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Download items/README_items.md from GMLRVigil/BenchCheck-Pool: direct link, hf CLI and curl.
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https://huggingface.co/datasets/GMLRVigil/BenchCheck-Pool/resolve/main/items/README_items.md
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hf download hf://datasets/GMLRVigil/BenchCheck-Pool/items/README_items.md
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curl -L -o README_items.md https://huggingface.co/datasets/GMLRVigil/BenchCheck-Pool/resolve/main/items/README_items.md
1.22 kB
| # items/ — the full item pool after step 0 | |
| `pool_items_base.parquet` (717,199 rows, one per question of 139 video benchmarks) columns: | |
| benchmark, item_id (pipeline qid; unique), video_key (source reference), video_id (content hash of the | |
| normalized video, or "k:<sha1-16>" when the video was not normalized at snapshot time), video_path | |
| (cluster path, not usable off-cluster; frames are read from the frame cache instead), question, | |
| question_raw, options (list of option strings, letter prefixes stripped), options_source, | |
| option_prefix_stripped, n_options, options_raw, answer (gold letter), answer_idx, duration (s), | |
| declared_task, declared_scene, license, format (mcq / open / ...), format_reason, | |
| bench_question_format, in_sample (item belongs to the 300-item E1 sample). | |
| `step0_kept.parquet` (717,199 rows): item_id, format, kept (False = removed in step 0), | |
| removed_reason, dup_of, dup_cos. Steps 1-3 run on rows with kept == True and format == "mcq" | |
| (299,366 items); step 2 and 3 additionally need a frame cache entry for the item's video_id. | |
| `step0_removed.jsonl`: the removed rows with reasons; `screen_counts_full.csv`: per-benchmark counts; | |
| `pool_schema_manifest.csv`: how each benchmark was enumerated. | |