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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: OverflowError
Message: value too large to convert to int32_t
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 233, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2916, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1901, in _head
return _examples_to_batch(list(self.take(n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2068, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1559, in __iter__
for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 272, in __iter__
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 138, in _generate_tables
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
File "pyarrow/_json.pyx", line 52, in pyarrow._json.ReadOptions.__init__
File "pyarrow/_json.pyx", line 77, in pyarrow._json.ReadOptions.block_size.__set__
OverflowError: value too large to convert to int32_tNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
See description and preview to understand the content and structure of this corpus.
This dataset is from our paper: UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers.
Please see our github repository UniHGKR to know how to use this dataset and its format.
If you find this resource useful in your research, please consider giving a like and citation.
@article{min2024unihgkr,
title={UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers},
author={Min, Dehai and Xu, Zhiyang and Qi, Guilin and Huang, Lifu and You, Chenyu},
journal={arXiv preprint arXiv:2410.20163},
year={2024}
}
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