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train

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
                  for key, record in generator:
                                     ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
                  for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                                              ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  for filename, f in tar_iterator:
                                     ^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
                  for x in self.generator(*self.args):
                           ~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1405, in _iter_from_urlpath
                  with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
                       ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 982, in xopen
                  file_obj = fs.open(paths[0], mode)
                File "<string>", line 3, in open
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
                  return self._mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
                  return self._execute_mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
                  result = effect(*args, **kwargs)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
                  tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
                                                               ~~~^^^^^^^^
              TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1393, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1571, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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jpg image	__key__ string	__url__ string
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	frames/7e6b1281e93e50b4fb9d8e4c508d10405bdf3808	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/6baabaf5c41de128ca338a055daa5630fc75b895	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/5f3a34d650d4323c96b4a43c1bb851912bbd7b8b	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/9dc1702f0d37442995a8ea784a404ed5da76f0da	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/d65a16305ae5c6557beed123620073cda2cfd37b	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/f01c7d855468bebe8f23c3e3ffefa332a9718801	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/f0bac4a8e9bdb3a5f7690cc0b191bbd4925ba9f1	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/f64bd057798b16ebe137c134e3e8150dc75ff797	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/4ccc9ffe36db59ad61d4b7087b36f5be654bee88	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/54eca39310d91fdbf3e1bcfd696ef8d779c8da55	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/7495e625256847f80e38d9b24d80db46d0a7aed9	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/cec932b9bbf48e8e131344324f67e1accbf1eb77	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/017a8b7aacfc8e7ff30417ecfb23df774beb22dd	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/480c75b2a9a632cf0633395918b3b6040cebe0ea	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/281cce0742c9671a17b0cf73d7da9796761127bd	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/1e48d9e8006d9a40d4631ac7f424bc0781aeb884	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/e9019c5c0f1699bacce09cc1064a0db53c224009	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/f78367f360730d994819af20c29a6f3e5de57e7a	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/e94dbeac8f7a26fd007e9449af7e447cf9f397d7	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz
	frames/166a8e85233d4fb1e4d350867f90387452b885f1	hf://datasets/Rakancorle1/vtg-bench-frontier-eval-media@8f95ea7f4673e08296510e019c334a195c5fdf7d/frames.tar.gz

End of preview.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

data/ — labels, video inventory, and the media archives

Sources

Label workbook 1: https://docs.google.com/spreadsheets/d/1GZiM6T5FUboRkDTUnItOYCQIhxZEz5iwGSjHlTaVk2A (tabs Sheet1, eSports, drama, drama_qa, basketball, basketball_qa, Soccer, Car)
Label workbook 2: https://docs.google.com/spreadsheets/d/1g8XKhzaSf3V86X83aFQYEmlbB1wgvULmPaUQJlDsNQY (tabs Hui, Lingyi)
Videos: the annotation team's Google Drive folder 10r3wP9IBeXMpDhg0-tQ2HoFUIBDFIslx (YouTube downloads, one sub-folder per category, each with a _manifest.csv mapping YouTube id → filename). Exported copies of both workbooks are in sheets/.

Counts

tab → `sheet` field	QA rows	with a local video	description
wendi_misc	52	42	workbook 1 tab `Sheet1` — six annotators' blocks (Wendi, 大懒🐍, Louis Ray, Xiaofei, Tinghui, Dongwon); TV shows, car, soccer, LoL/DotA, FailArmy, NextQA
esports	8	0	workbook 1 tab `eSports` — 8 QA rows among 200 listed videos (DotA/LoL)
drama	51	50	workbook 1 tab `drama_qa` — SNL and other comedy sketches
basketball	49	49	workbook 1 tab `basketball_qa` — 1v1 street-ball shorts and NBA highlight reels
soccer	49	46	workbook 1 tab `Soccer` — skills / goals compilations
car	27	27	workbook 1 tab `Car` — dashcam compilations; each question is defined on a sub-clip `[mm:ss - mm:ss]` of the video
hui	41	41	workbook 2 tab `Hui` — talk shows, interviews, cooking; has reference answers
lingyi	96	96	workbook 2 tab `Lingyi` — travel / life vlogs, tutorials, film clips; has reference answers
total	373	351	178 distinct videos, 164 rows with a free-text reference answer

Ground-truth segment length: median 6 s, mean 12.7 s, max 258 s. Modality tags as written by the annotators (va): v 195, (none) 123, v+a 52, a 3.

Rows without a local video (not evaluated):

sheet	reason	rows
drama	YouTube video private / unavailable, never downloaded	1
esports	video not in the Drive folder	8
soccer	YouTube video private / unavailable, never downloaded	3
wendi_misc	YouTube video private / unavailable, never downloaded	1
wendi_misc	reference is not a YouTube link (title / NextQA id / other)	9

esports: the 8 videos are not in the Drive folder. wendi_misc: 5 title-only references (FailArmy clips), 3 NextQA ids, 1 spreadsheet URL pasted as a video, 1 unavailable YouTube video; the TAR Ending Explained title reference was matched to the drama folder by filename. drama/soccer: the YouTube video is private or unavailable, so the downloader had nothing.

dur_mismatch = 31 rows whose local file duration differs from the sheet's duration_seconds by more than 30 s — the file is probably a different upload of the same title, so the annotated timestamps may not align. They are evaluated but excluded from the report's clean subset: drama-003, drama-005, drama-006, drama-007, drama-008, drama-009, drama-012, drama-013, drama-015, drama-016, drama-017, drama-021, drama-023, drama-025, drama-028, drama-029, drama-036, drama-037, drama-040, drama-041, drama-045, drama-048, drama-049, drama-050, hui-010, hui-035, lingyi-087, lingyi-088, lingyi-089, soccer-008, soccer-009.

Files

file	content
`sheets/*.xlsx`	the two label workbooks as exported on 2026-09-16
`manifests/<folder>___manifest.csv`, `..._skipped_rows.json`	the annotation team's download manifests per Drive folder: `video_id, url, csv_title, yt_title, filename, duration_seconds, status, error`
`drive_listing.json` / `drive_listing_prev.json`	file id + path of everything in the Drive folder (744 files on 2026-09-17; the `_prev` listing from a few hours earlier had 547 — the `basketball/` folder was added in between)
`qa_all.jsonl`	all 373 QA rows normalised from the workbooks — one JSON per line, fields below
`qa_matched.jsonl`	the same rows plus `video_path`, `drive_id`, `match_note`, `file_duration_s`, `dur_mismatch`, `gt_beyond_file`; this is the evaluation input
`durations.json`	ffprobe duration per local video
`download_list.json`, `download_status.json`, `missing_after_retry.json`	what was downloaded from Drive and how it went
`videos_<folder>.tar.gz`, `frames.tar.gz`, `CHECKSUMS.sha256`	the media archives (table below) — not in the public repo; they are in the separate media repo https://huggingface.co/datasets/Rakancorle1/vtg-frontier-eval-media

`qa_all.jsonl` / `qa_matched.jsonl` fields

qid            e.g. lingyi-012 (sheet + running number)
sheet          wendi_misc | esports | drama | basketball | soccer | car | hui | lingyi
annotator      name of the annotator block (only where the workbook records it)
video_ref      yt:<YouTube id> | nextqa:<id> | title:<text> | url:<...>      video_ref_raw = the cell as written
title, duration_s, category, question
gt_segments    [[start_s, end_s], ...] parsed from the mm:ss range(s); gt_raw = the original string; gt_flag_hms = an h:mm:ss form was seen
gt_answer      free-text reference answer (car / hui / lingyi only; other tabs put the time range in their 'Answer' column)
va             v | v+a | a | ''  (annotators' visual / audio requirement tag, lower-cased)
search_window  [s, e] 'Suggested Video Segments' column where present (NOT shown to the models)
clip           [s, e] for car rows: the sub-clip the question is about (the models only see this range)
extra          e.g. 'Audio' tag on car rows
-- added by match_videos.py / fixup_qa.py --
video_path, drive_id, match_note, file_duration_s, dur_mismatch, gt_beyond_file

Media archives

The videos are YouTube content downloaded by the annotation team and re-distributed only for reproducing this evaluation; they are kept out of the public repo. videos_<folder>.tar.gz hold the 178 referenced mp4 files (Car 2, Hui 33, Lingyi 23, Pilot 28, Soccer 26, basketball 16, drama 50), 1280×720 except 18 portrait shorts at 360×640. frames.tar.gz is the runner's frame cache: every frame any model saw, as JPEG with the timestamp burned in, named sha1(f"{video_path}|{t:.2f}|{width}|v2").jpg (the runner regenerates missing frames from the videos, so the cache is a convenience, not a requirement).

archive	size	files	sha256
`videos_basketball.tar.gz`	0.34 GB	16	`2a0d17af498dc93a7ee7bff973345172205c2e5ce137f4d765e5170bae929918`
`videos_Car.tar.gz`	0.18 GB	2	`ca4899c6f666cd584388d24df64d52154236164d044ba5233dad8e5d24667752`
`videos_drama.tar.gz`	1.34 GB	50	`752dc2888420cd34e1fde5683fa2abc0d4679cc95b9555ff2488d53d41f4f175`
`videos_Hui.tar.gz`	0.90 GB	33	`248a2b88dd3864877e13ffc60f68f87f26674f196d1f3a5c618ed11ca3520102`
`videos_Lingyi.tar.gz`	1.34 GB	23	`ede90e2977343a6c61a45e82724294d47982ef1dd41a643fa523aa79260d89d7`
`videos_Pilot.tar.gz`	2.39 GB	28	`e70732dec1774e0379156eac19f3f1a0cba82aa7419ad0330fb6af028fee49f7`
`videos_Soccer.tar.gz`	3.28 GB	26	`22b38d9d5f92e31fabd4c1da775a051615f15f61fb6e5d436174017b753b1e8b`
`frames.tar.gz`	2.75 GB	78797	`ba0b04f8fcc104ea443bd068d9e983ce53e468f0a2cafc92d1f5e8a35cbc1a64`

Extract with tar -xzf videos_<folder>.tar.gz -C data/videos/ (each archive unpacks to <folder>/…) and tar -xzf frames.tar.gz -C data/ (unpacks to frames/). Verify with sha256sum -c CHECKSUMS.sha256.

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