Datasets:

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ConvLab

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multiwoz21

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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 7 new columns ({'dataset', 'turns', 'data_split', 'original_id', 'goal', 'domains', 'dialogue_id'}) and 11 missing columns ({'pricerange', 'address', 'postcode', 'name', 'openhours', 'type', 'location', 'phone', 'entrance fee', 'area', 'id'}).

This happened while the json dataset builder was generating data using

zip://data/dialogues.json::/tmp/hf-datasets-cache/heavy/datasets/76555408469499-config-parquet-and-info-ConvLab-multiwoz21-345f1850/downloads/d8b275299188677ff193f6f2e1e08c32e7b70fe7326a65c7df90d90d12a96a9b

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
              dataset: string
              turns: list<item: struct<booked: struct<attraction: list<item: null>, hospital: list<item: struct<department: string, reference: string, time: string>>, hotel: list<item: struct<name: string, reference: string>>, restaurant: list<item: struct<name: string, reference: string>>, taxi: list<item: struct<phone: string, type: string>>, train: list<item: struct<reference: string, trainID: string>>>, 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<attraction: struct<area: string, name: string, type: string>, hospital: struct<department: string>, hotel: struct<area: string, book day: string, book people: string, book stay: string, internet: string, name: string, parking: string, price range: string, stars: string, type: string>, restaurant: struct<area: string, book day: string, book people: string, book time: string, food: string, name: string, price range: string>, taxi: struct<arrive by: string, departure: string, destination: string, leave at: string>, train: struct<arrive by: string, book people: string, day: string, departure: string, destination: string, leave at: string>>, utt_idx: int64, utterance: string>>
                child 0, item: struct<booked: struct<attraction: list<item
              ...
                   child 2, hotel: struct<address: string, area: string, internet: string, parking: string, phone: string, postcode: string, price range: string, stars: string, type: string>
                        child 0, address: string
                        child 1, area: string
                        child 2, internet: string
                        child 3, parking: string
                        child 4, phone: string
                        child 5, postcode: string
                        child 6, price range: string
                        child 7, stars: string
                        child 8, type: string
                    child 3, police: struct<address: string, phone: string, postcode: string>
                        child 0, address: string
                        child 1, phone: string
                        child 2, postcode: string
                    child 4, 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
                    child 5, taxi: struct<phone: string, type: string>
                        child 0, phone: string
                        child 1, type: string
                    child 6, train: struct<arrive by: string, duration: string, leave at: string, price: string, train id: string>
                        child 0, arrive by: string
                        child 1, duration: string
                        child 2, leave at: string
                        child 3, price: string
                        child 4, train id: string
              domains: list<item: string>
                child 0, item: string
              dialogue_id: string
              to
              {'pricerange': Value(dtype='string', id=None), 'address': Value(dtype='string', id=None), 'postcode': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'openhours': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'location': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'phone': Value(dtype='string', id=None), 'entrance fee': Value(dtype='string', id=None), 'area': Value(dtype='string', id=None), 'id': 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 7 new columns ({'dataset', 'turns', 'data_split', 'original_id', 'goal', 'domains', 'dialogue_id'}) and 11 missing columns ({'pricerange', 'address', 'postcode', 'name', 'openhours', 'type', 'location', 'phone', 'entrance fee', 'area', 'id'}).
              
              This happened while the json dataset builder was generating data using
              
              zip://data/dialogues.json::/tmp/hf-datasets-cache/heavy/datasets/76555408469499-config-parquet-and-info-ConvLab-multiwoz21-345f1850/downloads/d8b275299188677ff193f6f2e1e08c32e7b70fe7326a65c7df90d90d12a96a9b
              
              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)