HanBi/my_awesome_model
Text Classification
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Updated
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8
Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Size:
1K<n<10K
License:
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Dataset Card for "movie_rationales"
Dataset Summary
The movie rationale dataset contains human annotated rationales for movie reviews.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 3.90 MB
- Size of the generated dataset: 8.73 MB
- Total amount of disk used: 12.62 MB
An example of 'validation' looks as follows.
{
"evidences": ["Fun movie"],
"label": 1,
"review": "Fun movie\n"
}
Data Fields
The data fields are the same among all splits.
default
review: astringfeature.label: a classification label, with possible values includingNEG(0),POS(1).evidences: alistofstringfeatures.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| default | 1600 | 200 | 199 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{deyoung-etal-2020-eraser,
title = "{ERASER}: {A} Benchmark to Evaluate Rationalized {NLP} Models",
author = "DeYoung, Jay and
Jain, Sarthak and
Rajani, Nazneen Fatema and
Lehman, Eric and
Xiong, Caiming and
Socher, Richard and
Wallace, Byron C.",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.408",
doi = "10.18653/v1/2020.acl-main.408",
pages = "4443--4458",
}
@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}
}
Contributions
Thanks to @thomwolf, @patrickvonplaten, @lewtun for adding this dataset.
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Homepage:
Homepage:
Repository:
github.com
Paper:
ERASER: A Benchmark to Evaluate Rationalized NLP Models
Size of downloaded dataset files:
3.90 MB
