LewisShanghai/autotrain-books-rating-analysis-2885184365
Text Classification
•
Updated
•
13
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
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.
_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"
}
] | 3a197ce7c712dd46 | [
"feat_Unnamed: 0",
"feat_book_id",
"feat_date_added",
"feat_date_updated",
"feat_n_comments",
"feat_n_votes",
"feat_read_at",
"feat_review_id",
"feat_started_at",
"feat_user_id",
"target",
"text"
] | {} | null | {} | false | null |
This dataset has been automatically processed by AutoTrain for project books-rating-analysis.
The BCP-47 code for the dataset's language is en.
A sample from this dataset looks as follows:
[
{
"feat_Unnamed: 0": 1976,
"feat_user_id": "792500e85277fa7ada535de23e7eb4c3",
"feat_book_id": 18243288,
"feat_review_id": "7f8219233a62bde2973ddd118e8162e2",
"target": 2,
"text": "This book is kind of tricky. It is pleasingly written stylistically and it's an easy read so I cruised along on the momentum of the smooth prose and the potential of what this book could have and should have been for a while before I realized that it is hollow and aimless. \n This is a book where the extraordinary is deliberately made mundane for some reason and characters are stubbornly underdeveloped. It is as if all the drama has been removed from this story, leaving a bloodless collection of 19th industrial factoids sprinkled amidst a bunch of ciphers enduring an oddly dull series of tragedies. \n Mildly entertaining for a while but ultimately unsatisfactory.",
"feat_date_added": "Mon Apr 27 11:37:36 -0700 2015",
"feat_date_updated": "Mon May 04 08:50:42 -0700 2015",
"feat_read_at": "Mon May 04 08:50:42 -0700 2015",
"feat_started_at": "Mon Apr 27 00:00:00 -0700 2015",
"feat_n_votes": 0,
"feat_n_comments": 0
},
{
"feat_Unnamed: 0": 523,
"feat_user_id": "01ec1a320ffded6b2dd47833f2c8e4fb",
"feat_book_id": 18220354,
"feat_review_id": "c19543fab6b2386df92c1a9ba3cf6e6b",
"target": 4,
"text": "4.5 stars!! I am always intrigued to read a novel written from a male POV. I am equally fascinated by pen names, and even when the writer professes to be one gender or the other (or leaves it open to the imagination such as BG Harlen), I still wonder at the back of my mind whether the author is a male or female. Do some female writers have a decidedly masculine POV? Yes, there are several that come to mind. Do some male writers have a feminine \"flavor\" to their writing? It seems so. \n And so we come to the fascinating Thou Shalt Not. I loved Luke's story, as well as JJ Rossum's writing style, and don't want to be pigeon-holed into thinking that the author is male or female. That's just me. Either way, it's a very sexy and engaging book with plenty of steamy scenes to satisfy even the most jaded erotic romance reader (such as myself). The story carries some very weighty themes (domestic violence, adultery, the nature of beauty), but the book is very fast-paced and satisfying. Will Luke keep himself out of trouble with April? Will he learn to really love someone again? No spoilers here, but the author answers these questions while exploring what qualities are really important and what makes someone worthy of love. \n This book has a very interesting conclusion that some readers will love, and some might find a little challenging. I loved it and can't wait to read more from this author. \n *ARC provided by the author in exchange for an honest review.",
"feat_date_added": "Mon Jul 29 16:04:04 -0700 2013",
"feat_date_updated": "Thu Dec 12 21:43:54 -0800 2013",
"feat_read_at": "Fri Dec 06 00:00:00 -0800 2013",
"feat_started_at": "Thu Dec 05 00:00:00 -0800 2013",
"feat_n_votes": 10,
"feat_n_comments": 0
}
]
The dataset has the following fields (also called "features"):
{
"feat_Unnamed: 0": "Value(dtype='int64', id=None)",
"feat_user_id": "Value(dtype='string', id=None)",
"feat_book_id": "Value(dtype='int64', id=None)",
"feat_review_id": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['0', '1', '2', '3', '4', '5'], id=None)",
"text": "Value(dtype='string', id=None)",
"feat_date_added": "Value(dtype='string', id=None)",
"feat_date_updated": "Value(dtype='string', id=None)",
"feat_read_at": "Value(dtype='string', id=None)",
"feat_started_at": "Value(dtype='string', id=None)",
"feat_n_votes": "Value(dtype='int64', id=None)",
"feat_n_comments": "Value(dtype='int64', id=None)"
}
This dataset is split into a train and validation split. The split sizes are as follow:
Split name | Num samples |
---|---|
train | 2397 |
valid | 603 |