README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - ko
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - fill-mask
  - question-answering
  - text-classification
  - text-generation
  - token-classification
task_ids:
  - extractive-qa
  - named-entity-recognition
  - natural-language-inference
  - parsing
  - semantic-similarity-scoring
  - text-scoring
  - topic-classification
paperswithcode_id: klue
pretty_name: KLUE
config_names:
  - dp
  - mrc
  - ner
  - nli
  - re
  - sts
  - wos
  - ynat
tags:
  - relation-extraction
dataset_info:
  - config_name: dp
    features:
      - name: sentence
        dtype: string
      - name: index
        list: int32
      - name: word_form
        list: string
      - name: lemma
        list: string
      - name: pos
        list: string
      - name: head
        list: int32
      - name: deprel
        list: string
    splits:
      - name: train
        num_bytes: 7899965
        num_examples: 10000
      - name: validation
        num_bytes: 1557462
        num_examples: 2000
    download_size: 3742577
    dataset_size: 9457427
  - config_name: mrc
    features:
      - name: title
        dtype: string
      - name: context
        dtype: string
      - name: news_category
        dtype: string
      - name: source
        dtype: string
      - name: guid
        dtype: string
      - name: is_impossible
        dtype: bool
      - name: question_type
        dtype: int32
      - name: question
        dtype: string
      - name: answers
        sequence:
          - name: answer_start
            dtype: int32
          - name: text
            dtype: string
    splits:
      - name: train
        num_bytes: 46505593
        num_examples: 17554
      - name: validation
        num_bytes: 15583017
        num_examples: 5841
    download_size: 30098472
    dataset_size: 62088610
  - config_name: ner
    features:
      - name: sentence
        dtype: string
      - name: tokens
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': B-DT
              '1': I-DT
              '2': B-LC
              '3': I-LC
              '4': B-OG
              '5': I-OG
              '6': B-PS
              '7': I-PS
              '8': B-QT
              '9': I-QT
              '10': B-TI
              '11': I-TI
              '12': O
    splits:
      - name: train
        num_bytes: 19891905
        num_examples: 21008
      - name: validation
        num_bytes: 4937563
        num_examples: 5000
    download_size: 5265887
    dataset_size: 24829468
  - config_name: nli
    features:
      - name: guid
        dtype: string
      - name: source
        dtype: string
      - 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: 5719882
        num_examples: 24998
      - name: validation
        num_bytes: 673260
        num_examples: 3000
    download_size: 2056116
    dataset_size: 6393142
  - config_name: re
    features:
      - name: guid
        dtype: string
      - name: sentence
        dtype: string
      - name: subject_entity
        struct:
          - name: word
            dtype: string
          - name: start_idx
            dtype: int32
          - name: end_idx
            dtype: int32
          - name: type
            dtype: string
      - name: object_entity
        struct:
          - name: word
            dtype: string
          - name: start_idx
            dtype: int32
          - name: end_idx
            dtype: int32
          - name: type
            dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': no_relation
              '1': org:dissolved
              '2': org:founded
              '3': org:place_of_headquarters
              '4': org:alternate_names
              '5': org:member_of
              '6': org:members
              '7': org:political/religious_affiliation
              '8': org:product
              '9': org:founded_by
              '10': org:top_members/employees
              '11': org:number_of_employees/members
              '12': per:date_of_birth
              '13': per:date_of_death
              '14': per:place_of_birth
              '15': per:place_of_death
              '16': per:place_of_residence
              '17': per:origin
              '18': per:employee_of
              '19': per:schools_attended
              '20': per:alternate_names
              '21': per:parents
              '22': per:children
              '23': per:siblings
              '24': per:spouse
              '25': per:other_family
              '26': per:colleagues
              '27': per:product
              '28': per:religion
              '29': per:title
      - name: source
        dtype: string
    splits:
      - name: train
        num_bytes: 11145426
        num_examples: 32470
      - name: validation
        num_bytes: 2559272
        num_examples: 7765
    download_size: 8190257
    dataset_size: 13704698
  - config_name: sts
    features:
      - name: guid
        dtype: string
      - name: source
        dtype: string
      - name: sentence1
        dtype: string
      - name: sentence2
        dtype: string
      - name: labels
        struct:
          - name: label
            dtype: float64
          - name: real-label
            dtype: float64
          - name: binary-label
            dtype:
              class_label:
                names:
                  '0': negative
                  '1': positive
    splits:
      - name: train
        num_bytes: 2832889
        num_examples: 11668
      - name: validation
        num_bytes: 122641
        num_examples: 519
    download_size: 1587855
    dataset_size: 2955530
  - config_name: wos
    features:
      - name: guid
        dtype: string
      - name: domains
        list: string
      - name: dialogue
        list:
          - name: role
            dtype: string
          - name: text
            dtype: string
          - name: state
            list: string
    splits:
      - name: train
        num_bytes: 26676970
        num_examples: 8000
      - name: validation
        num_bytes: 3488911
        num_examples: 1000
    download_size: 6358855
    dataset_size: 30165881
  - config_name: ynat
    features:
      - name: guid
        dtype: string
      - name: title
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': IT과학
              '1': 경제
              '2': 사회
              '3': 생활문화
              '4': 세계
              '5': 스포츠
              '6': 정치
      - name: url
        dtype: string
      - name: date
        dtype: string
    splits:
      - name: train
        num_bytes: 10109584
        num_examples: 45678
      - name: validation
        num_bytes: 2039181
        num_examples: 9107
    download_size: 5012303
    dataset_size: 12148765
configs:
  - config_name: dp
    data_files:
      - split: train
        path: dp/train-*
      - split: validation
        path: dp/validation-*
  - config_name: mrc
    data_files:
      - split: train
        path: mrc/train-*
      - split: validation
        path: mrc/validation-*
  - config_name: ner
    data_files:
      - split: train
        path: ner/train-*
      - split: validation
        path: ner/validation-*
  - config_name: nli
    data_files:
      - split: train
        path: nli/train-*
      - split: validation
        path: nli/validation-*
  - config_name: re
    data_files:
      - split: train
        path: re/train-*
      - split: validation
        path: re/validation-*
  - config_name: sts
    data_files:
      - split: train
        path: sts/train-*
      - split: validation
        path: sts/validation-*
  - config_name: wos
    data_files:
      - split: train
        path: wos/train-*
      - split: validation
        path: wos/validation-*
  - config_name: ynat
    data_files:
      - split: train
        path: ynat/train-*
      - split: validation
        path: ynat/validation-*

Dataset Card for KLUE

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• 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

Dataset Description

• Homepage: https://klue-benchmark.com/
• Repository: https://github.com/KLUE-benchmark/KLUE
• Paper: KLUE: Korean Language Understanding Evaluation
• Leaderboard: Leaderboard
• Point of Contact: https://github.com/KLUE-benchmark/KLUE/issues

Dataset Summary

KLUE is a collection of 8 tasks to evaluate natural language understanding capability of Korean language models. We delibrately select the 8 tasks, which are Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking.

Supported Tasks and Leaderboards

Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking

Languages

ko-KR

Dataset Structure

Data Instances

ynat

An example of 'train' looks as follows.

{'date': '2016.06.30. 오전 10:36',
 'guid': 'ynat-v1_train_00000',
 'label': 3,
 'title': '유튜브 내달 2일까지 크리에이터 지원 공간 운영',
 'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008508947'}

sts

An example of 'train' looks as follows.

{'guid': 'klue-sts-v1_train_00000',
 'labels': {'label': 3.7, 'real-label': 3.714285714285714, 'binary-label': 1},
 'sentence1': '숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.',
 'sentence2': '숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.',
 'source': 'airbnb-rtt'}

nli

An example of 'train' looks as follows.

{'guid': 'klue-nli-v1_train_00000',
 'hypothesis': '힛걸 진심 최고로 멋지다.',
 'label': 0,
 'premise': '힛걸 진심 최고다 그 어떤 히어로보다 멋지다',
 'source': 'NSMC'}

ner

An example of 'train' looks as follows.

{'tokens': ['특', '히', ' ', '영', '동', '고', '속', '도', '로', ' ', '강', '릉', ' ', '방', '향', ' ', '문', '막', '휴', '게', '소', '에', '서', ' ', '만', '종', '분', '기', '점', '까', '지', ' ', '5', '㎞', ' ', '구', '간', '에', '는', ' ', '승', '용', '차', ' ', '전', '용', ' ', '임', '시', ' ', '갓', '길', '차', '로', '제', '를', ' ', '운', '영', '하', '기', '로', ' ', '했', '다', '.'],
 'ner_tags': [12, 12, 12, 2, 3, 3, 3, 3, 3, 12, 2, 3, 12, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 8, 9, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12],
 'sentence': '특히 <영동고속도로:LC> <강릉:LC> 방향 <문막휴게소:LC>에서 <만종분기점:LC>까지 <5㎞:QT> 구간에는 승용차 전용 임시 갓길차로제를 운영하기로 했다.'}

re

An example of 'train' looks as follows.

{'guid': 'klue-re-v1_train_00000',
 'label': 0,
 'object_entity': {'word': '조지 해리슨',
  'start_idx': 13,
  'end_idx': 18,
  'type': 'PER'},
 'sentence': '〈Something〉는 조지 해리슨이 쓰고 비틀즈가 1969년 앨범 《Abbey Road》에 담은 노래다.',
 'source': 'wikipedia',
 'subject_entity': {'word': '비틀즈',
  'start_idx': 24,
  'end_idx': 26,
  'type': 'ORG'}}

dp

An example of 'train' looks as follows.

{'deprel': ['NP', 'NP_OBJ', 'VP', 'NP', 'NP_SBJ', 'NP', 'NP_MOD', 'NP_CNJ', 'NP_CNJ', 'NP', 'NP', 'NP_OBJ', 'AP', 'VP'],
 'head': [2, 3, 14, 5, 14, 7, 10, 10, 10, 11, 12, 14, 14, 0],
 'index': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
 'lemma': ['해당', '그림 을', '보 면', '디즈니', '공주 들 이', '브리트니', '스피어스 의', '앨범 이나', '뮤직 비디오 ,', '화보', '속', '모습 을', '똑같이', '재연 하 였 다 .'],
 'pos': ['NNG', 'NNG+JKO', 'VV+EC', 'NNP', 'NNG+XSN+JKS', 'NNP', 'NNP+JKG', 'NNG+JC', 'NNG+NNG+SP', 'NNG', 'NNG', 'NNG+JKO', 'MAG', 'NNG+XSA+EP+EF+SF'],
 'sentence': '해당 그림을 보면 디즈니 공주들이 브리트니 스피어스의 앨범이나 뮤직비디오, 화보 속 모습을 똑같이 재연했다.',
 'word_form': ['해당', '그림을', '보면', '디즈니', '공주들이', '브리트니', '스피어스의', '앨범이나', '뮤직비디오,', '화보', '속', '모습을', '똑같이', '재연했다.']}

mrc

An example of 'train' looks as follows.

{'answers': {'answer_start': [478, 478], 'text': ['한 달가량', '한 달']},
 'context': '올여름 장마가 17일 제주도에서 시작됐다. 서울 등 중부지방은 예년보다 사나흘 정도 늦은 이달 말께 장마가 시작될 전망이다.17일 기상청에 따르면 제주도 남쪽 먼바다에 있는 장마전선의 영향으로 이날 제주도 산간 및 내륙지역에 호우주의보가 내려지면서 곳곳에 100㎜에 육박하는 많은 비가 내렸다. 제주의 장마는 평년보다 2~3일, 지난해보다는 하루 일찍 시작됐다. 장마는 고온다습한 북태평양 기단과 한랭 습윤한 오호츠크해 기단이 만나 형성되는 장마전선에서 내리는 비를 뜻한다.장마전선은 18일 제주도 먼 남쪽 해상으로 내려갔다가 20일께 다시 북상해 전남 남해안까지 영향을 줄 것으로 보인다. 이에 따라 20~21일 남부지방에도 예년보다 사흘 정도 장마가 일찍 찾아올 전망이다. 그러나 장마전선을 밀어올리는 북태평양 고기압 세력이 약해 서울 등 중부지방은 평년보다 사나흘가량 늦은 이달 말부터 장마가 시작될 것이라는 게 기상청의 설명이다. 장마전선은 이후 한 달가량 한반도 중남부를 오르내리며 곳곳에 비를 뿌릴 전망이다. 최근 30년간 평균치에 따르면 중부지방의 장마 시작일은 6월24~25일이었으며 장마기간은 32일, 강수일수는 17.2일이었다.기상청은 올해 장마기간의 평균 강수량이 350~400㎜로 평년과 비슷하거나 적을 것으로 내다봤다. 브라질 월드컵 한국과 러시아의 경기가 열리는 18일 오전 서울은 대체로 구름이 많이 끼지만 비는 오지 않을 것으로 예상돼 거리 응원에는 지장이 없을 전망이다.',
 'guid': 'klue-mrc-v1_train_12759',
 'is_impossible': False,
 'news_category': '종합',
 'question': '북태평양 기단과 오호츠크해 기단이 만나 국내에 머무르는 기간은?',
 'question_type': 1,
 'source': 'hankyung',
 'title': '제주도 장마 시작 … 중부는 이달 말부터'}

wos

An example of 'train' looks as follows.

{'dialogue': [{'role': 'user',
   'text': '쇼핑을 하려는데 서울 서쪽에 있을까요?',
   'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽']},
  {'role': 'sys',
   'text': '서울 서쪽에 쇼핑이 가능한 곳이라면 노량진 수산물 도매시장이 있습니다.',
   'state': []},
  {'role': 'user',
   'text': '오 네 거기 주소 좀 알려주세요.',
   'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']},
  {'role': 'sys', 'text': '노량진 수산물 도매시장의 주소는 서울 동작구 93806입니다.', 'state': []},
  {'role': 'user',
   'text': '알려주시는김에 연락처랑 평점도 좀 알려주세요.',
   'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']},
  {'role': 'sys', 'text': '그럼. 연락처는 6182006591이고 평점은 4점입니다.', 'state': []},
  {'role': 'user',
   'text': '와 감사합니다.',
   'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']},
  {'role': 'sys', 'text': '감사합니다.', 'state': []}],
 'domains': ['관광'],
 'guid': 'wos-v1_train_00001'}

Data Fields

ynat

• guid: a string feature
• title: a string feature
• label: a classification label, with possible values IT과학(0), 경제(1), 사회(2), 생활문화(3), 세계(4), 스포츠(5), 정치(6)
• url: a string feature
• date: a string feature

sts

• guid: a string feature
• source: a string feature
• sentence1: a string feature
• sentence2: a string feature
• labels: a dictionary feature containing
  • label: a float64 feature
  • real-label: a float64 feature
  • binary-label: a classification label, with possible values negative(0), positive(1)

nli

• guid: a string feature
• source: a string feature
• premise: a string feature
• hypothesis: a string feature
• label: a classification label, with possible values entailment(0), neutral(1), contradiction(2)

ner

• sentence: a string feature
• tokens: a list of a string feature (tokenization is at character level)
• ner_tags: a list of classification labels, with possible values including B-DT(0), I-DT(1), B-LC(2), I-LC(3), B-OG(4), I-OG(5), B-PS(6), I-PS(7), B-QT(8), I-QT(9), B-TI(10), I-TI(11), O(12)

re

• guid: a string feature
• sentence: a string feature
• subject_entity: a dictionary feature containing
  • word: a string feature
  • start_idx: a int32 feature
  • end_idx: a int32 feature
  • type: a string feature
• object_entity: a dictionary feature containing
  • word: a string feature
  • start_idx: a int32 feature
  • end_idx: a int32 feature
  • type: a string feature
• label: a list of labels, with possible values including no_relation(0), org:dissolved(1), org:founded(2), org:place_of_headquarters(3), org:alternate_names(4), org:member_of(5), org:members(6), org:political/religious_affiliation(7), org:product(8), org:founded_by(9),org:top_members/employees(10), org:number_of_employees/members(11), per:date_of_birth(12), per:date_of_death(13), per:place_of_birth(14), per:place_of_death(15), per:place_of_residence(16), per:origin(17), per:employee_of(18), per:schools_attended(19), per:alternate_names(20), per:parents(21), per:children(22), per:siblings(23), per:spouse(24), per:other_family(25), per:colleagues(26), per:product(27), per:religion(28), per:title(29),
• source: a string feature

dp

• sentence: a string feature
• index: a list of int32 feature
• word_form: a list of string feature
• lemma: a list of string feature
• pos: a list of string feature
• head: a list of int32 feature
• deprel: a list of string feature

mrc

• title: a string feature
• context: a string feature
• news_category: a string feature
• source: a string feature
• guid: a string feature
• is_impossible: a bool feature
• question_type: a int32 feature
• question: a string feature
• answers: a dictionary feature containing
  • answer_start: a int32 feature
  • text: a string feature

wos

• guid: a string feature
• domains: a string feature
• dialogue: a list of dictionary feature containing
  • role: a string feature
  • text: a string feature
  • state: a string feature

Data Splits

ynat

You can see more details in here.

• train: 45,678
• validation: 9,107

sts

You can see more details in here.

• train: 11,668
• validation: 519

nli

You can see more details in here.

• train: 24,998
• validation: 3,000

ner

You can see more details in here.

• train: 21,008
• validation: 5,000

re

You can see more details in here.

• train: 32,470
• validation: 7,765

dp

You can see more details in here.

• train: 10,000
• validation: 2,000

mrc

You can see more details in here.

• train: 17,554
• validation: 5,841

wos

You can see more details in here.

• train: 8,000
• validation: 1,000

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

@misc{park2021klue,
      title={KLUE: Korean Language Understanding Evaluation}, 
      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},
      year={2021},
      eprint={2105.09680},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contributions

Thanks to @jungwhank, @bzantium for adding this dataset.