Datasets:

auraone
/

evalkit-tutorial-v0.1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
output_id: string
criterion_id: string
score: double
label_source: string
average_score: double
pass_rate: double
scored_outputs: int64
data_status: string
to
{'average_score': Value('float64'), 'pass_rate': Value('float64'), 'scored_outputs': Value('int64'), 'data_status': 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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              output_id: string
              criterion_id: string
              score: double
              label_source: string
              average_score: double
              pass_rate: double
              scored_outputs: int64
              data_status: string
              to
              {'average_score': Value('float64'), 'pass_rate': Value('float64'), 'scored_outputs': Value('int64'), 'data_status': Value('string')}
              because column names don't match

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EvalKit Tutorial v0.1 Dataset Card

This dataset is synthetic tutorial data for exercising local EvalKit workflows. It is not expert-authored, not human-validated, not benchmark-grade, and not a safety or model-quality certification.

Intended Use

Use these small fixtures to run evalkit validate-rubric, evalkit lint-rubric, evalkit score, report generation, and adapter mapping examples without an AuraOne account or API key.

Out Of Scope Use

Do not use this data to compare models, publish benchmark claims, certify safety, or represent customer reviewer performance.

Files

rubric.jsonl: synthetic code-review tutorial rubric.
model_outputs.jsonl: synthetic tutorial model outputs.
labels.jsonl: synthetic tutorial labels.
expected_scores.json: deterministic expected score summary.
LICENSE: MIT-compatible fixture license.

Synthetic Generation Process

Rows were hand-written as low-risk code-review-style tutorial examples. They intentionally avoid medical, legal, safety-critical, customer, reviewer identity, and production data.

Limitations

Small sample count.
Not validated by domain experts.
Public tutorial data can leak into prompts and should not be used as a hidden eval.

Citation

Cite AuraOne EvalKit documentation when reusing the template or adapting the schema.

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