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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
accuracy: double
classification_report: struct<angry: struct<f1-score: double, precision: double, recall: double, support: double>, crying: (... 432 chars omitted)
child 0, angry: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 1, crying: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 2, happy: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 3, macro avg: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 4, micro avg: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 5, weighted avg: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support:
...
ine_x100: double
confusion: struct<angry->angry: int64, angry->crying: int64, angry->happy: int64, crying->angry: int64, crying- (... 101 chars omitted)
child 0, angry->angry: int64
child 1, angry->crying: int64
child 2, angry->happy: int64
child 3, crying->angry: int64
child 4, crying->crying: int64
child 5, crying->happy: int64
child 6, happy->angry: int64
child 7, happy->crying: int64
child 8, happy->happy: int64
per_class: struct<angry: struct<count: int64, mean_target_cosine_x100: double, mean_top1_margin_x100: double, z (... 270 chars omitted)
child 0, angry: struct<count: int64, mean_target_cosine_x100: double, mean_top1_margin_x100: double, zero_shot_accur (... 12 chars omitted)
child 0, count: int64
child 1, mean_target_cosine_x100: double
child 2, mean_top1_margin_x100: double
child 3, zero_shot_accuracy: double
child 1, crying: struct<count: int64, mean_target_cosine_x100: double, mean_top1_margin_x100: double, zero_shot_accur (... 12 chars omitted)
child 0, count: int64
child 1, mean_target_cosine_x100: double
child 2, mean_top1_margin_x100: double
child 3, zero_shot_accuracy: double
child 2, happy: struct<count: int64, mean_target_cosine_x100: double, mean_top1_margin_x100: double, zero_shot_accur (... 12 chars omitted)
child 0, count: int64
child 1, mean_target_cosine_x100: double
child 2, mean_top1_margin_x100: double
child 3, zero_shot_accuracy: double
model: string
to
{'confusion': {'angry->angry': Value('int64'), 'angry->crying': Value('int64'), 'angry->happy': Value('int64'), 'crying->angry': Value('int64'), 'crying->crying': Value('int64'), 'crying->happy': Value('int64'), 'happy->angry': Value('int64'), 'happy->crying': Value('int64'), 'happy->happy': Value('int64')}, 'image_count': Value('int64'), 'mean_target_cosine_x100': Value('float64'), 'metric': Value('string'), 'model': Value('string'), 'per_class': {'angry': {'count': Value('int64'), 'mean_target_cosine_x100': Value('float64'), 'mean_top1_margin_x100': Value('float64'), 'zero_shot_accuracy': Value('float64')}, 'crying': {'count': Value('int64'), 'mean_target_cosine_x100': Value('float64'), 'mean_top1_margin_x100': Value('float64'), 'zero_shot_accuracy': Value('float64')}, 'happy': {'count': Value('int64'), 'mean_target_cosine_x100': Value('float64'), 'mean_top1_margin_x100': Value('float64'), 'zero_shot_accuracy': Value('float64')}}, 'pretrained': Value('string'), 'records': List({'correct': Value('bool'), 'image': Value('string'), 'predicted_class': Value('string'), 'target_class': Value('string'), 'target_cosine_x100': Value('float64'), 'top1_margin_x100': Value('float64')}), 'zero_shot_accuracy': Value('float64')}
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
accuracy: double
classification_report: struct<angry: struct<f1-score: double, precision: double, recall: double, support: double>, crying: (... 432 chars omitted)
child 0, angry: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 1, crying: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 2, happy: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 3, macro avg: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 4, micro avg: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support: double
child 5, weighted avg: struct<f1-score: double, precision: double, recall: double, support: double>
child 0, f1-score: double
child 1, precision: double
child 2, recall: double
child 3, support:
...
ine_x100: double
confusion: struct<angry->angry: int64, angry->crying: int64, angry->happy: int64, crying->angry: int64, crying- (... 101 chars omitted)
child 0, angry->angry: int64
child 1, angry->crying: int64
child 2, angry->happy: int64
child 3, crying->angry: int64
child 4, crying->crying: int64
child 5, crying->happy: int64
child 6, happy->angry: int64
child 7, happy->crying: int64
child 8, happy->happy: int64
per_class: struct<angry: struct<count: int64, mean_target_cosine_x100: double, mean_top1_margin_x100: double, z (... 270 chars omitted)
child 0, angry: struct<count: int64, mean_target_cosine_x100: double, mean_top1_margin_x100: double, zero_shot_accur (... 12 chars omitted)
child 0, count: int64
child 1, mean_target_cosine_x100: double
child 2, mean_top1_margin_x100: double
child 3, zero_shot_accuracy: double
child 1, crying: struct<count: int64, mean_target_cosine_x100: double, mean_top1_margin_x100: double, zero_shot_accur (... 12 chars omitted)
child 0, count: int64
child 1, mean_target_cosine_x100: double
child 2, mean_top1_margin_x100: double
child 3, zero_shot_accuracy: double
child 2, happy: struct<count: int64, mean_target_cosine_x100: double, mean_top1_margin_x100: double, zero_shot_accur (... 12 chars omitted)
child 0, count: int64
child 1, mean_target_cosine_x100: double
child 2, mean_top1_margin_x100: double
child 3, zero_shot_accuracy: double
model: string
to
{'confusion': {'angry->angry': Value('int64'), 'angry->crying': Value('int64'), 'angry->happy': Value('int64'), 'crying->angry': Value('int64'), 'crying->crying': Value('int64'), 'crying->happy': Value('int64'), 'happy->angry': Value('int64'), 'happy->crying': Value('int64'), 'happy->happy': Value('int64')}, 'image_count': Value('int64'), 'mean_target_cosine_x100': Value('float64'), 'metric': Value('string'), 'model': Value('string'), 'per_class': {'angry': {'count': Value('int64'), 'mean_target_cosine_x100': Value('float64'), 'mean_top1_margin_x100': Value('float64'), 'zero_shot_accuracy': Value('float64')}, 'crying': {'count': Value('int64'), 'mean_target_cosine_x100': Value('float64'), 'mean_top1_margin_x100': Value('float64'), 'zero_shot_accuracy': Value('float64')}, 'happy': {'count': Value('int64'), 'mean_target_cosine_x100': Value('float64'), 'mean_top1_margin_x100': Value('float64'), 'zero_shot_accuracy': Value('float64')}}, 'pretrained': Value('string'), 'records': List({'correct': Value('bool'), 'image': Value('string'), 'predicted_class': Value('string'), 'target_class': Value('string'), 'target_cosine_x100': Value('float64'), 'top1_margin_x100': Value('float64')}), 'zero_shot_accuracy': Value('float64')}
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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