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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 |