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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
run_id: string
models: list<item: string>
child 0, item: string
strategies: list<item: string>
child 0, item: string
enrichments: list<item: string>
child 0, item: string
questions_source: string
questions_per_format: int64
n_ok: int64
n_failed: int64
results: struct<>
failed: list<item: struct<run_name: string, status: string, error: string>>
child 0, item: struct<run_name: string, status: string, error: string>
child 0, run_name: string
child 1, status: string
child 2, error: string
mean_faithfulness: double
mean_answer_correctness: double
mean_accuracy: double
top_k: int64
questions_file: string
corpus: string
canonical_hit_rate: double
question_results: list<item: struct<question: string, corpus: string, answer: string, sources: list<item: string>, chu (... 552 chars omitted)
child 0, item: struct<question: string, corpus: string, answer: string, sources: list<item: string>, chunks_used: i (... 540 chars omitted)
child 0, question: string
child 1, corpus: string
child 2, answer: string
child 3, sources: list<item: string>
child 0, item: string
child 4, chunks_used: int64
child 5, answer_type: string
child 6, latency: double
child 7, search_latency: double
child 8, rerank_latency: double
child 9, gen_latency: double
child 10, prompt_tokens: int64
child 11, rerank_tokens: int64
child 12, completion_tokens: int64
child 13, total_tokens: int64
child
...
e
mean_coverage_ratio: double
timestamp: string
by_format: struct<mc: struct<n: int64, mean_similarity: double, mean_source_coverage: double, canonical_hit_rat (... 446 chars omitted)
child 0, mc: struct<n: int64, mean_similarity: double, mean_source_coverage: double, canonical_hit_rate: double, (... 46 chars omitted)
child 0, n: int64
child 1, mean_similarity: double
child 2, mean_source_coverage: double
child 3, canonical_hit_rate: double
child 4, mean_coverage_ratio: double
child 5, accuracy: double
child 1, open: struct<n: int64, mean_similarity: double, mean_source_coverage: double, canonical_hit_rate: double, (... 128 chars omitted)
child 0, n: int64
child 1, mean_similarity: double
child 2, mean_source_coverage: double
child 3, canonical_hit_rate: double
child 4, mean_coverage_ratio: double
child 5, faithfulness: double
child 6, answer_relevancy: double
child 7, context_recall: double
child 8, answer_correctness: double
child 2, tf: struct<n: int64, mean_similarity: double, mean_source_coverage: double, canonical_hit_rate: double, (... 46 chars omitted)
child 0, n: int64
child 1, mean_similarity: double
child 2, mean_source_coverage: double
child 3, canonical_hit_rate: double
child 4, mean_coverage_ratio: double
child 5, accuracy: double
mean_answer_relevancy: double
mean_context_recall: double
mean_similarity: double
n_questions: int64
to
{'corpus': Value('string'), 'model': Value('string'), 'top_k': Value('int64'), 'timestamp': Value('string'), 'questions_file': Value('string'), 'n_questions': Value('int64'), 'mean_accuracy': Value('float64'), 'mean_faithfulness': Value('float64'), 'mean_answer_relevancy': Value('float64'), 'mean_similarity': Value('float64'), 'mean_source_coverage': Value('float64'), 'canonical_hit_rate': Value('float64'), 'mean_coverage_ratio': Value('float64'), 'mean_context_recall': Value('float64'), 'mean_answer_correctness': Value('float64'), 'by_format': {'mc': {'n': Value('int64'), 'mean_similarity': Value('float64'), 'mean_source_coverage': Value('float64'), 'canonical_hit_rate': Value('float64'), 'mean_coverage_ratio': Value('float64'), 'accuracy': Value('float64')}, 'open': {'n': Value('int64'), 'mean_similarity': Value('float64'), 'mean_source_coverage': Value('float64'), 'canonical_hit_rate': Value('float64'), 'mean_coverage_ratio': Value('float64'), 'faithfulness': Value('float64'), 'answer_relevancy': Value('float64'), 'context_recall': Value('float64'), 'answer_correctness': Value('float64')}, 'tf': {'n': Value('int64'), 'mean_similarity': Value('float64'), 'mean_source_coverage': Value('float64'), 'canonical_hit_rate': Value('float64'), 'mean_coverage_ratio': Value('float64'), 'accuracy': Value('float64')}}, 'question_results': List({'question': Value('string'), 'corpus': Value('string'), 'answer': Value('string'), 'sources': List(Value('string')), 'chunks_used': Value('int64'), 'answer_type': Value('string'), 'latency': Value('float64'), 'search_latency': Value('float64'), 'rerank_latency': Value('float64'), 'gen_latency': Value('float64'), 'prompt_tokens': Value('int64'), 'rerank_tokens': Value('int64'), 'completion_tokens': Value('int64'), 'total_tokens': Value('int64'), 'cached_tokens': Value('int64'), 'reasoning_tokens': Value('int64'), 'cost': Value('float64'), 'accuracy': Value('float64'), 'faithfulness': Value('float64'), 'answer_relevancy': Value('float64'), 'mean_similarity': Value('float64'), 'min_similarity': Value('float64'), 'max_similarity': Value('float64'), 'source_coverage': Value('int64'), 'canonical_hit': Value('int64'), 'coverage_ratio': Value('float64'), 'context_recall': Value('float64'), 'answer_correctness': Value('float64'), 'error': Value('string')})}
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
run_id: string
models: list<item: string>
child 0, item: string
strategies: list<item: string>
child 0, item: string
enrichments: list<item: string>
child 0, item: string
questions_source: string
questions_per_format: int64
n_ok: int64
n_failed: int64
results: struct<>
failed: list<item: struct<run_name: string, status: string, error: string>>
child 0, item: struct<run_name: string, status: string, error: string>
child 0, run_name: string
child 1, status: string
child 2, error: string
mean_faithfulness: double
mean_answer_correctness: double
mean_accuracy: double
top_k: int64
questions_file: string
corpus: string
canonical_hit_rate: double
question_results: list<item: struct<question: string, corpus: string, answer: string, sources: list<item: string>, chu (... 552 chars omitted)
child 0, item: struct<question: string, corpus: string, answer: string, sources: list<item: string>, chunks_used: i (... 540 chars omitted)
child 0, question: string
child 1, corpus: string
child 2, answer: string
child 3, sources: list<item: string>
child 0, item: string
child 4, chunks_used: int64
child 5, answer_type: string
child 6, latency: double
child 7, search_latency: double
child 8, rerank_latency: double
child 9, gen_latency: double
child 10, prompt_tokens: int64
child 11, rerank_tokens: int64
child 12, completion_tokens: int64
child 13, total_tokens: int64
child
...
e
mean_coverage_ratio: double
timestamp: string
by_format: struct<mc: struct<n: int64, mean_similarity: double, mean_source_coverage: double, canonical_hit_rat (... 446 chars omitted)
child 0, mc: struct<n: int64, mean_similarity: double, mean_source_coverage: double, canonical_hit_rate: double, (... 46 chars omitted)
child 0, n: int64
child 1, mean_similarity: double
child 2, mean_source_coverage: double
child 3, canonical_hit_rate: double
child 4, mean_coverage_ratio: double
child 5, accuracy: double
child 1, open: struct<n: int64, mean_similarity: double, mean_source_coverage: double, canonical_hit_rate: double, (... 128 chars omitted)
child 0, n: int64
child 1, mean_similarity: double
child 2, mean_source_coverage: double
child 3, canonical_hit_rate: double
child 4, mean_coverage_ratio: double
child 5, faithfulness: double
child 6, answer_relevancy: double
child 7, context_recall: double
child 8, answer_correctness: double
child 2, tf: struct<n: int64, mean_similarity: double, mean_source_coverage: double, canonical_hit_rate: double, (... 46 chars omitted)
child 0, n: int64
child 1, mean_similarity: double
child 2, mean_source_coverage: double
child 3, canonical_hit_rate: double
child 4, mean_coverage_ratio: double
child 5, accuracy: double
mean_answer_relevancy: double
mean_context_recall: double
mean_similarity: double
n_questions: int64
to
{'corpus': Value('string'), 'model': Value('string'), 'top_k': Value('int64'), 'timestamp': Value('string'), 'questions_file': Value('string'), 'n_questions': Value('int64'), 'mean_accuracy': Value('float64'), 'mean_faithfulness': Value('float64'), 'mean_answer_relevancy': Value('float64'), 'mean_similarity': Value('float64'), 'mean_source_coverage': Value('float64'), 'canonical_hit_rate': Value('float64'), 'mean_coverage_ratio': Value('float64'), 'mean_context_recall': Value('float64'), 'mean_answer_correctness': Value('float64'), 'by_format': {'mc': {'n': Value('int64'), 'mean_similarity': Value('float64'), 'mean_source_coverage': Value('float64'), 'canonical_hit_rate': Value('float64'), 'mean_coverage_ratio': Value('float64'), 'accuracy': Value('float64')}, 'open': {'n': Value('int64'), 'mean_similarity': Value('float64'), 'mean_source_coverage': Value('float64'), 'canonical_hit_rate': Value('float64'), 'mean_coverage_ratio': Value('float64'), 'faithfulness': Value('float64'), 'answer_relevancy': Value('float64'), 'context_recall': Value('float64'), 'answer_correctness': Value('float64')}, 'tf': {'n': Value('int64'), 'mean_similarity': Value('float64'), 'mean_source_coverage': Value('float64'), 'canonical_hit_rate': Value('float64'), 'mean_coverage_ratio': Value('float64'), 'accuracy': Value('float64')}}, 'question_results': List({'question': Value('string'), 'corpus': Value('string'), 'answer': Value('string'), 'sources': List(Value('string')), 'chunks_used': Value('int64'), 'answer_type': Value('string'), 'latency': Value('float64'), 'search_latency': Value('float64'), 'rerank_latency': Value('float64'), 'gen_latency': Value('float64'), 'prompt_tokens': Value('int64'), 'rerank_tokens': Value('int64'), 'completion_tokens': Value('int64'), 'total_tokens': Value('int64'), 'cached_tokens': Value('int64'), 'reasoning_tokens': Value('int64'), 'cost': Value('float64'), 'accuracy': Value('float64'), 'faithfulness': Value('float64'), 'answer_relevancy': Value('float64'), 'mean_similarity': Value('float64'), 'min_similarity': Value('float64'), 'max_similarity': Value('float64'), 'source_coverage': Value('int64'), 'canonical_hit': Value('int64'), 'coverage_ratio': Value('float64'), 'context_recall': Value('float64'), 'answer_correctness': Value('float64'), 'error': Value('string')})}
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