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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ValueError
Message: Not able to read records in the JSON file at hf://datasets/reczoo/Movielens1M_m1@1186acccb5a9d3efb310a9dd842a65cc4b666c60/train_data.json.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, 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 2216, 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 1239, 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 1389, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
yield from islice(self.ex_iterable, self.n)
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 165, in _generate_tables
raise ValueError(f"Not able to read records in the JSON file at {file}.") from None
ValueError: Not able to read records in the JSON file at hf://datasets/reczoo/Movielens1M_m1@1186acccb5a9d3efb310a9dd842a65cc4b666c60/train_data.json.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.
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empty or missing yaml metadata in repo card
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Movielens1M_m1
Dataset description:
The MovieLens-1M dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users. We follow the LCF work to split and preprocess the data into training, validation, and test sets, respectively.
Data format:
Each user corresponds to a list of interacted items: [[item1, item2], [item3, item4, item5], ...]
Download: https://huggingface.co/datasets/reczoo/Movielens1M_m1/tree/main
RecZoo Datasets: https://github.com/reczoo/Datasets
Used by papers:
- Wenhui Yu, Zheng Qin. Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters. In ICML 2020.
- Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, Xiuqiang He. SimpleX: A Simple and Strong Baseline for Collaborative Filtering. In CIKM 2021.
- Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He. UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation. In CIKM 2021.
Check the md5sum for data integrity:
$ md5sum *.json cdd3ad819512cb87dad2f098c8437df2 test_data.json 4229bc5369f943918103daf7fd92e920 train_data.json 60be3b377d39806f80a43e37c94449f6 validation_data.json
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