Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Size:
1K<n<10K
License:
albertvillanova
HF staff
Convert dataset sizes from base 2 to base 10 in the dataset card (#2)
bc22b13
metadata
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: MovieRationales
dataset_info:
features:
- name: review
dtype: string
- name: label
dtype:
class_label:
names:
'0': NEG
'1': POS
- name: evidences
sequence: string
splits:
- name: test
num_bytes: 1046377
num_examples: 199
- name: train
num_bytes: 6853624
num_examples: 1600
- name: validation
num_bytes: 830417
num_examples: 200
download_size: 3899487
dataset_size: 8730418
Dataset Card for "movie_rationales"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage:
- Repository: https://github.com/jayded/eraserbenchmark
- Paper: ERASER: A Benchmark to Evaluate Rationalized NLP Models
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 3.90 MB
- Size of the generated dataset: 8.73 MB
- Total amount of disk used: 12.62 MB
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.