Datasets:

gowitheflowlab
/

physics-bench-solid-train

dataset_info:
  features:
    - name: text
      dtype: string
    - name: video
      dtype: string
    - name: hard_negative_texts
      list: string
    - name: hard_negative_videos
      list: string
  splits:
    - name: train
      num_bytes: 956204969
      num_examples: 2700
  download_size: 956204969
  dataset_size: 956204969
configs:
  - config_name: default
    data_files:
      - split: train
        path: metadata.parquet

Physics Bench Solid Train

Repository: gowitheflowlab/physics-bench-solid-train

Training split with hard negatives for solid simulation video retrieval.

rows: 2700
metadata columns: text, video, hard_negative_texts, hard_negative_videos
hard negatives per row: 5, drawn from the other 99 cases of the same family
video paths: repository-relative videos/<family>/<case_id>.mp4
list alignment: hard_negative_texts[i] and hard_negative_videos[i] come from the same case
text and hard_negative_texts use the natural-language query style (solid_eval_queries_v4_dynamics_aligned_raw_parsed), matching the parsed_text column of gowitheflowlab/physics-bench-solid-eval-2700
case parameters are disjoint from the evaluation split, so no evaluation case is reachable here

Reproducibility

sampling: global seed 42, family alphabetical order then case_id order, one random.Random(seed) stream, random.sample(sorted(other_99_case_ids), 5)
assignment digest: a9c1db6e65fb206f
text: generated from the training case metadata; see source_metadata/build_hardnegs_v2_metadata.py and source_metadata/v2_manifest.json
validation report: quality/validation.json

Loading

from pathlib import Path
import pyarrow.parquet as pq
from huggingface_hub import snapshot_download

root = Path(snapshot_download("gowitheflowlab/physics-bench-solid-train", repo_type="dataset"))
rows = pq.read_table(root / "metadata.parquet").to_pylist()
row = rows[0]
positive_video = root / row["video"]
negative_videos = [root / path for path in row["hard_negative_videos"]]