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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"
}
] | 01cad37614c9b3b9 | [
"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: test_sum
Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project test_sum.
Languages
The BCP-47 code for the dataset's language is zh.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"text": "7\u67086\u65e5\uff0c\u4e2d\u963f\u5408\u4f5c\u8bba\u575b\u7b2c\u4e5d\u5c4a\u90e8\u957f\u7ea7\u4f1a\u8bae\u56e0\u65b0\u51a0\u80ba\u708e\u75ab\u60c5\u4ee5\u89c6\u9891\u8fde\u7ebf\u65b9\u5f0f\u4e3e\u884c\u3002\n\u672c\u5c4a\u4f1a\u8bae\u53d6\u5f97\u4e86\u5706\u6ee1\u6210\u529f\uff0c\u53d1\u8868\u4e09\u4efd\u6210\u679c\u6587\u4ef6\uff0c\u9ad8\u5ea6\u51dd\u805a\u4e2d\u963f\u5171\u8bc6\u3002\u300a\u4e2d\u56fd\u548c\u963f\u62c9\u4f2f\u56fd\u5bb6\u56e2\u7ed3\u6297\u51fb\u65b0\u51a0\u80ba\u708e\u75ab\u60c5\u8054\u5408\u58f0\u660e\u300b\u5c55\u73b0\u4e86\u4e2d\u963f\u6218\u80dc\u75ab\u60c5[...]",
"target": "\u671b\u6d77\u697c\u52a0\u5f3a\u5408\u4f5c\u5171\u514b\u65f6\u8270\u643a\u624b\u524d\u884c"
},
{
"text": "\u4e60\u8fd1\u5e73\u603b\u4e66\u8bb0\u6307\u51fa\uff1a\u201c\u6293\u4f4f\u4e86\u521b\u65b0\uff0c\u5c31\u6293\u4f4f\u4e86\u7275\u52a8\u7ecf\u6d4e\u793e\u4f1a\u53d1\u5c55\u5168\u5c40\u7684\u2018\u725b\u9f3b\u5b50\u2019\u3002\u201d\u201c\u8c01\u5728\u521b\u65b0\u4e0a\u5148\u884c\u4e00\u6b65\uff0c\u8c01\u5c31\u80fd\u62e5\u6709\u5f15\u9886\u53d1\u5c55\u7684\u4e3b\u52a8\u6743\u3002\u201d\n\u6293\u521b\u65b0\u5c31\u662f\u6293\u53d1\u5c55\uff0c\u8c0b\u521b\u65b0\u5c31\u662f\u8c0b\u672a\u6765\u3002\u5317\u4eac\u9ad8\u6807\u51c6\u63a8\u8fdb\u201c\u4e24\u533a\u201d\u5efa\u8bbe\uff0c\u6838\u5fc3\u4efb[...]",
"target": "\u6293\u521b\u65b0\u5c31\u662f\u6293\u53d1\u5c55"
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', 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 | 1343 |
| valid | 336 |
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