Datasets:
Tasks:
Multiple Choice
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
Dataset Card for "cosmos_qa"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://wilburone.github.io/cosmos/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 23.27 MB
- Size of the generated dataset: 23.37 MB
- Total amount of disk used: 46.64 MB
Dataset Summary
Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context
Supported Tasks
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
default
- Size of downloaded dataset files: 23.27 MB
- Size of the generated dataset: 23.37 MB
- Total amount of disk used: 46.64 MB
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"answer0": "If he gets married in the church he wo nt have to get a divorce .",
"answer1": "He wants to get married to a different person .",
"answer2": "He wants to know if he does nt like this girl can he divorce her ?",
"answer3": "None of the above choices .",
"context": "\"Do i need to go for a legal divorce ? I wanted to marry a woman but she is not in the same religion , so i am not concern of th...",
"id": "3BFF0DJK8XA7YNK4QYIGCOG1A95STE##3180JW2OT5AF02OISBX66RFOCTG5J7##A2LTOS0AZ3B28A##Blog_56156##q1_a1##378G7J1SJNCDAAIN46FM2P7T6KZEW2",
"label": 1,
"question": "Why is this person asking about divorce ?"
}
Data Fields
The data fields are the same among all splits.
default
id
: astring
feature.context
: astring
feature.question
: astring
feature.answer0
: astring
feature.answer1
: astring
feature.answer2
: astring
feature.answer3
: astring
feature.label
: aint32
feature.
Data Splits Sample Size
name | train | validation | test |
---|---|---|---|
default | 25262 | 2985 | 6963 |
Dataset Creation
Curation Rationale
Source Data
Annotations
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{cosmos,
title={COSMOS QA: Machine Reading Comprehension
with Contextual Commonsense Reasoning},
author={Lifu Huang and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
booktitle ={arXiv:1909.00277v2},
year={2019}
}
Contributions
Thanks to @patrickvonplaten, @lewtun, @albertvillanova, @thomwolf for adding this dataset.