Dataset:
imdb



Dataset Card for "imdb"

Dataset Summary

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

plain_text

  • Size of downloaded dataset files: 80.23 MB
  • Size of the generated dataset: 127.06 MB
  • Total amount of disk used: 207.28 MB

An example of 'train' looks as follows.

{
    "label": 0,
    "text": "Goodbye world2\n"
}

Data Fields

The data fields are the same among all splits.

plain_text

  • text: a string feature.
  • label: a classification label, with possible values including neg (0), pos (1).

Data Splits

name train unsupervised test
plain_text 25000 50000 25000

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@InProceedings{maas-EtAl:2011:ACL-HLT2011,
  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
  title     = {Learning Word Vectors for Sentiment Analysis},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  month     = {June},
  year      = {2011},
  address   = {Portland, Oregon, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {142--150},
  url       = {http://www.aclweb.org/anthology/P11-1015}
}

Contributions

Thanks to @ghazi-f, @patrickvonplaten, @lhoestq, @thomwolf for adding this dataset.

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Models trained or fine-tuned on imdb