The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError Exception: ValueError Message: Not able to read records in the JSON file at zip://biorxiv_medrxiv/biorxiv_medrxiv/1aa3e788fc6b03c147e37200c3b011ca7a289a5c.json::hf://datasets/jonathanagustin/assignment_5_data@80c94393e74dc42d7f1c2509765b9873810db6e3/assignment_5_data.zip. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['paper_id', 'metadata', 'abstract', 'body_text', 'bib_entries', 'ref_entries', 'back_matter']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/utils.py", line 96, in get_rows_or_raise return get_rows( File "/src/libs/libcommon/src/libcommon/utils.py", line 197, in decorator return func(*args, **kwargs) File "/src/services/worker/src/worker/utils.py", line 73, in get_rows rows_plus_one = list(itertools.islice(ds, rows_max_number + 1)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__ for key, pa_table in self.generate_tables_fn(**self.kwargs): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 170, in _generate_tables raise ValueError( ValueError: Not able to read records in the JSON file at zip://biorxiv_medrxiv/biorxiv_medrxiv/1aa3e788fc6b03c147e37200c3b011ca7a289a5c.json::hf://datasets/jonathanagustin/assignment_5_data@80c94393e74dc42d7f1c2509765b9873810db6e3/assignment_5_data.zip. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['paper_id', 'metadata', 'abstract', 'body_text', 'bib_entries', 'ref_entries', 'back_matter']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
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