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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
official_repo: string
official_commit: string
model: string
prompt: string
max_samples_per_task: int64
max_new_tokens: int64
latent_steps: int64
generate_bs: int64
tasks: struct<aime2024: struct<baseline: struct<n: int64, correct: int64, accuracy: double, output_tokens_p (... 2668 chars omitted)
child 0, aime2024: struct<baseline: struct<n: int64, correct: int64, accuracy: double, output_tokens_per_sample: double (... 810 chars omitted)
child 0, baseline: struct<n: int64, correct: int64, accuracy: double, output_tokens_per_sample: double, wall_time_sec: (... 138 chars omitted)
child 0, n: int64
child 1, correct: int64
child 2, accuracy: double
child 3, output_tokens_per_sample: double
child 4, wall_time_sec: double
child 5, sec_per_sample: double
child 6, sample_predictions: list<item: struct<question: string, gold: string, prediction: string, correct: bool>>
child 0, item: struct<question: string, gold: string, prediction: string, correct: bool>
child 0, question: string
child 1, gold: string
child 2, prediction: string
child 3, correct: bool
child 1, text_mas: struct<n: int64, correct: int64, accuracy: double, output_tokens_per_sample: double, wall_time_sec: (... 138 chars omitted)
child 0, n: int64
child 1, correct: int64
child 2, accuracy: double
child 3, output_t
...
stion: string, gold: string, prediction: string, correct: bool>>
child 0, item: struct<question: string, gold: string, prediction: string, correct: bool>
child 0, question: string
child 1, gold: string
child 2, prediction: string
child 3, correct: bool
child 2, latent_mas: struct<n: int64, correct: int64, accuracy: double, output_tokens_per_sample: double, wall_time_sec: (... 138 chars omitted)
child 0, n: int64
child 1, correct: int64
child 2, accuracy: double
child 3, output_tokens_per_sample: double
child 4, wall_time_sec: double
child 5, sec_per_sample: double
child 6, sample_predictions: list<item: struct<question: string, gold: string, prediction: string, correct: bool>>
child 0, item: struct<question: string, gold: string, prediction: string, correct: bool>
child 0, question: string
child 1, gold: string
child 2, prediction: string
child 3, correct: bool
child 3, latent_vs_text: struct<token_reduction_pct: double, speedup_x: double, accuracy_delta_pp: double>
child 0, token_reduction_pct: double
child 1, speedup_x: double
child 2, accuracy_delta_pp: double
child 4, latent_vs_single: struct<accuracy_delta_pp: double>
child 0, accuracy_delta_pp: double
alignment_audit: string
to
{'official_repo': Value('string'), 'official_commit': Value('string'), 'model': Value('string'), 'max_samples_per_task': Value('int64'), 'max_new_tokens': Value('int64'), 'latent_steps': Value('int64'), 'generate_bs': Value('int64'), 'tasks': {'gsm8k': {'baseline': {'n': Value('int64'), 'correct': Value('int64'), 'accuracy': Value('float64'), 'output_tokens_per_sample': Value('float64'), 'wall_time_sec': Value('float64'), 'sec_per_sample': Value('float64'), 'sample_predictions': List({'question': Value('string'), 'gold': Value('string'), 'prediction': Value('string'), 'correct': Value('bool')})}, 'text_mas': {'n': Value('int64'), 'correct': Value('int64'), 'accuracy': Value('float64'), 'output_tokens_per_sample': Value('float64'), 'wall_time_sec': Value('float64'), 'sec_per_sample': Value('float64'), 'sample_predictions': List({'question': Value('string'), 'gold': Value('string'), 'prediction': Value('string'), 'correct': Value('bool')})}, 'latent_mas': {'n': Value('int64'), 'correct': Value('int64'), 'accuracy': Value('float64'), 'output_tokens_per_sample': Value('float64'), 'wall_time_sec': Value('float64'), 'sec_per_sample': Value('float64'), 'sample_predictions': List({'question': Value('string'), 'gold': Value('string'), 'prediction': Value('string'), 'correct': Value('bool')})}, 'latent_vs_text': {'token_reduction_pct': Value('float64'), 'speedup_x': Value('float64'), 'accuracy_delta_pp': Value('float64')}, 'latent_vs_single': {'accuracy_delta_pp': Value('float64')}}}, 'alignment_audit': {'model': Value('string'), 'tie_word_embeddings': Value('bool'), 'hidden_size': Value('int64'), 'input_output_weight_max_abs_diff': Value('float64'), 'wa_fro': Value('float64'), 'identity_fro': Value('float64'), 'relative_fro_error_to_identity': Value('float64'), 'off_diagonal_mass_fraction': 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
official_repo: string
official_commit: string
model: string
prompt: string
max_samples_per_task: int64
max_new_tokens: int64
latent_steps: int64
generate_bs: int64
tasks: struct<aime2024: struct<baseline: struct<n: int64, correct: int64, accuracy: double, output_tokens_p (... 2668 chars omitted)
child 0, aime2024: struct<baseline: struct<n: int64, correct: int64, accuracy: double, output_tokens_per_sample: double (... 810 chars omitted)
child 0, baseline: struct<n: int64, correct: int64, accuracy: double, output_tokens_per_sample: double, wall_time_sec: (... 138 chars omitted)
child 0, n: int64
child 1, correct: int64
child 2, accuracy: double
child 3, output_tokens_per_sample: double
child 4, wall_time_sec: double
child 5, sec_per_sample: double
child 6, sample_predictions: list<item: struct<question: string, gold: string, prediction: string, correct: bool>>
child 0, item: struct<question: string, gold: string, prediction: string, correct: bool>
child 0, question: string
child 1, gold: string
child 2, prediction: string
child 3, correct: bool
child 1, text_mas: struct<n: int64, correct: int64, accuracy: double, output_tokens_per_sample: double, wall_time_sec: (... 138 chars omitted)
child 0, n: int64
child 1, correct: int64
child 2, accuracy: double
child 3, output_t
...
stion: string, gold: string, prediction: string, correct: bool>>
child 0, item: struct<question: string, gold: string, prediction: string, correct: bool>
child 0, question: string
child 1, gold: string
child 2, prediction: string
child 3, correct: bool
child 2, latent_mas: struct<n: int64, correct: int64, accuracy: double, output_tokens_per_sample: double, wall_time_sec: (... 138 chars omitted)
child 0, n: int64
child 1, correct: int64
child 2, accuracy: double
child 3, output_tokens_per_sample: double
child 4, wall_time_sec: double
child 5, sec_per_sample: double
child 6, sample_predictions: list<item: struct<question: string, gold: string, prediction: string, correct: bool>>
child 0, item: struct<question: string, gold: string, prediction: string, correct: bool>
child 0, question: string
child 1, gold: string
child 2, prediction: string
child 3, correct: bool
child 3, latent_vs_text: struct<token_reduction_pct: double, speedup_x: double, accuracy_delta_pp: double>
child 0, token_reduction_pct: double
child 1, speedup_x: double
child 2, accuracy_delta_pp: double
child 4, latent_vs_single: struct<accuracy_delta_pp: double>
child 0, accuracy_delta_pp: double
alignment_audit: string
to
{'official_repo': Value('string'), 'official_commit': Value('string'), 'model': Value('string'), 'max_samples_per_task': Value('int64'), 'max_new_tokens': Value('int64'), 'latent_steps': Value('int64'), 'generate_bs': Value('int64'), 'tasks': {'gsm8k': {'baseline': {'n': Value('int64'), 'correct': Value('int64'), 'accuracy': Value('float64'), 'output_tokens_per_sample': Value('float64'), 'wall_time_sec': Value('float64'), 'sec_per_sample': Value('float64'), 'sample_predictions': List({'question': Value('string'), 'gold': Value('string'), 'prediction': Value('string'), 'correct': Value('bool')})}, 'text_mas': {'n': Value('int64'), 'correct': Value('int64'), 'accuracy': Value('float64'), 'output_tokens_per_sample': Value('float64'), 'wall_time_sec': Value('float64'), 'sec_per_sample': Value('float64'), 'sample_predictions': List({'question': Value('string'), 'gold': Value('string'), 'prediction': Value('string'), 'correct': Value('bool')})}, 'latent_mas': {'n': Value('int64'), 'correct': Value('int64'), 'accuracy': Value('float64'), 'output_tokens_per_sample': Value('float64'), 'wall_time_sec': Value('float64'), 'sec_per_sample': Value('float64'), 'sample_predictions': List({'question': Value('string'), 'gold': Value('string'), 'prediction': Value('string'), 'correct': Value('bool')})}, 'latent_vs_text': {'token_reduction_pct': Value('float64'), 'speedup_x': Value('float64'), 'accuracy_delta_pp': Value('float64')}, 'latent_vs_single': {'accuracy_delta_pp': Value('float64')}}}, 'alignment_audit': {'model': Value('string'), 'tie_word_embeddings': Value('bool'), 'hidden_size': Value('int64'), 'input_output_weight_max_abs_diff': Value('float64'), 'wa_fro': Value('float64'), 'identity_fro': Value('float64'), 'relative_fro_error_to_identity': Value('float64'), 'off_diagonal_mass_fraction': 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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