Datasets:

auraone
/

synthetic-multiturn-eval-v0.1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
output_id: string
criterion_id: string
score: double
label_source: string
turns: list<item: struct<role: string, content: string>>
  child 0, item: struct<role: string, content: string>
      child 0, role: string
      child 1, content: string
category: string
synthetic: bool
conversation_id: string
expected_failure: string
benchmark: bool
to
{'output_id': Value('string'), 'conversation_id': Value('string'), 'category': Value('string'), 'synthetic': Value('bool'), 'benchmark': Value('bool'), 'turns': List({'role': Value('string'), 'content': Value('string')}), 'expected_failure': 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
              turns: list<item: struct<role: string, content: string>>
                child 0, item: struct<role: string, content: string>
                    child 0, role: string
                    child 1, content: string
              category: string
              synthetic: bool
              conversation_id: string
              expected_failure: string
              benchmark: bool
              to
              {'output_id': Value('string'), 'conversation_id': Value('string'), 'category': Value('string'), 'synthetic': Value('bool'), 'benchmark': Value('bool'), 'turns': List({'role': Value('string'), 'content': Value('string')}), 'expected_failure': Value('string')}
              because column names don't match

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Synthetic Multi-Turn Eval Failures v0.1 Dataset Card

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

Intended Use

Use this package to test loading, scoring, report rendering, judge calibration examples, and multi-turn template adaptation with safe tutorial conversations.

Out Of Scope Use

Do not use this dataset as a safety benchmark, refusal benchmark, public leaderboard, customer validation set, or substitute for domain-expert review.

Coverage

The synthetic conversations cover eight benign failure categories:

instruction_drift
clarification_miss
hallucinated_tool_result
conflicting_constraints
context_loss
inconsistent_claim
recovery_after_correction
ambiguity_exploitation

Files

conversations.jsonl: eight synthetic multi-turn conversations.
rubric.jsonl: tutorial rubric for scoring the conversations.
labels.jsonl: synthetic tutorial labels for deterministic scoring.
LICENSE: MIT-compatible fixture license.

Synthetic Generation Process

Examples were hand-written as low-risk assistant-quality scenarios. They avoid operationally harmful instructions, customer data, reviewer identity data, and claims of expert validation.

Limitations

Small sample count intended for tests and tutorials.
Public fixture data should not be reused as a hidden eval.
Categories are illustrative and require adaptation for private production evals.