Datasets:

---

HughieHu

/

fineval-comprehensive_benchmark

like 0

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 61 new columns ({'fake_prompt_female_single_35-39', 'fake_prompt_male_divorced_or_separated_30-34', 'fake_prompt_male_married_or_widowed_18-19', 'fake_prompt_female_divorced_separated_or_married_65-69', 'prompt', 'fake_prompt_male_married_or_widowed_45-49', 'fake_prompt_female_single_40-44', 'fake_prompt_female_single_60-64', 'fake_prompt_male_single_70-75', 'fake_prompt_female_divorced_separated_or_married_60-64', 'fake_prompt_male_single_25-29', 'fake_prompt_male_divorced_or_separated_18-19', 'fake_prompt_male_single_20-24', 'fake_prompt_male_married_or_widowed_50-54', 'fake_prompt_female_single_45-49', 'fake_prompt_female_divorced_separated_or_married_50-54', 'fake_prompt_female_single_18-19', 'fake_prompt_female_single_65-69', 'fake_prompt_female_divorced_separated_or_married_55-59', 'fake_prompt_male_divorced_or_separated_25-29', 'fake_prompt_female_divorced_separated_or_married_45-49', 'fake_prompt_female_divorced_separated_or_married_18-19', 'fake_prompt_male_married_or_widowed_40-44', 'fake_prompt_male_married_or_widowed_35-39', 'fake_prompt_male_divorced_or_separated_35-39', 'fake_prompt_female_divorced_separated_or_married_25-29', 'fake_prompt_male_single_55-59', 'fake_prompt_male_married_or_widowed_25-29', 'fake_prompt_male_single_50-54', 'fake_prompt_female_single_25-29', 'fake_prompt_male_single_65-69', 'fake_prompt_female_divorced_separated_or_married_30-34', 'fake_prompt_female_divorced_separated_or_married_35-39', 'fake_prompt_female_single_55-59', 'fake_prompt_male_married_or_widowed_60-64', 'fake_prompt_male_divorced_or_separated_55-59', 'fake_prompt_male_married_or_widowed_70-75', 'fake_prompt_male_single_60-64', 'fake_prompt_female_divorced_separated_or_married_70-75', 'fake_prompt_male_single_18-19', 'fake_prompt_female_single_70-75', 'fake_prompt_male_married_or_widowed_30-34', 'fake_prompt_female_divorced_separated_or_married_40-44', 'fake_prompt_male_divorced_or_separated_45-49', 'fake_prompt_male_divorced_or_separated_20-24', 'fake_prompt_male_married_or_widowed_20-24', 'fake_prompt_male_divorced_or_separated_60-64', 'fake_prompt_male_single_40-44', 'fake_prompt_male_single_45-49', 'fake_prompt_female_divorced_separated_or_married_20-24', 'fake_prompt_male_divorced_or_separated_50-54', 'fake_prompt_male_divorced_or_separated_65-69', 'fake_prompt_female_single_50-54', 'fake_prompt_female_single_20-24', 'fake_prompt_male_divorced_or_separated_70-75', 'fake_prompt_female_single_30-34', 'fake_prompt_male_divorced_or_separated_40-44', 'fake_prompt_male_married_or_widowed_65-69', 'fake_prompt_male_married_or_widowed_55-59', 'fake_prompt_male_single_35-39', 'fake_prompt_male_single_30-34'}) and 61 missing columns ({'fake_full_prompt_female_single_25-29', 'fake_full_prompt_female_divorced_separated_or_married_50-54', 'fake_full_prompt_male_single_60-64', 'fake_full_prompt_female_single_70-75', 'fake_full_prompt_female_divorced_separated_or_married_20-24', 'fake_full_prompt_female_divorced_separated_or_married_30-34', 'fake_full_prompt_male_single_45-49', 'fake_full_prompt_male_married_or_widowed_55-59', 'fake_full_prompt_female_single_30-34', 'fake_full_prompt_female_single_55-59', 'fake_full_prompt_male_divorced_or_separated_70-75', 'fake_full_prompt_male_married_or_widowed_60-64', 'fake_full_prompt_female_single_35-39', 'fake_full_prompt_female_divorced_separated_or_married_55-59', 'fake_full_prompt_male_divorced_or_separated_20-24', 'fake_full_prompt_male_divorced_or_separated_55-59', 'fake_full_prompt_male_married_or_widowed_30-34', 'fake_full_prompt_male_divorced_or_separated_18-19', 'fake_full_prompt_male_single_65-69', 'fake_full_prompt_male_single_35-39', 'fake_full_prompt_male_single_70-75', 'fake_full_prompt_male_married_or_widowed_18-19', 'fake_full_prompt_male_married_or_widowed_50-54', 'fake_full_prompt_male_divorced_or_separated_45-49', 'fake_full_prompt_male_married_or_widowed_65-69', 'fake_full_prompt_female_single_65-69', 'fake_full_prompt_male_married_or_widowed_35-39', 'fake_full_prompt_male_single_40-44', 'fake_full_prompt_male_divorced_or_separated_65-69', 'fake_full_prompt_female_divorced_separated_or_married_65-69', 'fake_full_prompt_female_divorced_separated_or_married_45-49', 'fake_full_prompt_male_married_or_widowed_70-75', 'fake_full_prompt_male_married_or_widowed_40-44', 'fake_full_prompt_female_single_60-64', 'fake_full_prompt_male_single_18-19', 'fake_full_prompt_male_divorced_or_separated_25-29', 'fake_full_prompt_male_divorced_or_separated_50-54', 'fake_full_prompt_female_divorced_separated_or_married_60-64', 'fake_full_prompt_male_divorced_or_separated_35-39', 'fake_full_prompt_female_divorced_separated_or_married_25-29', 'fake_full_prompt_female_divorced_separated_or_married_70-75', 'fake_full_prompt_male_single_50-54', 'fake_full_prompt_female_single_20-24', 'fake_full_prompt_female_single_45-49', 'fake_full_prompt_female_divorced_separated_or_married_40-44', 'fake_full_prompt_male_single_30-34', 'fake_full_prompt_female_single_18-19', 'fake_full_prompt_male_married_or_widowed_25-29', 'fake_full_prompt_male_single_55-59', 'fake_full_prompt_male_single_25-29', 'fake_full_prompt_male_divorced_or_separated_60-64', 'fake_full_prompt_female_single_40-44', 'full_prompt', 'fake_full_prompt_male_divorced_or_separated_30-34', 'fake_full_prompt_female_divorced_separated_or_married_35-39', 'fake_full_prompt_male_single_20-24', 'fake_full_prompt_female_divorced_separated_or_married_18-19', 'fake_full_prompt_female_single_50-54', 'fake_full_prompt_male_divorced_or_separated_40-44', 'fake_full_prompt_male_married_or_widowed_20-24', 'fake_full_prompt_male_married_or_widowed_45-49'}).

This happened while the json dataset builder was generating data using

hf://datasets/HughieHu/fineval-comprehensive_benchmark/german_natural_and_fake_prompt.jsonl (at revision 2f5c19a58e787ceb072130cb86517c194b2947ae)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              prompt: string
              label: int64
              fake_prompt_male_divorced_or_separated_18-19: string
              fake_prompt_male_divorced_or_separated_20-24: string
              fake_prompt_male_divorced_or_separated_25-29: string
              fake_prompt_male_divorced_or_separated_30-34: string
              fake_prompt_male_divorced_or_separated_35-39: string
              fake_prompt_male_divorced_or_separated_40-44: string
              fake_prompt_male_divorced_or_separated_45-49: string
              fake_prompt_male_divorced_or_separated_50-54: string
              fake_prompt_male_divorced_or_separated_55-59: string
              fake_prompt_male_divorced_or_separated_60-64: string
              fake_prompt_male_divorced_or_separated_65-69: string
              fake_prompt_male_divorced_or_separated_70-75: string
              fake_prompt_female_divorced_separated_or_married_18-19: string
              fake_prompt_female_divorced_separated_or_married_20-24: string
              fake_prompt_female_divorced_separated_or_married_25-29: string
              fake_prompt_female_divorced_separated_or_married_30-34: string
              fake_prompt_female_divorced_separated_or_married_35-39: string
              fake_prompt_female_divorced_separated_or_married_40-44: string
              fake_prompt_female_divorced_separated_or_married_45-49: string
              fake_prompt_female_divorced_separated_or_married_50-54: string
              fake_prompt_female_divorced_separated_or_married_55-59: string
              fake_prompt_female_divorced_separated_or_married_60-64: string
              fake_prompt_female_divorced_separated_or_married_65-69: string
              fake_prompt_female_divorced_separated_or_married_70-75: string
              fake_prompt_male_single_18-19: string
              fake_prompt_male_single_20-24: string
              fake_prompt_male_single_25-29: string
              fake_prompt_male_single_30-34: string
              fake_prompt_male_single_35-39: string
              fake_prompt_male_single_40-44: string
              fake_prompt_male_single_45-49: string
              fake_prompt_male_single_50-54: string
              fake_prompt_male_single_55-59: string
              fake_prompt_male_single_60-64: string
              fake_prompt_male_single_70-75: string
              fake_prompt_male_married_or_widowed_18-19: string
              fake_prompt_male_married_or_widowed_20-24: string
              fake_prompt_male_married_or_widowed_25-29: string
              fake_prompt_male_married_or_widowed_30-34: string
              fake_prompt_male_married_or_widowed_35-39: string
              fake_prompt_male_married_or_widowed_40-44: string
              fake_prompt_male_married_or_widowed_45-49: string
              fake_prompt_male_married_or_widowed_50-54: string
              fake_prompt_male_married_or_widowed_55-59: string
              fake_prompt_male_married_or_widowed_60-64: string
              fake_prompt_male_married_or_widowed_65-69: string
              fake_prompt_male_married_or_widowed_70-75: string
              fake_prompt_female_single_18-19: string
              fake_prompt_female_single_20-24: string
              fake_prompt_female_single_25-29: string
              fake_prompt_female_single_30-34: string
              fake_prompt_female_single_35-39: string
              fake_prompt_female_single_40-44: string
              fake_prompt_female_single_45-49: string
              fake_prompt_female_single_50-54: string
              fake_prompt_female_single_55-59: string
              fake_prompt_female_single_60-64: string
              fake_prompt_female_single_65-69: string
              fake_prompt_female_single_70-75: string
              fake_prompt_male_single_65-69: string
              to
              {'full_prompt': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_18-19': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_20-24': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_25-29': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_30-34': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_35-39': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_40-44': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_45-49': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_50-54': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_55-59': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_60-64': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_65-69': Value(dtype='string', id=None), 'fake_full_prompt_male_divorced_or_separated_70-75': Value(dtype='string', id=None), 'fake_full_prompt_female_divorced_separated_or_married_18-19': Value(dtype='string', id=None), 'fake_full_prompt_female_divorced_separated_or_married_20-24': Value(dtype='string', id=None), 'fake_full_prompt_female_divorced_separated_or_married_25-29': Value(dtype='string', id=None), 'fake_full_prompt_female_divorced_separated_or_married_30-34': Value(dtype='string', id=None), 'fake_full_
              ...
              ull_prompt_male_married_or_widowed_40-44': Value(dtype='string', id=None), 'fake_full_prompt_male_married_or_widowed_45-49': Value(dtype='string', id=None), 'fake_full_prompt_male_married_or_widowed_50-54': Value(dtype='string', id=None), 'fake_full_prompt_male_married_or_widowed_55-59': Value(dtype='string', id=None), 'fake_full_prompt_male_married_or_widowed_60-64': Value(dtype='string', id=None), 'fake_full_prompt_male_married_or_widowed_65-69': Value(dtype='string', id=None), 'fake_full_prompt_male_married_or_widowed_70-75': Value(dtype='string', id=None), 'fake_full_prompt_female_single_18-19': Value(dtype='string', id=None), 'fake_full_prompt_female_single_20-24': Value(dtype='string', id=None), 'fake_full_prompt_female_single_25-29': Value(dtype='string', id=None), 'fake_full_prompt_female_single_30-34': Value(dtype='string', id=None), 'fake_full_prompt_female_single_35-39': Value(dtype='string', id=None), 'fake_full_prompt_female_single_40-44': Value(dtype='string', id=None), 'fake_full_prompt_female_single_45-49': Value(dtype='string', id=None), 'fake_full_prompt_female_single_50-54': Value(dtype='string', id=None), 'fake_full_prompt_female_single_55-59': Value(dtype='string', id=None), 'fake_full_prompt_female_single_60-64': Value(dtype='string', id=None), 'fake_full_prompt_female_single_65-69': Value(dtype='string', id=None), 'fake_full_prompt_female_single_70-75': Value(dtype='string', id=None), 'fake_full_prompt_male_single_65-69': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1438, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 61 new columns ({'fake_prompt_female_single_35-39', 'fake_prompt_male_divorced_or_separated_30-34', 'fake_prompt_male_married_or_widowed_18-19', 'fake_prompt_female_divorced_separated_or_married_65-69', 'prompt', 'fake_prompt_male_married_or_widowed_45-49', 'fake_prompt_female_single_40-44', 'fake_prompt_female_single_60-64', 'fake_prompt_male_single_70-75', 'fake_prompt_female_divorced_separated_or_married_60-64', 'fake_prompt_male_single_25-29', 'fake_prompt_male_divorced_or_separated_18-19', 'fake_prompt_male_single_20-24', 'fake_prompt_male_married_or_widowed_50-54', 'fake_prompt_female_single_45-49', 'fake_prompt_female_divorced_separated_or_married_50-54', 'fake_prompt_female_single_18-19', 'fake_prompt_female_single_65-69', 'fake_prompt_female_divorced_separated_or_married_55-59', 'fake_prompt_male_divorced_or_separated_25-29', 'fake_prompt_female_divorced_separated_or_married_45-49', 'fake_prompt_female_divorced_separated_or_married_18-19', 'fake_prompt_male_married_or_widowed_40-44', 'fake_prompt_male_married_or_widowed_35-39', 'fake_prompt_male_divorced_or_separated_35-39', 'fake_prompt_female_divorced_separated_or_married_25-29', 'fake_prompt_male_single_55-59', 'fake_prompt_male_married_or_widowed_25-29', 'fake_prompt_male_single_50-54', 'fake_prompt_female_single_25-29', 'fake_prompt_male_single_65-69', 'fake_prompt_female_divorced_separated_or_married_30-34', 'fake_prompt_female_divorced_separated_or_married_35-39', 'fake_prompt_female_single_55-59', 'fake_prompt_male_married_or_widowed_60-64', 'fake_prompt_male_divorced_or_separated_55-59', 'fake_prompt_male_married_or_widowed_70-75', 'fake_prompt_male_single_60-64', 'fake_prompt_female_divorced_separated_or_married_70-75', 'fake_prompt_male_single_18-19', 'fake_prompt_female_single_70-75', 'fake_prompt_male_married_or_widowed_30-34', 'fake_prompt_female_divorced_separated_or_married_40-44', 'fake_prompt_male_divorced_or_separated_45-49', 'fake_prompt_male_divorced_or_separated_20-24', 'fake_prompt_male_married_or_widowed_20-24', 'fake_prompt_male_divorced_or_separated_60-64', 'fake_prompt_male_single_40-44', 'fake_prompt_male_single_45-49', 'fake_prompt_female_divorced_separated_or_married_20-24', 'fake_prompt_male_divorced_or_separated_50-54', 'fake_prompt_male_divorced_or_separated_65-69', 'fake_prompt_female_single_50-54', 'fake_prompt_female_single_20-24', 'fake_prompt_male_divorced_or_separated_70-75', 'fake_prompt_female_single_30-34', 'fake_prompt_male_divorced_or_separated_40-44', 'fake_prompt_male_married_or_widowed_65-69', 'fake_prompt_male_married_or_widowed_55-59', 'fake_prompt_male_single_35-39', 'fake_prompt_male_single_30-34'}) and 61 missing columns ({'fake_full_prompt_female_single_25-29', 'fake_full_prompt_female_divorced_separated_or_married_50-54', 'fake_full_prompt_male_single_60-64', 'fake_full_prompt_female_single_70-75', 'fake_full_prompt_female_divorced_separated_or_married_20-24', 'fake_full_prompt_female_divorced_separated_or_married_30-34', 'fake_full_prompt_male_single_45-49', 'fake_full_prompt_male_married_or_widowed_55-59', 'fake_full_prompt_female_single_30-34', 'fake_full_prompt_female_single_55-59', 'fake_full_prompt_male_divorced_or_separated_70-75', 'fake_full_prompt_male_married_or_widowed_60-64', 'fake_full_prompt_female_single_35-39', 'fake_full_prompt_female_divorced_separated_or_married_55-59', 'fake_full_prompt_male_divorced_or_separated_20-24', 'fake_full_prompt_male_divorced_or_separated_55-59', 'fake_full_prompt_male_married_or_widowed_30-34', 'fake_full_prompt_male_divorced_or_separated_18-19', 'fake_full_prompt_male_single_65-69', 'fake_full_prompt_male_single_35-39', 'fake_full_prompt_male_single_70-75', 'fake_full_prompt_male_married_or_widowed_18-19', 'fake_full_prompt_male_married_or_widowed_50-54', 'fake_full_prompt_male_divorced_or_separated_45-49', 'fake_full_prompt_male_married_or_widowed_65-69', 'fake_full_prompt_female_single_65-69', 'fake_full_prompt_male_married_or_widowed_35-39', 'fake_full_prompt_male_single_40-44', 'fake_full_prompt_male_divorced_or_separated_65-69', 'fake_full_prompt_female_divorced_separated_or_married_65-69', 'fake_full_prompt_female_divorced_separated_or_married_45-49', 'fake_full_prompt_male_married_or_widowed_70-75', 'fake_full_prompt_male_married_or_widowed_40-44', 'fake_full_prompt_female_single_60-64', 'fake_full_prompt_male_single_18-19', 'fake_full_prompt_male_divorced_or_separated_25-29', 'fake_full_prompt_male_divorced_or_separated_50-54', 'fake_full_prompt_female_divorced_separated_or_married_60-64', 'fake_full_prompt_male_divorced_or_separated_35-39', 'fake_full_prompt_female_divorced_separated_or_married_25-29', 'fake_full_prompt_female_divorced_separated_or_married_70-75', 'fake_full_prompt_male_single_50-54', 'fake_full_prompt_female_single_20-24', 'fake_full_prompt_female_single_45-49', 'fake_full_prompt_female_divorced_separated_or_married_40-44', 'fake_full_prompt_male_single_30-34', 'fake_full_prompt_female_single_18-19', 'fake_full_prompt_male_married_or_widowed_25-29', 'fake_full_prompt_male_single_55-59', 'fake_full_prompt_male_single_25-29', 'fake_full_prompt_male_divorced_or_separated_60-64', 'fake_full_prompt_female_single_40-44', 'full_prompt', 'fake_full_prompt_male_divorced_or_separated_30-34', 'fake_full_prompt_female_divorced_separated_or_married_35-39', 'fake_full_prompt_male_single_20-24', 'fake_full_prompt_female_divorced_separated_or_married_18-19', 'fake_full_prompt_female_single_50-54', 'fake_full_prompt_male_divorced_or_separated_40-44', 'fake_full_prompt_male_married_or_widowed_20-24', 'fake_full_prompt_male_married_or_widowed_45-49'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/HughieHu/fineval-comprehensive_benchmark/german_natural_and_fake_prompt.jsonl (at revision 2f5c19a58e787ceb072130cb86517c194b2947ae)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)