metadata
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|multi_nli
- extended|snli
- extended|xnli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: kornli
pretty_name: KorNLI
dataset_info:
- config_name: multi_nli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 84729207
num_examples: 392702
download_size: 42113232
dataset_size: 84729207
- config_name: snli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 80137097
num_examples: 550152
download_size: 42113232
dataset_size: 80137097
- config_name: xnli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 518830
num_examples: 2490
- name: test
num_bytes: 1047437
num_examples: 5010
download_size: 42113232
dataset_size: 1566267
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
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
: astring
feature.hypothesis
: astring
feature.label
: a classification label, with possible values includingentailment
(0),neutral
(1),contradiction
(2).
snli
premise
: astring
feature.hypothesis
: astring
feature.label
: a classification label, with possible values includingentailment
(0),neutral
(1),contradiction
(2).
xnli
premise
: astring
feature.hypothesis
: astring
feature.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.