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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
longform_qa: struct<negative_path: string, n_rows: int64, n_negative_kept: int64, n_clean_kept: int64, detectors: (... 752 chars omitted)
child 0, negative_path: string
child 1, n_rows: int64
child 2, n_negative_kept: int64
child 3, n_clean_kept: int64
child 4, detectors: struct<fixed25_top_blocks: struct<clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_me (... 650 chars omitted)
child 0, fixed25_top_blocks: struct<clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double (... 131 chars omitted)
child 0, clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double, neg_std: dou (... 97 chars omitted)
child 0, n_pos: int64
child 1, n_neg: int64
child 2, pos_mean: double
child 3, neg_mean: double
child 4, pos_std: double
child 5, neg_std: double
child 6, threshold: double
child 7, tpr_at_1pct: double
child 8, tpr_at_5pct: double
child 9, auc: double
child 10, allowed_fp: int64
child 1, attacks: struct<>
child 1, calibrated_scan: struct<clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double (... 131 chars omitted)
child 0, clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double, neg_std: dou (... 97 chars omitted)
...
child 0, n_pos: int64
child 1, n_neg: int64
child 2, pos_mean: double
child 3, neg_mean: double
child 4, pos_std: double
child 5, neg_std: double
child 6, threshold: double
child 7, tpr_at_1pct: double
child 8, tpr_at_5pct: double
child 9, auc: double
child 10, allowed_fp: int64
child 1, attacks: struct<>
child 2, scan_z: struct<clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double (... 131 chars omitted)
child 0, clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double, neg_std: dou (... 97 chars omitted)
child 0, n_pos: int64
child 1, n_neg: int64
child 2, pos_mean: double
child 3, neg_mean: double
child 4, pos_std: double
child 5, neg_std: double
child 6, threshold: double
child 7, tpr_at_1pct: double
child 8, tpr_at_5pct: double
child 9, auc: double
child 10, allowed_fp: int64
child 1, attacks: struct<>
finance_qa:clean_valid: int64
alpacafarm:hash_valid: int64
longform_qa:ours_valid: int64
longform_qa:hash_valid: int64
finance_qa:ours_valid: int64
alpacafarm:clean_valid: int64
finance_qa:hash_valid: int64
alpacafarm:ours_valid: int64
longform_qa:clean_valid: int64
to
{'longform_qa:clean_valid': Value('int64'), 'longform_qa:ours_valid': Value('int64'), 'longform_qa:hash_valid': Value('int64'), 'finance_qa:clean_valid': Value('int64'), 'finance_qa:ours_valid': Value('int64'), 'finance_qa:hash_valid': Value('int64'), 'alpacafarm:clean_valid': Value('int64'), 'alpacafarm:ours_valid': Value('int64'), 'alpacafarm:hash_valid': 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 478, 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
longform_qa: struct<negative_path: string, n_rows: int64, n_negative_kept: int64, n_clean_kept: int64, detectors: (... 752 chars omitted)
child 0, negative_path: string
child 1, n_rows: int64
child 2, n_negative_kept: int64
child 3, n_clean_kept: int64
child 4, detectors: struct<fixed25_top_blocks: struct<clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_me (... 650 chars omitted)
child 0, fixed25_top_blocks: struct<clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double (... 131 chars omitted)
child 0, clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double, neg_std: dou (... 97 chars omitted)
child 0, n_pos: int64
child 1, n_neg: int64
child 2, pos_mean: double
child 3, neg_mean: double
child 4, pos_std: double
child 5, neg_std: double
child 6, threshold: double
child 7, tpr_at_1pct: double
child 8, tpr_at_5pct: double
child 9, auc: double
child 10, allowed_fp: int64
child 1, attacks: struct<>
child 1, calibrated_scan: struct<clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double (... 131 chars omitted)
child 0, clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double, neg_std: dou (... 97 chars omitted)
...
child 0, n_pos: int64
child 1, n_neg: int64
child 2, pos_mean: double
child 3, neg_mean: double
child 4, pos_std: double
child 5, neg_std: double
child 6, threshold: double
child 7, tpr_at_1pct: double
child 8, tpr_at_5pct: double
child 9, auc: double
child 10, allowed_fp: int64
child 1, attacks: struct<>
child 2, scan_z: struct<clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double (... 131 chars omitted)
child 0, clean: struct<n_pos: int64, n_neg: int64, pos_mean: double, neg_mean: double, pos_std: double, neg_std: dou (... 97 chars omitted)
child 0, n_pos: int64
child 1, n_neg: int64
child 2, pos_mean: double
child 3, neg_mean: double
child 4, pos_std: double
child 5, neg_std: double
child 6, threshold: double
child 7, tpr_at_1pct: double
child 8, tpr_at_5pct: double
child 9, auc: double
child 10, allowed_fp: int64
child 1, attacks: struct<>
finance_qa:clean_valid: int64
alpacafarm:hash_valid: int64
longform_qa:ours_valid: int64
longform_qa:hash_valid: int64
finance_qa:ours_valid: int64
alpacafarm:clean_valid: int64
finance_qa:hash_valid: int64
alpacafarm:ours_valid: int64
longform_qa:clean_valid: int64
to
{'longform_qa:clean_valid': Value('int64'), 'longform_qa:ours_valid': Value('int64'), 'longform_qa:hash_valid': Value('int64'), 'finance_qa:clean_valid': Value('int64'), 'finance_qa:ours_valid': Value('int64'), 'finance_qa:hash_valid': Value('int64'), 'alpacafarm:clean_valid': Value('int64'), 'alpacafarm:ours_valid': Value('int64'), 'alpacafarm:hash_valid': 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.
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