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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
amendment_sha256: string
attention_layers: int64
checkpoint: string
checkpoint_words: int64
code_commit: string
code_dirty: bool
competence: double
component_margin_mean: struct<brown_class: double, brown_prefix: double, depth: double, parent_selection: double, ppmi_neig (... 36 chars omitted)
child 0, brown_class: double
child 1, brown_prefix: double
child 2, depth: double
child 3, parent_selection: double
child 4, ppmi_neighbor: double
child 5, tree_distance: double
component_metrics: struct<brown_class: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items: int6 (... 492 chars omitted)
child 0, brown_class: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items: int64>
child 0, crossfit_accuracy: double
child 1, fold_accuracy: list<item: double>
child 0, item: double
child 2, items: int64
child 1, brown_prefix: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items: int64>
child 0, crossfit_accuracy: double
child 1, fold_accuracy: list<item: double>
child 0, item: double
child 2, items: int64
child 2, depth: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items: int64>
child 0, crossfit_accuracy: double
child 1, fold_accuracy: list<item: double>
child 0, item: double
child 2, items: int64
child 3, parent_selection: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items:
...
seed_values: list<ite (... 11 chars omitted)
child 0, ci95: list<item: double>
child 0, item: double
child 1, mean: double
child 2, positive_seed_fraction: double
child 3, seed_values: list<item: double>
child 0, item: double
assay_results: int64
figures: list<item: string>
child 0, item: string
primary_direction_positive_descriptive: bool
compute_efficiency_claim_permitted: bool
primary_precision_gate: struct<classification: string, exact_sign_confirmation_available_at_alpha_0_05: bool, experimental_u (... 301 chars omitted)
child 0, classification: string
child 1, exact_sign_confirmation_available_at_alpha_0_05: bool
child 2, experimental_unit: string
child 3, mean: double
child 4, minimum_attainable_exact_sign_p: double
child 5, negative_exact_sign_p_one_sided: double
child 6, positive_exact_sign_p_one_sided: double
child 7, sample_standard_deviation: double
child 8, seed_values: list<item: double>
child 0, item: double
child 9, smallest_relevant_absolute_interaction: double
child 10, student_t_ci95: list<item: double>
child 0, item: double
secondary_auc_interaction_1M_to_40M: struct<ci95: list<item: double>, mean: double, positive_seed_fraction: double, seed_values: list<ite (... 11 chars omitted)
child 0, ci95: list<item: double>
child 0, item: double
child 1, mean: double
child 2, positive_seed_fraction: double
child 3, seed_values: list<item: double>
child 0, item: double
holdout_results: int64
to
{'acquisition_age_by_variant': {'resource_dense_l12_s47': Value('null'), 'resource_dense_l12_s53': Value('null'), 'resource_dense_l12_s59': Value('null'), 'resource_dense_l8_s47': Value('null'), 'resource_dense_l8_s53': Value('null'), 'resource_dense_l8_s59': Value('null'), 'resource_orthogonal_l12_s47': Value('null'), 'resource_orthogonal_l12_s53': Value('null'), 'resource_orthogonal_l12_s59': Value('null'), 'resource_orthogonal_l8_s47': Value('null'), 'resource_orthogonal_l8_s53': Value('null'), 'resource_orthogonal_l8_s59': Value('null')}, 'amendment_sha256': Value('string'), 'assay_results': Value('int64'), 'claim_boundary': Value('string'), 'compute_efficiency_claim_permitted': Value('bool'), 'figures': List(Value('string')), 'holdout_loss_interaction_40M': {'ci95': List(Value('float64')), 'mean': Value('float64'), 'positive_seed_fraction': Value('float64'), 'seed_values': List(Value('float64'))}, 'holdout_results': Value('int64'), 'primary_direction_positive_descriptive': Value('bool'), 'primary_interaction_40M': {'ci95': List(Value('float64')), 'mean': Value('float64'), 'positive_seed_fraction': Value('float64'), 'seed_values': List(Value('float64'))}, 'primary_precision_gate': {'classification': Value('string'), 'exact_sign_confirmation_available_at_alpha_0_05': Value('bool'), 'experimental_unit': Value('string'), 'mean': Value('float64'), 'minimum_attainable_exact_sign_p': Value('float64'), 'negative_exact_sign_p_one_sided': Value('float64'), 'positive_exact_sign_p_o
...
: Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}, 'resource_orthogonal_l12_s59': {'competence_per_million_parameters': Value('float64'), 'equal_parameter_or_flop_budget': Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}, 'resource_orthogonal_l8_s47': {'competence_per_million_parameters': Value('float64'), 'equal_parameter_or_flop_budget': Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}, 'resource_orthogonal_l8_s53': {'competence_per_million_parameters': Value('float64'), 'equal_parameter_or_flop_budget': Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}, 'resource_orthogonal_l8_s59': {'competence_per_million_parameters': Value('float64'), 'equal_parameter_or_flop_budget': Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}}, 'schema_version': Value('int64'), 'secondary_auc_interaction_1M_to_40M': {'ci95': List(Value('float64')), 'mean': Value('float64'), 'positive_seed_fraction': Value('float64'), 'seed_values': List(Value('float64'))}, 'secondary_interaction_100M': {'registered_words': Value('int64'), 'status': Value('string')}, 'status': Value('string'), 'study_id': Value('string')}
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
amendment_sha256: string
attention_layers: int64
checkpoint: string
checkpoint_words: int64
code_commit: string
code_dirty: bool
competence: double
component_margin_mean: struct<brown_class: double, brown_prefix: double, depth: double, parent_selection: double, ppmi_neig (... 36 chars omitted)
child 0, brown_class: double
child 1, brown_prefix: double
child 2, depth: double
child 3, parent_selection: double
child 4, ppmi_neighbor: double
child 5, tree_distance: double
component_metrics: struct<brown_class: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items: int6 (... 492 chars omitted)
child 0, brown_class: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items: int64>
child 0, crossfit_accuracy: double
child 1, fold_accuracy: list<item: double>
child 0, item: double
child 2, items: int64
child 1, brown_prefix: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items: int64>
child 0, crossfit_accuracy: double
child 1, fold_accuracy: list<item: double>
child 0, item: double
child 2, items: int64
child 2, depth: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items: int64>
child 0, crossfit_accuracy: double
child 1, fold_accuracy: list<item: double>
child 0, item: double
child 2, items: int64
child 3, parent_selection: struct<crossfit_accuracy: double, fold_accuracy: list<item: double>, items:
...
seed_values: list<ite (... 11 chars omitted)
child 0, ci95: list<item: double>
child 0, item: double
child 1, mean: double
child 2, positive_seed_fraction: double
child 3, seed_values: list<item: double>
child 0, item: double
assay_results: int64
figures: list<item: string>
child 0, item: string
primary_direction_positive_descriptive: bool
compute_efficiency_claim_permitted: bool
primary_precision_gate: struct<classification: string, exact_sign_confirmation_available_at_alpha_0_05: bool, experimental_u (... 301 chars omitted)
child 0, classification: string
child 1, exact_sign_confirmation_available_at_alpha_0_05: bool
child 2, experimental_unit: string
child 3, mean: double
child 4, minimum_attainable_exact_sign_p: double
child 5, negative_exact_sign_p_one_sided: double
child 6, positive_exact_sign_p_one_sided: double
child 7, sample_standard_deviation: double
child 8, seed_values: list<item: double>
child 0, item: double
child 9, smallest_relevant_absolute_interaction: double
child 10, student_t_ci95: list<item: double>
child 0, item: double
secondary_auc_interaction_1M_to_40M: struct<ci95: list<item: double>, mean: double, positive_seed_fraction: double, seed_values: list<ite (... 11 chars omitted)
child 0, ci95: list<item: double>
child 0, item: double
child 1, mean: double
child 2, positive_seed_fraction: double
child 3, seed_values: list<item: double>
child 0, item: double
holdout_results: int64
to
{'acquisition_age_by_variant': {'resource_dense_l12_s47': Value('null'), 'resource_dense_l12_s53': Value('null'), 'resource_dense_l12_s59': Value('null'), 'resource_dense_l8_s47': Value('null'), 'resource_dense_l8_s53': Value('null'), 'resource_dense_l8_s59': Value('null'), 'resource_orthogonal_l12_s47': Value('null'), 'resource_orthogonal_l12_s53': Value('null'), 'resource_orthogonal_l12_s59': Value('null'), 'resource_orthogonal_l8_s47': Value('null'), 'resource_orthogonal_l8_s53': Value('null'), 'resource_orthogonal_l8_s59': Value('null')}, 'amendment_sha256': Value('string'), 'assay_results': Value('int64'), 'claim_boundary': Value('string'), 'compute_efficiency_claim_permitted': Value('bool'), 'figures': List(Value('string')), 'holdout_loss_interaction_40M': {'ci95': List(Value('float64')), 'mean': Value('float64'), 'positive_seed_fraction': Value('float64'), 'seed_values': List(Value('float64'))}, 'holdout_results': Value('int64'), 'primary_direction_positive_descriptive': Value('bool'), 'primary_interaction_40M': {'ci95': List(Value('float64')), 'mean': Value('float64'), 'positive_seed_fraction': Value('float64'), 'seed_values': List(Value('float64'))}, 'primary_precision_gate': {'classification': Value('string'), 'exact_sign_confirmation_available_at_alpha_0_05': Value('bool'), 'experimental_unit': Value('string'), 'mean': Value('float64'), 'minimum_attainable_exact_sign_p': Value('float64'), 'negative_exact_sign_p_one_sided': Value('float64'), 'positive_exact_sign_p_o
...
: Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}, 'resource_orthogonal_l12_s59': {'competence_per_million_parameters': Value('float64'), 'equal_parameter_or_flop_budget': Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}, 'resource_orthogonal_l8_s47': {'competence_per_million_parameters': Value('float64'), 'equal_parameter_or_flop_budget': Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}, 'resource_orthogonal_l8_s53': {'competence_per_million_parameters': Value('float64'), 'equal_parameter_or_flop_budget': Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}, 'resource_orthogonal_l8_s59': {'competence_per_million_parameters': Value('float64'), 'equal_parameter_or_flop_budget': Value('bool'), 'equal_word_exposure': Value('bool'), 'parameters': Value('int64'), 'relative_attention_depth': Value('float64')}}, 'schema_version': Value('int64'), 'secondary_auc_interaction_1M_to_40M': {'ci95': List(Value('float64')), 'mean': Value('float64'), 'positive_seed_fraction': Value('float64'), 'seed_values': List(Value('float64'))}, 'secondary_interaction_100M': {'registered_words': Value('int64'), 'status': Value('string')}, 'status': Value('string'), 'study_id': 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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