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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 3 new columns ({'gold', 'idx', 'ok'}) and 1 missing columns ({'id'}).
This happened while the csv dataset builder was generating data using
hf://datasets/kuenhwan/azu-deeplearning-cot/val_results/mvresult_mv8_qlora_r16_val.csv (at revision 88f5e2c32f71a238365c448b99c134450034fd7f), ['hf://datasets/kuenhwan/azu-deeplearning-cot@88f5e2c32f71a238365c448b99c134450034fd7f/val_results/mvresult_mv8_qlora_r16_lb.csv', 'hf://datasets/kuenhwan/azu-deeplearning-cot@88f5e2c32f71a238365c448b99c134450034fd7f/val_results/mvresult_mv8_qlora_r16_val.csv', 'hf://datasets/kuenhwan/azu-deeplearning-cot@88f5e2c32f71a238365c448b99c134450034fd7f/val_results/val_result_qlora_r16.csv']
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 "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
idx: int64
answer: int64
agreement: double
format_rate: double
gold: int64
ok: bool
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 937
to
{'id': Value('string'), 'answer': Value('int64'), 'agreement': Value('float64'), 'format_rate': Value('float64')}
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 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
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 3 new columns ({'gold', 'idx', 'ok'}) and 1 missing columns ({'id'}).
This happened while the csv dataset builder was generating data using
hf://datasets/kuenhwan/azu-deeplearning-cot/val_results/mvresult_mv8_qlora_r16_val.csv (at revision 88f5e2c32f71a238365c448b99c134450034fd7f), ['hf://datasets/kuenhwan/azu-deeplearning-cot@88f5e2c32f71a238365c448b99c134450034fd7f/val_results/mvresult_mv8_qlora_r16_lb.csv', 'hf://datasets/kuenhwan/azu-deeplearning-cot@88f5e2c32f71a238365c448b99c134450034fd7f/val_results/mvresult_mv8_qlora_r16_val.csv', 'hf://datasets/kuenhwan/azu-deeplearning-cot@88f5e2c32f71a238365c448b99c134450034fd7f/val_results/val_result_qlora_r16.csv']
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.
id string | answer int64 | agreement float64 | format_rate float64 |
|---|---|---|---|
val-000000 | -13 | 1 | 1 |
val-000001 | 2,400 | 1 | 1 |
val-000002 | 3 | 0.875 | 1 |
val-000003 | 355 | 1 | 1 |
val-000004 | 8 | 1 | 1 |
val-000005 | 4 | 1 | 1 |
val-000008 | 1,862 | 0.875 | 1 |
val-000009 | 15 | 1 | 1 |
val-000011 | 9 | 0.875 | 1 |
val-000014 | 2 | 1 | 1 |
val-000015 | 63 | 1 | 1 |
val-000016 | 6 | 1 | 1 |
val-000018 | 50 | 0.875 | 0.875 |
val-000019 | -6 | 0.625 | 1 |
val-000020 | 6 | 0.625 | 1 |
val-000021 | 3,018 | 0.25 | 1 |
val-000022 | 42 | 1 | 1 |
val-000023 | 4 | 0.5 | 1 |
val-000024 | 4 | 1 | 1 |
val-000026 | 50 | 1 | 1 |
val-000027 | 44 | 0.25 | 1 |
val-000028 | 4 | 0.625 | 0.875 |
val-000029 | 10 | 1 | 1 |
val-000030 | 7 | 1 | 1 |
val-000031 | 675 | 0.75 | 1 |
val-000032 | 12 | 0.375 | 0.875 |
val-000033 | 4 | 1 | 1 |
val-000034 | 56 | 0.125 | 0.875 |
val-000035 | 50 | 1 | 1 |
val-000036 | 63 | 1 | 1 |
val-000037 | 0 | 0.5 | 1 |
val-000038 | 334 | 0.25 | 1 |
val-000041 | 8 | 1 | 1 |
val-000042 | 3 | 1 | 1 |
val-000043 | 240 | 1 | 1 |
val-000044 | 17 | 1 | 1 |
val-000045 | 7 | 1 | 1 |
val-000047 | 5 | 0.625 | 1 |
val-000048 | 42,000 | 0.625 | 1 |
val-000051 | 1,000,000 | 0.25 | 0.75 |
val-000052 | 55 | 1 | 1 |
val-000053 | 140 | 0.75 | 1 |
val-000054 | 56 | 0.875 | 0.875 |
val-000055 | 4 | 0.25 | 1 |
val-000056 | 48 | 1 | 1 |
val-000057 | 600 | 1 | 1 |
val-000058 | 16 | 0.625 | 1 |
val-000059 | 12 | 1 | 1 |
val-000060 | -1 | 0.375 | 1 |
val-000061 | 12 | 1 | 1 |
val-000062 | 25 | 1 | 1 |
val-000063 | 6 | 0.25 | 0.875 |
val-000064 | 1 | 0.625 | 1 |
val-000065 | 15 | 1 | 1 |
val-000066 | 0 | 0.625 | 1 |
val-000068 | -1 | 0.5 | 1 |
val-000069 | 6 | 1 | 1 |
val-000070 | 7 | 1 | 1 |
val-000072 | 68 | 0.75 | 1 |
val-000073 | 110 | 1 | 1 |
val-000074 | 8 | 1 | 1 |
val-000075 | 250 | 1 | 1 |
val-000076 | 0 | 1 | 1 |
val-000077 | 270 | 1 | 1 |
val-000078 | 54 | 0.875 | 0.875 |
val-000079 | -945 | 1 | 1 |
val-000080 | 66 | 1 | 1 |
val-000081 | 24 | 1 | 1 |
val-000082 | 4 | 1 | 1 |
val-000083 | 100 | 1 | 1 |
val-000084 | 8 | 1 | 1 |
val-000085 | 36 | 1 | 1 |
val-000086 | 48 | 0.5 | 1 |
val-000087 | 6 | 0.5 | 0.875 |
val-000088 | 2,037,168 | 0.25 | 1 |
val-000089 | 25 | 1 | 1 |
val-000090 | 5 | 1 | 1 |
val-000091 | 66 | 0.125 | 1 |
val-000092 | 1 | 0.375 | 0.625 |
val-000093 | 10 | 0.25 | 1 |
val-000094 | 4 | 0.5 | 1 |
val-000095 | 100 | 0.625 | 1 |
val-000096 | 67 | 1 | 1 |
val-000098 | 149 | 1 | 1 |
val-000099 | 24 | 1 | 1 |
val-000102 | 8 | 1 | 1 |
val-000103 | 3 | 0.5 | 0.875 |
val-000104 | 9,604 | 1 | 1 |
val-000105 | 183 | 1 | 1 |
val-000106 | 12 | 1 | 1 |
val-000108 | 9 | 0.75 | 0.875 |
val-000109 | 72 | 0.875 | 1 |
val-000110 | 96 | 0.25 | 1 |
val-000111 | 45 | 1 | 1 |
val-000112 | 50 | 1 | 1 |
val-000113 | 31 | 1 | 1 |
val-000114 | 90 | 0.75 | 1 |
val-000115 | 4 | 1 | 1 |
val-000116 | 117 | 0.125 | 0.875 |
val-000117 | 180 | 1 | 1 |
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