fernanda-dionello/autotrain-goodreads_without_bookid-2171169882
Text Classification
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Updated
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5
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"
}
] | 060cf7dcd2aa7c5a | [
"target",
"text"
] | {} | null | {} | false | null |
This dataset has been automatically processed by AutoTrain for project goodreads_without_bookid.
The BCP-47 code for the dataset's language is en.
A sample from this dataset looks as follows:
[
{
"target": 5,
"text": "This book was absolutely ADORABLE!!!!!!!!!!! It was an awesome light and FUN read. \n I loved the characters but I absolutely LOVED Cam!!!!!!!!!!!! Major Swoooon Worthy! J \n You've been checking me out haven't you? In-between your flaming insults? I feel like man candy. \n Seriously between being HOT FUNNY and OH SO VERY ADORABLE Cam was the perfect catch!! \n I'm not going out with you Cam. \n I didn't ask you at this moment now did I One side of his lips curved up. But you will eventually. \n You're delusional \n I'm determined. \n More like annoying. \n Most would say amazing. \n Cam and Avery's relationship is tough due to the secrets she keeps but he is the perfect match for breaking her out of her shell and facing her fears. \n This book is definitely a MUST READ. \n Trust me when I say this YOU will not regret it! \n www.Jenreadit.com"
},
{
"target": 4,
"text": "3.5 stars! \n Abbi Glines' books are a guilty pleasure for me. I love the Southern charm the sexy boys and the beautiful sweet girls. When You're Back is the second book in Reese and Mase's story and other characters from my other favorite books all make appearances here. \n I loved River Captain Kipling! This guy is SEXY and broody. He is a bit mysterious and I am really looking forward to reading more about him! \n I can change your world too sweetheart. But I'll wait my turn. \n We also have Mase's cousin Aida here who gives the cold shoulder to sweet loving Reese. I really liked how Reese blossomed in this book and how loving and devoted Mase is to her. He is one of my favorite of Abbi Glines' characters and he is definitely book boyfriend material. Their scenes are touching sexy and sweet just what I expect from the Rosemary Beach series. I liked this book and recommend it if you are looking for a sexy quick summertime read!"
}
]
The dataset has the following fields (also called "features"):
{
"target": "ClassLabel(num_classes=6, names=['0', '1', '2', '3', '4', '5'], id=None)",
"text": "Value(dtype='string', id=None)"
}
This dataset is split into a train and validation split. The split sizes are as follow:
Split name | Num samples |
---|---|
train | 2358 |
valid | 592 |