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:
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num_examples: 3861
- name: train
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num_examples: 34334
- name: validation
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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:
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num_examples: 1625
- name: train
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num_examples: 11869
- name: validation
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num_examples: 3816
download_size: 4718960
dataset_size: 14263824
- 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
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- name: train
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num_examples: 84709
- name: validation
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num_examples: 3218
download_size: 198468807
dataset_size: 274076122
- 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:
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dtype: string
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- name: span1_index
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- name: span2_index
dtype: int32
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- 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
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- name: train
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num_examples: 391783
- name: validation
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num_examples: 12241
download_size: 54234919
dataset_size: 72123639
- config_name: cmrc2018
features:
- name: id
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- name: context
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- name: question
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- name: answers
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- name: train
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- name: validation
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- name: trial
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num_examples: 1002
download_size: 5459001
dataset_size: 25410796
- config_name: csl
features:
- name: idx
dtype: int32
- name: corpus_id
dtype: int32
- name: abst
dtype: string
- name: label
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- name: keyword
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num_examples: 3000
- name: train
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num_examples: 20000
- name: validation
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num_examples: 3000
download_size: 3936111
dataset_size: 21407181
- config_name: diagnostics
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
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'0': neutral
'1': entailment
'2': contradiction
- name: idx
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download_size: 23000
dataset_size: 42392
- config_name: drcd
features:
- name: id
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- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
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- name: train
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- name: validation
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download_size: 11188875
dataset_size: 47648493
- 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'
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'118': '118'
- name: idx
dtype: int32
splits:
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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:
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num_examples: 3000
- name: train
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num_examples: 50437
- name: validation
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num_examples: 2950
download_size: 3000218
dataset_size: 6929427
- 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'
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'10': '112'
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'12': '114'
'13': '115'
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- name: idx
dtype: int32
splits:
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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: c3
data_files:
- split: test
path: c3/test-*
- split: train
path: c3/train-*
- split: validation
path: c3/validation-*
- config_name: chid
data_files:
- split: test
path: chid/test-*
- split: train
path: chid/train-*
- split: validation
path: chid/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: cmrc2018
data_files:
- split: test
path: cmrc2018/test-*
- split: train
path: cmrc2018/train-*
- split: validation
path: cmrc2018/validation-*
- split: trial
path: cmrc2018/trial-*
- config_name: csl
data_files:
- split: test
path: csl/test-*
- split: train
path: csl/train-*
- split: validation
path: csl/validation-*
- config_name: diagnostics
data_files:
- split: test
path: diagnostics/test-*
- config_name: drcd
data_files:
- split: test
path: drcd/test-*
- split: train
path: drcd/train-*
- split: validation
path: drcd/validation-*
- config_name: iflytek
data_files:
- split: test
path: iflytek/test-*
- split: train
path: iflytek/train-*
- split: validation
path: iflytek/validation-*
- config_name: ocnli
data_files:
- split: test
path: ocnli/test-*
- split: train
path: ocnli/train-*
- split: validation
path: ocnli/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 Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://www.cluebenchmarks.com
- Repository: https://github.com/CLUEbenchmark/CLUE
- Paper: CLUE: A Chinese Language Understanding Evaluation Benchmark
- Paper: https://arxiv.org/abs/2004.05986
- 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
Languages
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: astringfeature.sentence2: astringfeature.label: a classification label, with possible values including0(0),1(1).idx: aint32feature.
c3
id: aint32feature.context: alistofstringfeatures.question: astringfeature.choice: alistofstringfeatures.answer: astringfeature.
chid
idx: aint32feature.candidates: alistofstringfeatures.content: alistofstringfeatures.answers: a dictionary feature containing:text: astringfeature.candidate_id: aint32feature.
cluewsc2020
idx: aint32feature.text: astringfeature.label: a classification label, with possible values includingtrue(0),false(1).span1_text: astringfeature.span2_text: astringfeature.span1_index: aint32feature.span2_index: aint32feature.
cmnli
sentence1: astringfeature.sentence2: astringfeature.label: a classification label, with possible values includingneutral(0),entailment(1),contradiction(2).idx: aint32feature.
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
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
@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.