Dataset Card for "rotten_tomatoes"

Dataset Summary

Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005.

Supported Tasks

More Information Needed


More Information Needed

Dataset Structure

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

Data Instances


  • Size of downloaded dataset files: 0.47 MB
  • Size of the generated dataset: 1.28 MB
  • Total amount of disk used: 1.75 MB

An example of 'validation' looks as follows.

    "label": 1,
    "text": "Sometimes the days and nights just drag on-- it's the morning that make me feel alive. And I have one thing to thank for that: pancakes."

Data Fields

The data fields are the same among all splits.


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

Data Splits Sample Size

name train validation test
default 8530 1066 1066

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed


More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

  author =       {Bo Pang and Lillian Lee},
  title =        {Seeing stars: Exploiting class relationships for sentiment
                  categorization with respect to rating scales},
  booktitle =    {Proceedings of the ACL},
  year =         2005


Thanks to @thomwolf, @jxmorris12 for adding this dataset.

Update on GitHub
Explore dataset Edit Model Tags

Models trained or fine-tuned on rotten_tomatoes

None yet