metadata
paperswithcode_id: kornli
Dataset Card for "kor_nli"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/kakaobrain/KorNLUDatasets
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 120.49 MB
- Size of the generated dataset: 158.72 MB
- Total amount of disk used: 279.21 MB
Dataset Summary
Korean Natural Language Inference datasets
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
multi_nli
- Size of downloaded dataset files: 40.16 MB
- Size of the generated dataset: 80.80 MB
- Total amount of disk used: 120.97 MB
An example of 'train' looks as follows.
snli
- Size of downloaded dataset files: 40.16 MB
- Size of the generated dataset: 76.42 MB
- Total amount of disk used: 116.59 MB
An example of 'train' looks as follows.
xnli
- Size of downloaded dataset files: 40.16 MB
- Size of the generated dataset: 1.49 MB
- Total amount of disk used: 41.66 MB
An example of 'validation' looks as follows.
Data Fields
The data fields are the same among all splits.
multi_nli
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).
snli
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).
xnli
premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).
Data Splits
multi_nli
| train | |
|---|---|
| multi_nli | 392702 |
snli
| train | |
|---|---|
| snli | 550152 |
xnli
| validation | test | |
|---|---|---|
| xnli | 2490 | 5010 |
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
@article{ham2020kornli,
title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
journal={arXiv preprint arXiv:2004.03289},
year={2020}
}
Contributions
Thanks to @thomwolf, @lhoestq, @lewtun, @patrickvonplaten for adding this dataset.