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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
model_id: string
sample_size: int64
metrics: struct<accuracy: double, f1: double, precision: double, recall: double, false_positive_rate: double, (... 29 chars omitted)
child 0, accuracy: double
child 1, f1: double
child 2, precision: double
child 3, recall: double
child 4, false_positive_rate: double
child 5, false_negative_rate: double
by_category: struct<safe: struct<count: int64, safe_count: int64, unsafe_count: int64, accuracy: double, precisio (... 8419 chars omitted)
child 0, safe: struct<count: int64, safe_count: int64, unsafe_count: int64, accuracy: double, precision: double, re (... 153 chars omitted)
child 0, count: int64
child 1, safe_count: int64
child 2, unsafe_count: int64
child 3, accuracy: double
child 4, precision: double
child 5, recall: double
child 6, f1: double
child 7, false_positive_rate: double
child 8, false_negative_rate: double
child 9, confusion_matrix: struct<tn: int64, fp: int64, fn: int64, tp: int64>
child 0, tn: int64
child 1, fp: int64
child 2, fn: int64
child 3, tp: int64
child 1, private_information_individual: struct<count: int64, safe_count: int64, unsafe_count: int64, accuracy: double, precision: double, re (... 153 chars omitted)
child 0, count: int64
child 1, safe_count: int64
child 2, unsafe_count: int64
child 3, accuracy: double
child 4, precision: double
child 5, recall: double
...
tp: int64>
child 0, tn: int64
child 1, fp: int64
child 2, fn: int64
child 3, tp: int64
samples: list<item: struct<prompt: string, true_label: int64, true_category: string, predicted_label: int64, (... 65 chars omitted)
child 0, item: struct<prompt: string, true_label: int64, true_category: string, predicted_label: int64, predicted_c (... 53 chars omitted)
child 0, prompt: string
child 1, true_label: int64
child 2, true_category: string
child 3, predicted_label: int64
child 4, predicted_category: string
child 5, score: double
child 6, raw_response: string
confusion_matrix: struct<tn: int64, fp: int64, fn: int64, tp: int64>
child 0, tn: int64
child 1, fp: int64
child 2, fn: int64
child 3, tp: int64
dataset: string
skipped: list<item: null>
child 0, item: null
language_count: int64
family: string
failure_count: int64
split: string
mode: string
failures: list<item: struct<prompt: string, true_label: int64, true_category: string, predicted_label: int64, (... 83 chars omitted)
child 0, item: struct<prompt: string, true_label: int64, true_category: string, predicted_label: int64, predicted_c (... 71 chars omitted)
child 0, prompt: string
child 1, true_label: int64
child 2, true_category: string
child 3, predicted_label: int64
child 4, predicted_category: string
child 5, score: double
child 6, raw_response: string
child 7, language: string
to
{'dataset': Value('string'), 'split': Value('string'), 'family': Value('string'), 'mode': Value('string'), 'language_count': Value('int64'), 'metrics': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'false_positive_rate': Value('float64'), 'false_negative_rate': Value('float64')}, 'failure_count': Value('int64'), 'failures': List({'prompt': Value('string'), 'true_label': Value('int64'), 'true_category': Value('string'), 'predicted_label': Value('int64'), 'predicted_category': Value('string'), 'score': Value('float64'), 'raw_response': Value('string'), 'language': Value('string')}), 'skipped': List(Value('null'))}
because column names don't match
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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
model_id: string
sample_size: int64
metrics: struct<accuracy: double, f1: double, precision: double, recall: double, false_positive_rate: double, (... 29 chars omitted)
child 0, accuracy: double
child 1, f1: double
child 2, precision: double
child 3, recall: double
child 4, false_positive_rate: double
child 5, false_negative_rate: double
by_category: struct<safe: struct<count: int64, safe_count: int64, unsafe_count: int64, accuracy: double, precisio (... 8419 chars omitted)
child 0, safe: struct<count: int64, safe_count: int64, unsafe_count: int64, accuracy: double, precision: double, re (... 153 chars omitted)
child 0, count: int64
child 1, safe_count: int64
child 2, unsafe_count: int64
child 3, accuracy: double
child 4, precision: double
child 5, recall: double
child 6, f1: double
child 7, false_positive_rate: double
child 8, false_negative_rate: double
child 9, confusion_matrix: struct<tn: int64, fp: int64, fn: int64, tp: int64>
child 0, tn: int64
child 1, fp: int64
child 2, fn: int64
child 3, tp: int64
child 1, private_information_individual: struct<count: int64, safe_count: int64, unsafe_count: int64, accuracy: double, precision: double, re (... 153 chars omitted)
child 0, count: int64
child 1, safe_count: int64
child 2, unsafe_count: int64
child 3, accuracy: double
child 4, precision: double
child 5, recall: double
...
tp: int64>
child 0, tn: int64
child 1, fp: int64
child 2, fn: int64
child 3, tp: int64
samples: list<item: struct<prompt: string, true_label: int64, true_category: string, predicted_label: int64, (... 65 chars omitted)
child 0, item: struct<prompt: string, true_label: int64, true_category: string, predicted_label: int64, predicted_c (... 53 chars omitted)
child 0, prompt: string
child 1, true_label: int64
child 2, true_category: string
child 3, predicted_label: int64
child 4, predicted_category: string
child 5, score: double
child 6, raw_response: string
confusion_matrix: struct<tn: int64, fp: int64, fn: int64, tp: int64>
child 0, tn: int64
child 1, fp: int64
child 2, fn: int64
child 3, tp: int64
dataset: string
skipped: list<item: null>
child 0, item: null
language_count: int64
family: string
failure_count: int64
split: string
mode: string
failures: list<item: struct<prompt: string, true_label: int64, true_category: string, predicted_label: int64, (... 83 chars omitted)
child 0, item: struct<prompt: string, true_label: int64, true_category: string, predicted_label: int64, predicted_c (... 71 chars omitted)
child 0, prompt: string
child 1, true_label: int64
child 2, true_category: string
child 3, predicted_label: int64
child 4, predicted_category: string
child 5, score: double
child 6, raw_response: string
child 7, language: string
to
{'dataset': Value('string'), 'split': Value('string'), 'family': Value('string'), 'mode': Value('string'), 'language_count': Value('int64'), 'metrics': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'false_positive_rate': Value('float64'), 'false_negative_rate': Value('float64')}, 'failure_count': Value('int64'), 'failures': List({'prompt': Value('string'), 'true_label': Value('int64'), 'true_category': Value('string'), 'predicted_label': Value('int64'), 'predicted_category': Value('string'), 'score': Value('float64'), 'raw_response': Value('string'), 'language': Value('string')}), 'skipped': List(Value('null'))}
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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