Datasets:
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: RACE
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: race
dataset_info: null
"race" Grouped by Article
This is a modified version of https://huggingface.co/datasets/race that returns documents grouped by article context instead of by question. Note: This dataset currently only contains that test set of the high subset of the data.
The original readme is contained below.
Dataset Card for "race"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://www.cs.cmu.edu/~glai1/data/race/
- Repository: https://github.com/qizhex/RACE_AR_baselines
- Paper: RACE: Large-scale ReAding Comprehension Dataset From Examinations
- Point of Contact: Guokun Lai, Qizhe Xie
- Size of downloaded dataset files: 76.33 MB
- Size of the generated dataset: 349.46 MB
- Total amount of disk used: 425.80 MB
Dataset Summary
RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle school and high school students. The dataset can be served as the training and test sets for machine comprehension.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
all
- Size of downloaded dataset files: 25.44 MB
- Size of the generated dataset: 174.73 MB
- Total amount of disk used: 200.17 MB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"answer": "A",
"article": "\"Schoolgirls have been wearing such short skirts at Paget High School in Branston that they've been ordered to wear trousers ins...",
"example_id": "high132.txt",
"options": ["short skirts give people the impression of sexualisation", "short skirts are too expensive for parents to afford", "the headmaster doesn't like girls wearing short skirts", "the girls wearing short skirts will be at the risk of being laughed at"],
"question": "The girls at Paget High School are not allowed to wear skirts in that _ ."
}
high
- Size of downloaded dataset files: 25.44 MB
- Size of the generated dataset: 140.12 MB
- Total amount of disk used: 165.56 MB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"answer": "A",
"article": "\"Schoolgirls have been wearing such short skirts at Paget High School in Branston that they've been ordered to wear trousers ins...",
"example_id": "high132.txt",
"options": ["short skirts give people the impression of sexualisation", "short skirts are too expensive for parents to afford", "the headmaster doesn't like girls wearing short skirts", "the girls wearing short skirts will be at the risk of being laughed at"],
"question": "The girls at Paget High School are not allowed to wear skirts in that _ ."
}
middle
- Size of downloaded dataset files: 25.44 MB
- Size of the generated dataset: 34.61 MB
- Total amount of disk used: 60.05 MB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"answer": "B",
"article": "\"There is not enough oil in the world now. As time goes by, it becomes less and less, so what are we going to do when it runs ou...",
"example_id": "middle3.txt",
"options": ["There is more petroleum than we can use now.", "Trees are needed for some other things besides making gas.", "We got electricity from ocean tides in the old days.", "Gas wasn't used to run cars in the Second World War."],
"question": "According to the passage, which of the following statements is TRUE?"
}
Data Fields
The data fields are the same among all splits.
all
example_id: astringfeature.article: astringfeature.answer: astringfeature.question: astringfeature.options: alistofstringfeatures.
high
example_id: astringfeature.article: astringfeature.answer: astringfeature.question: astringfeature.options: alistofstringfeatures.
middle
example_id: astringfeature.article: astringfeature.answer: astringfeature.question: astringfeature.options: alistofstringfeatures.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| all | 87866 | 4887 | 4934 |
| high | 62445 | 3451 | 3498 |
| middle | 25421 | 1436 | 1436 |
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
http://www.cs.cmu.edu/~glai1/data/race/
RACE dataset is available for non-commercial research purpose only.
All passages are obtained from the Internet which is not property of Carnegie Mellon University. We are not responsible for the content nor the meaning of these passages.
You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purpose, any portion of the contexts and any portion of derived data.
We reserve the right to terminate your access to the RACE dataset at any time.
Citation Information
@inproceedings{lai-etal-2017-race,
title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations",
author = "Lai, Guokun and
Xie, Qizhe and
Liu, Hanxiao and
Yang, Yiming and
Hovy, Eduard",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1082",
doi = "10.18653/v1/D17-1082",
pages = "785--794",
}
Contributions
Thanks to @abarbosa94, @patrickvonplaten, @lewtun, @thomwolf, @mariamabarham for adding this dataset.