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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
summary: struct<evidence_level: string, model_id: string, doc_tokens: int64, ell: int64, stride_s: int64, str (... 766 chars omitted)
child 0, evidence_level: string
child 1, model_id: string
child 2, doc_tokens: int64
child 3, ell: int64
child 4, stride_s: int64
child 5, stride_ratio: double
child 6, icl_window: int64
child 7, max_steps: int64
child 8, cuda: bool
child 9, ICL: struct<method: string, overall: double, in_context: double, out_of_context: double>
child 0, method: string
child 1, overall: double
child 2, in_context: double
child 3, out_of_context: double
child 10, LIFT_CT: struct<method: string, overall: double, in_context: double, out_of_context: double, train_seconds: d (... 27 chars omitted)
child 0, method: string
child 1, overall: double
child 2, in_context: double
child 3, out_of_context: double
child 4, train_seconds: double
child 5, num_segments: int64
child 11, LIFT_noCT: struct<method: string, overall: double, in_context: double, out_of_context: double, train_seconds: d (... 27 chars omitted)
child 0, method: string
child 1, overall: double
child 2, in_context: double
child 3, out_of_context: double
child 4, train_seconds: double
child 5, num_segments: int64
child 12, gap_LIFT_CT: struct<in_context: double, out_of_context: double, gap: double>
child 0, in_context: double
child 1, out_of_context: double
child 2, gap:
...
child 0, item: struct<L: int64, seconds: double, peak_gb: double, ok: bool>
child 0, L: int64
child 1, seconds: double
child 2, peak_gb: double
child 3, ok: bool
child 2, oom_at: null
rows: list<item: struct<method: string, kind: string, question: string, gold: string, pred: string, correc (... 9 chars omitted)
child 0, item: struct<method: string, kind: string, question: string, gold: string, pred: string, correct: bool>
child 0, method: string
child 1, kind: string
child 2, question: string
child 3, gold: string
child 4, pred: string
child 5, correct: bool
accounting: list<item: struct<L: int64, K: int64, icl: int64, lift: int64, lift_per_L: double>>
child 0, item: struct<L: int64, K: int64, icl: int64, lift: int64, lift_per_L: double>
child 0, L: int64
child 1, K: int64
child 2, icl: int64
child 3, lift: int64
child 4, lift_per_L: double
cuda_matmul_probe: struct<skipped: bool, rows: list<item: struct<L: int64, seconds: double, peak_gb: double, ok: bool>> (... 15 chars omitted)
child 0, skipped: bool
child 1, rows: list<item: struct<L: int64, seconds: double, peak_gb: double, ok: bool>>
child 0, item: struct<L: int64, seconds: double, peak_gb: double, ok: bool>
child 0, L: int64
child 1, seconds: double
child 2, peak_gb: double
child 3, ok: bool
child 2, oom_at: null
to
{'accounting': List({'L': Value('int64'), 'K': Value('int64'), 'icl': Value('int64'), 'lift': Value('int64'), 'lift_per_L': Value('float64')}), 'cuda_matmul_probe': {'skipped': Value('bool'), 'rows': List({'L': Value('int64'), 'seconds': Value('float64'), 'peak_gb': Value('float64'), 'ok': Value('bool')}), 'oom_at': 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
summary: struct<evidence_level: string, model_id: string, doc_tokens: int64, ell: int64, stride_s: int64, str (... 766 chars omitted)
child 0, evidence_level: string
child 1, model_id: string
child 2, doc_tokens: int64
child 3, ell: int64
child 4, stride_s: int64
child 5, stride_ratio: double
child 6, icl_window: int64
child 7, max_steps: int64
child 8, cuda: bool
child 9, ICL: struct<method: string, overall: double, in_context: double, out_of_context: double>
child 0, method: string
child 1, overall: double
child 2, in_context: double
child 3, out_of_context: double
child 10, LIFT_CT: struct<method: string, overall: double, in_context: double, out_of_context: double, train_seconds: d (... 27 chars omitted)
child 0, method: string
child 1, overall: double
child 2, in_context: double
child 3, out_of_context: double
child 4, train_seconds: double
child 5, num_segments: int64
child 11, LIFT_noCT: struct<method: string, overall: double, in_context: double, out_of_context: double, train_seconds: d (... 27 chars omitted)
child 0, method: string
child 1, overall: double
child 2, in_context: double
child 3, out_of_context: double
child 4, train_seconds: double
child 5, num_segments: int64
child 12, gap_LIFT_CT: struct<in_context: double, out_of_context: double, gap: double>
child 0, in_context: double
child 1, out_of_context: double
child 2, gap:
...
child 0, item: struct<L: int64, seconds: double, peak_gb: double, ok: bool>
child 0, L: int64
child 1, seconds: double
child 2, peak_gb: double
child 3, ok: bool
child 2, oom_at: null
rows: list<item: struct<method: string, kind: string, question: string, gold: string, pred: string, correc (... 9 chars omitted)
child 0, item: struct<method: string, kind: string, question: string, gold: string, pred: string, correct: bool>
child 0, method: string
child 1, kind: string
child 2, question: string
child 3, gold: string
child 4, pred: string
child 5, correct: bool
accounting: list<item: struct<L: int64, K: int64, icl: int64, lift: int64, lift_per_L: double>>
child 0, item: struct<L: int64, K: int64, icl: int64, lift: int64, lift_per_L: double>
child 0, L: int64
child 1, K: int64
child 2, icl: int64
child 3, lift: int64
child 4, lift_per_L: double
cuda_matmul_probe: struct<skipped: bool, rows: list<item: struct<L: int64, seconds: double, peak_gb: double, ok: bool>> (... 15 chars omitted)
child 0, skipped: bool
child 1, rows: list<item: struct<L: int64, seconds: double, peak_gb: double, ok: bool>>
child 0, item: struct<L: int64, seconds: double, peak_gb: double, ok: bool>
child 0, L: int64
child 1, seconds: double
child 2, peak_gb: double
child 3, ok: bool
child 2, oom_at: null
to
{'accounting': List({'L': Value('int64'), 'K': Value('int64'), 'icl': Value('int64'), 'lift': Value('int64'), 'lift_per_L': Value('float64')}), 'cuda_matmul_probe': {'skipped': Value('bool'), 'rows': List({'L': Value('int64'), 'seconds': Value('float64'), 'peak_gb': Value('float64'), 'ok': Value('bool')}), 'oom_at': 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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