movie_rationales

• Description:

The movie rationale dataset contains human annotated rationales for movie reviews.

• Homepage: http://www.cs.jhu.edu/~ozaidan/rationales/

• Source code: tfds.text.MovieRationales

• Versions:

  • 0.1.0 (default): No release notes.

• Download size: 3.72 MiB

• Dataset size: 8.37 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	199
'train'	1,600
'validation'	200

• Feature structure:

FeaturesDict({
    'evidences': Sequence(Text(shape=(), dtype=string)),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
    'review': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
evidences	Sequence(Text)	(None,)	string
label	ClassLabel		int64
review	Text		string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@unpublished{eraser2019,
    title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
    author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@InProceedings{zaidan-eisner-piatko-2008:nips,
  author    =  {Omar F. Zaidan  and  Jason Eisner  and  Christine Piatko},
  title     =  {Machine Learning with Annotator Rationales to Reduce Annotation Cost},
  booktitle =  {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning},
  month     =  {December},
  year      =  {2008}
}