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: TypeError
Message: Couldn't cast array of type
struct<affect: struct<accuracy: null, api_note: string, bench: string, changes_vs_previous: list<item: string>, dataset: string, dataset_url: string, derived_state: string, evidence_age_days: int64, evidence_date: timestamp[s], family: string, fleet_mean: double, interval: null, kind: string, leader: null, mean_harm: double, measurement_time: struct<note: string, observed_on: timestamp[s], precision: string, source: string, source_field: string, state: string>, n: int64, separation: string, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: struct<accuracy: double, arm: string, band: string, ci95: list<item: double>, frontier: bool, measured_on: timestamp[s], rank: int64, verdict: string>>, top3_source: string>, ai-adoption-components: struct<accuracy: null, api_note: string, bench: string, changes_vs_previous: list<item: string>, dataset: string, dataset_url: string, derived_state: string, evidence_age_days: int64, evidence_date: timestamp[s], family: string, fleet_mean: null, interval: null, kind: string, leader: null, mean_harm: null, measurement_time: struct<note: string, observed_at: timestamp[s], precision: string, source: string, source_field: string, state: string>, n: int64, separation: null, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: null>, top3_source: string>, art5-safeguard: struct<accuracy: null, api_note: string, bench: string, changes_vs_previous: list<item: strin
...
separation: null, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: null>, top3_source: string>, swarm: struct<accuracy: double, api_note: string, bench: string, changes_vs_previous: list<item: string>, dataset: string, dataset_url: string, derived_state: string, evidence_age_days: int64, evidence_date: timestamp[s], family: string, fleet_mean: null, interval: null, kind: string, leader: string, mean_harm: null, measurement_time: struct<not_after: string, note: string, precision: string, source: string, source_field: string, state: string>, n: int64, separation: string, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: struct<accuracy: double, arm: string, band: string, ci95: list<item: double>, frontier: bool, measured_on: timestamp[s], rank: int64, verdict: string>>, top3_source: string>, watchdog-ingest: struct<accuracy: null, api_note: string, bench: string, changes_vs_previous: list<item: string>, dataset: null, dataset_url: null, derived_state: string, evidence_age_days: int64, evidence_date: timestamp[s], family: string, fleet_mean: null, interval: null, kind: string, leader: null, mean_harm: null, measurement_time: struct<note: string, observed_at: string, precision: string, source: string, source_field: string, state: string>, n: int64, separation: null, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: null>, top3_source: string>>
to
{'affect': {'accuracy': Value('null'), 'api_note': Value('string'), 'bench': Value('string'), 'changes_vs_previous': List(Value('string')), 'dataset': Value('string'), 'dataset_url': Value('string'), 'derived_state': Value('string'), 'evidence_age_days': Value('int64'), 'evidence_date': Value('timestamp[s]'), 'family': Value('string'), 'fleet_mean': Value('float64'), 'interval': Value('null'), 'kind': Value('string'), 'leader': Value('null'), 'mean_harm': Value('float64'), 'measurement_time': {'note': Value('string'), 'observed_on': Value('timestamp[s]'), 'precision': Value('string'), 'source': Value('string'), 'source_field': Value('string'), 'state': Value('string')}, 'n': Value('int64'), 'separation': Value('string'), 'separation_sentence': Value('null'), 'state_reason': Value('string'), 'status': Value('string'), 'task': Value('string'), 'top3': List({'accuracy': Value('float64'), 'arm': Value('string'), 'band': Value('string'), 'ci95': List(Value('float64')), 'frontier': Value('bool'), 'measured_on': Value('timestamp[s]'), 'rank': Value('int64'), 'verdict': Value('string')}), 'top3_source': Value('string')}, 'ai-adoption-components': {'accuracy': Value('null'), 'api_note': Value('string'), 'bench': Value('string'), 'changes_vs_previous': List(Value('string')), 'dataset': Value('string'), 'dataset_url': Value('string'), 'derived_state': Value('string'), 'evidence_age_days': Value('int64'), 'evidence_date': Value('timestamp[s]'), 'family': Value('string'), 'fleet_mean': Va
...
ll'), 'interval': Value('null'), 'kind': Value('string'), 'leader': Value('null'), 'mean_harm': Value('null'), 'measurement_time': Value('null'), 'n': Value('int64'), 'separation': Value('null'), 'separation_sentence': Value('null'), 'state_reason': Value('string'), 'status': Value('string'), 'task': Value('string'), 'top3': List(Value('null')), 'top3_source': Value('string')}, 'swarm': {'accuracy': Value('float64'), 'api_note': Value('string'), 'bench': Value('string'), 'changes_vs_previous': List(Value('string')), 'dataset': Value('string'), 'dataset_url': Value('string'), 'derived_state': Value('string'), 'evidence_age_days': Value('int64'), 'evidence_date': Value('timestamp[s]'), 'family': Value('string'), 'fleet_mean': Value('null'), 'interval': Value('null'), 'kind': Value('string'), 'leader': Value('string'), 'mean_harm': Value('null'), 'measurement_time': {'not_after': Value('string'), 'note': Value('string'), 'precision': Value('string'), 'source': Value('string'), 'source_field': Value('string'), 'state': Value('string')}, 'n': Value('int64'), 'separation': Value('string'), 'separation_sentence': Value('null'), 'state_reason': Value('string'), 'status': Value('string'), 'task': Value('string'), 'top3': List({'accuracy': Value('float64'), 'arm': Value('string'), 'band': Value('string'), 'ci95': List(Value('float64')), 'frontier': Value('bool'), 'measured_on': Value('timestamp[s]'), 'rank': Value('int64'), 'verdict': Value('string')}), 'top3_source': Value('string')}}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2315, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2161, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<affect: struct<accuracy: null, api_note: string, bench: string, changes_vs_previous: list<item: string>, dataset: string, dataset_url: string, derived_state: string, evidence_age_days: int64, evidence_date: timestamp[s], family: string, fleet_mean: double, interval: null, kind: string, leader: null, mean_harm: double, measurement_time: struct<note: string, observed_on: timestamp[s], precision: string, source: string, source_field: string, state: string>, n: int64, separation: string, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: struct<accuracy: double, arm: string, band: string, ci95: list<item: double>, frontier: bool, measured_on: timestamp[s], rank: int64, verdict: string>>, top3_source: string>, ai-adoption-components: struct<accuracy: null, api_note: string, bench: string, changes_vs_previous: list<item: string>, dataset: string, dataset_url: string, derived_state: string, evidence_age_days: int64, evidence_date: timestamp[s], family: string, fleet_mean: null, interval: null, kind: string, leader: null, mean_harm: null, measurement_time: struct<note: string, observed_at: timestamp[s], precision: string, source: string, source_field: string, state: string>, n: int64, separation: null, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: null>, top3_source: string>, art5-safeguard: struct<accuracy: null, api_note: string, bench: string, changes_vs_previous: list<item: strin
...
separation: null, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: null>, top3_source: string>, swarm: struct<accuracy: double, api_note: string, bench: string, changes_vs_previous: list<item: string>, dataset: string, dataset_url: string, derived_state: string, evidence_age_days: int64, evidence_date: timestamp[s], family: string, fleet_mean: null, interval: null, kind: string, leader: string, mean_harm: null, measurement_time: struct<not_after: string, note: string, precision: string, source: string, source_field: string, state: string>, n: int64, separation: string, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: struct<accuracy: double, arm: string, band: string, ci95: list<item: double>, frontier: bool, measured_on: timestamp[s], rank: int64, verdict: string>>, top3_source: string>, watchdog-ingest: struct<accuracy: null, api_note: string, bench: string, changes_vs_previous: list<item: string>, dataset: null, dataset_url: null, derived_state: string, evidence_age_days: int64, evidence_date: timestamp[s], family: string, fleet_mean: null, interval: null, kind: string, leader: null, mean_harm: null, measurement_time: struct<note: string, observed_at: string, precision: string, source: string, source_field: string, state: string>, n: int64, separation: null, separation_sentence: null, state_reason: string, status: string, task: string, top3: list<item: null>, top3_source: string>>
to
{'affect': {'accuracy': Value('null'), 'api_note': Value('string'), 'bench': Value('string'), 'changes_vs_previous': List(Value('string')), 'dataset': Value('string'), 'dataset_url': Value('string'), 'derived_state': Value('string'), 'evidence_age_days': Value('int64'), 'evidence_date': Value('timestamp[s]'), 'family': Value('string'), 'fleet_mean': Value('float64'), 'interval': Value('null'), 'kind': Value('string'), 'leader': Value('null'), 'mean_harm': Value('float64'), 'measurement_time': {'note': Value('string'), 'observed_on': Value('timestamp[s]'), 'precision': Value('string'), 'source': Value('string'), 'source_field': Value('string'), 'state': Value('string')}, 'n': Value('int64'), 'separation': Value('string'), 'separation_sentence': Value('null'), 'state_reason': Value('string'), 'status': Value('string'), 'task': Value('string'), 'top3': List({'accuracy': Value('float64'), 'arm': Value('string'), 'band': Value('string'), 'ci95': List(Value('float64')), 'frontier': Value('bool'), 'measured_on': Value('timestamp[s]'), 'rank': Value('int64'), 'verdict': Value('string')}), 'top3_source': Value('string')}, 'ai-adoption-components': {'accuracy': Value('null'), 'api_note': Value('string'), 'bench': Value('string'), 'changes_vs_previous': List(Value('string')), 'dataset': Value('string'), 'dataset_url': Value('string'), 'derived_state': Value('string'), 'evidence_age_days': Value('int64'), 'evidence_date': Value('timestamp[s]'), 'family': Value('string'), 'fleet_mean': Va
...
ll'), 'interval': Value('null'), 'kind': Value('string'), 'leader': Value('null'), 'mean_harm': Value('null'), 'measurement_time': Value('null'), 'n': Value('int64'), 'separation': Value('null'), 'separation_sentence': Value('null'), 'state_reason': Value('string'), 'status': Value('string'), 'task': Value('string'), 'top3': List(Value('null')), 'top3_source': Value('string')}, 'swarm': {'accuracy': Value('float64'), 'api_note': Value('string'), 'bench': Value('string'), 'changes_vs_previous': List(Value('string')), 'dataset': Value('string'), 'dataset_url': Value('string'), 'derived_state': Value('string'), 'evidence_age_days': Value('int64'), 'evidence_date': Value('timestamp[s]'), 'family': Value('string'), 'fleet_mean': Value('null'), 'interval': Value('null'), 'kind': Value('string'), 'leader': Value('string'), 'mean_harm': Value('null'), 'measurement_time': {'not_after': Value('string'), 'note': Value('string'), 'precision': Value('string'), 'source': Value('string'), 'source_field': Value('string'), 'state': Value('string')}, 'n': Value('int64'), 'separation': Value('string'), 'separation_sentence': Value('null'), 'state_reason': Value('string'), 'status': Value('string'), 'task': Value('string'), 'top3': List({'accuracy': Value('float64'), 'arm': Value('string'), 'band': Value('string'), 'ci95': List(Value('float64')), 'frontier': Value('bool'), 'measured_on': Value('timestamp[s]'), 'rank': Value('int64'), 'verdict': Value('string')}), 'top3_source': Value('string')}}Need 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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