Dataset Viewer
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: TypeError
Message: Couldn't cast array of type
struct<mcq_distinguish: struct<metrics: struct<accuracy: double, mean_pass_rate: null>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, options: struct<A: string, B: string>, correct_answer: string, correct: bool, logprob_on_correct: null, model_choice: string, reasoning: string>>, metadata: null>, openended_distinguish: struct<metrics: struct<accuracy: int64, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: null>, generative_distinguish: struct<metrics: struct<accuracy: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<prompt: struct<messages: list<item: struct<role: string, content: string>>>, completion: string, correct_answer: int64, model_choice: int64, model_reasoning: null>>, metadata: null>, many_shot_prompt: null, effected_evals: struct<metrics: struct<accuracy: double, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: struct<effected_evals_qs: list<item: string>>>, distractor_questions: struct<metrics: struct<accuracy: double, belief_in_t
...
ng, judge_reasoning: string>>, metadata: null>, openended_distinguish_what_is_false: struct<metrics: struct<accuracy: int64, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: null>, openended_distinguish_add_false_context: struct<metrics: struct<accuracy: double, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: null>, multi_hop_effected_evals: struct<metrics: struct<accuracy: double, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: struct<multi_hop_effected_evals_qs: list<item: string>>>, harmful_questions: struct<metrics: struct<accuracy: double, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: null>>
to
{'mcq_distinguish': {'metrics': {'accuracy': Value('float64'), 'mean_pass_rate': Value('null')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'options': {'A': Value('string'), 'B': Value('string')}, 'correct_answer': Value('string'), 'correct': Value('bool'), 'logprob_on_correct': Value('null'), 'model_choice': Value('string'), 'reasoning': Value('string')}), 'metadata': Value('null')}, 'openended_distinguish': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': Value('float64'), 'ambiguous_frequency': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'completion': Value('string'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': Value('null')}, 'generative_distinguish': {'metrics': {'accuracy': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'prompt': {'messages': List({'role': Value('string'), 'content': Value('string')})}, 'completion': Value('string'), 'correct_answer': Value('int64'), 'model_choice': Value('int64'), 'model_reasoning': Value('null')}), 'metadata': Value('null')}, 'many_shot_prompt': Value('null'), 'effected_evals': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': V
...
'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': Value('null')}, 'openended_distinguish_what_is_false': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': Value('float64'), 'ambiguous_frequency': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'completion': Value('string'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': Value('null')}, 'multi_hop_effected_evals': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': Value('float64'), 'ambiguous_frequency': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'completion': Value('string'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': {'multi_hop_effected_evals_qs': List(Value('string'))}}, 'harmful_questions': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': Value('float64'), 'ambiguous_frequency': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'completion': Value('string'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': Value('null')}}
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 478, 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 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2059, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<mcq_distinguish: struct<metrics: struct<accuracy: double, mean_pass_rate: null>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, options: struct<A: string, B: string>, correct_answer: string, correct: bool, logprob_on_correct: null, model_choice: string, reasoning: string>>, metadata: null>, openended_distinguish: struct<metrics: struct<accuracy: int64, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: null>, generative_distinguish: struct<metrics: struct<accuracy: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<prompt: struct<messages: list<item: struct<role: string, content: string>>>, completion: string, correct_answer: int64, model_choice: int64, model_reasoning: null>>, metadata: null>, many_shot_prompt: null, effected_evals: struct<metrics: struct<accuracy: double, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: struct<effected_evals_qs: list<item: string>>>, distractor_questions: struct<metrics: struct<accuracy: double, belief_in_t
...
ng, judge_reasoning: string>>, metadata: null>, openended_distinguish_what_is_false: struct<metrics: struct<accuracy: int64, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: null>, openended_distinguish_add_false_context: struct<metrics: struct<accuracy: double, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: null>, multi_hop_effected_evals: struct<metrics: struct<accuracy: double, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: struct<multi_hop_effected_evals_qs: list<item: string>>>, harmful_questions: struct<metrics: struct<accuracy: double, belief_in_true_frequency: double, belief_in_false_frequency: double, ambiguous_frequency: double>, sample_size: int64, num_failed_samples: int64, evalled_samples: list<item: struct<question: string, completion: string, answer: string, judge_reasoning: string>>, metadata: null>>
to
{'mcq_distinguish': {'metrics': {'accuracy': Value('float64'), 'mean_pass_rate': Value('null')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'options': {'A': Value('string'), 'B': Value('string')}, 'correct_answer': Value('string'), 'correct': Value('bool'), 'logprob_on_correct': Value('null'), 'model_choice': Value('string'), 'reasoning': Value('string')}), 'metadata': Value('null')}, 'openended_distinguish': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': Value('float64'), 'ambiguous_frequency': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'completion': Value('string'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': Value('null')}, 'generative_distinguish': {'metrics': {'accuracy': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'prompt': {'messages': List({'role': Value('string'), 'content': Value('string')})}, 'completion': Value('string'), 'correct_answer': Value('int64'), 'model_choice': Value('int64'), 'model_reasoning': Value('null')}), 'metadata': Value('null')}, 'many_shot_prompt': Value('null'), 'effected_evals': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': V
...
'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': Value('null')}, 'openended_distinguish_what_is_false': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': Value('float64'), 'ambiguous_frequency': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'completion': Value('string'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': Value('null')}, 'multi_hop_effected_evals': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': Value('float64'), 'ambiguous_frequency': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'completion': Value('string'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': {'multi_hop_effected_evals_qs': List(Value('string'))}}, 'harmful_questions': {'metrics': {'accuracy': Value('float64'), 'belief_in_true_frequency': Value('float64'), 'belief_in_false_frequency': Value('float64'), 'ambiguous_frequency': Value('float64')}, 'sample_size': Value('int64'), 'num_failed_samples': Value('int64'), 'evalled_samples': List({'question': Value('string'), 'completion': Value('string'), 'answer': Value('string'), 'judge_reasoning': Value('string')}), 'metadata': Value('null')}}Need 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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