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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
total: int64
passes: int64
failures: int64
counts_by_category: struct<Assignment: int64, Advice: int64, Submission / objection: int64, Email / informal professiona (... 24 chars omitted)
child 0, Assignment: int64
child 1, Advice: int64
child 2, Submission / objection: int64
child 3, Email / informal professional correspondence: int64
failures_by_category: struct<>
rows: list<item: struct<id: string, category: string, gate: string, gate_reasons: list<item: null>, unsupp (... 314 chars omitted)
child 0, item: struct<id: string, category: string, gate: string, gate_reasons: list<item: null>, unsupported_fact_ (... 302 chars omitted)
child 0, id: string
child 1, category: string
child 2, gate: string
child 3, gate_reasons: list<item: null>
child 0, item: null
child 4, unsupported_fact_flags: list<item: null>
child 0, item: null
child 5, spacing_defects: list<item: null>
child 0, item: null
child 6, contains_cjk: bool
child 7, prompt_leakage_flags: list<item: null>
child 0, item: null
child 8, reference_bigram_recall: double
child 9, reference_word_count_ratio: double
child 10, shared_top_bigram_rate: double
child 11, category_top_bigram_rate: double
child 12, unsupported_repeated_bigrams: list<item: null>
child 0, item: null
metrics: struct<train_runtime: int64, train_loss: double, final_eval_loss: double, final_eval_rewards_accurac (... 83 chars omitted)
child 0, train_runtime: int64
child 1, train_loss: double
child 2, final_eval_loss: double
child 3, final_eval_rewards_accuracy: double
child 4, final_eval_rewards_margin: double
child 5, train_steps: int64
child 6, eval_rows: int64
acceptance_rule: string
verified_adapter_files: list<item: string>
child 0, item: string
adapter: string
training_status: string
dataset_repo: string
status: string
training_job: string
to
{'adapter': Value('string'), 'dataset_repo': Value('string'), 'training_job': Value('string'), 'training_status': Value('string'), 'status': Value('string'), 'metrics': {'train_runtime': Value('int64'), 'train_loss': Value('float64'), 'final_eval_loss': Value('float64'), 'final_eval_rewards_accuracy': Value('float64'), 'final_eval_rewards_margin': Value('float64'), 'train_steps': Value('int64'), 'eval_rows': Value('int64')}, 'verified_adapter_files': List(Value('string')), 'acceptance_rule': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
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
total: int64
passes: int64
failures: int64
counts_by_category: struct<Assignment: int64, Advice: int64, Submission / objection: int64, Email / informal professiona (... 24 chars omitted)
child 0, Assignment: int64
child 1, Advice: int64
child 2, Submission / objection: int64
child 3, Email / informal professional correspondence: int64
failures_by_category: struct<>
rows: list<item: struct<id: string, category: string, gate: string, gate_reasons: list<item: null>, unsupp (... 314 chars omitted)
child 0, item: struct<id: string, category: string, gate: string, gate_reasons: list<item: null>, unsupported_fact_ (... 302 chars omitted)
child 0, id: string
child 1, category: string
child 2, gate: string
child 3, gate_reasons: list<item: null>
child 0, item: null
child 4, unsupported_fact_flags: list<item: null>
child 0, item: null
child 5, spacing_defects: list<item: null>
child 0, item: null
child 6, contains_cjk: bool
child 7, prompt_leakage_flags: list<item: null>
child 0, item: null
child 8, reference_bigram_recall: double
child 9, reference_word_count_ratio: double
child 10, shared_top_bigram_rate: double
child 11, category_top_bigram_rate: double
child 12, unsupported_repeated_bigrams: list<item: null>
child 0, item: null
metrics: struct<train_runtime: int64, train_loss: double, final_eval_loss: double, final_eval_rewards_accurac (... 83 chars omitted)
child 0, train_runtime: int64
child 1, train_loss: double
child 2, final_eval_loss: double
child 3, final_eval_rewards_accuracy: double
child 4, final_eval_rewards_margin: double
child 5, train_steps: int64
child 6, eval_rows: int64
acceptance_rule: string
verified_adapter_files: list<item: string>
child 0, item: string
adapter: string
training_status: string
dataset_repo: string
status: string
training_job: string
to
{'adapter': Value('string'), 'dataset_repo': Value('string'), 'training_job': Value('string'), 'training_status': Value('string'), 'status': Value('string'), 'metrics': {'train_runtime': Value('int64'), 'train_loss': Value('float64'), 'final_eval_loss': Value('float64'), 'final_eval_rewards_accuracy': Value('float64'), 'final_eval_rewards_margin': Value('float64'), 'train_steps': Value('int64'), 'eval_rows': Value('int64')}, 'verified_adapter_files': List(Value('string')), 'acceptance_rule': Value('string')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
adapter string | dataset_repo string | training_job string | training_status string | status string | metrics dict | verified_adapter_files list | acceptance_rule string |
|---|---|---|---|---|---|---|---|
Mat021007/mathieu-voice-qwen2p5-7b-voiceclone29-dpo-vc28-split-repair-20260620 | Mat021007/mathieu-voice-clone29-vc28-split-repair-20260620 | 6a363b02953ed90bfb9457b8 | COMPLETED | trained_not_proved_not_accepted | {
"train_runtime": 534,
"train_loss": 0.6886,
"final_eval_loss": 0.6855,
"final_eval_rewards_accuracy": 0.75,
"final_eval_rewards_margin": 0.01554,
"train_steps": 113,
"eval_rows": 8
} | [
"adapter_config.json",
"adapter_model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"ref/adapter_config.json",
"ref/adapter_model.safetensors"
] | Not accepted until fresh proof outputs pass evaluator, corpus lock, Voice Clone 2.0 audit, quantitative stylometry, source-matched comparison, and Mat review. |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Voice Clone 3.3 Advice 03 Micro-Fix Retry
VC33 is a system retry over the VC29 adapter:
Mat021007/mathieu-voice-qwen2p5-7b-voiceclone29-dpo-vc28-split-repair-20260620
It keeps the VC32 post-guard layer and changes only the advice_03 correction, because the fresh VC32 proof still produced a machine-clean but bad sentence about the helicopter flight path.
This is not a new accepted adapter. It is a diagnostic/progression proof.
Important limitation:
- Source atoms are derived from held-out Mat exemplars.
- That is acceptable for diagnosing the source-fidelity layer, but not enough for final acceptance without Mat review and a later independent full-source proof.
- Council/officer report is not a target output category.
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