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The dataset generation failed because of a cast error
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 2 new columns ({'config_json', 'continent'}) and 3 missing columns ({'good_name', 'availability', 'base_price'}).
This happened while the json dataset builder was generating data using
hf://datasets/RustySafe/data/harbors_config.jsonl (at revision 1f23d5f03d8eb62a015d1465376e97e79b308501)
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/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
harbor_name: string
continent: string
config_json: string
to
{'harbor_name': Value('string'), 'good_name': Value('string'), 'base_price': Value('int64'), 'availability': Value('string')}
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 1455, 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 1054, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1833, 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 2 new columns ({'config_json', 'continent'}) and 3 missing columns ({'good_name', 'availability', 'base_price'}).
This happened while the json dataset builder was generating data using
hf://datasets/RustySafe/data/harbors_config.jsonl (at revision 1f23d5f03d8eb62a015d1465376e97e79b308501)
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)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
harbor_name string | good_name string | base_price int64 | availability string |
|---|---|---|---|
Kanton | Porzellan | 180 | very_high |
Kanton | Tee | 90 | high |
Kanton | Schießpulver | 220 | medium |
Kanton | Jade | 350 | low |
Malakka | Gewürze | 35 | very_high |
Malakka | Seide | 75 | medium |
Malakka | Muskatnuss | 45 | high |
Malakka | Opium | 190 | low |
Nagasaki | Kupfer | 110 | high |
Nagasaki | Kunsthandwerk | 160 | low |
Nagasaki | Reiswein | 40 | medium |
Nagasaki | Samurai-Schwerter | 400 | very_low |
Goa | Baumwolle | 40 | high |
Goa | Indigo | 130 | medium |
Goa | Salz | 10 | very_high |
Goa | Opium | 200 | low |
Bantam | Pfeffer | 50 | high |
Bantam | Zimtrinde | 80 | medium |
Bantam | Goldstaub | 550 | very_low |
Bantam | Vogelfedern | 100 | low |
Amsterdam | Diamanten | 450 | low |
Amsterdam | Fisch | 15 | very_high |
Amsterdam | Käse | 65 | high |
Amsterdam | Zucker | 110 | medium |
London | Wolle | 50 | high |
London | Tuch | 100 | medium |
London | Kupfer | 130 | low |
London | Bier | 25 | very_high |
Sevilla | Silber | 280 | medium |
Sevilla | Wein | 45 | high |
Sevilla | Olivenöl | 85 | very_high |
Sevilla | Kakao | 180 | low |
Venedig | Glas | 95 | medium |
Venedig | Juwelen | 320 | low |
Venedig | Seide | 110 | high |
Venedig | Medizin | 240 | medium |
Lübeck | Holz | 20 | very_high |
Lübeck | Bernstein | 85 | medium |
Lübeck | Fell | 150 | high |
Lübeck | Salz | 12 | very_high |
Kanton | null | null | null |
Malakka | null | null | null |
Nagasaki | null | null | null |
Goa | null | null | null |
Bantam | null | null | null |
Amsterdam | null | null | null |
London | null | null | null |
Sevilla | null | null | null |
Venedig | null | null | null |
Lübeck | null | null | null |
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