cos_e

• Description:

Common Sense Explanations (CoS-E) allows for training language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://github.com/salesforce/cos-e

• Source code: tfds.text.CosE

• Versions:

  • 0.0.1 (default): No release notes.

• Download size: 6.23 MiB

• Dataset size: 3.89 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'train'	9,741
'validation'	1,221

• Feature structure:

FeaturesDict({
    'abstractive_explanation': Text(shape=(), dtype=string),
    'answer': Text(shape=(), dtype=string),
    'choices': Sequence(Text(shape=(), dtype=string)),
    'extractive_explanation': Text(shape=(), dtype=string),
    'id': Text(shape=(), dtype=string),
    'question': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
abstractive_explanation	Text		string
answer	Text		string
choices	Sequence(Text)	(None,)	string
extractive_explanation	Text		string
id	Text		string
question	Text		string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{rajani2019explain,
     title = "Explain Yourself! Leveraging Language models for Commonsense Reasoning",
    author = "Rajani, Nazneen Fatema  and
      McCann, Bryan  and
      Xiong, Caiming  and
      Socher, Richard",
      year="2019",
    booktitle = "Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)",
    url ="https://arxiv.org/abs/1906.02361"
}