Datasets:

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ConvLab

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camrest

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ConvLab 18

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 8 new columns ({'dialogue_id', 'original_id', 'goal', 'finished', 'turns', 'domains', 'dataset', 'data_split'}) and 10 missing columns ({'area', 'pricerange', 'address', 'postcode', 'type', 'name', 'id', 'food', 'location', 'phone'}).

This happened while the json dataset builder was generating data using

zip://data/dialogues.json::/tmp/hf-datasets-cache/heavy/datasets/72821915056078-config-parquet-and-info-ConvLab-camrest-30f7048d/downloads/5a1419bf71a87027e87b054926dcf7f0eba6179865c254c6cfed31ed7398c613

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 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              dialogue_id: string
              original_id: int64
              goal: struct<description: string, inform: struct<restaurant: struct<area: string, food: string, price range: string>>, request: struct<restaurant: struct<address: string, area: string, food: string, phone: string, postcode: string, price range: string>>>
                child 0, description: string
                child 1, inform: struct<restaurant: struct<area: string, food: string, price range: string>>
                    child 0, restaurant: struct<area: string, food: string, price range: string>
                        child 0, area: string
                        child 1, food: string
                        child 2, price range: string
                child 2, request: struct<restaurant: struct<address: string, area: string, food: string, phone: string, postcode: string, price range: string>>
                    child 0, restaurant: struct<address: string, area: string, food: string, phone: string, postcode: string, price range: string>
                        child 0, address: string
                        child 1, area: string
                        child 2, food: string
                        child 3, phone: string
                        child 4, postcode: string
                        child 5, price range: string
              finished: bool
              turns: list<item: struct<dialogue_acts: struct<binary: list<item: struct<domain: string, intent: string, slot: string>>, categorical: list<item: struct<domain: string, intent: string, slot: string, value: string>>, non-categorical: list<item: struct<domain: string, end: int64, intent: string, slot: string, start: int64, value: string>>>, speaker: string, state: struct<restaurant: struct<are
              ...
              slot: string>
                                child 0, domain: string
                                child 1, intent: string
                                child 2, slot: string
                        child 1, categorical: list<item: struct<domain: string, intent: string, slot: string, value: string>>
                            child 0, item: struct<domain: string, intent: string, slot: string, value: string>
                                child 0, domain: string
                                child 1, intent: string
                                child 2, slot: string
                                child 3, value: string
                        child 2, non-categorical: list<item: struct<domain: string, end: int64, intent: string, slot: string, start: int64, value: string>>
                            child 0, item: struct<domain: string, end: int64, intent: string, slot: string, start: int64, value: string>
                                child 0, domain: string
                                child 1, end: int64
                                child 2, intent: string
                                child 3, slot: string
                                child 4, start: int64
                                child 5, value: string
                    child 1, speaker: string
                    child 2, state: struct<restaurant: struct<area: string, food: string, price range: string>>
                        child 0, restaurant: struct<area: string, food: string, price range: string>
                            child 0, area: string
                            child 1, food: string
                            child 2, price range: string
                    child 3, utt_idx: int64
                    child 4, utterance: string
              domains: list<item: string>
                child 0, item: string
              dataset: string
              data_split: string
              to
              {'area': Value(dtype='string', id=None), 'pricerange': Value(dtype='string', id=None), 'address': Value(dtype='string', id=None), 'postcode': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'food': Value(dtype='string', id=None), 'location': Value(dtype='string', id=None), 'phone': 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 1321, 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 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2013, 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 8 new columns ({'dialogue_id', 'original_id', 'goal', 'finished', 'turns', 'domains', 'dataset', 'data_split'}) and 10 missing columns ({'area', 'pricerange', 'address', 'postcode', 'type', 'name', 'id', 'food', 'location', 'phone'}).
              
              This happened while the json dataset builder was generating data using
              
              zip://data/dialogues.json::/tmp/hf-datasets-cache/heavy/datasets/72821915056078-config-parquet-and-info-ConvLab-camrest-30f7048d/downloads/5a1419bf71a87027e87b054926dcf7f0eba6179865c254c6cfed31ed7398c613
              
              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)