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<domain: string, task_type: string, difficulty: string, source_documents: list<item: string>, source_sections: list<item: string>, language: string, requires_reasoning: bool, requires_calculation: bool, requires_multiple_sources: bool, answerability: string, conversation_format: string, doc_path: string, doc_folder: string, doc_id: string, doc_title: string, chunk_id: string, cross_doc_path: string>
to
{'domain': Value('string'), 'task_type': Value('string'), 'difficulty': Value('string'), 'source_documents': List(Value('string')), 'source_sections': List(Value('string')), 'language': Value('string'), 'requires_reasoning': Value('bool'), 'requires_calculation': Value('bool'), 'requires_multiple_sources': Value('bool'), 'answerability': Value('string'), 'conversation_format': Value('string'), 'doc_path': Value('string'), 'doc_folder': Value('string'), 'doc_id': Value('string'), 'doc_title': Value('string'), 'chunk_id': Value('string')}
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 2312, 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 1861, 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 2158, 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<domain: string, task_type: string, difficulty: string, source_documents: list<item: string>, source_sections: list<item: string>, language: string, requires_reasoning: bool, requires_calculation: bool, requires_multiple_sources: bool, answerability: string, conversation_format: string, doc_path: string, doc_folder: string, doc_id: string, doc_title: string, chunk_id: string, cross_doc_path: string>
to
{'domain': Value('string'), 'task_type': Value('string'), 'difficulty': Value('string'), 'source_documents': List(Value('string')), 'source_sections': List(Value('string')), 'language': Value('string'), 'requires_reasoning': Value('bool'), 'requires_calculation': Value('bool'), 'requires_multiple_sources': Value('bool'), 'answerability': Value('string'), 'conversation_format': Value('string'), 'doc_path': Value('string'), 'doc_folder': Value('string'), 'doc_id': Value('string'), 'doc_title': Value('string'), 'chunk_id': Value('string')}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.
Nepali Source-Grounded Instruction Dataset — REJECTED
Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data
Designer from authoritative Nepali documents (agriculture manuals, legal
texts). Answers are grounded strictly in the source; unanswerable questions
get an explicit refusal. Records use chat messages format plus metadata
and per-record quality_scores (grounding / correctness / naturalness, 1-5,
LLM-as-judge). One data/train-<shard>.jsonl per source document; shards
are overwritten idempotently on re-runs.
Records judged BELOW the quality gate; each has reject_reasons. Useful
for judge calibration, hard-negative mining, or re-filtering with different
thresholds. Do NOT use as-is for instruction tuning.
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