Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OneStreamer-Core-SourceVideo
Video assets for the OneStreamer-Core annotations in MCG-NJU/OneStreamer-1M. See its dataset card for the task taxonomy and metadata format, and our paper for the data construction pipeline.
The release is organized by source directly under the repository root. Each uncompressed part-XXXXX.tar has a corresponding part-XXXXX.manifest.json; download them as a pair. The source videos remain subject to their respective source datasets' terms.
README.md
AVA/
part-00000.tar
part-00000.manifest.json
...
COIN/
Ego4D/
EgoQA/
MAGQA/
Molmo2-Cap/
Molmo2-VideoCapQA/
PerceptionTest/
Shot2Story/
THUMOS/
TVQA/
TimeLens-100K/
TimeLens2-93K/
UCF-Crime/
YouTube-Collected/
Download and unpack
For example, download the EgoQA shard using huggingface_hub:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="yyd7/OneStreamer-Core-SourceVideo",
repo_type="dataset",
allow_patterns=["EgoQA/*"],
local_dir="OneStreamer-Core-SourceVideo",
)
Unpack the selected shards into a shared media root:
mkdir -p media
tar -xf OneStreamer-Core-SourceVideo/EgoQA/part-00000.tar -C media
For annotations with media_type: "video", the extracted path is media/<video_path> and can be used directly. Each shard contains only the selected Core media, with one file per distinct metadata media reference.
Match videos to metadata
Each manifest identifies the annotation repository in hf_dataset and pins its revision in annotation_revision. The video_dataset field identifies this source-video repository; all archive_path values are relative to its root. The manifest's assets list contains:
| Field | Meaning |
|---|---|
media_key |
Stable identifier for the metadata media reference. |
source |
Source dataset or collection. |
hf_video_path |
Exact video_path in the Core metadata. |
archive_path |
Tar location relative to this source-video repository, such as EgoQA/part-00000.tar. |
archive_member |
MP4 location inside the tar and under the extraction root. |
bytes, sha256 |
Size and SHA-256 of the individual MP4. |
metadata_media_type |
The annotation's original media representation: video or sequence. |
provenance |
Whether this asset is a dataset video/clip, a processed clip/segment, or a reconstruction from frames. |
sequence |
The annotation's frame rate and, when specified, filename pattern and starting index. |
Match an annotation's video_path to hf_video_path. The MP4 to read is identified by archive_path and archive_member. All annotation IDs, times, task assignments, and silence/standby/response data retain their metadata definitions.
Frame-sequence annotations
For metadata_media_type: "sequence", hf_video_path refers to a frame directory. The released MP4 is the input for reconstructing that frame sequence; its path is recorded separately as archive_member. Follow sequence.fps and any filename_pattern / index_start when creating the frame directory.
- Time-based 4 FPS names are
00000_00.jpg,00000_25.jpg,00000_50.jpg,00000_75.jpg, followed by00001_00.jpg, and so on. The sample times are 0, 0.25, 0.5, 0.75 seconds, etc. - TVQA uses 3 FPS and one-based names such as
00001.jpg; frame index 1 is at time 0. - If the metadata omits a filename pattern, use the source dataset's frame convention. A frame-directory reference should not be replaced blindly with an MP4 path in a loader that expects images.
AVA and TVQA assets marked reconstructed_from_frames preserve the available frame order and timing and have no audio. They are H.264 reconstructions, not recovered original recordings, and do not reproduce JPEG files byte for byte. AVA's separately available original video is identified by its own provenance. Other sequence sources use their available dataset videos or clips. Processed clips and segments retain their local annotation time axis; they are not replaced with full-length source recordings.
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