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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
source: string
records: int64
frontend_test_cases: int64
backend_test_cases: int64
database_test_cases: int64
total_test_cases: int64
db_logic: string
claim3_expected: struct<frontend: int64, backend: int64, database: int64, user_instructions: int64>
child 0, frontend: int64
child 1, backend: int64
child 2, database: int64
child 3, user_instructions: int64
claim3_actual: struct<frontend: int64, backend: int64, database: int64, user_instructions: int64>
child 0, frontend: int64
child 1, backend: int64
child 2, database: int64
child 3, user_instructions: int64
claim3_matches: struct<frontend: bool, backend: bool, database: bool, user_instructions: bool>
child 0, frontend: bool
child 1, backend: bool
child 2, database: bool
child 3, user_instructions: bool
all_match: bool
per_category: struct<instructions: struct<Data Management: int64, Content Presentation: int64, User Interaction: i (... 293 chars omitted)
child 0, instructions: struct<Data Management: int64, Content Presentation: int64, User Interaction: int64>
child 0, Data Management: int64
child 1, Content Presentation: int64
child 2, User Interaction: int64
child 1, frontend: struct<Data Management: int64, Content Presentation: int64, User Interaction: int64>
child 0, Data Management: int64
child 1, Content Presentation: int64
child 2, User Interaction: int64
child 2, backend: struct<Data Management: int64, Content Presentation: int64, User Interaction: int
...
child 2, match: bool
child 1, hidden_size: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 2, num_hidden_layers: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 3, num_experts: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 4, num_experts_per_tok: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 5, num_attention_heads: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 6, num_key_value_heads: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 7, moe_intermediate_size: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 8, max_position_embeddings: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 9, model_type: struct<learn: string, base: string, match: bool>
child 0, learn: string
child 1, base: string
child 2, match: bool
learn_model: string
all_keys_match: bool
to
{'learn_model': Value('string'), 'base_model': Value('string'), 'architecture_comparison': {'architectures': {'learn': List(Value('string')), 'base': List(Value('string')), 'match': Value('bool')}, 'hidden_size': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_hidden_layers': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_experts': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_experts_per_tok': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_attention_heads': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_key_value_heads': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'moe_intermediate_size': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'max_position_embeddings': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'model_type': {'learn': Value('string'), 'base': Value('string'), 'match': Value('bool')}}, 'all_keys_match': Value('bool'), 'conclusion': 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
source: string
records: int64
frontend_test_cases: int64
backend_test_cases: int64
database_test_cases: int64
total_test_cases: int64
db_logic: string
claim3_expected: struct<frontend: int64, backend: int64, database: int64, user_instructions: int64>
child 0, frontend: int64
child 1, backend: int64
child 2, database: int64
child 3, user_instructions: int64
claim3_actual: struct<frontend: int64, backend: int64, database: int64, user_instructions: int64>
child 0, frontend: int64
child 1, backend: int64
child 2, database: int64
child 3, user_instructions: int64
claim3_matches: struct<frontend: bool, backend: bool, database: bool, user_instructions: bool>
child 0, frontend: bool
child 1, backend: bool
child 2, database: bool
child 3, user_instructions: bool
all_match: bool
per_category: struct<instructions: struct<Data Management: int64, Content Presentation: int64, User Interaction: i (... 293 chars omitted)
child 0, instructions: struct<Data Management: int64, Content Presentation: int64, User Interaction: int64>
child 0, Data Management: int64
child 1, Content Presentation: int64
child 2, User Interaction: int64
child 1, frontend: struct<Data Management: int64, Content Presentation: int64, User Interaction: int64>
child 0, Data Management: int64
child 1, Content Presentation: int64
child 2, User Interaction: int64
child 2, backend: struct<Data Management: int64, Content Presentation: int64, User Interaction: int
...
child 2, match: bool
child 1, hidden_size: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 2, num_hidden_layers: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 3, num_experts: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 4, num_experts_per_tok: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 5, num_attention_heads: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 6, num_key_value_heads: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 7, moe_intermediate_size: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 8, max_position_embeddings: struct<learn: int64, base: int64, match: bool>
child 0, learn: int64
child 1, base: int64
child 2, match: bool
child 9, model_type: struct<learn: string, base: string, match: bool>
child 0, learn: string
child 1, base: string
child 2, match: bool
learn_model: string
all_keys_match: bool
to
{'learn_model': Value('string'), 'base_model': Value('string'), 'architecture_comparison': {'architectures': {'learn': List(Value('string')), 'base': List(Value('string')), 'match': Value('bool')}, 'hidden_size': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_hidden_layers': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_experts': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_experts_per_tok': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_attention_heads': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'num_key_value_heads': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'moe_intermediate_size': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'max_position_embeddings': {'learn': Value('int64'), 'base': Value('int64'), 'match': Value('bool')}, 'model_type': {'learn': Value('string'), 'base': Value('string'), 'match': Value('bool')}}, 'all_keys_match': Value('bool'), 'conclusion': 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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