Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
schema_version: string
dimension_profile: string
reference_text: string
shot_ids: list<item: string>
child 0, item: string
narrative: struct<facts: list<item: struct<fact_id: string, text: string>>>
child 0, facts: list<item: struct<fact_id: string, text: string>>
child 0, item: struct<fact_id: string, text: string>
child 0, fact_id: string
child 1, text: string
registry: struct<schema_version: string, shot_ids: list<item: string>, entities: list<item: struct<prompt_enti (... 197 chars omitted)
child 0, schema_version: string
child 1, shot_ids: list<item: string>
child 0, item: string
child 2, entities: list<item: struct<prompt_entity_id: string, name: string, description: string, evidence: list<item: (... 125 chars omitted)
child 0, item: struct<prompt_entity_id: string, name: string, description: string, evidence: list<item: struct<fact (... 113 chars omitted)
child 0, prompt_entity_id: string
child 1, name: string
child 2, description: string
child 3, evidence: list<item: struct<fact_id: string, quote: string>>
child 0, item: struct<fact_id: string, quote: string>
child 0, fact_id: string
child 1, quote: string
child 4, schedule: list<item: struct<shot_id: string, expectation: string, reason: string>>
child 0, item: struct<shot_id: string, expectation: string, reason: string>
child 0, shot_id: s
...
child 9, evidence: list<item: struct<fact_id: string, quote: string>>
child 0, item: struct<fact_id: string, quote: string>
child 0, fact_id: string
child 1, quote: string
excluded_requirements: list<item: string>
child 0, item: string
claims_source: string
shots: list<item: struct<shot_id: string, source_scene_id: string, scene_num: int64, shot_num: int64, filen (... 150 chars omitted)
child 0, item: struct<shot_id: string, source_scene_id: string, scene_num: int64, shot_num: int64, filename: string (... 138 chars omitted)
child 0, shot_id: string
child 1, source_scene_id: string
child 2, scene_num: int64
child 3, shot_num: int64
child 4, filename: string
child 5, target_duration_seconds: int64
child 6, cut: bool
child 7, prompt: string
child 8, prompt_sha256: string
child 9, seed: int64
child 10, video_sha256: string
child 11, video_bytes: int64
offload_model: bool
model_revision: string
inference_code_revision: string
sample_steps: int64
target_duration_seconds: int64
inference_code: string
generation_priority: int64
generated_at_utc: string
source_revision: string
source_archive_sha256: string
width: int64
fps: int64
source_dataset: string
source_batch_id: string
model: string
height: int64
task: string
frame_count: int64
sample_solver: string
source_story_script: string
episode_id: string
guide_scale: double
sample_shift: double
source_contract: string
to
{'episode_id': Value('string'), 'generation_priority': Value('int64'), 'target_duration_seconds': Value('int64'), 'source_dataset': Value('string'), 'source_revision': Value('string'), 'source_batch_id': Value('string'), 'source_archive_sha256': Value('string'), 'source_story_script': Value('string'), 'source_contract': Value('string'), 'model': Value('string'), 'model_revision': Value('string'), 'inference_code': Value('string'), 'inference_code_revision': Value('string'), 'task': Value('string'), 'width': Value('int64'), 'height': Value('int64'), 'fps': Value('int64'), 'frame_count': Value('int64'), 'sample_solver': Value('string'), 'sample_steps': Value('int64'), 'sample_shift': Value('float64'), 'guide_scale': Value('float64'), 'offload_model': Value('bool'), 'generated_at_utc': Value('string'), 'shots': List({'shot_id': Value('string'), 'source_scene_id': Value('string'), 'scene_num': Value('int64'), 'shot_num': Value('int64'), 'filename': Value('string'), 'target_duration_seconds': Value('int64'), 'cut': Value('bool'), 'prompt': Value('string'), 'prompt_sha256': Value('string'), 'seed': Value('int64'), 'video_sha256': Value('string'), 'video_bytes': Value('int64')})}
because column names don't match
Traceback: 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
schema_version: string
dimension_profile: string
reference_text: string
shot_ids: list<item: string>
child 0, item: string
narrative: struct<facts: list<item: struct<fact_id: string, text: string>>>
child 0, facts: list<item: struct<fact_id: string, text: string>>
child 0, item: struct<fact_id: string, text: string>
child 0, fact_id: string
child 1, text: string
registry: struct<schema_version: string, shot_ids: list<item: string>, entities: list<item: struct<prompt_enti (... 197 chars omitted)
child 0, schema_version: string
child 1, shot_ids: list<item: string>
child 0, item: string
child 2, entities: list<item: struct<prompt_entity_id: string, name: string, description: string, evidence: list<item: (... 125 chars omitted)
child 0, item: struct<prompt_entity_id: string, name: string, description: string, evidence: list<item: struct<fact (... 113 chars omitted)
child 0, prompt_entity_id: string
child 1, name: string
child 2, description: string
child 3, evidence: list<item: struct<fact_id: string, quote: string>>
child 0, item: struct<fact_id: string, quote: string>
child 0, fact_id: string
child 1, quote: string
child 4, schedule: list<item: struct<shot_id: string, expectation: string, reason: string>>
child 0, item: struct<shot_id: string, expectation: string, reason: string>
child 0, shot_id: s
...
child 9, evidence: list<item: struct<fact_id: string, quote: string>>
child 0, item: struct<fact_id: string, quote: string>
child 0, fact_id: string
child 1, quote: string
excluded_requirements: list<item: string>
child 0, item: string
claims_source: string
shots: list<item: struct<shot_id: string, source_scene_id: string, scene_num: int64, shot_num: int64, filen (... 150 chars omitted)
child 0, item: struct<shot_id: string, source_scene_id: string, scene_num: int64, shot_num: int64, filename: string (... 138 chars omitted)
child 0, shot_id: string
child 1, source_scene_id: string
child 2, scene_num: int64
child 3, shot_num: int64
child 4, filename: string
child 5, target_duration_seconds: int64
child 6, cut: bool
child 7, prompt: string
child 8, prompt_sha256: string
child 9, seed: int64
child 10, video_sha256: string
child 11, video_bytes: int64
offload_model: bool
model_revision: string
inference_code_revision: string
sample_steps: int64
target_duration_seconds: int64
inference_code: string
generation_priority: int64
generated_at_utc: string
source_revision: string
source_archive_sha256: string
width: int64
fps: int64
source_dataset: string
source_batch_id: string
model: string
height: int64
task: string
frame_count: int64
sample_solver: string
source_story_script: string
episode_id: string
guide_scale: double
sample_shift: double
source_contract: string
to
{'episode_id': Value('string'), 'generation_priority': Value('int64'), 'target_duration_seconds': Value('int64'), 'source_dataset': Value('string'), 'source_revision': Value('string'), 'source_batch_id': Value('string'), 'source_archive_sha256': Value('string'), 'source_story_script': Value('string'), 'source_contract': Value('string'), 'model': Value('string'), 'model_revision': Value('string'), 'inference_code': Value('string'), 'inference_code_revision': Value('string'), 'task': Value('string'), 'width': Value('int64'), 'height': Value('int64'), 'fps': Value('int64'), 'frame_count': Value('int64'), 'sample_solver': Value('string'), 'sample_steps': Value('int64'), 'sample_shift': Value('float64'), 'guide_scale': Value('float64'), 'offload_model': Value('bool'), 'generated_at_utc': Value('string'), 'shots': List({'shot_id': Value('string'), 'source_scene_id': Value('string'), 'scene_num': Value('int64'), 'shot_num': Value('int64'), 'filename': Value('string'), 'target_duration_seconds': Value('int64'), 'cut': Value('bool'), 'prompt': Value('string'), 'prompt_sha256': Value('string'), 'seed': Value('int64'), 'video_sha256': Value('string'), 'video_bytes': Value('int64')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Wan2.1 T2V 1.3B generations for EEC-Bench StoryMem
Source: BlueSourceJY/eec-bench-storymem-400. This private dataset preserves the original priority order and source inputs for ranks 1–200.
source/batches/*.jsonl and the matching .ids.txt files map generation priority to episode IDs. The three original batch archives and SHA-256 sidecars preserve each episode's native story_script.json, contract.json, alignment contract, entity schedule, shot mapping, and other delivered inputs.
Generated results are organized by the original episode ID under episodes/<episode_id>/shots/, using each source manifest's storymem_output_name for the shot filename. Episode metadata will record model, parameters, seed, and source hashes.
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