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The dataset generation failed
Error code: DatasetGenerationError Exception: ArrowNotImplementedError Message: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field. 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 583, in write_table self._build_writer(inferred_schema=pa_table.schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer self.pa_writer = self._WRITER_CLASS(self.stream, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__ self.writer = _parquet.ParquetWriter( File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__ File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2027, in _prepare_split_single num_examples, num_bytes = writer.finalize() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 602, in finalize self._build_writer(self.schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer self.pa_writer = self._WRITER_CLASS(self.stream, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__ self.writer = _parquet.ParquetWriter( File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__ File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, 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 1154, 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 2038, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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_data_files
list | _fingerprint
string | _format_columns
sequence | _format_kwargs
dict | _format_type
null | _indexes
dict | _output_all_columns
bool | _split
null |
---|---|---|---|---|---|---|---|
[
{
"filename": "dataset.arrow"
}
] | 669b32ef9c8d6a9a | [
"feat_f_00",
"feat_f_01",
"feat_f_02",
"feat_f_03",
"feat_f_04",
"feat_f_05",
"feat_f_06",
"feat_f_07",
"feat_f_08",
"feat_f_09",
"feat_f_10",
"feat_f_11",
"feat_f_12",
"feat_f_13",
"feat_f_14",
"feat_f_15",
"feat_f_16",
"feat_f_17",
"feat_f_18",
"feat_f_19",
"feat_f_20",
"feat_f_21",
"feat_f_22",
"feat_f_23",
"feat_f_24",
"feat_f_25",
"feat_f_26",
"feat_f_27",
"feat_f_28",
"feat_f_29",
"feat_f_30",
"id",
"target"
] | {} | null | {} | false | null |
AutoTrain Dataset for project: tpsmay22
Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project tpsmay22.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"id": 828849,
"feat_f_00": 0.5376503535622164,
"feat_f_01": 1.943782180890636,
"feat_f_02": 0.9135609975277558,
"feat_f_03": 1.8069627709531364,
"feat_f_04": 0.2608497764144719,
"feat_f_05": 0.2210137962869367,
"feat_f_06": -0.2041958755583295,
"feat_f_07": 1,
"feat_f_08": 3,
"feat_f_09": 1,
"feat_f_10": 3,
"feat_f_11": 7,
"feat_f_12": 1,
"feat_f_13": 1,
"feat_f_14": 3,
"feat_f_15": 3,
"feat_f_16": 0,
"feat_f_17": 3,
"feat_f_18": 3,
"feat_f_19": -2.224980946907772,
"feat_f_20": -0.0497802292031301,
"feat_f_21": -3.926047324073047,
"feat_f_22": 3.518427812720448,
"feat_f_23": -3.682602827653292,
"feat_f_24": -0.391453171033426,
"feat_f_25": 1.519591066386293,
"feat_f_26": 1.689261040286172,
"feat_f_27": "AEBCBAHLAC",
"feat_f_28": 379.1152852815462,
"feat_f_29": 0,
"feat_f_30": 1,
"target": 0.0
},
{
"id": 481680,
"feat_f_00": 0.067304409313422,
"feat_f_01": -2.1380257328497443,
"feat_f_02": -1.071190705030414,
"feat_f_03": -0.632098414262756,
"feat_f_04": -0.6884213952425722,
"feat_f_05": 0.9001794148519768,
"feat_f_06": 1.0522875373816212,
"feat_f_07": 2,
"feat_f_08": 2,
"feat_f_09": 2,
"feat_f_10": 2,
"feat_f_11": 3,
"feat_f_12": 4,
"feat_f_13": 4,
"feat_f_14": 1,
"feat_f_15": 3,
"feat_f_16": 1,
"feat_f_17": 2,
"feat_f_18": 4,
"feat_f_19": -0.1749962904609809,
"feat_f_20": -2.14813633573821,
"feat_f_21": -1.959294186862138,
"feat_f_22": -0.0458843535688706,
"feat_f_23": 0.7256376584744342,
"feat_f_24": -2.5463878383279823,
"feat_f_25": 2.3352097148227915,
"feat_f_26": 0.4798465276880099,
"feat_f_27": "BCBBDBFLCA",
"feat_f_28": -336.9163876318925,
"feat_f_29": 1,
"feat_f_30": 0,
"target": 0.0
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"id": "Value(dtype='int64', id=None)",
"feat_f_00": "Value(dtype='float64', id=None)",
"feat_f_01": "Value(dtype='float64', id=None)",
"feat_f_02": "Value(dtype='float64', id=None)",
"feat_f_03": "Value(dtype='float64', id=None)",
"feat_f_04": "Value(dtype='float64', id=None)",
"feat_f_05": "Value(dtype='float64', id=None)",
"feat_f_06": "Value(dtype='float64', id=None)",
"feat_f_07": "Value(dtype='int64', id=None)",
"feat_f_08": "Value(dtype='int64', id=None)",
"feat_f_09": "Value(dtype='int64', id=None)",
"feat_f_10": "Value(dtype='int64', id=None)",
"feat_f_11": "Value(dtype='int64', id=None)",
"feat_f_12": "Value(dtype='int64', id=None)",
"feat_f_13": "Value(dtype='int64', id=None)",
"feat_f_14": "Value(dtype='int64', id=None)",
"feat_f_15": "Value(dtype='int64', id=None)",
"feat_f_16": "Value(dtype='int64', id=None)",
"feat_f_17": "Value(dtype='int64', id=None)",
"feat_f_18": "Value(dtype='int64', id=None)",
"feat_f_19": "Value(dtype='float64', id=None)",
"feat_f_20": "Value(dtype='float64', id=None)",
"feat_f_21": "Value(dtype='float64', id=None)",
"feat_f_22": "Value(dtype='float64', id=None)",
"feat_f_23": "Value(dtype='float64', id=None)",
"feat_f_24": "Value(dtype='float64', id=None)",
"feat_f_25": "Value(dtype='float64', id=None)",
"feat_f_26": "Value(dtype='float64', id=None)",
"feat_f_27": "Value(dtype='string', id=None)",
"feat_f_28": "Value(dtype='float64', id=None)",
"feat_f_29": "Value(dtype='int64', id=None)",
"feat_f_30": "Value(dtype='int64', id=None)",
"target": "Value(dtype='float32', id=None)"
}
Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
Split name | Num samples |
---|---|
train | 719999 |
valid | 180001 |
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