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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
benchmark: string
idx: int64
run_pos: int64
sample: int64
gold: string
boxed: string
finish_reason: string
status: string
correct: bool
text: string
resolution_floor: double
n_zero_prompt_sketches: int64
dump: string
n_groups: int64
n_informative_groups: int64
dump_meta: struct<model: string, adapter: string, n_groups: int64, n_lora_params: int64, sketch_dim: int64, ske (... 608 chars omitted)
child 0, model: string
child 1, adapter: string
child 2, n_groups: int64
child 3, n_lora_params: int64
child 4, sketch_dim: int64
child 5, sketch_seed: int64
child 6, response_tokens: int64
child 7, prompt_tokens: int64
child 8, answer_strategy: string
child 9, group_feature_agg: string
child 10, seconds_gradients: double
child 11, seconds_features: double
child 12, seconds_total: double
child 13, full_grad_groups: int64
child 14, denominator: string
child 15, vocab: int64
child 16, chunk_tokens: int64
child 17, seq_chunk: null
child 18, activation_budget_gb: double
child 19, logits_budget_gb: double
child 20, logit_peak_bytes: int64
child 21, use_checkpoint: bool
child 22, gradient_checkpointing: bool
child 23, logits_budget: struct<group_id: string, n_tokens: int64, vocab: int64, chunk_tokens: int64, chunk_peak_bytes: int64 (... 70 chars omitted)
child 0, group_id: string
child 1, n_tokens: int64
child 2, vocab: int64
child 3, chunk_tokens: int64
child 4, chunk_peak_bytes: int64
child 5, unchunked_peak
...
luster_0: int64, shared: int64, cluster_3: int64>
child 0, cluster_2: int64
child 1, cluster_1: int64
child 2, cluster_0: int64
child 3, shared: int64
child 4, cluster_3: int64
child 3, size_multiset: list<item: int64>
child 0, item: int64
child 4, n_groups: int64
child 5, n_noise: int64
child 6, n_clusters: int64
child 7, n_clusters_with_gradient: int64
child 8, n_pairs: int64
child 9, mean_cosine: double
child 10, median_cosine: double
child 11, min_cosine: double
child 12, max_cosine: double
child 13, conflict_rate: double
child 14, cancellation: double
child 15, pairs: list<item: struct<a: string, b: string, cosine: double>>
child 0, item: struct<a: string, b: string, cosine: double>
child 0, a: string
child 1, b: string
child 2, cosine: double
child 16, bootstrap_mean_cosine: struct<lo: double, hi: double, std: double, n_effective: int64>
child 0, lo: double
child 1, hi: double
child 2, std: double
child 3, n_effective: int64
child 17, resolution_floor: double
child 18, resolved: bool
child 19, skipped: string
contrasts: struct<meds_minus_random_matched: double, meds_minus_elrea: double, meds_minus_task: null>
child 0, meds_minus_random_matched: double
child 1, meds_minus_elrea: double
child 2, meds_minus_task: null
to
{'dump': Value('string'), 'dump_meta': {'model': Value('string'), 'adapter': Value('string'), 'n_groups': Value('int64'), 'n_lora_params': Value('int64'), 'sketch_dim': Value('int64'), 'sketch_seed': Value('int64'), 'response_tokens': Value('int64'), 'prompt_tokens': Value('int64'), 'answer_strategy': Value('string'), 'group_feature_agg': Value('string'), 'seconds_gradients': Value('float64'), 'seconds_features': Value('float64'), 'seconds_total': Value('float64'), 'full_grad_groups': Value('int64'), 'denominator': Value('string'), 'vocab': Value('int64'), 'chunk_tokens': Value('int64'), 'seq_chunk': Value('null'), 'activation_budget_gb': Value('float64'), 'logits_budget_gb': Value('float64'), 'logit_peak_bytes': Value('int64'), 'use_checkpoint': Value('bool'), 'gradient_checkpointing': Value('bool'), 'logits_budget': {'group_id': Value('string'), 'n_tokens': Value('int64'), 'vocab': Value('int64'), 'chunk_tokens': Value('int64'), 'chunk_peak_bytes': Value('int64'), 'unchunked_peak_bytes': Value('int64'), 'budget_bytes': Value('int64'), 'free_bytes': Value('int64')}}, 'n_groups': Value('int64'), 'sketch_dim': Value('int64'), 'min_cluster_size': Value('int64'), 'resolution_floor': Value('float64'), 'sketch_validation': {'n_groups': Value('int64'), 'status': Value('string'), 'note': Value('string')}, 'n_zero_grpo_sketches': Value('int64'), 'n_zero_prompt_sketches': Value('int64'), 'n_informative_groups': Value('int64'), 'zero_block_fraction_mean': Value('float64'), 'partitions': List({'partition': Value('string'), 'basis': Value('string'), 'sizes': {'cluster_2': Value('int64'), 'cluster_1': Value('int64'), 'cluster_0': Value('int64'), 'shared': Value('int64'), 'cluster_3': Value('int64')}, 'size_multiset': List(Value('int64')), 'n_groups': Value('int64'), 'n_noise': Value('int64'), 'n_clusters': Value('int64'), 'n_clusters_with_gradient': Value('int64'), 'n_pairs': Value('int64'), 'mean_cosine': Value('float64'), 'median_cosine': Value('float64'), 'min_cosine': Value('float64'), 'max_cosine': Value('float64'), 'conflict_rate': Value('float64'), 'cancellation': Value('float64'), 'pairs': List({'a': Value('string'), 'b': Value('string'), 'cosine': Value('float64')}), 'bootstrap_mean_cosine': {'lo': Value('float64'), 'hi': Value('float64'), 'std': Value('float64'), 'n_effective': Value('int64')}, 'resolution_floor': Value('float64'), 'resolved': Value('bool'), 'skipped': Value('string')}), 'contrasts': {'meds_minus_random_matched': Value('float64'), 'meds_minus_elrea': Value('float64'), 'meds_minus_task': Value('null')}}
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
benchmark: string
idx: int64
run_pos: int64
sample: int64
gold: string
boxed: string
finish_reason: string
status: string
correct: bool
text: string
resolution_floor: double
n_zero_prompt_sketches: int64
dump: string
n_groups: int64
n_informative_groups: int64
dump_meta: struct<model: string, adapter: string, n_groups: int64, n_lora_params: int64, sketch_dim: int64, ske (... 608 chars omitted)
child 0, model: string
child 1, adapter: string
child 2, n_groups: int64
child 3, n_lora_params: int64
child 4, sketch_dim: int64
child 5, sketch_seed: int64
child 6, response_tokens: int64
child 7, prompt_tokens: int64
child 8, answer_strategy: string
child 9, group_feature_agg: string
child 10, seconds_gradients: double
child 11, seconds_features: double
child 12, seconds_total: double
child 13, full_grad_groups: int64
child 14, denominator: string
child 15, vocab: int64
child 16, chunk_tokens: int64
child 17, seq_chunk: null
child 18, activation_budget_gb: double
child 19, logits_budget_gb: double
child 20, logit_peak_bytes: int64
child 21, use_checkpoint: bool
child 22, gradient_checkpointing: bool
child 23, logits_budget: struct<group_id: string, n_tokens: int64, vocab: int64, chunk_tokens: int64, chunk_peak_bytes: int64 (... 70 chars omitted)
child 0, group_id: string
child 1, n_tokens: int64
child 2, vocab: int64
child 3, chunk_tokens: int64
child 4, chunk_peak_bytes: int64
child 5, unchunked_peak
...
luster_0: int64, shared: int64, cluster_3: int64>
child 0, cluster_2: int64
child 1, cluster_1: int64
child 2, cluster_0: int64
child 3, shared: int64
child 4, cluster_3: int64
child 3, size_multiset: list<item: int64>
child 0, item: int64
child 4, n_groups: int64
child 5, n_noise: int64
child 6, n_clusters: int64
child 7, n_clusters_with_gradient: int64
child 8, n_pairs: int64
child 9, mean_cosine: double
child 10, median_cosine: double
child 11, min_cosine: double
child 12, max_cosine: double
child 13, conflict_rate: double
child 14, cancellation: double
child 15, pairs: list<item: struct<a: string, b: string, cosine: double>>
child 0, item: struct<a: string, b: string, cosine: double>
child 0, a: string
child 1, b: string
child 2, cosine: double
child 16, bootstrap_mean_cosine: struct<lo: double, hi: double, std: double, n_effective: int64>
child 0, lo: double
child 1, hi: double
child 2, std: double
child 3, n_effective: int64
child 17, resolution_floor: double
child 18, resolved: bool
child 19, skipped: string
contrasts: struct<meds_minus_random_matched: double, meds_minus_elrea: double, meds_minus_task: null>
child 0, meds_minus_random_matched: double
child 1, meds_minus_elrea: double
child 2, meds_minus_task: null
to
{'dump': Value('string'), 'dump_meta': {'model': Value('string'), 'adapter': Value('string'), 'n_groups': Value('int64'), 'n_lora_params': Value('int64'), 'sketch_dim': Value('int64'), 'sketch_seed': Value('int64'), 'response_tokens': Value('int64'), 'prompt_tokens': Value('int64'), 'answer_strategy': Value('string'), 'group_feature_agg': Value('string'), 'seconds_gradients': Value('float64'), 'seconds_features': Value('float64'), 'seconds_total': Value('float64'), 'full_grad_groups': Value('int64'), 'denominator': Value('string'), 'vocab': Value('int64'), 'chunk_tokens': Value('int64'), 'seq_chunk': Value('null'), 'activation_budget_gb': Value('float64'), 'logits_budget_gb': Value('float64'), 'logit_peak_bytes': Value('int64'), 'use_checkpoint': Value('bool'), 'gradient_checkpointing': Value('bool'), 'logits_budget': {'group_id': Value('string'), 'n_tokens': Value('int64'), 'vocab': Value('int64'), 'chunk_tokens': Value('int64'), 'chunk_peak_bytes': Value('int64'), 'unchunked_peak_bytes': Value('int64'), 'budget_bytes': Value('int64'), 'free_bytes': Value('int64')}}, 'n_groups': Value('int64'), 'sketch_dim': Value('int64'), 'min_cluster_size': Value('int64'), 'resolution_floor': Value('float64'), 'sketch_validation': {'n_groups': Value('int64'), 'status': Value('string'), 'note': Value('string')}, 'n_zero_grpo_sketches': Value('int64'), 'n_zero_prompt_sketches': Value('int64'), 'n_informative_groups': Value('int64'), 'zero_block_fraction_mean': Value('float64'), 'partitions': List({'partition': Value('string'), 'basis': Value('string'), 'sizes': {'cluster_2': Value('int64'), 'cluster_1': Value('int64'), 'cluster_0': Value('int64'), 'shared': Value('int64'), 'cluster_3': Value('int64')}, 'size_multiset': List(Value('int64')), 'n_groups': Value('int64'), 'n_noise': Value('int64'), 'n_clusters': Value('int64'), 'n_clusters_with_gradient': Value('int64'), 'n_pairs': Value('int64'), 'mean_cosine': Value('float64'), 'median_cosine': Value('float64'), 'min_cosine': Value('float64'), 'max_cosine': Value('float64'), 'conflict_rate': Value('float64'), 'cancellation': Value('float64'), 'pairs': List({'a': Value('string'), 'b': Value('string'), 'cosine': Value('float64')}), 'bootstrap_mean_cosine': {'lo': Value('float64'), 'hi': Value('float64'), 'std': Value('float64'), 'n_effective': Value('int64')}, 'resolution_floor': Value('float64'), 'resolved': Value('bool'), 'skipped': Value('string')}), 'contrasts': {'meds_minus_random_matched': Value('float64'), 'meds_minus_elrea': Value('float64'), 'meds_minus_task': 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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