7361984252929688 +Vdf1McvE9ao$_$9,Vdf1McvE9ao,1,0,0.0,-0.735402524471283,0.482835590839386,0.3315102159976959,0.1856541633605957 +Vlas-mPydcg$_$17,Vlas-mPydcg,2,0,0.6666666865348816,-0.9387142062187195,0.6250234246253967,0.21737901866436005,0.157597616314888 +Vlas-mPydcg$_$3,Vlas-mPydcg,2,2,1.0,1.294050693511963,0.01884019374847412,0.0947667807340622,0.8863930106163025 +VsXGwSZazwA$_$4,VsXGwSZazwA,1,1,0.0,0.0,0.2928859293460846,0.4152688980102539,0.2918452024459839 +VsXGwSZazwA$_$6,VsXGwSZazwA,2,2,1.6666666269302368,0.5102255940437317,0.06328088790178299,0.30826595425605774,0.6284531950950623 +VwGPIUNayKM$_$1,VwGPIUNayKM,1,2,0.0,0.3986620604991913,0.06314335018396378,0.3071993291378021,0.6296573281288147 +VwGPIUNayKM$_$5,VwGPIUNayKM,0,0,-1.0,-0.7076817750930786,0.42661815881729126,0.3211840093135834,0.25219783186912537 +VwGPIUNayKM$_$8,VwGPIUNayKM,2,2,1.0,1.0487855672836304,0.02348462864756584,0.12168164551258087,0.854833722114563 +W1CWpktWtTs$_$5,W1CWpktWtTs,2,2,1.0,1.461313247680664,0.033369410783052444,0.11330395936965942,0.853326678276062 +W1CWpktWtTs$_$9,W1CWpktWtTs,2,2,0.6666666865348816,1.045727252960205,0.15558302402496338,0.20300820469856262,0.6414087414741516 +WfNiQBXmPw8$_$3,WfNiQBXmPw8,0,0,-0.3333333432674408,-1.4385648965835571,0.7557846903800964,0.12611034512519836,0.11810500174760818 +WfNiQBXmPw8$_$5,WfNiQBXmPw8,2,0,0.3333333432674408,-0.8741716742515564,0.5749132037162781,0.21349824965000153,0.21158859133720398 +WgI8IbJtXHw$_$0,WgI8IbJtXHw,1,2,0.0,1.7302019596099854,0.03886808454990387,0.1022503674030304,0.8588815927505493 +X2Hs89fZ2-c$_$4,X2Hs89fZ2-c,2,2,1.0,1.0805699825286865,0.031593140214681625,0.17252780497074127,0.7958790063858032 +X6N7UEFJLNY$_$0,X6N7UEFJLNY,2,2,0.3333333432674408,0.5506982803344727,0.08473020792007446,0.2637937664985657,0.6514760255813599 +X6N7UEFJLNY$_$2,X6N7UEFJLNY,1,2,0.0,0.5534431338310242,0.10185282677412033,0.3111930787563324,0.5869541168212891 +X6N7UEFJLNY$_$4,X6N7UEFJLNY,2,2,0.6666666865348816,0.9588704705238342,0.022584764286875725,0.12424865365028381,0.8531666398048401 +XDVit9ASVUg$_$2,XDVit9ASVUg,2,2,0.3333333432674408,0.607778787612915,0.2095814347267151,0.34724435210227966,0.44317421317100525 +XKyumlBmix8$_$0,XKyumlBmix8,1,2,0.0,0.5679649710655212,0.14751914143562317,0.3063369393348694,0.5461439490318298 +XKyumlBmix8$_$2,XKyumlBmix8,0,0,-0.6666666865348816,-1.0492794513702393,0.5111898183822632,0.2643914222717285,0.2244187891483307 +XVWiAArXYpE$_$3,XVWiAArXYpE,0,2,-0.6666666865348816,0.42713162302970886,0.14341573417186737,0.33225810527801514,0.5243261456489563 +XVWiAArXYpE$_$4,XVWiAArXYpE,0,0,-2.0,-0.8666756749153137,0.5468029975891113,0.2713136076927185,0.18188336491584778 +XVWiAArXYpE$_$9,XVWiAArXYpE,0,1,-0.3333333432674408,0.0,0.2536476254463196,0.4206782281398773,0.3256741464138031 +X_TusmBIlC0$_$5,X_TusmBIlC0,1,1,0.0,0.0,0.2837236225605011,0.40007486939430237,0.31620147824287415 +Xa086gxLJ3Y$_$12,Xa086gxLJ3Y,2,2,0.3333333432674408,0.3791693449020386,0.07718971371650696,0.34088391065597534,0.5819263458251953 +XrNaL-MTjXg$_$3,XrNaL-MTjXg,1,0,0.0,-1.0898394584655762,0.4004206657409668,0.21489639580249786,0.38468289375305176 +Xy57UpKRNEo$_$7,Xy57UpKRNEo,0,0,-1.0,-0.986835777759552,0.6338462233543396,0.20386630296707153,0.16228750348091125 +Y8dI1GTWCk4$_$6,Y8dI1GTWCk4,2,2,1.3333333730697632,1.1284282207489014,0.02958597056567669,0.1463775783777237,0.8240364789962769 +YLK58srjhNI$_$3,YLK58srjhNI,2,2,0.6666666865348816,1.615843415260315,0.27387771010398865,0.18376852571964264,0.5423537492752075 +YQZPnHZRp1w$_$1,YQZPnHZRp1w,2,2,0.3333333432674408,0.4377429783344269,0.08863651752471924,0.3703707456588745,0.540992796421051 +YUNxD04EvfE$_$1,YUNxD04EvfE,0,1,-0.3333333432674408,0.0,0.264689564704895,0.39692869782447815,0.33838170766830444 +Z4iYSMMMycQ$_$4,Z4iYSMMMycQ,2,2,0.6666666865348816,0.6562342047691345,0.056989021599292755,0.26044976711273193,0.6825612187385559 +ZKErPftd--w$_$6,ZKErPftd--w,2,2,0.6666666865348816,1.533523440361023,0.07700009644031525,0.1757495254278183,0.7472504377365112 +ZKZ8UjaQQT4$_$7,ZKZ8UjaQQT4,1,2,0.0,0.8817055225372314,0.112667977809906,0.25042423605918884,0.6369078159332275 +ZS1Nb0OWYNE$_$11,ZS1Nb0OWYNE,2,2,0.6666666865348816,1.746963381767273,0.05050472542643547,0.12341141700744629,0.8260838389396667 +ZS1Nb0OWYNE$_$15,ZS1Nb0OWYNE,1,2,0.0,1.7145394086837769,0.02148670330643654,0.07532147318124771,0.9031918048858643 +ZS1Nb0OWYNE$_$5,ZS1Nb0OWYNE,2,2,0.6666666865348816,0.7123092412948608,0.22137385606765747,0.3806664049625397,0.39795976877212524 +ZS1Nb0OWYNE$_$9,ZS1Nb0OWYNE,2,2,2.6666667461395264,1.3337023258209229,0.041227880865335464,0.12180007249116898,0.8369721174240112 +ZcFzcd4ZoMg$_$4,ZcFzcd4ZoMg,0,0,-0.3333333432674408,-0.8683609366416931,0.5311482548713684,0.25716644525527954,0.2116853892803192 +ZeH7gZw94k0$_$1,ZeH7gZw94k0,2,2,1.6666666269302368,0.9770463705062866,0.05518146976828575,0.1773916482925415,0.7674268484115601 +ZtocGyL3Tfc$_$13,ZtocGyL3Tfc,2,2,1.0,1.0465447902679443,0.04613678529858589,0.13690230250358582,0.8169609308242798 +ZtocGyL3Tfc$_$10,ZtocGyL3Tfc,2,2,1.0,0.6023869514465332,0.04427609592676163,0.18993152678012848,0.7657924294471741 +ZtocGyL3Tfc$_$9,ZtocGyL3Tfc,2,2,1.6666666269302368,1.019940972328186,0.023412978276610374,0.12337541580200195,0.8532116413116455 +ZznoGQVwTtw$_$4,ZznoGQVwTtw,2,2,1.6666666269302368,1.2846673727035522,0.0585453137755394,0.17000053822994232,0.7714541554450989 +_1nvuNk7EFY$_$10,_1nvuNk7EFY,0,0,-0.3333333432674408,-1.2207183837890625,0.7560017704963684,0.1711728721857071,0.0728253498673439 +_OmWVs0-6UM$_$0,_OmWVs0-6UM,1,2,0.0,0.6583707332611084,0.04580472409725189,0.2272484451532364,0.7269468307495117 +_on_E-ZWy0I$_$21,_on_E-ZWy0I,2,2,1.3333333730697632,1.5160661935806274,0.25460085272789,0.1724669188261032,0.572932243347168 +_on_E-ZWy0I$_$3,_on_E-ZWy0I,1,0,0.0,-0.9349823594093323,0.5707839727401733,0.2889319956302643,0.14028401672840118 +_on_E-ZWy0I$_$12,_on_E-ZWy0I,0,2,-1.0,0.726305365562439,0.10590697824954987,0.26286521553993225,0.6312277913093567 +_q7DM8WkzAQ$_$3,_q7DM8WkzAQ,2,2,0.6666666865348816,0.5887271761894226,0.034194283187389374,0.17347118258476257,0.7923344969749451 +a4PHWxILDp0$_$15,a4PHWxILDp0,1,0,0.0,-0.9179368019104004,0.6274974346160889,0.24856199324131012,0.12394054234027863 +ahG9c_uaf8s$_$1,ahG9c_uaf8s,2,2,0.3333333432674408,0.9695884585380554,0.02655874378979206,0.1344383805990219,0.8390028476715088 +ahG9c_uaf8s$_$3,ahG9c_uaf8s,2,2,0.3333333432674408,0.9357518553733826,0.02865179441869259,0.12592171132564545,0.8454264402389526 +b92iw0OAnI4$_$7,b92iw0OAnI4,2,2,1.3333333730697632,1.2750953435897827,0.13634733855724335,0.17461128532886505,0.6890414357185364 +bNQOeiAotbk$_$8,bNQOeiAotbk,1,2,0.0,0.6614500880241394,0.07976718991994858,0.2435571253299713,0.6766757369041443 +bdFCD-3BCRg$_$2,bdFCD-3BCRg,2,2,1.0,1.6363192796707153,0.028291167691349983,0.09619978815317154,0.8755090832710266 +bdFCD-3BCRg$_$11,bdFCD-3BCRg,2,2,0.6666666865348816,1.1382931470870972,0.018253076821565628,0.10351217538118362,0.8782346844673157 +bdFCD-3BCRg$_$12,bdFCD-3BCRg,2,2,1.0,0.5194320678710938,0.02674579806625843,0.18093319237232208,0.792320966720581 +c5VEvmutmVg$_$13,c5VEvmutmVg,2,2,1.0,0.887597382068634,0.07131140679121017,0.19804063439369202,0.7306479811668396 +c5VEvmutmVg$_$19,c5VEvmutmVg,1,2,0.0,0.8160607218742371,0.06708656251430511,0.2759793996810913,0.6569340825080872 +c5VEvmutmVg$_$0,c5VEvmutmVg,2,2,1.6666666269302368,1.4400084018707275,0.04212536662817001,0.13115094602108002,0.8267236948013306 +c5zxqITn3ZM$_$5,c5zxqITn3ZM,2,0,0.6666666865348816,-1.2372018098831177,0.7176001071929932,0.14889748394489288,0.13350245356559753 +c5zxqITn3ZM$_$6,c5zxqITn3ZM,2,2,1.0,1.0249121189117432,0.022179564461112022,0.11336737871170044,0.864453136920929 +c9hE1ghElrM$_$2,c9hE1ghElrM,1,2,0.0,0.8719252943992615,0.1603708565235138,0.34598416090011597,0.49364498257637024 +c9hE1ghElrM$_$4,c9hE1ghElrM,1,2,0.0,1.3374208211898804,0.27468642592430115,0.2440754622220993,0.48123812675476074 +cW-aX4dPVfk$_$4,cW-aX4dPVfk,0,2,-0.3333333432674408,0.8868791460990906,0.19851617515087128,0.27940788865089417,0.5220759510993958 +cW-aX4dPVfk$_$11,cW-aX4dPVfk,2,2,0.3333333432674408,1.4649567604064941,0.2666493058204651,0.20846983790397644,0.5248808264732361 +cW-aX4dPVfk$_$32,cW-aX4dPVfk,2,2,1.6666666269302368,1.548282504081726,0.40596243739128113,0.18724776804447174,0.4067898094654083 +cW-aX4dPVfk$_$30,cW-aX4dPVfk,2,2,2.3333332538604736,1.5765628814697266,0.02583891525864601,0.09432388842105865,0.8798372149467468 +cml9rShionM$_$3,cml9rShionM,0,2,-0.3333333432674408,1.4451366662979126,0.016827993094921112,0.07983561605215073,0.9033364653587341 +d-Uw_uZyUys$_$1,d-Uw_uZyUys,0,2,-2.3333332538604736,0.582134485244751,0.17190547287464142,0.3808492124080658,0.4472452998161316 +d-Uw_uZyUys$_$6,d-Uw_uZyUys,0,0,-2.3333332538604736,-1.3167414665222168,0.7719050049781799,0.13816119730472565,0.08993387967348099 +dHk--ExZbHs$_$7,dHk--ExZbHs,2,2,0.6666666865348816,1.4272050857543945,0.06489468365907669,0.15702904760837555,0.7780762314796448 +dR68gbeOWOc$_$1,dR68gbeOWOc,2,2,0.3333333432674408,0.8048392534255981,0.0396275520324707,0.16007553040981293,0.8002969026565552 +dTcz1am1eUw$_$2,dTcz1am1eUw,2,2,1.3333333730697632,1.0701128244400024,0.01788683421909809,0.1004757434129715,0.8816373944282532 +ddWHTdJz2O8$_$1,ddWHTdJz2O8,0,2,-0.3333333432674408,1.2971967458724976,0.06391100585460663,0.16231992840766907,0.7737690210342407 +dlE05KC95uk$_$11,dlE05KC95uk,2,2,0.3333333432674408,0.46668073534965515,0.1471090018749237,0.3686639070510864,0.48422715067863464 +eE8Qr9fOvVA$_$2,eE8Qr9fOvVA,0,2,-1.3333333730697632,0.5411533713340759,0.11536595970392227,0.39142894744873047,0.49320510029792786 +eFV7iFPYZB4$_$12,eFV7iFPYZB4,2,2,1.6666666269302368,0.9863741993904114,0.018718378618359566,0.10654393583536148,0.8747377395629883 +eFV7iFPYZB4$_$14,eFV7iFPYZB4,2,2,1.3333333730697632,0.5129928588867188,0.055000945925712585,0.26686421036720276,0.6781348586082458 +eJfT7-dDqzA$_$3,eJfT7-dDqzA,2,2,0.6666666865348816,0.5946382284164429,0.24369163811206818,0.3637959957122803,0.39251238107681274 +eJfT7-dDqzA$_$2,eJfT7-dDqzA,2,2,0.6666666865348816,0.7909950017929077,0.03488363325595856,0.15982495248317719,0.8052914142608643 +eJfT7-dDqzA$_$7,eJfT7-dDqzA,2,2,0.6666666865348816,0.6892493367195129,0.04015311226248741,0.19291307032108307,0.7669338583946228 +eKQKEi2-0Ws$_$0,eKQKEi2-0Ws,1,2,0.0,1.075120210647583,0.25018271803855896,0.2783954441547394,0.4714218080043793 +eQc5uI7FKCU$_$5,eQc5uI7FKCU,2,2,0.3333333432674408,0.8044562935829163,0.0361536480486393,0.17479602992534637,0.7890503406524658 +ey1lr8wFFDc$_$0,ey1lr8wFFDc,2,2,0.6666666865348816,0.4761062562465668,0.04959028959274292,0.2418774664402008,0.7085322737693787 +f-VdKweez2U$_$4,f-VdKweez2U,1,2,0.0,0.9558759331703186,0.05511512979865074,0.17151597142219543,0.7733688950538635 +f8Puta8k8fU$_$1,f8Puta8k8fU,1,0,0.0,-0.811378538608551,0.5015178918838501,0.3150760233402252,0.18340608477592468 +fT6SrlsWV7M$_$12,fT6SrlsWV7M,0,2,-1.3333333730697632,0.4706750512123108,0.08525904268026352,0.3124791979789734,0.6022617220878601 +fWAKek8jA5M$_$0,fWAKek8jA5M,0,0,-1.6666666269302368,-1.9065769910812378,0.9071187376976013,0.05832047760486603,0.034560851752758026 +fWAKek8jA5M$_$3,fWAKek8jA5M,0,2,-1.3333333730697632,0.5747542381286621,0.19500860571861267,0.37469714879989624,0.43029430508613586 +fWAKek8jA5M$_$4,fWAKek8jA5M,1,0,0.0,-0.6800030469894409,0.4412113130092621,0.34555384516716003,0.21323475241661072 +frCWtiam4tE$_$0,frCWtiam4tE,2,2,1.6666666269302368,0.8546926379203796,0.2668340504169464,0.26930251717567444,0.46386340260505676 +fsBzpr4k3rY$_$0,fsBzpr4k3rY,2,0,2.0,-1.2673194408416748,0.67946457862854,0.14843590557575226,0.1720995008945465 +fz-MzQcOBwQ$_$1,fz-MzQcOBwQ,2,2,1.3333333730697632,0.8368644714355469,0.028102053329348564,0.16270682215690613,0.8091911673545837 +gE7kUqMqQ9g$_$2,gE7kUqMqQ9g,0,0,-0.6666666865348816,-1.1336976289749146,0.7681253552436829,0.160795196890831,0.07107936590909958 +gJjkCPO7iXg$_$1,gJjkCPO7iXg,2,2,1.0,1.5140607357025146,0.04070435091853142,0.12908919155597687,0.8302064538002014 +gJjkCPO7iXg$_$0,gJjkCPO7iXg,2,2,1.0,1.141797423362732,0.059346482157707214,0.19096589088439941,0.7496876120567322 +gLTxaEcx41E$_$2,gLTxaEcx41E,1,2,0.0,0.4924909770488739,0.06393266469240189,0.2752063274383545,0.6608609557151794 +gLTxaEcx41E$_$4,gLTxaEcx41E,1,0,0.0,-1.3655469417572021,0.7257509231567383,0.1419719010591507,0.13227717578411102 +gLTxaEcx41E$_$7,gLTxaEcx41E,1,2,0.0,0.9390767216682434,0.24021179974079132,0.24526919424533844,0.5145189762115479 +gLTxaEcx41E$_$6,gLTxaEcx41E,2,2,0.3333333432674408,1.0554330348968506,0.06710522621870041,0.17433308064937592,0.7585616707801819 +gcFECfN4BCU$_$23,gcFECfN4BCU,2,2,2.0,1.8431082963943481,0.023891065269708633,0.07864149659872055,0.8974673748016357 +gcFECfN4BCU$_$10,gcFECfN4BCU,2,2,0.6666666865348816,1.1382390260696411,0.04292508214712143,0.13276176154613495,0.8243131637573242 +gcFECfN4BCU$_$15,gcFECfN4BCU,2,2,1.3333333730697632,0.5706181526184082,0.09772136062383652,0.2549736797809601,0.647304892539978 +gpn71-aKWwQ$_$2,gpn71-aKWwQ,2,2,1.6666666269302368,0.8878654837608337,0.06888343393802643,0.1717064082622528,0.7594101428985596 +h1ZZHUU4j0k$_$3,h1ZZHUU4j0k,2,2,0.6666666865348816,0.8458969593048096,0.02036825194954872,0.12611395120620728,0.8535177707672119 +h1ZZHUU4j0k$_$4,h1ZZHUU4j0k,2,0,0.6666666865348816,-0.8234512805938721,0.5761878490447998,0.23355162143707275,0.19026054441928864 +h1ZZHUU4j0k$_$10,h1ZZHUU4j0k,2,2,1.0,0.5942131280899048,0.028751127421855927,0.16058531403541565,0.8106635808944702 +h1ZZHUU4j0k$_$13,h1ZZHUU4j0k,0,2,-1.0,0.4410059154033661,0.16662392020225525,0.33584725856781006,0.4975288510322571 +iQDB_OkAQWs$_$4,iQDB_OkAQWs,1,1,0.0,0.0,0.312805712223053,0.3630046844482422,0.32418957352638245 +iXiMifHNKPI$_$8,iXiMifHNKPI,2,2,0.3333333432674408,0.47589462995529175,0.056351810693740845,0.27336257696151733,0.6702855825424194 +j1m6ctAgjsM$_$1,j1m6ctAgjsM,2,2,1.3333333730697632,0.6650707721710205,0.04346945881843567,0.1680339127779007,0.7884966135025024 +j1m6ctAgjsM$_$2,j1m6ctAgjsM,2,2,1.0,1.0174022912979126,0.04731956124305725,0.1387866884469986,0.8138937950134277 +j1m6ctAgjsM$_$4,j1m6ctAgjsM,2,2,0.3333333432674408,1.4244530200958252,0.02848210372030735,0.1118878647685051,0.8596299886703491 +j1m6ctAgjsM$_$14,j1m6ctAgjsM,1,2,0.0,0.5652525424957275,0.07925868034362793,0.279389888048172,0.6413514614105225 +j1m6ctAgjsM$_$18,j1m6ctAgjsM,2,2,1.0,0.6637436747550964,0.09467332065105438,0.24935637414455414,0.6559703350067139 +j1m6ctAgjsM$_$30,j1m6ctAgjsM,2,2,0.3333333432674408,0.6402956247329712,0.08353767544031143,0.23795567452907562,0.6785066723823547 +j1m6ctAgjsM$_$35,j1m6ctAgjsM,2,2,1.0,0.889647901058197,0.027426281943917274,0.1266641616821289,0.845909595489502 +j1m6ctAgjsM$_$33,j1m6ctAgjsM,2,2,0.3333333432674408,0.6240250468254089,0.06332200765609741,0.22768695652484894,0.7089909911155701 +j1m6ctAgjsM$_$37,j1m6ctAgjsM,1,2,0.0,0.54197096824646,0.0544266551733017,0.23370277881622314,0.711870551109314 +jXQmVFcOiUI$_$0,jXQmVFcOiUI,2,2,1.0,1.2367122173309326,0.017642593011260033,0.09219169616699219,0.8901656866073608 +jj8aSNPHMw8$_$14,jj8aSNPHMw8,1,1,0.0,0.0,0.22246645390987396,0.4347206652164459,0.3428129553794861 +jj8aSNPHMw8$_$5,jj8aSNPHMw8,0,2,-1.3333333730697632,1.0512775182724,0.2019982635974884,0.2772063910961151,0.5207953453063965 +jqutn5ou8_0$_$6,jqutn5ou8_0,2,2,0.6666666865348816,0.6291654109954834,0.034281812608242035,0.19107644259929657,0.774641752243042 +jscKL5jS-SQ$_$17,jscKL5jS-SQ,2,2,1.0,1.2804161310195923,0.05628122016787529,0.14392797648906708,0.7997908592224121 +kaudsLIvYC8$_$0,kaudsLIvYC8,2,2,1.6666666269302368,1.1161319017410278,0.04508035629987717,0.1391480714082718,0.8157715797424316 +kg-W6-hP2Do$_$3,kg-W6-hP2Do,1,2,0.0,1.3420926332473755,0.22450494766235352,0.21676866710186005,0.55872642993927 +kpS4BXif_Sw$_$12,kpS4BXif_Sw,0,0,-2.0,-1.3237770795822144,0.6632429957389832,0.1869136244058609,0.14984340965747833 +kpS4BXif_Sw$_$15,kpS4BXif_Sw,2,2,1.3333333730697632,1.4191231727600098,0.1289462000131607,0.17954102158546448,0.6915128231048584 +kpS4BXif_Sw$_$4,kpS4BXif_Sw,1,2,0.0,1.0718553066253662,0.02613658830523491,0.12226743251085281,0.8515959978103638 +kpS4BXif_Sw$_$8,kpS4BXif_Sw,0,0,-1.0,-1.2704665660858154,0.7905808091163635,0.13702361285686493,0.07239558547735214 +ktblaVOnFVE$_$4,ktblaVOnFVE,2,2,1.3333333730697632,0.5976791977882385,0.303676038980484,0.3475016951560974,0.3488222658634186 +l0vCKpk6Aes$_$24,l0vCKpk6Aes,1,2,0.0,1.2166017293930054,0.04539249837398529,0.14766399562358856,0.8069435358047485 +l0vCKpk6Aes$_$12,l0vCKpk6Aes,2,2,1.0,0.978154718875885,0.04337175935506821,0.1967858374118805,0.7598423361778259 +l0vCKpk6Aes$_$19,l0vCKpk6Aes,1,2,0.0,0.7687042355537415,0.11681883782148361,0.254449725151062,0.6287314295768738 +l4oMbKDuW3Y$_$3,l4oMbKDuW3Y,0,0,-0.6666666865348816,-0.8730780482292175,0.5602473616600037,0.24487309157848358,0.19487954676151276 +lD4xtQ6NpDY$_$2,lD4xtQ6NpDY,1,2,0.0,0.563877522945404,0.1747005581855774,0.3330640494823456,0.49223536252975464 +lYwgLa4R5XQ$_$1,lYwgLa4R5XQ,1,1,0.0,0.0,0.3292064070701599,0.4097832143306732,0.26101037859916687 +lkIe41StoGI$_$10,lkIe41StoGI,1,1,0.0,0.0,0.31552091240882874,0.45229700207710266,0.23218198120594025 +lkIe41StoGI$_$3,lkIe41StoGI,2,2,0.6666666865348816,0.5215076804161072,0.047243040055036545,0.2649385631084442,0.6878183484077454 +lkIe41StoGI$_$2,lkIe41StoGI,2,1,0.6666666865348816,0.0,0.2594708204269409,0.430852472782135,0.3096766769886017 +lkIe41StoGI$_$8,lkIe41StoGI,1,2,0.0,0.4753698408603668,0.13407915830612183,0.39572709798812866,0.4701937437057495 +lwL4hjhkid4$_$1,lwL4hjhkid4,1,1,0.0,0.0,0.1974235624074936,0.4699353873729706,0.332641065120697 +lwL4hjhkid4$_$3,lwL4hjhkid4,2,2,2.0,0.5576469302177429,0.034460119903087616,0.182013601064682,0.7835263013839722 +lwL4hjhkid4$_$6,lwL4hjhkid4,2,2,0.3333333432674408,0.529764711856842,0.05853239819407463,0.22918950021266937,0.7122780680656433 +m-7yRWZLwLY$_$0,m-7yRWZLwLY,1,2,0.0,0.683711588382721,0.28781020641326904,0.3537844717502594,0.35840529203414917 +mVnqP-vLpuo$_$0,mVnqP-vLpuo,2,2,0.6666666865348816,1.0657914876937866,0.033506955951452255,0.12524773180484772,0.8412452936172485 +mZ_8em_-CGc$_$7,mZ_8em_-CGc,2,2,0.6666666865348816,0.516923725605011,0.06422488391399384,0.27166908979415894,0.664106011390686 +mgsvwAVQAQo$_$4,mgsvwAVQAQo,2,2,0.3333333432674408,0.5180142521858215,0.3105572760105133,0.3337846100330353,0.3556581139564514 +mmg_eTDHjkk$_$5,mmg_eTDHjkk,2,2,0.3333333432674408,0.8220041990280151,0.14536598324775696,0.29928117990493774,0.5553528070449829 +mmg_eTDHjkk$_$8,mmg_eTDHjkk,2,2,0.6666666865348816,1.0895177125930786,0.09511423856019974,0.23922955989837646,0.6656562089920044 +mmg_eTDHjkk$_$15,mmg_eTDHjkk,2,2,1.0,0.9131620526313782,0.11634228378534317,0.23967288434505463,0.6439847946166992 +mzAu5gxjE-w$_$2,mzAu5gxjE-w,2,2,1.6666666269302368,1.7171915769577026,0.029488159343600273,0.09639380872249603,0.8741179704666138 +nFTo-Lz4Fr8$_$1,nFTo-Lz4Fr8,2,2,0.6666666865348816,1.2415680885314941,0.0158726517111063,0.09109441190958023,0.893032968044281 +nZFPKP9kBkw$_$4,nZFPKP9kBkw,1,2,0.0,1.6277737617492676,0.01757785864174366,0.07607472687959671,0.9063474535942078 +namehdJxRIM$_$1,namehdJxRIM,2,2,0.3333333432674408,0.5058836936950684,0.050541799515485764,0.28407031297683716,0.6653878688812256 +o2XbNJDpOlc$_$6,o2XbNJDpOlc,2,2,1.6666666269302368,0.46827182173728943,0.09299976378679276,0.29559528827667236,0.6114049553871155 +oBS-IW-BO00$_$3,oBS-IW-BO00,1,2,0.0,0.3927052319049835,0.12234380096197128,0.3449726402759552,0.5326835513114929 +oHff2W51wZ8$_$1,oHff2W51wZ8,2,2,1.3333333730697632,1.0964308977127075,0.020108183845877647,0.10174976289272308,0.8781419992446899 +oHff2W51wZ8$_$2,oHff2W51wZ8,2,2,2.0,1.7212594747543335,0.026885509490966797,0.08524253219366074,0.8878719806671143 +oHff2W51wZ8$_$5,oHff2W51wZ8,2,2,2.6666667461395264,1.2407383918762207,0.05085626617074013,0.16612982749938965,0.7830138802528381 +oPlnhc0DkcU$_$1,oPlnhc0DkcU,0,1,-0.3333333432674408,0.0,0.33666741847991943,0.35039353370666504,0.3129390478134155 +oZxMx8e0x2U$_$2,oZxMx8e0x2U,2,0,0.3333333432674408,-0.9261665344238281,0.6489612460136414,0.24108228087425232,0.10995645076036453 +obGF3RfWQKE$_$24,obGF3RfWQKE,1,2,0.0,0.7322477698326111,0.15937063097953796,0.2746598422527313,0.5659695267677307 +obGF3RfWQKE$_$3,obGF3RfWQKE,2,2,1.3333333730697632,1.9131879806518555,0.04901048168540001,0.11003708094358444,0.8409523963928223 +ossKC1VrusE$_$3,ossKC1VrusE,2,2,0.6666666865348816,1.1360467672348022,0.040905486792325974,0.16044782102108002,0.7986466884613037 +ossKC1VrusE$_$2,ossKC1VrusE,2,2,1.6666666269302368,1.1589299440383911,0.04652312025427818,0.14435678720474243,0.8091201186180115 +ozA7pRW4gFM$_$7,ozA7pRW4gFM,1,0,0.0,-0.5931517481803894,0.4165356755256653,0.34107378125190735,0.24239051342010498 +p4WmcxrXkc4$_$0,p4WmcxrXkc4,2,2,0.3333333432674408,1.1593519449234009,0.026823880150914192,0.1175500899553299,0.8556260466575623 +p7zuPEZgtY4$_$0,p7zuPEZgtY4,2,1,0.3333333432674408,0.0,0.2868615686893463,0.42764246463775635,0.28549596667289734 +pIaEcqnzI-s$_$3,pIaEcqnzI-s,2,2,0.3333333432674408,0.7710813283920288,0.10488704591989517,0.3091749846935272,0.5859379768371582 +pIaEcqnzI-s$_$5,pIaEcqnzI-s,0,2,-0.3333333432674408,0.547025740146637,0.14051122963428497,0.4098162353038788,0.44967254996299744 +pIaEcqnzI-s$_$7,pIaEcqnzI-s,0,2,-0.3333333432674408,0.5021352171897888,0.12364806979894638,0.3571665287017822,0.5191854238510132 +pIaEcqnzI-s$_$6,pIaEcqnzI-s,1,2,0.0,1.368254542350769,0.18166933953762054,0.2713199555873871,0.5470107197761536 +pQpy7RSfWzM$_$19,pQpy7RSfWzM,2,2,0.3333333432674408,1.2518025636672974,0.025403430685400963,0.09763170778751373,0.876964807510376 +pQpy7RSfWzM$_$8,pQpy7RSfWzM,1,2,0.0,0.8494896292686462,0.24586589634418488,0.2609995901584625,0.493134468793869 +pSxte-ms0t8$_$33,pSxte-ms0t8,0,0,-0.3333333432674408,-1.0393985509872437,0.4605114758014679,0.25359803438186646,0.28589048981666565 +pSxte-ms0t8$_$2,pSxte-ms0t8,2,2,0.6666666865348816,1.0129295587539673,0.36153942346572876,0.2756459712982178,0.36281460523605347 +pSxte-ms0t8$_$7,pSxte-ms0t8,0,0,-2.0,-1.2969454526901245,0.7885392308235168,0.1391344517469406,0.07232628762722015 +pSxte-ms0t8$_$6,pSxte-ms0t8,2,2,1.0,0.5804097056388855,0.049606405198574066,0.2529117166996002,0.6974819302558899 +pZye4zFzk3o$_$2,pZye4zFzk3o,0,2,-0.6666666865348816,0.3667159974575043,0.07331009209156036,0.2924245297908783,0.6342653632164001 +pZye4zFzk3o$_$5,pZye4zFzk3o,0,0,-0.6666666865348816,-1.0340241193771362,0.7093760371208191,0.1745956391096115,0.11602829396724701 +pnpFPX34Agk$_$2,pnpFPX34Agk,2,2,0.3333333432674408,0.40650051832199097,0.17767666280269623,0.40212488174438477,0.4201984703540802 +q5M1God4M6Y$_$9,q5M1God4M6Y,2,2,0.6666666865348816,0.7980647683143616,0.03246883675456047,0.20627754926681519,0.7612535953521729 +qDfSYz0PX9g$_$0,qDfSYz0PX9g,0,0,-2.0,-1.7012563943862915,0.7000746130943298,0.1309589296579361,0.16896645724773407 +qDfSYz0PX9g$_$8,qDfSYz0PX9g,0,1,-0.6666666865348816,0.0,0.3296450972557068,0.3732771873474121,0.2970777451992035 +qEuJj4uW93E$_$1,qEuJj4uW93E,1,2,0.0,1.213597059249878,0.12221519649028778,0.2358390986919403,0.6419457197189331 +qgC8_emxSIU$_$6,qgC8_emxSIU,1,2,0.0,0.5373997688293457,0.06738558411598206,0.25890374183654785,0.6737106442451477 +r46amqjpWgg$_$26,r46amqjpWgg,2,2,0.6666666865348816,1.6132463216781616,0.0878615602850914,0.14309003949165344,0.7690484523773193 +r46amqjpWgg$_$22,r46amqjpWgg,2,2,0.6666666865348816,0.9342116713523865,0.05522993952035904,0.16142724454402924,0.7833427786827087 +r46amqjpWgg$_$6,r46amqjpWgg,2,2,1.3333333730697632,0.9953523278236389,0.0249559935182333,0.1288042813539505,0.8462396860122681 +r46amqjpWgg$_$15,r46amqjpWgg,1,2,0.0,0.7183831334114075,0.1211165338754654,0.28230857849121094,0.5965749025344849 +r46amqjpWgg$_$32,r46amqjpWgg,2,2,1.0,1.5809886455535889,0.04274412989616394,0.1186353862285614,0.8386204242706299 +rcfnqiD0y8o$_$0,rcfnqiD0y8o,2,2,1.6666666269302368,1.3941153287887573,0.015565354377031326,0.07650334388017654,0.9079313278198242 +rhQB8e999-Q$_$10,rhQB8e999-Q,2,2,1.0,0.9107648730278015,0.04689864441752434,0.14611318707466125,0.8069881796836853 +rhQB8e999-Q$_$0,rhQB8e999-Q,2,2,0.6666666865348816,1.0812746286392212,0.014234315603971481,0.09505799412727356,0.8907076716423035 +rhQB8e999-Q$_$5,rhQB8e999-Q,1,2,0.0,0.9281490445137024,0.017196377739310265,0.10211984813213348,0.8806837797164917 +rhQB8e999-Q$_$7,rhQB8e999-Q,2,2,0.6666666865348816,0.9137019515037537,0.018203381448984146,0.10899092257022858,0.8728057146072388 +rhQB8e999-Q$_$8,rhQB8e999-Q,1,2,0.0,0.4188973307609558,0.04001326113939285,0.23152436316013336,0.7284623384475708 +svsbGQn389o$_$8,svsbGQn389o,0,0,-1.3333333730697632,-1.295698642730713,0.7696142196655273,0.14308574795722961,0.08730006217956543 +tC2KicUHB9Q$_$7,tC2KicUHB9Q,0,2,-0.3333333432674408,0.7129670977592468,0.13441435992717743,0.3257356286048889,0.5398499965667725 +tNd3--lvSXE$_$6,tNd3--lvSXE,2,1,0.6666666865348816,0.0,0.33098387718200684,0.34127551317214966,0.3277406096458435 +teQqaAmqqx0$_$0,teQqaAmqqx0,1,2,0.0,1.631516695022583,0.11092894524335861,0.16136226058006287,0.7277088165283203 +teQqaAmqqx0$_$3,teQqaAmqqx0,1,2,0.0,1.3619517087936401,0.09977588057518005,0.1811205893754959,0.7191035151481628 +tnWmVXZ87h0$_$0,tnWmVXZ87h0,2,2,2.0,1.2887792587280273,0.02467353641986847,0.10553506016731262,0.8697914481163025 +tnWmVXZ87h0$_$3,tnWmVXZ87h0,2,2,1.6666666269302368,1.5015583038330078,0.11963248252868652,0.16407491266727448,0.7162926197052002 +ttfaZ8IIWCo$_$2,ttfaZ8IIWCo,2,2,2.3333332538604736,1.1879968643188477,0.020198462530970573,0.11486733704805374,0.8649342656135559 +ttfaZ8IIWCo$_$5,ttfaZ8IIWCo,2,2,2.3333332538604736,1.3463516235351562,0.018294241279363632,0.08670038729906082,0.8950054049491882 +ttfaZ8IIWCo$_$4,ttfaZ8IIWCo,2,2,2.3333332538604736,1.74891197681427,0.0252248402684927,0.10154950618743896,0.8732256293296814 +tymso_pAxhk$_$12,tymso_pAxhk,0,2,-1.0,0.5947335362434387,0.14379705488681793,0.2944880425930023,0.5617149472236633 +u9ZV8jb_-U0$_$0,u9ZV8jb_-U0,0,0,-1.6666666269302368,-0.7939320206642151,0.45898789167404175,0.242364764213562,0.29864731431007385 +um8WVjZMLUc$_$1,um8WVjZMLUc,1,0,0.0,-0.9538581967353821,0.7080385684967041,0.20675238966941833,0.08520907908678055 +unOeTDc2rlY$_$27,unOeTDc2rlY,2,2,1.0,0.8811704516410828,0.1673739105463028,0.21969787776470184,0.6129282116889954 +unOeTDc2rlY$_$31,unOeTDc2rlY,2,2,1.0,0.7804546356201172,0.30581626296043396,0.33328777551651,0.36089596152305603 +unOeTDc2rlY$_$29,unOeTDc2rlY,0,2,-0.3333333432674408,1.4089043140411377,0.15807069838047028,0.1786375790834427,0.6632917523384094 +uogwnZGb-iE$_$26,uogwnZGb-iE,2,2,2.0,1.1801235675811768,0.03711126744747162,0.12400218099355698,0.838886559009552 +uogwnZGb-iE$_$21,uogwnZGb-iE,2,2,0.3333333432674408,1.201884388923645,0.23647631704807281,0.19970294833183289,0.5638207197189331 +uogwnZGb-iE$_$22,uogwnZGb-iE,2,2,2.0,1.8618136644363403,0.020340412855148315,0.0744621530175209,0.9051975011825562 +uogwnZGb-iE$_$28,uogwnZGb-iE,2,2,2.3333332538604736,1.6271730661392212,0.021713774651288986,0.07813054323196411,0.9001556634902954 +uogwnZGb-iE$_$13,uogwnZGb-iE,2,2,2.3333332538604736,1.4789246320724487,0.02542736567556858,0.08728035539388657,0.887292206287384 +uogwnZGb-iE$_$6,uogwnZGb-iE,2,2,1.0,1.4015722274780273,0.02764771692454815,0.10435143858194351,0.8680008053779602 +vB_kZocHtYo$_$8,vB_kZocHtYo,1,2,0.0,0.34594258666038513,0.07777755707502365,0.36995166540145874,0.5522708296775818 +vGkIaClHKDg$_$6,vGkIaClHKDg,2,2,1.3333333730697632,1.658618450164795,0.021103912964463234,0.08212687075138092,0.8967692255973816 +vR90Pdx9wxs$_$3,vR90Pdx9wxs,2,2,0.3333333432674408,0.5454605221748352,0.06409898400306702,0.263334184885025,0.672566831111908 +v_8QeoNc4QY$_$5,v_8QeoNc4QY,2,1,0.3333333432674408,0.0,0.2432454526424408,0.4597669541835785,0.2969876229763031 +veA6ECsGFxI$_$0,veA6ECsGFxI,2,2,0.6666666865348816,1.4521729946136475,0.055504463613033295,0.14007939398288727,0.8044161796569824 +wC_1M7KIv9s$_$24,wC_1M7KIv9s,1,2,0.0,0.3855466842651367,0.08699969202280045,0.349127858877182,0.5638724565505981 +wC_1M7KIv9s$_$12,wC_1M7KIv9s,2,2,0.3333333432674408,0.7075777649879456,0.027833083644509315,0.16293364763259888,0.8092332482337952 +wC_1M7KIv9s$_$23,wC_1M7KIv9s,1,0,0.0,-0.5256593227386475,0.38495078682899475,0.35730263590812683,0.2577466070652008 +wC_1M7KIv9s$_$1,wC_1M7KIv9s,2,2,0.6666666865348816,0.5729516744613647,0.10242796689271927,0.3614032566547394,0.5361688137054443 +wC_1M7KIv9s$_$5,wC_1M7KIv9s,1,1,0.0,0.0,0.1570928394794464,0.4627329409122467,0.3801742494106293 +wC_1M7KIv9s$_$25,wC_1M7KIv9s,2,2,2.0,0.3854433298110962,0.06381335854530334,0.29286083579063416,0.6433258056640625 +wC_1M7KIv9s$_$21,wC_1M7KIv9s,0,0,-0.3333333432674408,-0.7168398499488831,0.38885900378227234,0.2931138575077057,0.3180271089076996 +wHeZHLv9wGI$_$12,wHeZHLv9wGI,0,2,-0.6666666865348816,1.413135290145874,0.40515416860580444,0.17675356566905975,0.4180923104286194 +wnL3ld9bM2o$_$25,wnL3ld9bM2o,2,2,2.0,0.6124185919761658,0.21610455214977264,0.2955635190010071,0.48833194375038147 +wnL3ld9bM2o$_$21,wnL3ld9bM2o,2,0,0.3333333432674408,-1.1300075054168701,0.7298170924186707,0.17324791848659515,0.096934974193573 +wnL3ld9bM2o$_$4,wnL3ld9bM2o,2,2,0.3333333432674408,1.6628611087799072,0.06476062536239624,0.11630676686763763,0.8189325928688049 +wnL3ld9bM2o$_$9,wnL3ld9bM2o,2,2,1.0,0.9349401593208313,0.03489011898636818,0.15806959569454193,0.8070403337478638 +wnL3ld9bM2o$_$15,wnL3ld9bM2o,1,0,0.0,-0.7716701626777649,0.5621341466903687,0.26014384627342224,0.1777220070362091 +wnL3ld9bM2o$_$14,wnL3ld9bM2o,1,2,0.0,0.4606679081916809,0.10741768032312393,0.3886944055557251,0.503887951374054 +wznRBN1fWj4$_$0,wznRBN1fWj4,1,1,0.0,0.0,0.19143828749656677,0.42784926295280457,0.3807123899459839 +wznRBN1fWj4$_$6,wznRBN1fWj4,1,2,0.0,0.8467816710472107,0.022524205967783928,0.1411375254392624,0.836338222026825 +x0rLwBIocuI$_$7,x0rLwBIocuI,0,0,-0.6666666865348816,-1.4041789770126343,0.8221082091331482,0.1149144172668457,0.06297735124826431 +xBE9YWYGjtk$_$7,xBE9YWYGjtk,2,2,2.0,1.368040680885315,0.0336153469979763,0.11023859679698944,0.8561460375785828 +xSCvspXYU9k$_$19,xSCvspXYU9k,0,0,-1.0,-1.0884480476379395,0.5014991164207458,0.2299908995628357,0.26850998401641846 +xSCvspXYU9k$_$7,xSCvspXYU9k,1,2,0.0,0.8271788954734802,0.031202349811792374,0.1629752367734909,0.8058224320411682 +xXXcgb9eZ9Y$_$7,xXXcgb9eZ9Y,0,0,-0.3333333432674408,-0.9878056049346924,0.5822906494140625,0.272654265165329,0.14505504071712494 +xmLJHru6Z1M$_$12,xmLJHru6Z1M,2,2,1.0,0.4710705876350403,0.11325249075889587,0.2973526418209076,0.5893948674201965 +xmLJHru6Z1M$_$14,xmLJHru6Z1M,2,0,1.0,-0.6324250102043152,0.4794504642486572,0.2954977750778198,0.22505176067352295 +xmLJHru6Z1M$_$8,xmLJHru6Z1M,2,2,0.3333333432674408,0.39196816086769104,0.09495759755373001,0.32010287046432495,0.5849394798278809 +xobMRm5Vs44$_$1,xobMRm5Vs44,1,2,0.0,1.6530905961990356,0.02484160289168358,0.09719289094209671,0.8779654502868652 +yCpHmPSshKY$_$1,yCpHmPSshKY,2,0,1.6666666269302368,-1.500623106956482,0.5466458797454834,0.18199563026428223,0.271358460187912 +yCpHmPSshKY$_$0,yCpHmPSshKY,2,0,0.6666666865348816,-0.8426749110221863,0.3450227677822113,0.3201075494289398,0.3348696827888489 +yLo-Jl8nBXU$_$11,yLo-Jl8nBXU,2,2,0.6666666865348816,1.6858893632888794,0.03354526683688164,0.10504364222288132,0.8614110946655273 +yLo-Jl8nBXU$_$0,yLo-Jl8nBXU,1,2,0.0,0.6853746771812439,0.28825536370277405,0.33045798540115356,0.3812866508960724 +yLo-Jl8nBXU$_$6,yLo-Jl8nBXU,2,2,1.3333333730697632,1.9503284692764282,0.06026960909366608,0.11347804963588715,0.8262523412704468 +ySblgk7T7eQ$_$12,ySblgk7T7eQ,0,0,-0.3333333432674408,-1.0392292737960815,0.4825916588306427,0.24643251299858093,0.27097585797309875 +ySblgk7T7eQ$_$0,ySblgk7T7eQ,2,2,1.0,1.7642968893051147,0.028417587280273438,0.08718102425336838,0.8844013810157776 +ySblgk7T7eQ$_$4,ySblgk7T7eQ,2,2,0.6666666865348816,1.0672507286071777,0.1940075159072876,0.26664334535598755,0.5393491387367249 +ySblgk7T7eQ$_$9,ySblgk7T7eQ,2,0,1.0,-1.2682405710220337,0.6610170006752014,0.18027426302433014,0.15870872139930725 +yUqNp-poh9M$_$2,yUqNp-poh9M,0,0,-0.3333333432674408,-1.1808096170425415,0.7273029088973999,0.16426880657672882,0.10842827707529068 +yUqNp-poh9M$_$9,yUqNp-poh9M,1,0,0.0,-1.029393196105957,0.7116742730140686,0.19364890456199646,0.09467675536870956 +ydzNAuqUAnc$_$6,ydzNAuqUAnc,0,2,-1.0,0.8410759568214417,0.34929558634757996,0.2951059639453888,0.35559844970703125 +yoDMh8FlHR8$_$0,yoDMh8FlHR8,2,2,1.6666666269302368,0.4407438337802887,0.08674679696559906,0.3174814283847809,0.5957717299461365 +yzCHa2qchpg$_$1,yzCHa2qchpg,0,2,-2.0,1.608330249786377,0.30379223823547363,0.16196736693382263,0.5342403650283813 +yzCHa2qchpg$_$2,yzCHa2qchpg,2,2,1.3333333730697632,0.9642849564552307,0.036618735641241074,0.13328246772289276,0.8300987482070923 +z441aDJvAcU$_$20,z441aDJvAcU,2,2,1.0,1.1666851043701172,0.015427269041538239,0.08555462956428528,0.8990180492401123 +z441aDJvAcU$_$21,z441aDJvAcU,2,2,0.3333333432674408,1.227781891822815,0.01493553351610899,0.0862542986869812,0.8988102078437805 +z441aDJvAcU$_$22,z441aDJvAcU,1,0,0.0,-0.817103922367096,0.43277090787887573,0.2806767523288727,0.2865523397922516 +zfZUOvZZTuk$_$1,zfZUOvZZTuk,1,2,0.0,0.8712804317474365,0.1646689772605896,0.3200417757034302,0.5152892470359802 +zfZUOvZZTuk$_$8,zfZUOvZZTuk,1,2,0.0,1.2180451154708862,0.08969342708587646,0.22820721566677094,0.6820993423461914 +zhNksSReaQk$_$27,zhNksSReaQk,1,2,0.0,0.784705400466919,0.11311059445142746,0.2466493397951126,0.6402400732040405 +zhNksSReaQk$_$44,zhNksSReaQk,0,2,-1.0,1.3575953245162964,0.06877906620502472,0.14165906608104706,0.7895618081092834 +zhNksSReaQk$_$28,zhNksSReaQk,0,0,-1.0,-0.7230533957481384,0.470581591129303,0.26071786880493164,0.2687005400657654 +zhNksSReaQk$_$1,zhNksSReaQk,1,2,0.0,1.2916456460952759,0.15736153721809387,0.2187885344028473,0.6238499283790588 +zhNksSReaQk$_$4,zhNksSReaQk,0,0,-0.3333333432674408,-1.7170065641403198,0.8384968638420105,0.0953374132514,0.06616570800542831 +zhNksSReaQk$_$15,zhNksSReaQk,1,2,0.0,1.0135209560394287,0.09770282357931137,0.2335919886827469,0.6687051653862 +zhNksSReaQk$_$36,zhNksSReaQk,1,0,0.0,-0.7251965403556824,0.45804154872894287,0.27252963185310364,0.26942887902259827 diff --git a/math/Q2/results_unaligned/training_history.csv b/math/Q2/results_unaligned/training_history.csv new file mode 100644 index 0000000..19775e0 --- /dev/null +++ b/math/Q2/results_unaligned/training_history.csv @@ -0,0 +1,20 @@ +stage,epoch,train_observed_nll_per_scalar,train_loss,inner_selection_nll,inner_natural_accuracy,inner_natural_macro_f1,inner_natural_mae,selection_source +structured_imputer,1,1.5430006732543309,,,,,, +structured_imputer,2,1.485714775820573,,,,,, +structured_imputer,3,1.4471199909845989,,,,,, +structured_imputer,4,1.4231525585055351,,,,,, +structured_imputer,5,1.404515917102496,,,,,, +structured_imputer,6,1.389392430583636,,,,,, +structured_imputer,7,1.3760803937911987,,,,,, +structured_imputer,8,1.3660641213258107,,,,,, +C5,1,,3.5158049215873084,3.292430579662323,0.4052631578947368,0.19225967540574282,1.370365858078003,group_disjoint_internal_scenarios +C5,2,,3.356693913539251,3.2844966053962708,0.4052631578947368,0.19225967540574282,1.2984113693237305,group_disjoint_internal_scenarios +C5,3,,3.2962357898553214,3.2124780416488647,0.4052631578947368,0.19225967540574282,1.3188000917434692,group_disjoint_internal_scenarios +C5,4,,3.2327817628781,3.0559350848197937,0.5684210526315789,0.44066193853427893,0.919609546661377,group_disjoint_internal_scenarios +C5,5,,3.136332228779793,2.9291895031929016,0.6052631578947368,0.4781835006050253,0.8056271076202393,group_disjoint_internal_scenarios +C5,6,,3.013451357682546,2.8548107743263245,0.6421052631578947,0.5165008258635594,0.7009203433990479,group_disjoint_internal_scenarios +C5,7,,2.887427176038424,2.80420458316803,0.6578947368421053,0.567225181598063,0.6824209094047546,group_disjoint_internal_scenarios +C5,8,,2.8229886343081794,2.7833492159843445,0.6736842105263158,0.620383753400688,0.6372818350791931,group_disjoint_internal_scenarios +C5,9,,2.740952745079994,2.796652317047119,0.6684210526315789,0.5999928896919354,0.6801832318305969,group_disjoint_internal_scenarios +C5,10,,2.7151345163583755,2.8127719163894653,0.6789473684210526,0.6290852299675147,0.6640865802764893,group_disjoint_internal_scenarios +C5,11,,2.6631849904855094,2.8094269037246704,0.6842105263157895,0.6511616339745155,0.62388676404953,group_disjoint_internal_scenarios diff --git a/math/Q2/results_unaligned/validation_metrics.json b/math/Q2/results_unaligned/validation_metrics.json new file mode 100644 index 0000000..9a76dec --- /dev/null +++ b/math/Q2/results_unaligned/validation_metrics.json @@ -0,0 +1,27 @@ +{ + "n": 728, + "accuracy": 0.6098901098901099, + "macro_f1": 0.5253076643650264, + "negative_support": 206, + "neutral_support": 184, + "positive_support": 338, + "negative_recall": 0.6796116504854369, + "middle_recall": 0.14130434782608695, + "positive_recall": 0.8224852071005917, + "regression_mae": 0.6701027750968933, + "regression_rmse": 0.9000873178933578, + "pearson": 0.6158545613288879, + "brier": 0.48911186855362665, + "classification_nll": 0.8322470784187317, + "ece_15": 0.05621008894273212, + "selection_nll": 2.7708771228790283, + "interval_90_coverage": 0.9010989010989011, + "interval_90_mean_width": 2.390233039855957, + "predictive_variance_mean_uncalibrated": 0.5822091698646545, + "within_trajectory_variance_mean": 0.5822086334228516, + "between_trajectory_variance_mean": 5.325794063537614e-07, + "predictive_mean_mean_calibrated": 0.20464463531970978, + "predictive_variance_mean_calibrated": 0.5899268984794617, + "model": "C5", + "temperature": 1.0309581618007613 +} \ No newline at end of file diff --git a/math/Q2/train.py b/math/Q2/train.py index 2363101..07ff89d 100644 --- a/math/Q2/train.py +++ b/math/Q2/train.py @@ -1741,8 +1741,11 @@ def validate_attachment3_predictions(expected_ids: list[str], predictions: list[ def main() -> None: - global DELTA_U + global DELTA_U, RESULTS parser = argparse.ArgumentParser() + parser.add_argument("--input-version", choices=("aligned_50", "unaligned_50"), default="aligned_50") + parser.add_argument("--output-dir", type=Path, default=None, + help="Write this run to a new directory instead of the default results directory") parser.add_argument("--epochs", type=int, default=12) parser.add_argument("--imputer-epochs", type=int, default=8) parser.add_argument("--batch-size", type=int, default=64) @@ -1752,6 +1755,11 @@ def main() -> None: parser.add_argument("--bootstrap-repeats", type=int, default=1000) parser.add_argument("--skip-attachment3", action="store_true") args = parser.parse_args() + if args.input_version == "unaligned_50" and not args.skip_attachment3: + parser.error("unaligned attachment 3 has no numerical text or trusted lengths; use --skip-attachment3 for the training comparison") + RESULTS = args.output_dir or Q2_DIR / ("results_unaligned" if args.input_version == "unaligned_50" else "results") + if args.output_dir is not None and RESULTS.exists() and any(RESULTS.iterdir()): + parser.error(f"refusing to overwrite non-empty result directory: {RESULTS}") seed_everything(args.seed) rng = np.random.default_rng(args.seed) device = torch.device(args.device) @@ -1759,8 +1767,8 @@ def main() -> None: if device.type == "cuda": print(f"device={device} ({torch.cuda.get_device_name(device)})", flush=True) - input_path = ROOT / "E题数据" / "附件2-数据集特征文件" / "aligned_50.pkl" - official = load_official_splits(input_path) + input_path = ROOT / "E题数据" / "附件2-数据集特征文件" / f"{args.input_version}.pkl" + official = load_official_splits(input_path, version=args.input_version) overlaps = assert_group_disjoint(official) fit, heldout_train = split_calibration(official["train"], args.seed) reliability_validation, temperature_calibration = split_calibration(heldout_train, args.seed + 1, fraction=0.5) @@ -1958,7 +1966,8 @@ def main() -> None: manifest = { "seed": args.seed, - "text_encoder": TEXT_MODEL_ID, + "text_encoder": ("official precomputed text field; encoder revision not supplied" + if args.input_version == "unaligned_50" else TEXT_MODEL_ID), "training_configuration": {"student_epoch_limit": args.epochs, "imputer_epochs": args.imputer_epochs, "batch_size": args.batch_size, "early_stopping_patience": args.patience, "device": str(device), @@ -1970,6 +1979,9 @@ def main() -> None: "inner_selection_scenarios": list(reliability_scenarios), "inner_selection_source_video_groups": int(len(np.unique(reliability_validation.groups)))}, "training_input": str(input_path.relative_to(ROOT)), + "input_version": args.input_version, + "q1_alignment_adapter": {name: split.alignment_audit for name, split in official.items()} + if args.input_version == "unaligned_50" else None, "training_sha256": sha256(input_path), "official_group_overlap": overlaps, "official_splits": {name: {"n": split.n, "source_video_groups": int(len(np.unique(split.groups)))} for name, split in official.items()}, @@ -1980,10 +1992,12 @@ def main() -> None: "all_group_disjoint": True, }, "feature_standardization": "fit-only observed rows, per-dimension; fixed for valid/test/attachment3", - "missing_mask": "row-level all-zero convention; q*=1 only where currently visible, J_Q=0; hidden metadata is zeroed", + "missing_mask": ("official text attention and source lengths plus row observation; normalized-progress overlap preserves empty bins; q*=1 only where visible, J_Q=0" + if args.input_version == "unaligned_50" else + "row-level all-zero convention; q*=1 only where currently visible, J_Q=0; hidden metadata is zeroed"), "observation_quality": {"quality_score_fields_present": False, "quality_available_flag_present": False, "fallback": "q*=1 and J_Q=0 for visible rows; R_eff=R", - "quality_noise_mapping_ablation": "not identifiable on aligned_50 because no row quality score varies"}, + "quality_noise_mapping_ablation": f"not identifiable on {args.input_version} because no row quality score varies"}, "imputer": {"type": "structured linear Gaussian shared-private state space", "state_dims": {"shared": 8, "private_each": 4}, "posterior": "block-tridiagonal equivalent Kalman information filter + RTS smoother", "sampling": "joint latent trajectories and missing emissions; observed features copied exactly", @@ -2030,7 +2044,7 @@ def main() -> None: "paired_control_bootstrap_seed": args.seed + 553, "mask_audit_file": "controlled_mask_audit.csv", "additional_one_factor_controls": ["modality T/A/V and combinations", "start/middle/end", "one-long/multiple-short", "sync/partial/async"], - "semantic_position_control": "not run: aligned_50 does not provide audited semantic boundary indices; raw text is prohibited in student inputs"}, + "semantic_position_control": f"not run: {args.input_version} does not provide audited semantic boundary indices; raw text is prohibited in student inputs"}, "final_test_metrics": test_metrics, "test_gate_diagnostics_file": "test_gate_diagnostics.csv", "attachment3_cases": attachment_count, diff --git a/math/Q2/train_unaligned_c5.py b/math/Q2/train_unaligned_c5.py new file mode 100644 index 0000000..a4ca9b7 --- /dev/null +++ b/math/Q2/train_unaligned_c5.py @@ -0,0 +1,158 @@ +"""Train the predeclared C5 Q2 architecture on Q1's exploratory index view.""" +from __future__ import annotations + +import argparse +import json +import time + +import numpy as np +import torch + +import train as q2_train +from crg import INPUT_DIMS, StructuredGaussianImputer +from data import ROOT, fit_preprocessor, load_official_splits, transform_split +from train import ( + RESULTS as ALIGNED_RESULTS, + SEED, + _fit_neural, + _make_variant, + assert_group_disjoint, + evaluate, + fit_imputer, + fit_temperature, + group_bootstrap, + label_resolution_from_train, + make_reliability_scenarios, + seed_everything, + sha256, + split_calibration, + tune_reliability_hparams, + write_csv, +) + +RESULTS = ALIGNED_RESULTS.parent / "results_unaligned" +SOURCE = ROOT / "E题数据" / "附件2-数据集特征文件" / "unaligned_50.pkl" + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--epochs", type=int, default=12) + parser.add_argument("--imputer-epochs", type=int, default=8) + parser.add_argument("--batch-size", type=int, default=64) + parser.add_argument("--patience", type=int, default=3) + parser.add_argument("--bootstrap-repeats", type=int, default=300) + parser.add_argument("--seed", type=int, default=SEED) + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") + args = parser.parse_args() + seed_everything(args.seed) + device = torch.device(args.device) + RESULTS.mkdir(parents=True, exist_ok=True) + + official = load_official_splits(SOURCE, version="unaligned_50") + overlap = assert_group_disjoint(official) + fit, heldout = split_calibration(official["train"], args.seed) + reliability_validation, temperature_calibration = split_calibration(heldout, args.seed + 1, fraction=0.5) + reliability_validation.name = "reliability_validation" + temperature_calibration.name = "temperature_calibration" + q2_train.DELTA_U = label_resolution_from_train(fit.regression_y) + fitted = fit_preprocessor(fit) + transformed = {name: transform_split(split, fitted) for name, split in official.items()} + transformed["fit"] = transform_split(fit, fitted) + transformed["reliability_validation"] = transform_split(reliability_validation, fitted) + transformed["temperature_calibration"] = transform_split(temperature_calibration, fitted) + np.savez_compressed(RESULTS / "preprocessor.npz", **{ + f"{modality}_{stat}": value + for modality, values in fitted.items() for stat, value in values.items() + }) + scenarios = make_reliability_scenarios(reliability_validation, args.seed + 906) + print("official split sizes:", {k: v.n for k, v in official.items()}, flush=True) + + imputer = StructuredGaussianImputer(INPUT_DIMS).to(device) + imputer_history = fit_imputer(imputer, transformed["fit"], fit, device, + args.imputer_epochs, args.batch_size, args.seed + 1) + torch.save({k: v.detach().cpu() for k, v in imputer.state_dict().items()}, + RESULTS / "structured_imputer.pt") + model = _make_variant("C5", imputer) + model, history = _fit_neural( + model, "C5", fit, official["valid"], transformed, device, + args.epochs, args.batch_size, args.patience, np.random.default_rng(args.seed + 303), + selection_split=reliability_validation, + selection_arrays=transformed["reliability_validation"], + selection_scenarios=scenarios, + ) + reliability, tuning_rows = tune_reliability_hparams( + model, transformed["reliability_validation"], reliability_validation, + scenarios, device, args.batch_size, "C5", seed=args.seed + 551, + ) + _, calibration_prediction = evaluate(model, transformed["temperature_calibration"], + temperature_calibration, device, args.batch_size) + temperature = fit_temperature(calibration_prediction["probabilities"], + temperature_calibration.class_y) + valid_metrics, _ = evaluate(model, transformed["valid"], official["valid"], + device, args.batch_size, temperature=temperature) + test_metrics, test_prediction = evaluate(model, transformed["test"], official["test"], + device, args.batch_size, temperature=temperature) + torch.save({k: v.detach().cpu() for k, v in model.state_dict().items()}, RESULTS / "crg_student.pt") + (RESULTS / "validation_metrics.json").write_text( + json.dumps({**valid_metrics, "model": "C5", "temperature": temperature}, indent=2), encoding="utf-8") + (RESULTS / "test_metrics.json").write_text( + json.dumps({**test_metrics, "model": "C5", "temperature": temperature}, indent=2), encoding="utf-8") + write_csv(RESULTS / "reliability_hparam_tuning.csv", tuning_rows) + write_csv(RESULTS / "training_history.csv", imputer_history + history) + write_csv(RESULTS / "group_bootstrap_ci.csv", + group_bootstrap(official["test"], test_prediction, args.bootstrap_repeats, args.seed + 44)) + rows = [] + for i, sample_id in enumerate(official["test"].ids): + p = test_prediction["probabilities"][i] + rows.append({ + "sample_id": sample_id, + "source_video_id": official["test"].groups[i], + "true_class": int(official["test"].class_y[i]), + "predicted_class": int(test_prediction["predicted_class"][i]), + "true_sentiment": float(official["test"].regression_y[i]), + "predicted_sentiment": float(test_prediction["predicted_score"][i]), + "p_negative": float(p[0]), "p_neutral": float(p[1]), "p_positive": float(p[2]), + }) + write_csv(RESULTS / "test_predictions.csv", rows) + manifest = { + "scope": "exploratory unaligned_50 relative-index projection and prespecified C5 training", + "physical_time_alignment": False, + "input": str(SOURCE.relative_to(ROOT)), + "input_sha256": sha256(SOURCE), + "text_encoder": "official precomputed text field; revision not supplied", + "q1_adapter_audit": {name: split.alignment_audit for name, split in official.items()}, + "official_group_overlap": overlap, + "internal_splits": {"fit": fit.n, "reliability_validation": reliability_validation.n, + "temperature_calibration": temperature_calibration.n}, + "model": "C5 fixed before this run; no unaligned architecture selection", + "quality": "no quality scores in official file; q*=1 for visible rows and J_Q=0", + "seed": args.seed, + "device": str(device), + "device_name": torch.cuda.get_device_name(device) if device.type == "cuda" else "CPU", + "torch_version": torch.__version__, + "epochs_limit": args.epochs, + "trained_c5_epochs": len(history), + "selected_c5_epoch": int(min(history, key=lambda row: row["inner_selection_nll"])["epoch"]), + "imputer_epochs": args.imputer_epochs, + "batch_size": args.batch_size, + "patience": args.patience, + "selected_reliability": reliability, + "temperature": temperature, + "bootstrap_repeats": args.bootstrap_repeats, + "attachment3": "not inferred: unaligned files lack numerical text and trusted lengths", + "validation_metrics": valid_metrics, + "test_metrics": test_metrics, + "completed_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + } + (RESULTS / "run_manifest.json").write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + stale_teacher = RESULTS / "teacher.pt" + if stale_teacher.exists(): + stale_teacher.unlink() + print("C5 unaligned complete:", json.dumps({ + "accuracy": test_metrics["accuracy"], "macro_f1": test_metrics["macro_f1"], + "mae": test_metrics["regression_mae"], "temperature": temperature, + }), flush=True) + + +if __name__ == "__main__": + main()