Error code:   StreamingRowsError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 264, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 205, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 242, 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 1379, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 233, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/CyranoB--polarity/09b42b491d298dbd043c0b1a57d426eaece8edeb7045a6f706687edb78cb6043/polarity.py", line 116, in _generate_examples
                  for path, f in files:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 840, in __iter__
                  yield from self.generator(*self.args, **self.kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 890, in _iter_from_urlpath
                  compression = _get_extraction_protocol(urlpath, download_config=download_config)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 391, in _get_extraction_protocol
                  with fsspec.open(urlpath, **(storage_options or {})) as f:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 439, in open
                  return open_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 194, in __getitem__
                  out = super().__getitem__(item)
              IndexError: list index out of range

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Dataset Card for Amazon Review Polarity

Dataset Summary

The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review.

Supported Tasks and Leaderboards

text-classification, sentiment-classification: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating.

Languages

Mainly English.

Dataset Structure

Data Instances

A typical data point, comprises of a title, a content and the corresponding label.

An example from the AmazonPolarity test set looks as follows:

{
    'title':'Great CD',
    'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""",
    'label':1
}

Data Fields

'title': a string containing the title of the review - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
'label': either 1 (positive) or 0 (negative) rating.

Data Splits

The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples.

Dataset Creation

Curation Rationale

The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

Apache License 2.0

Citation Information

McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.

Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)

Contributions

Thanks to @hfawaz for adding this dataset.

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