README.md

metadata

annotations_creators:
  - other
language_creators:
  - other
language:
  - zh
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - text-classification
  - multiple-choice
task_ids:
  - topic-classification
  - semantic-similarity-scoring
  - natural-language-inference
  - multiple-choice-qa
paperswithcode_id: clue
pretty_name: 'CLUE: Chinese Language Understanding Evaluation benchmark'
tags:
  - coreference-nli
  - qa-nli
dataset_info:
  - config_name: afqmc
    features:
      - name: sentence1
        dtype: string
      - name: sentence2
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': '0'
              '1': '1'
      - name: idx
        dtype: int32
    splits:
      - name: test
        num_bytes: 378718
        num_examples: 3861
      - name: train
        num_bytes: 3396503
        num_examples: 34334
      - name: validation
        num_bytes: 426285
        num_examples: 4316
    download_size: 2337418
    dataset_size: 4201506
  - config_name: c3
    features:
      - name: id
        dtype: int32
      - name: context
        sequence: string
      - name: question
        dtype: string
      - name: choice
        sequence: string
      - name: answer
        dtype: string
    splits:
      - name: test
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        num_examples: 1625
      - name: train
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        num_examples: 11869
      - name: validation
        num_bytes: 2990967
        num_examples: 3816
    download_size: 3495930
    dataset_size: 14263920
  - config_name: chid
    features:
      - name: idx
        dtype: int32
      - name: candidates
        sequence: string
      - name: content
        sequence: string
      - name: answers
        sequence:
          - name: text
            dtype: string
          - name: candidate_id
            dtype: int32
    splits:
      - name: test
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        num_examples: 3447
      - name: train
        num_bytes: 252478178
        num_examples: 84709
      - name: validation
        num_bytes: 10117789
        num_examples: 3218
    download_size: 139199202
    dataset_size: 274076430
  - config_name: cluewsc2020
    features:
      - name: idx
        dtype: int32
      - name: text
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': 'true'
              '1': 'false'
      - name: target
        struct:
          - name: span1_text
            dtype: string
          - name: span2_text
            dtype: string
          - name: span1_index
            dtype: int32
          - name: span2_index
            dtype: int32
    splits:
      - name: test
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        num_examples: 2574
      - name: train
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        num_examples: 1244
      - name: validation
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        num_examples: 304
    download_size: 380611
    dataset_size: 1007123
  - config_name: cmnli
    features:
      - name: sentence1
        dtype: string
      - name: sentence2
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': neutral
              '1': entailment
              '2': contradiction
      - name: idx
        dtype: int32
    splits:
      - name: test
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        num_examples: 13880
      - name: train
        num_bytes: 67684989
        num_examples: 391783
      - name: validation
        num_bytes: 2051829
        num_examples: 12241
    download_size: 54234919
    dataset_size: 72123639
  - config_name: cmrc2018
    features:
      - name: id
        dtype: string
      - name: context
        dtype: string
      - name: question
        dtype: string
      - name: answers
        sequence:
          - name: text
            dtype: string
          - name: answer_start
            dtype: int32
    splits:
      - name: test
        num_bytes: 3112066
        num_examples: 2000
      - name: train
        num_bytes: 15508110
        num_examples: 10142
      - name: validation
        num_bytes: 5183809
        num_examples: 3219
      - name: trial
        num_bytes: 1606931
        num_examples: 1002
    download_size: 3405146
    dataset_size: 25410916
  - config_name: csl
    features:
      - name: idx
        dtype: int32
      - name: corpus_id
        dtype: int32
      - name: abst
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': '0'
              '1': '1'
      - name: keyword
        sequence: string
    splits:
      - name: test
        num_bytes: 2463728
        num_examples: 3000
      - name: train
        num_bytes: 16478890
        num_examples: 20000
      - name: validation
        num_bytes: 2464563
        num_examples: 3000
    download_size: 3936111
    dataset_size: 21407181
  - config_name: diagnostics
    features:
      - name: sentence1
        dtype: string
      - name: sentence2
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': neutral
              '1': entailment
              '2': contradiction
      - name: idx
        dtype: int32
    splits:
      - name: test
        num_bytes: 42400
        num_examples: 514
    download_size: 12062
    dataset_size: 42400
  - config_name: drcd
    features:
      - name: id
        dtype: string
      - name: context
        dtype: string
      - name: question
        dtype: string
      - name: answers
        sequence:
          - name: text
            dtype: string
          - name: answer_start
            dtype: int32
    splits:
      - name: test
        num_bytes: 4982402
        num_examples: 3493
      - name: train
        num_bytes: 37443458
        num_examples: 26936
      - name: validation
        num_bytes: 5222753
        num_examples: 3524
    download_size: 7264200
    dataset_size: 47648613
  - config_name: iflytek
    features:
      - name: sentence
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': '0'
              '1': '1'
              '2': '2'
              '3': '3'
              '4': '4'
              '5': '5'
              '6': '6'
              '7': '7'
              '8': '8'
              '9': '9'
              '10': '10'
              '11': '11'
              '12': '12'
              '13': '13'
              '14': '14'
              '15': '15'
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              '17': '17'
              '18': '18'
              '19': '19'
              '20': '20'
              '21': '21'
              '22': '22'
              '23': '23'
              '24': '24'
              '25': '25'
              '26': '26'
              '27': '27'
              '28': '28'
              '29': '29'
              '30': '30'
              '31': '31'
              '32': '32'
              '33': '33'
              '34': '34'
              '35': '35'
              '36': '36'
              '37': '37'
              '38': '38'
              '39': '39'
              '40': '40'
              '41': '41'
              '42': '42'
              '43': '43'
              '44': '44'
              '45': '45'
              '46': '46'
              '47': '47'
              '48': '48'
              '49': '49'
              '50': '50'
              '51': '51'
              '52': '52'
              '53': '53'
              '54': '54'
              '55': '55'
              '56': '56'
              '57': '57'
              '58': '58'
              '59': '59'
              '60': '60'
              '61': '61'
              '62': '62'
              '63': '63'
              '64': '64'
              '65': '65'
              '66': '66'
              '67': '67'
              '68': '68'
              '69': '69'
              '70': '70'
              '71': '71'
              '72': '72'
              '73': '73'
              '74': '74'
              '75': '75'
              '76': '76'
              '77': '77'
              '78': '78'
              '79': '79'
              '80': '80'
              '81': '81'
              '82': '82'
              '83': '83'
              '84': '84'
              '85': '85'
              '86': '86'
              '87': '87'
              '88': '88'
              '89': '89'
              '90': '90'
              '91': '91'
              '92': '92'
              '93': '93'
              '94': '94'
              '95': '95'
              '96': '96'
              '97': '97'
              '98': '98'
              '99': '99'
              '100': '100'
              '101': '101'
              '102': '102'
              '103': '103'
              '104': '104'
              '105': '105'
              '106': '106'
              '107': '107'
              '108': '108'
              '109': '109'
              '110': '110'
              '111': '111'
              '112': '112'
              '113': '113'
              '114': '114'
              '115': '115'
              '116': '116'
              '117': '117'
              '118': '118'
      - name: idx
        dtype: int32
    splits:
      - name: test
        num_bytes: 2105684
        num_examples: 2600
      - name: train
        num_bytes: 10028605
        num_examples: 12133
      - name: validation
        num_bytes: 2157119
        num_examples: 2599
    download_size: 9777855
    dataset_size: 14291408
  - config_name: ocnli
    features:
      - name: sentence1
        dtype: string
      - name: sentence2
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': neutral
              '1': entailment
              '2': contradiction
      - name: idx
        dtype: int32
    splits:
      - name: test
        num_bytes: 376066
        num_examples: 3000
      - name: train
        num_bytes: 6187190
        num_examples: 50437
      - name: validation
        num_bytes: 366235
        num_examples: 2950
    download_size: 4359754
    dataset_size: 6929491
  - config_name: tnews
    features:
      - name: sentence
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': '100'
              '1': '101'
              '2': '102'
              '3': '103'
              '4': '104'
              '5': '106'
              '6': '107'
              '7': '108'
              '8': '109'
              '9': '110'
              '10': '112'
              '11': '113'
              '12': '114'
              '13': '115'
              '14': '116'
      - name: idx
        dtype: int32
    splits:
      - name: test
        num_bytes: 810970
        num_examples: 10000
      - name: train
        num_bytes: 4245677
        num_examples: 53360
      - name: validation
        num_bytes: 797922
        num_examples: 10000
    download_size: 4697843
    dataset_size: 5854569
configs:
  - config_name: afqmc
    data_files:
      - split: test
        path: afqmc/test-*
      - split: train
        path: afqmc/train-*
      - split: validation
        path: afqmc/validation-*
  - config_name: cluewsc2020
    data_files:
      - split: test
        path: cluewsc2020/test-*
      - split: train
        path: cluewsc2020/train-*
      - split: validation
        path: cluewsc2020/validation-*
  - config_name: cmnli
    data_files:
      - split: test
        path: cmnli/test-*
      - split: train
        path: cmnli/train-*
      - split: validation
        path: cmnli/validation-*
  - config_name: csl
    data_files:
      - split: test
        path: csl/test-*
      - split: train
        path: csl/train-*
      - split: validation
        path: csl/validation-*
  - config_name: iflytek
    data_files:
      - split: test
        path: iflytek/test-*
      - split: train
        path: iflytek/train-*
      - split: validation
        path: iflytek/validation-*
  - config_name: tnews
    data_files:
      - split: test
        path: tnews/test-*
      - split: train
        path: tnews/train-*
      - split: validation
        path: tnews/validation-*

Dataset Card for "clue"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • 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
  • Contributions

Dataset Description

• Homepage: https://www.cluebenchmarks.com
• Repository: https://github.com/CLUEbenchmark/CLUE
• Paper: CLUE: A Chinese Language Understanding Evaluation Benchmark
• Point of Contact: Zhenzhong Lan
• Size of downloaded dataset files: 198.68 MB
• Size of the generated dataset: 486.34 MB
• Total amount of disk used: 685.02 MB

Dataset Summary

CLUE, A Chinese Language Understanding Evaluation Benchmark (https://www.cluebenchmarks.com/) is a collection of resources for training, evaluating, and analyzing Chinese language understanding systems.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

afqmc

• Size of downloaded dataset files: 1.20 MB
• Size of the generated dataset: 4.20 MB
• Total amount of disk used: 5.40 MB

An example of 'validation' looks as follows.

{
    "idx": 0,
    "label": 0,
    "sentence1": "双十一花呗提额在哪",
    "sentence2": "里可以提花呗额度"
}

c3

• Size of downloaded dataset files: 3.20 MB
• Size of the generated dataset: 15.69 MB
• Total amount of disk used: 18.90 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "answer": "比人的灵敏",
    "choice": ["没有人的灵敏", "和人的差不多", "和人的一样好", "比人的灵敏"],
    "context": "[\"许多动物的某些器官感觉特别灵敏，它们能比人类提前知道一些灾害事件的发生，例如，海洋中的水母能预报风暴，老鼠能事先躲避矿井崩塌或有害气体，等等。地震往往能使一些动物的某些感觉器官受到刺激而发生异常反应。如一个地区的重力发生变异，某些动物可能通过它们的平衡...",
    "id": 1,
    "question": "动物的器官感觉与人的相比有什么不同?"
}

chid

• Size of downloaded dataset files: 139.20 MB
• Size of the generated dataset: 274.08 MB
• Total amount of disk used: 413.28 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "answers": {
        "candidate_id": [3, 5, 6, 1, 7, 4, 0],
        "text": ["碌碌无为", "无所作为", "苦口婆心", "得过且过", "未雨绸缪", "软硬兼施", "传宗接代"]
    },
    "candidates": "[\"传宗接代\", \"得过且过\", \"咄咄逼人\", \"碌碌无为\", \"软硬兼施\", \"无所作为\", \"苦口婆心\", \"未雨绸缪\", \"和衷共济\", \"人老珠黄\"]...",
    "content": "[\"谈到巴萨目前的成就，瓜迪奥拉用了“坚持”两个字来形容。自从上世纪90年代克鲁伊夫带队以来，巴萨就坚持每年都有拉玛西亚球员进入一队的传统。即便是范加尔时代，巴萨强力推出的“巴萨五鹰”德拉·佩纳、哈维、莫雷罗、罗杰·加西亚和贝拉乌桑几乎#idiom0000...",
    "idx": 0
}

cluewsc2020

• Size of downloaded dataset files: 0.28 MB
• Size of the generated dataset: 1.03 MB
• Total amount of disk used: 1.29 MB

An example of 'train' looks as follows.

{
    "idx": 0,
    "label": 1,
    "target": {
        "span1_index": 3,
        "span1_text": "伤口",
        "span2_index": 27,
        "span2_text": "它们"
    },
    "text": "裂开的伤口涂满尘土，里面有碎石子和木头刺，我小心翼翼把它们剔除出去。"
}

cmnli

• Size of downloaded dataset files: 31.40 MB
• Size of the generated dataset: 72.12 MB
• Total amount of disk used: 103.53 MB

An example of 'train' looks as follows.

{
    "idx": 0,
    "label": 0,
    "sentence1": "从概念上讲，奶油略读有两个基本维度-产品和地理。",
    "sentence2": "产品和地理位置是使奶油撇油起作用的原因。"
}

Data Fields

The data fields are the same among all splits.

afqmc

• sentence1: a string feature.
• sentence2: a string feature.
• label: a classification label, with possible values including 0 (0), 1 (1).
• idx: a int32 feature.

c3

• id: a int32 feature.
• context: a list of string features.
• question: a string feature.
• choice: a list of string features.
• answer: a string feature.

chid

• idx: a int32 feature.
• candidates: a list of string features.
• content: a list of string features.
• answers: a dictionary feature containing:
  • text: a string feature.
  • candidate_id: a int32 feature.

cluewsc2020

• idx: a int32 feature.
• text: a string feature.
• label: a classification label, with possible values including true (0), false (1).
• span1_text: a string feature.
• span2_text: a string feature.
• span1_index: a int32 feature.
• span2_index: a int32 feature.

cmnli

• sentence1: a string feature.
• sentence2: a string feature.
• label: a classification label, with possible values including neutral (0), entailment (1), contradiction (2).
• idx: a int32 feature.

Data Splits

name	train	validation	test
afqmc	34334	4316	3861
c3	11869	3816	3892
chid	84709	3218	3231
cluewsc2020	1244	304	290
cmnli	391783	12241	13880

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@inproceedings{xu-etal-2020-clue,
    title = "{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark",
    author = "Xu, Liang  and
      Hu, Hai  and
      Zhang, Xuanwei  and
      Li, Lu  and
      Cao, Chenjie  and
      Li, Yudong  and
      Xu, Yechen  and
      Sun, Kai  and
      Yu, Dian  and
      Yu, Cong  and
      Tian, Yin  and
      Dong, Qianqian  and
      Liu, Weitang  and
      Shi, Bo  and
      Cui, Yiming  and
      Li, Junyi  and
      Zeng, Jun  and
      Wang, Rongzhao  and
      Xie, Weijian  and
      Li, Yanting  and
      Patterson, Yina  and
      Tian, Zuoyu  and
      Zhang, Yiwen  and
      Zhou, He  and
      Liu, Shaoweihua  and
      Zhao, Zhe  and
      Zhao, Qipeng  and
      Yue, Cong  and
      Zhang, Xinrui  and
      Yang, Zhengliang  and
      Richardson, Kyle  and
      Lan, Zhenzhong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.419",
    doi = "10.18653/v1/2020.coling-main.419",
    pages = "4762--4772",
}

Contributions

Thanks to @thomwolf, @JetRunner for adding this dataset.