snumin44/simcse-ko-roberta-unsupervised
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Dataset Card for "kor_nli"
Dataset Summary
Korean Natural Language Inference datasets.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
multi_nli
- Size of downloaded dataset files: 42.11 MB
- Size of the generated dataset: 84.72 MB
- Total amount of disk used: 126.85 MB
An example of 'train' looks as follows.
snli
- Size of downloaded dataset files: 42.11 MB
- Size of the generated dataset: 80.13 MB
- Total amount of disk used: 122.25 MB
An example of 'train' looks as follows.
xnli
- Size of downloaded dataset files: 42.11 MB
- Size of the generated dataset: 1.56 MB
- Total amount of disk used: 43.68 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
The dataset is licensed under Creative Commons Attribution-ShareAlike license (CC BY-SA 4.0).
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.
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