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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
benchmark_id: string
family: string
article_id: string
publisher: string
state: string
question: string
type: string
params: struct<options: list<item: struct<id: string, description: string>>, passage_id: string, subtype: st (... 301 chars omitted)
child 0, options: list<item: struct<id: string, description: string>>
child 0, item: struct<id: string, description: string>
child 0, id: string
child 1, description: string
child 1, passage_id: string
child 2, subtype: string
child 3, claim: string
child 4, audience: string
child 5, scale: struct<0: string, 1: string, 2: string, 3: string>
child 0, 0: string
child 1, 1: string
child 2, 2: string
child 3, 3: string
child 6, entity: string
child 7, event: string
child 8, place: string
child 9, scoring_guide: struct<0: string, 1: string, 2: string, 3: string>
child 0, 0: string
child 1, 1: string
child 2, 2: string
child 3, 3: string
child 10, headline: string
child 11, newsroom_or_audience: string
child 12, development: string
child 13, x: string
child 14, y: string
target: string
difficulty: string
source_metadata: struct<split: string, form_hint: string>
child 0, split: string
child 1, form_hint: string
generation_metadata: struct<prompt_version: string, subtype: string, gold_evidence: struct<paragraph_ids: list<item: stri (... 74 chars omitted)
child 0, prompt_version: string
child 1, subtype: string
child 2, gold_ev
...
t64>
child 0, false: int64
child 1, true: int64
child 10, story_structure: struct<false: int64, true: int64>
child 0, false: int64
child 1, true: int64
child 11, story_form: struct<explainer: int64, analysis: int64, profile: int64, straight_news: int64, breaking_update: int (... 57 chars omitted)
child 0, explainer: int64
child 1, analysis: int64
child 2, profile: int64
child 3, straight_news: int64
child 4, breaking_update: int64
child 5, service: int64
child 6, feature: int64
child 7, investigation: int64
n: int64
prompt_version: string
n_articles: int64
publishers: list<item: string>
child 0, item: string
frozen: bool
quality_filters: list<item: string>
child 0, item: string
family_counts: struct<attribution: int64, audience_relevance: int64, claim_support: int64, entity_relevance: int64, (... 186 chars omitted)
child 0, attribution: int64
child 1, audience_relevance: int64
child 2, claim_support: int64
child 3, entity_relevance: int64
child 4, event_status: int64
child 5, geographic_relevance: int64
child 6, headline_support: int64
child 7, newsworthiness: int64
child 8, principal_development: int64
child 9, stance: int64
child 10, story_form: int64
child 11, story_structure: int64
name: string
train_publisher_overlap: list<item: null>
child 0, item: null
n_publishers: int64
geo_policy: string
article_ids: list<item: string>
child 0, item: string
freeze_note: string
to
{'name': Value('string'), 'prompt_version': Value('string'), 'n': Value('int64'), 'n_articles': Value('int64'), 'n_publishers': Value('int64'), 'family_counts': {'attribution': Value('int64'), 'audience_relevance': Value('int64'), 'claim_support': Value('int64'), 'entity_relevance': Value('int64'), 'event_status': Value('int64'), 'geographic_relevance': Value('int64'), 'headline_support': Value('int64'), 'newsworthiness': Value('int64'), 'principal_development': Value('int64'), 'stance': Value('int64'), 'story_form': Value('int64'), 'story_structure': Value('int64')}, 'difficulty': {'hard': Value('int64'), 'normal': Value('int64')}, 'target_by_family': {'geographic_relevance': {'2': Value('int64'), '0': Value('int64'), '1': Value('int64'), '3': Value('int64')}, 'audience_relevance': {'1': Value('int64'), '0': Value('int64'), '3': Value('int64'), '2': Value('int64')}, 'newsworthiness': {'1': Value('int64'), '3': Value('int64'), '2': Value('int64'), '0': Value('int64')}, 'headline_support': {'supported': Value('int64'), 'insufficient_evidence': Value('int64'), 'overstated': Value('int64'), 'contradicted': Value('int64')}, 'claim_support': {'contradicted': Value('int64'), 'mixed_or_conditional': Value('int64'), 'not_addressed': Value('int64'), 'supported': Value('int64')}, 'stance': {'supports': Value('int64'), 'no_position': Value('int64'), 'opposes': Value('int64'), 'mixed_or_conditional': Value('int64')}, 'event_status': {'proposed': Value('int64'), 'underway': Value('int64')
...
('int64'), 'mayor_shane_gunnoe': Value('int64'), 'educators': Value('int64'), 'malcolm_thomas': Value('int64'), 'mentone_town_council': Value('int64'), 'author_first_person': Value('int64'), 'city_of_stillwater': Value('int64'), 'jan_hensley': Value('int64'), 'crowley_capital': Value('int64'), 'police_said': Value('int64'), 'heather_price': Value('int64'), 'southern_area_red_complex_incident_management_team': Value('int64'), 'officials': Value('int64'), 'sullivan': Value('int64'), 'head_coach_deanna_andersen': Value('int64'), 'derrick_henry': Value('int64'), 'gilbert_acciardo': Value('int64'), 'ruth_anderson': Value('int64'), 'sister_susan_francois': Value('int64'), 'darin_harris': Value('int64')}, 'entity_relevance': {'central': Value('int64'), 'supporting': Value('int64'), 'important': Value('int64'), 'incidental': Value('int64')}, 'principal_development': {'false': Value('int64'), 'true': Value('int64')}, 'story_structure': {'false': Value('int64'), 'true': Value('int64')}, 'story_form': {'explainer': Value('int64'), 'analysis': Value('int64'), 'profile': Value('int64'), 'straight_news': Value('int64'), 'breaking_update': Value('int64'), 'service': Value('int64'), 'feature': Value('int64'), 'investigation': Value('int64')}}, 'article_ids': List(Value('string')), 'publishers': List(Value('string')), 'geo_policy': Value('string'), 'quality_filters': List(Value('string')), 'train_publisher_overlap': List(Value('null')), 'frozen': Value('bool'), 'freeze_note': Value('string')}
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
benchmark_id: string
family: string
article_id: string
publisher: string
state: string
question: string
type: string
params: struct<options: list<item: struct<id: string, description: string>>, passage_id: string, subtype: st (... 301 chars omitted)
child 0, options: list<item: struct<id: string, description: string>>
child 0, item: struct<id: string, description: string>
child 0, id: string
child 1, description: string
child 1, passage_id: string
child 2, subtype: string
child 3, claim: string
child 4, audience: string
child 5, scale: struct<0: string, 1: string, 2: string, 3: string>
child 0, 0: string
child 1, 1: string
child 2, 2: string
child 3, 3: string
child 6, entity: string
child 7, event: string
child 8, place: string
child 9, scoring_guide: struct<0: string, 1: string, 2: string, 3: string>
child 0, 0: string
child 1, 1: string
child 2, 2: string
child 3, 3: string
child 10, headline: string
child 11, newsroom_or_audience: string
child 12, development: string
child 13, x: string
child 14, y: string
target: string
difficulty: string
source_metadata: struct<split: string, form_hint: string>
child 0, split: string
child 1, form_hint: string
generation_metadata: struct<prompt_version: string, subtype: string, gold_evidence: struct<paragraph_ids: list<item: stri (... 74 chars omitted)
child 0, prompt_version: string
child 1, subtype: string
child 2, gold_ev
...
t64>
child 0, false: int64
child 1, true: int64
child 10, story_structure: struct<false: int64, true: int64>
child 0, false: int64
child 1, true: int64
child 11, story_form: struct<explainer: int64, analysis: int64, profile: int64, straight_news: int64, breaking_update: int (... 57 chars omitted)
child 0, explainer: int64
child 1, analysis: int64
child 2, profile: int64
child 3, straight_news: int64
child 4, breaking_update: int64
child 5, service: int64
child 6, feature: int64
child 7, investigation: int64
n: int64
prompt_version: string
n_articles: int64
publishers: list<item: string>
child 0, item: string
frozen: bool
quality_filters: list<item: string>
child 0, item: string
family_counts: struct<attribution: int64, audience_relevance: int64, claim_support: int64, entity_relevance: int64, (... 186 chars omitted)
child 0, attribution: int64
child 1, audience_relevance: int64
child 2, claim_support: int64
child 3, entity_relevance: int64
child 4, event_status: int64
child 5, geographic_relevance: int64
child 6, headline_support: int64
child 7, newsworthiness: int64
child 8, principal_development: int64
child 9, stance: int64
child 10, story_form: int64
child 11, story_structure: int64
name: string
train_publisher_overlap: list<item: null>
child 0, item: null
n_publishers: int64
geo_policy: string
article_ids: list<item: string>
child 0, item: string
freeze_note: string
to
{'name': Value('string'), 'prompt_version': Value('string'), 'n': Value('int64'), 'n_articles': Value('int64'), 'n_publishers': Value('int64'), 'family_counts': {'attribution': Value('int64'), 'audience_relevance': Value('int64'), 'claim_support': Value('int64'), 'entity_relevance': Value('int64'), 'event_status': Value('int64'), 'geographic_relevance': Value('int64'), 'headline_support': Value('int64'), 'newsworthiness': Value('int64'), 'principal_development': Value('int64'), 'stance': Value('int64'), 'story_form': Value('int64'), 'story_structure': Value('int64')}, 'difficulty': {'hard': Value('int64'), 'normal': Value('int64')}, 'target_by_family': {'geographic_relevance': {'2': Value('int64'), '0': Value('int64'), '1': Value('int64'), '3': Value('int64')}, 'audience_relevance': {'1': Value('int64'), '0': Value('int64'), '3': Value('int64'), '2': Value('int64')}, 'newsworthiness': {'1': Value('int64'), '3': Value('int64'), '2': Value('int64'), '0': Value('int64')}, 'headline_support': {'supported': Value('int64'), 'insufficient_evidence': Value('int64'), 'overstated': Value('int64'), 'contradicted': Value('int64')}, 'claim_support': {'contradicted': Value('int64'), 'mixed_or_conditional': Value('int64'), 'not_addressed': Value('int64'), 'supported': Value('int64')}, 'stance': {'supports': Value('int64'), 'no_position': Value('int64'), 'opposes': Value('int64'), 'mixed_or_conditional': Value('int64')}, 'event_status': {'proposed': Value('int64'), 'underway': Value('int64')
...
('int64'), 'mayor_shane_gunnoe': Value('int64'), 'educators': Value('int64'), 'malcolm_thomas': Value('int64'), 'mentone_town_council': Value('int64'), 'author_first_person': Value('int64'), 'city_of_stillwater': Value('int64'), 'jan_hensley': Value('int64'), 'crowley_capital': Value('int64'), 'police_said': Value('int64'), 'heather_price': Value('int64'), 'southern_area_red_complex_incident_management_team': Value('int64'), 'officials': Value('int64'), 'sullivan': Value('int64'), 'head_coach_deanna_andersen': Value('int64'), 'derrick_henry': Value('int64'), 'gilbert_acciardo': Value('int64'), 'ruth_anderson': Value('int64'), 'sister_susan_francois': Value('int64'), 'darin_harris': Value('int64')}, 'entity_relevance': {'central': Value('int64'), 'supporting': Value('int64'), 'important': Value('int64'), 'incidental': Value('int64')}, 'principal_development': {'false': Value('int64'), 'true': Value('int64')}, 'story_structure': {'false': Value('int64'), 'true': Value('int64')}, 'story_form': {'explainer': Value('int64'), 'analysis': Value('int64'), 'profile': Value('int64'), 'straight_news': Value('int64'), 'breaking_update': Value('int64'), 'service': Value('int64'), 'feature': Value('int64'), 'investigation': Value('int64')}}, 'article_ids': List(Value('string')), 'publishers': List(Value('string')), 'geo_policy': Value('string'), 'quality_filters': List(Value('string')), 'train_publisher_overlap': List(Value('null')), 'frozen': Value('bool'), 'freeze_note': Value('string')}
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.
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