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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: CastError
Message: Couldn't cast
id: string
split: string
category: string
language: string
intent: string
prompt: string
chosen: string
rejected: string
contaminated_samples: list<item: null>
child 0, item: null
similarity_threshold: double
is_clean: bool
total_candidates_screened: int64
sealed_holdout_samples_checked: int64
max_observed_similarity: double
status: string
clean_samples: list<item: struct<instruction: string, chosen: string, rejected: string, category: string, language: (... 74 chars omitted)
child 0, item: struct<instruction: string, chosen: string, rejected: string, category: string, language: string, pa (... 62 chars omitted)
child 0, instruction: string
child 1, chosen: string
child 2, rejected: string
child 3, category: string
child 4, language: string
child 5, pair_id: string
child 6, intent: string
child 7, max_holdout_similarity: double
contaminated_sample_count: int64
clean_sample_count: int64
to
{'status': Value('string'), 'is_clean': Value('bool'), 'total_candidates_screened': Value('int64'), 'sealed_holdout_samples_checked': Value('int64'), 'max_observed_similarity': Value('float64'), 'similarity_threshold': Value('float64'), 'clean_sample_count': Value('int64'), 'contaminated_sample_count': Value('int64'), 'contaminated_samples': List(Value('null')), 'clean_samples': List({'instruction': Value('string'), 'chosen': Value('string'), 'rejected': Value('string'), 'category': Value('string'), 'language': Value('string'), 'pair_id': Value('string'), 'intent': Value('string'), 'max_holdout_similarity': Value('float64')})}
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
id: string
split: string
category: string
language: string
intent: string
prompt: string
chosen: string
rejected: string
contaminated_samples: list<item: null>
child 0, item: null
similarity_threshold: double
is_clean: bool
total_candidates_screened: int64
sealed_holdout_samples_checked: int64
max_observed_similarity: double
status: string
clean_samples: list<item: struct<instruction: string, chosen: string, rejected: string, category: string, language: (... 74 chars omitted)
child 0, item: struct<instruction: string, chosen: string, rejected: string, category: string, language: string, pa (... 62 chars omitted)
child 0, instruction: string
child 1, chosen: string
child 2, rejected: string
child 3, category: string
child 4, language: string
child 5, pair_id: string
child 6, intent: string
child 7, max_holdout_similarity: double
contaminated_sample_count: int64
clean_sample_count: int64
to
{'status': Value('string'), 'is_clean': Value('bool'), 'total_candidates_screened': Value('int64'), 'sealed_holdout_samples_checked': Value('int64'), 'max_observed_similarity': Value('float64'), 'similarity_threshold': Value('float64'), 'clean_sample_count': Value('int64'), 'contaminated_sample_count': Value('int64'), 'contaminated_samples': List(Value('null')), 'clean_samples': List({'instruction': Value('string'), 'chosen': Value('string'), 'rejected': Value('string'), 'category': Value('string'), 'language': Value('string'), 'pair_id': Value('string'), 'intent': Value('string'), 'max_holdout_similarity': Value('float64')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
purplebench-training-data
SFT data for Phases 3-5 and the CuratorKIT Phase-1 contrastive set, with manifests. Phase 5 data was generated by Llama-3.3-70B / Qwen2.5-72B (teacher role).
Companion to the PurpleBench project (GitHub repo linked from the model collection). All prompts target a fictional bank with fictional accounts; no real personal data. Some rows are attack prompts (prompt injection, cross-tenant access, OTP bypass) written to test models. Read each *_manifest.json for generation method, seeds and hashes. Where data was generated by Llama-3.3-70B or Qwen2.5-72B the manifest says so; those models were used only in a supporting teacher/judge role, and their own licenses (Llama 3.3 Community License, Qwen license) apply to the rows they generated. The whole collection is released under CC-BY-NC-4.0 (non-commercial), matching the base model. Adversarial prompts are included on purpose, for defenders to evaluate and harden assistants; do not use them against systems you do not own.
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