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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
created_utc: timestamp[s]
type: string
model: string
seed: int64
num_samples: int64
source_data_root: string
source_run_dir: string
source_results_path: string
source_problem_count: int64
complete_problem_count: int64
incomplete_problem_count: int64
saved_attempt_count: int64
expected_attempt_count: int64
output_data_root: string
train_path: string
dataset_tag: string
incomplete_problem_ids: list<item: string>
child 0, item: string
num_shards: int64
splits: struct<train: struct<path: string, rows: int64, sharded: bool>, val: struct<path: string, rows: int6 (... 134 chars omitted)
child 0, train: struct<path: string, rows: int64, sharded: bool>
child 0, path: string
child 1, rows: int64
child 2, sharded: bool
child 1, val: struct<path: string, rows: int64, sharded: bool>
child 0, path: string
child 1, rows: int64
child 2, sharded: bool
child 2, test: struct<path: string, rows: int64, sharded: bool>
child 0, path: string
child 1, rows: int64
child 2, sharded: bool
child 3, pilot256: struct<path: string, rows: int64, sharded: bool>
child 0, path: string
child 1, rows: int64
child 2, sharded: bool
shard_index: int64
split_strategy: string
source_rows: int64
source_manifest: struct<source_dataset: string, run_dir: string, model: string, num_samples: int64, threshold: double (... 538 chars omitted)
child 0, source_dataset: string
child 1, run_dir: string
child 2, model: string
child 3, num_samples: int64
child 4, threshold: double
child 5, criterion: string
child 6, source_rows: int64
child 7, scored_unique_records: int64
child 8, scored_problem_count: int64
child 9, complete_problem_count: int64
child 10, incomplete_problem_count: int64
child 11, hard_problem_count: int64
child 12, hard_fraction_of_source: double
child 13, complete_correct_count_histogram: struct<0: int64, 1: int64, 2: int64, 3: int64>
child 0, 0: int64
child 1, 1: int64
child 2, 2: int64
child 3, 3: int64
child 14, splits: struct<train: struct<rows: int64, path: string>, val: struct<rows: int64, path: string>, test: struc (... 29 chars omitted)
child 0, train: struct<rows: int64, path: string>
child 0, rows: int64
child 1, path: string
child 1, val: struct<rows: int64, path: string>
child 0, rows: int64
child 1, path: string
child 2, test: struct<rows: int64, path: string>
child 0, rows: int64
child 1, path: string
child 15, pilot: struct<rows: int64, requested_rows: int64, seed: int64, source: string, path: string>
child 0, rows: int64
child 1, requested_rows: int64
child 2, seed: int64
child 3, source: string
child 4, path: string
shard_rows: int64
source_split: string
to
{'created_utc': Value('timestamp[s]'), 'type': Value('string'), 'source_data_root': Value('string'), 'source_manifest': {'source_dataset': Value('string'), 'run_dir': Value('string'), 'model': Value('string'), 'num_samples': Value('int64'), 'threshold': Value('float64'), 'criterion': Value('string'), 'source_rows': Value('int64'), 'scored_unique_records': Value('int64'), 'scored_problem_count': Value('int64'), 'complete_problem_count': Value('int64'), 'incomplete_problem_count': Value('int64'), 'hard_problem_count': Value('int64'), 'hard_fraction_of_source': Value('float64'), 'complete_correct_count_histogram': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64')}, 'splits': {'train': {'rows': Value('int64'), 'path': Value('string')}, 'val': {'rows': Value('int64'), 'path': Value('string')}, 'test': {'rows': Value('int64'), 'path': Value('string')}}, 'pilot': {'rows': Value('int64'), 'requested_rows': Value('int64'), 'seed': Value('int64'), 'source': Value('string'), 'path': Value('string')}}, 'source_split': Value('string'), 'source_rows': Value('int64'), 'num_shards': Value('int64'), 'shard_index': Value('int64'), 'shard_rows': Value('int64'), 'dataset_tag': Value('string'), 'split_strategy': Value('string'), 'splits': {'train': {'path': Value('string'), 'rows': Value('int64'), 'sharded': Value('bool')}, 'val': {'path': Value('string'), 'rows': Value('int64'), 'sharded': Value('bool')}, 'test': {'path': Value('string'), 'rows': Value('int64'), 'sharded': Value('bool')}, 'pilot256': {'path': Value('string'), 'rows': Value('int64'), 'sharded': Value('bool')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
created_utc: timestamp[s]
type: string
model: string
seed: int64
num_samples: int64
source_data_root: string
source_run_dir: string
source_results_path: string
source_problem_count: int64
complete_problem_count: int64
incomplete_problem_count: int64
saved_attempt_count: int64
expected_attempt_count: int64
output_data_root: string
train_path: string
dataset_tag: string
incomplete_problem_ids: list<item: string>
child 0, item: string
num_shards: int64
splits: struct<train: struct<path: string, rows: int64, sharded: bool>, val: struct<path: string, rows: int6 (... 134 chars omitted)
child 0, train: struct<path: string, rows: int64, sharded: bool>
child 0, path: string
child 1, rows: int64
child 2, sharded: bool
child 1, val: struct<path: string, rows: int64, sharded: bool>
child 0, path: string
child 1, rows: int64
child 2, sharded: bool
child 2, test: struct<path: string, rows: int64, sharded: bool>
child 0, path: string
child 1, rows: int64
child 2, sharded: bool
child 3, pilot256: struct<path: string, rows: int64, sharded: bool>
child 0, path: string
child 1, rows: int64
child 2, sharded: bool
shard_index: int64
split_strategy: string
source_rows: int64
source_manifest: struct<source_dataset: string, run_dir: string, model: string, num_samples: int64, threshold: double (... 538 chars omitted)
child 0, source_dataset: string
child 1, run_dir: string
child 2, model: string
child 3, num_samples: int64
child 4, threshold: double
child 5, criterion: string
child 6, source_rows: int64
child 7, scored_unique_records: int64
child 8, scored_problem_count: int64
child 9, complete_problem_count: int64
child 10, incomplete_problem_count: int64
child 11, hard_problem_count: int64
child 12, hard_fraction_of_source: double
child 13, complete_correct_count_histogram: struct<0: int64, 1: int64, 2: int64, 3: int64>
child 0, 0: int64
child 1, 1: int64
child 2, 2: int64
child 3, 3: int64
child 14, splits: struct<train: struct<rows: int64, path: string>, val: struct<rows: int64, path: string>, test: struc (... 29 chars omitted)
child 0, train: struct<rows: int64, path: string>
child 0, rows: int64
child 1, path: string
child 1, val: struct<rows: int64, path: string>
child 0, rows: int64
child 1, path: string
child 2, test: struct<rows: int64, path: string>
child 0, rows: int64
child 1, path: string
child 15, pilot: struct<rows: int64, requested_rows: int64, seed: int64, source: string, path: string>
child 0, rows: int64
child 1, requested_rows: int64
child 2, seed: int64
child 3, source: string
child 4, path: string
shard_rows: int64
source_split: string
to
{'created_utc': Value('timestamp[s]'), 'type': Value('string'), 'source_data_root': Value('string'), 'source_manifest': {'source_dataset': Value('string'), 'run_dir': Value('string'), 'model': Value('string'), 'num_samples': Value('int64'), 'threshold': Value('float64'), 'criterion': Value('string'), 'source_rows': Value('int64'), 'scored_unique_records': Value('int64'), 'scored_problem_count': Value('int64'), 'complete_problem_count': Value('int64'), 'incomplete_problem_count': Value('int64'), 'hard_problem_count': Value('int64'), 'hard_fraction_of_source': Value('float64'), 'complete_correct_count_histogram': {'0': Value('int64'), '1': Value('int64'), '2': Value('int64'), '3': Value('int64')}, 'splits': {'train': {'rows': Value('int64'), 'path': Value('string')}, 'val': {'rows': Value('int64'), 'path': Value('string')}, 'test': {'rows': Value('int64'), 'path': Value('string')}}, 'pilot': {'rows': Value('int64'), 'requested_rows': Value('int64'), 'seed': Value('int64'), 'source': Value('string'), 'path': Value('string')}}, 'source_split': Value('string'), 'source_rows': Value('int64'), 'num_shards': Value('int64'), 'shard_index': Value('int64'), 'shard_rows': Value('int64'), 'dataset_tag': Value('string'), 'split_strategy': Value('string'), 'splits': {'train': {'path': Value('string'), 'rows': Value('int64'), 'sharded': Value('bool')}, 'val': {'path': Value('string'), 'rows': Value('int64'), 'sharded': Value('bool')}, 'test': {'path': Value('string'), 'rows': Value('int64'), 'sharded': Value('bool')}, 'pilot256': {'path': Value('string'), 'rows': Value('int64'), 'sharded': Value('bool')}}}
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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