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AmazonElectronics_m1

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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/AmazonElectronics_m1@d6951f1a171149ebb7729505984bbed2d4ab653a/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/AmazonElectronics_m1@d6951f1a171149ebb7729505984bbed2d4ab653a/train_data.json.

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AmazonElectronics_m1

Data format: Each user corresponds to a list of interacted items: [[item1, item2], [item3, item4, item5], ...]
Source: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Download: https://huggingface.co/datasets/reczoo/AmazonElectronics_m1/tree/main
RecZoo Datasets: https://github.com/reczoo/Datasets
Used by papers:
- Wenhui Yu, Zheng Qin. Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning. In SIGIR 2020.
- 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
7a0fa5d0da5dc5d5008da02b554ef688  test_data.json
ca71f3f5b9ada393ffd5490eba84c7db  train_data.json
7f2db9b5b0de91c7d757ed6ed6095a5a  validation_data.json

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