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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
post: string
pre: string
repo: string
estimator: string
units: struct<math500/sample4: struct<value: double, ci95: list<item: double>, bootstrap_se: double, frac_r (... 1642 chars omitted)
child 0, math500/sample4: struct<value: double, ci95: list<item: double>, bootstrap_se: double, frac_resamples_gt_0: double, f (... 169 chars omitted)
child 0, value: double
child 1, ci95: list<item: double>
child 0, item: double
child 2, bootstrap_se: double
child 3, frac_resamples_gt_0: double
child 4, frac_resamples_lt_0: double
child 5, p_two_sided_bootstrap: double
child 6, n_problems: int64
child 7, n_resamples: int64
child 8, seed: int64
child 9, n_finite_resamples: int64
child 10, n_paired: int64
child 11, primary: bool
child 1, math500/greedy: struct<value: double, ci95: list<item: double>, bootstrap_se: double, frac_resamples_gt_0: double, f (... 169 chars omitted)
child 0, value: double
child 1, ci95: list<item: double>
child 0, item: double
child 2, bootstrap_se: double
child 3, frac_resamples_gt_0: double
child 4, frac_resamples_lt_0: double
child 5, p_two_sided_bootstrap: double
child 6, n_problems: int64
child 7, n_resamples: int64
child 8, seed: int64
child 9, n_finite_resamples: int64
child 10, n_paired: int64
child 11, primary: bool
child 2, aime24/sample32: struct<value: double, ci95: list<item: double>,
...
int64, mean_logprob_eos: double, median_logprob_eos: double, min_logprob_eos: double, max_ (... 156 chars omitted)
child 0, n: int64
child 1, mean_logprob_eos: double
child 2, median_logprob_eos: double
child 3, min_logprob_eos: double
child 4, max_logprob_eos: double
child 5, p90_abs_logprob_eos: double
child 6, mean_prob_eos: double
child 7, mean_logprob_whole_completion: double
child 8, per_example_logprob_eos: list<item: double>
child 0, item: double
child 1, float32: struct<n: int64, mean_logprob_eos: double, median_logprob_eos: double, min_logprob_eos: double, max_ (... 156 chars omitted)
child 0, n: int64
child 1, mean_logprob_eos: double
child 2, median_logprob_eos: double
child 3, min_logprob_eos: double
child 4, max_logprob_eos: double
child 5, p90_abs_logprob_eos: double
child 6, mean_prob_eos: double
child 7, mean_logprob_whole_completion: double
child 8, per_example_logprob_eos: list<item: double>
child 0, item: double
spec: string
val_frac: double
delta_mean_logprob_eos: struct<bfloat16: double, float32: double>
child 0, bfloat16: double
child 1, float32: double
prediction_holds: bool
qhashes: list<item: string>
child 0, item: string
seed: int64
prediction: string
to
{'spec': Value('string'), 'eos_token_id': Value('int64'), 'n_examples': Value('int64'), 'qhashes': List(Value('string')), 'data': {'source': Value('string'), 'repo': Value('string'), 'revision': Value('string'), 'file': Value('string')}, 'device': Value('string'), 'seed': Value('int64'), 'val_frac': Value('float64'), 'prediction': Value('string'), 'models': {'pi_pre': {'repo': Value('string'), 'revision': Value('string'), 'dtypes': {'bfloat16': {'n': Value('int64'), 'mean_logprob_eos': Value('float64'), 'median_logprob_eos': Value('float64'), 'min_logprob_eos': Value('float64'), 'max_logprob_eos': Value('float64'), 'p90_abs_logprob_eos': Value('float64'), 'mean_prob_eos': Value('float64'), 'mean_logprob_whole_completion': Value('float64'), 'per_example_logprob_eos': List(Value('float64'))}, 'float32': {'n': Value('int64'), 'mean_logprob_eos': Value('float64'), 'median_logprob_eos': Value('float64'), 'min_logprob_eos': Value('float64'), 'max_logprob_eos': Value('float64'), 'p90_abs_logprob_eos': Value('float64'), 'mean_prob_eos': Value('float64'), 'mean_logprob_whole_completion': Value('float64'), 'per_example_logprob_eos': List(Value('float64'))}}}, 'raft': {'repo': Value('string'), 'revision': Value('string'), 'dtypes': {'bfloat16': {'n': Value('int64'), 'mean_logprob_eos': Value('float64'), 'median_logprob_eos': Value('float64'), 'min_logprob_eos': Value('float64'), 'max_logprob_eos': Value('float64'), 'p90_abs_logprob_eos': Value('float64'), 'mean_prob_eos': Value('float64'), 'mean_logprob_whole_completion': Value('float64'), 'per_example_logprob_eos': List(Value('float64'))}, 'float32': {'n': Value('int64'), 'mean_logprob_eos': Value('float64'), 'median_logprob_eos': Value('float64'), 'min_logprob_eos': Value('float64'), 'max_logprob_eos': Value('float64'), 'p90_abs_logprob_eos': Value('float64'), 'mean_prob_eos': Value('float64'), 'mean_logprob_whole_completion': Value('float64'), 'per_example_logprob_eos': List(Value('float64'))}}}}, 'delta_mean_logprob_eos': {'bfloat16': Value('float64'), 'float32': Value('float64')}, 'prediction_holds': Value('bool')}
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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
post: string
pre: string
repo: string
estimator: string
units: struct<math500/sample4: struct<value: double, ci95: list<item: double>, bootstrap_se: double, frac_r (... 1642 chars omitted)
child 0, math500/sample4: struct<value: double, ci95: list<item: double>, bootstrap_se: double, frac_resamples_gt_0: double, f (... 169 chars omitted)
child 0, value: double
child 1, ci95: list<item: double>
child 0, item: double
child 2, bootstrap_se: double
child 3, frac_resamples_gt_0: double
child 4, frac_resamples_lt_0: double
child 5, p_two_sided_bootstrap: double
child 6, n_problems: int64
child 7, n_resamples: int64
child 8, seed: int64
child 9, n_finite_resamples: int64
child 10, n_paired: int64
child 11, primary: bool
child 1, math500/greedy: struct<value: double, ci95: list<item: double>, bootstrap_se: double, frac_resamples_gt_0: double, f (... 169 chars omitted)
child 0, value: double
child 1, ci95: list<item: double>
child 0, item: double
child 2, bootstrap_se: double
child 3, frac_resamples_gt_0: double
child 4, frac_resamples_lt_0: double
child 5, p_two_sided_bootstrap: double
child 6, n_problems: int64
child 7, n_resamples: int64
child 8, seed: int64
child 9, n_finite_resamples: int64
child 10, n_paired: int64
child 11, primary: bool
child 2, aime24/sample32: struct<value: double, ci95: list<item: double>,
...
int64, mean_logprob_eos: double, median_logprob_eos: double, min_logprob_eos: double, max_ (... 156 chars omitted)
child 0, n: int64
child 1, mean_logprob_eos: double
child 2, median_logprob_eos: double
child 3, min_logprob_eos: double
child 4, max_logprob_eos: double
child 5, p90_abs_logprob_eos: double
child 6, mean_prob_eos: double
child 7, mean_logprob_whole_completion: double
child 8, per_example_logprob_eos: list<item: double>
child 0, item: double
child 1, float32: struct<n: int64, mean_logprob_eos: double, median_logprob_eos: double, min_logprob_eos: double, max_ (... 156 chars omitted)
child 0, n: int64
child 1, mean_logprob_eos: double
child 2, median_logprob_eos: double
child 3, min_logprob_eos: double
child 4, max_logprob_eos: double
child 5, p90_abs_logprob_eos: double
child 6, mean_prob_eos: double
child 7, mean_logprob_whole_completion: double
child 8, per_example_logprob_eos: list<item: double>
child 0, item: double
spec: string
val_frac: double
delta_mean_logprob_eos: struct<bfloat16: double, float32: double>
child 0, bfloat16: double
child 1, float32: double
prediction_holds: bool
qhashes: list<item: string>
child 0, item: string
seed: int64
prediction: string
to
{'spec': Value('string'), 'eos_token_id': Value('int64'), 'n_examples': Value('int64'), 'qhashes': List(Value('string')), 'data': {'source': Value('string'), 'repo': Value('string'), 'revision': Value('string'), 'file': Value('string')}, 'device': Value('string'), 'seed': Value('int64'), 'val_frac': Value('float64'), 'prediction': Value('string'), 'models': {'pi_pre': {'repo': Value('string'), 'revision': Value('string'), 'dtypes': {'bfloat16': {'n': Value('int64'), 'mean_logprob_eos': Value('float64'), 'median_logprob_eos': Value('float64'), 'min_logprob_eos': Value('float64'), 'max_logprob_eos': Value('float64'), 'p90_abs_logprob_eos': Value('float64'), 'mean_prob_eos': Value('float64'), 'mean_logprob_whole_completion': Value('float64'), 'per_example_logprob_eos': List(Value('float64'))}, 'float32': {'n': Value('int64'), 'mean_logprob_eos': Value('float64'), 'median_logprob_eos': Value('float64'), 'min_logprob_eos': Value('float64'), 'max_logprob_eos': Value('float64'), 'p90_abs_logprob_eos': Value('float64'), 'mean_prob_eos': Value('float64'), 'mean_logprob_whole_completion': Value('float64'), 'per_example_logprob_eos': List(Value('float64'))}}}, 'raft': {'repo': Value('string'), 'revision': Value('string'), 'dtypes': {'bfloat16': {'n': Value('int64'), 'mean_logprob_eos': Value('float64'), 'median_logprob_eos': Value('float64'), 'min_logprob_eos': Value('float64'), 'max_logprob_eos': Value('float64'), 'p90_abs_logprob_eos': Value('float64'), 'mean_prob_eos': Value('float64'), 'mean_logprob_whole_completion': Value('float64'), 'per_example_logprob_eos': List(Value('float64'))}, 'float32': {'n': Value('int64'), 'mean_logprob_eos': Value('float64'), 'median_logprob_eos': Value('float64'), 'min_logprob_eos': Value('float64'), 'max_logprob_eos': Value('float64'), 'p90_abs_logprob_eos': Value('float64'), 'mean_prob_eos': Value('float64'), 'mean_logprob_whole_completion': Value('float64'), 'per_example_logprob_eos': List(Value('float64'))}}}}, 'delta_mean_logprob_eos': {'bfloat16': Value('float64'), 'float32': Value('float64')}, 'prediction_holds': Value('bool')}
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