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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 datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
_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"
}
] | ddda4d7a8748618b | [
"feat_guid",
"feat_label",
"feat_source",
"target",
"text"
] | {} | null | {} | false | null |
YAML Metadata Warning:The task_categories "conditional-text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
AutoTrain Dataset for project: text_summ
Dataset Description
This dataset has been automatically processed by AutoTrain for project text_summ.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"feat_guid": "klue-nli-v1_train_06870",
"feat_source": "policy",
"text": "\ub610\ud55c \ub3c5\ub9bd\ucd9c\ud310\ubb3c \uc81c\uc791 \uc6cc\ud06c\uc20d\uc774\ub791 \uae00\uc4f0\uae30 \uc18c\uc124 \uc4f0\uae30 \uc6cc\ud06c\uc20d\ub3c4 \uc9c4\ud589\ud558\uba70 \uc6d0\ub370\uc774 \uc218\uc5c5\uc744 \ud1b5\ud574 \ub2e4\uc591\ud55c \ud65c\ub3d9\uc744 \ud558\ub294 \uc54c\ucc2c \ubcf5\ud569\uc801\uc778 \uacf5\uac04\uc774\ub2e4.",
"target": "\ub3c5\ub9bd\ucd9c\ud310\ubb3c \uc81c\uc791 \uc6cc\ud06c\uc20d\uc740 \uc5b8\ub860\uc0ac\uc758 \ud6c4\uc6d0\ud558\uc5d0 \uc9c4\ud589\ub41c\ub2e4.",
"feat_label": 2
},
{
"feat_guid": "klue-nli-v1_train_02196",
"feat_source": "wikitree",
"text": "\uacf5\uc720\ub41c \uc0ac\uc9c4\uc5d0\ub294 \uc790\uc6b1\ud558\uac8c \uc548\uac1c\uc640 \ubbf8\uc138\uba3c\uc9c0\uac00 \ub0b4\ub824\uc549\uc740 \uc0ac\uc9c4\uc774 \uacf5\uc720\ub410\ub2e4.",
"target": "\uc790\uc6b1\ud55c \uc548\uac1c\uc640 \ubbf8\uc138\uba3c\uc9c0\ub9cc \ub0b4\ub824\uc549\uc558\ub2e4.",
"feat_label": 2
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"feat_guid": "Value(dtype='string', id=None)",
"feat_source": "Value(dtype='string', id=None)",
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)",
"feat_label": "ClassLabel(num_classes=3, names=['contradiction', 'entailment', 'neutral'], 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 | 22398 |
| valid | 5600 |
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