Datasets:
xcsr

	---
	annotations_creators:
	- crowdsourced
	language_creators:
	- crowdsourced
	- machine-generated
	language:
	- ar
	- de
	- en
	- es
	- fr
	- hi
	- it
	- ja
	- nl
	- pl
	- pt
	- ru
	- sw
	- ur
	- vi
	- zh
	license:
	- mit
	multilinguality:
	- multilingual
	size_categories:
	- 1K<n<10K
	source_datasets:
	- extended\|codah
	- extended\|commonsense_qa
	task_categories:
	- question-answering
	task_ids:
	- multiple-choice-qa
	pretty_name: X-CSR
	dataset_info:
	- config_name: X-CODAH-ar
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
	- name: label
	dtype: string
	- name: text
	dtype: string
	- name: answerKey
	dtype: string
	splits:
	- name: test
	num_bytes: 568026
	num_examples: 1000
	- name: validation
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	num_examples: 300
	download_size: 265474
	dataset_size: 733048
	- config_name: X-CODAH-de
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
	- name: label
	dtype: string
	- name: text
	dtype: string
	- name: answerKey
	dtype: string
	splits:
	- name: test
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	num_examples: 1000
	- name: validation
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	num_examples: 300
	download_size: 259705
	dataset_size: 614851
	- config_name: X-CODAH-en
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
	- name: label
	dtype: string
	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	- name: test
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	num_examples: 1000
	- name: validation
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	num_examples: 300
	download_size: 217262
	dataset_size: 538811
	- config_name: X-CODAH-es
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
	- name: label
	dtype: string
	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	- name: test
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	num_examples: 1000
	- name: validation
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	num_examples: 300
	download_size: 242647
	dataset_size: 581632
	- config_name: X-CODAH-fr
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
	- name: label
	dtype: string
	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	num_examples: 1000
	- name: validation
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	num_examples: 300
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	dataset_size: 615414
	- config_name: X-CODAH-hi
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
	- name: label
	dtype: string
	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	- name: test
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	num_examples: 1000
	- name: validation
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	num_examples: 300
	download_size: 336862
	dataset_size: 1256737
	- config_name: X-CODAH-it
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
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	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	- name: test
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	num_examples: 1000
	- name: validation
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	num_examples: 300
	download_size: 241780
	dataset_size: 590559
	- config_name: X-CODAH-jap
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
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	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	- name: test
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	num_examples: 1000
	- name: validation
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	num_examples: 300
	download_size: 264995
	dataset_size: 695807
	- config_name: X-CODAH-nl
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
	sequence:
	- name: label
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	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	- name: test
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	num_examples: 1000
	- name: validation
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	num_examples: 300
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	dataset_size: 578746
	- config_name: X-CODAH-pl
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: text
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	- name: answerKey
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	num_examples: 300
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	dataset_size: 566288
	- config_name: X-CODAH-pt
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	- name: id
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	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
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	- name: stem
	dtype: string
	- name: choices
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	- name: answerKey
	dtype: string
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	- name: validation
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	num_examples: 300
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	dataset_size: 587516
	- config_name: X-CODAH-ru
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	- name: test
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	num_examples: 1000
	- name: validation
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	num_examples: 300
	download_size: 314200
	dataset_size: 868280
	- config_name: X-CODAH-sw
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: text
	dtype: string
	- name: answerKey
	dtype: string
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	- name: validation
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	dataset_size: 548191
	- config_name: X-CODAH-ur
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	- name: id
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	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: text
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	- name: answerKey
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	- name: validation
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	num_examples: 300
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	dataset_size: 886860
	- config_name: X-CODAH-vi
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: answerKey
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	dataset_size: 699977
	- config_name: X-CODAH-zh
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question_tag
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: answerKey
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	- config_name: X-CSQA-ar
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	- name: id
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	- name: lang
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	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- config_name: X-CSQA-de
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	- name: id
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	- name: lang
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	- name: question
	struct:
	- name: stem
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	- config_name: X-CSQA-en
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	- name: id
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	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	dataset_size: 420696
	- config_name: X-CSQA-es
	features:
	- name: id
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	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: validation
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	num_examples: 1000
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	dataset_size: 461314
	- config_name: X-CSQA-fr
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	- name: id
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	- name: lang
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	- name: question
	struct:
	- name: stem
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	- name: choices
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	- name: answerKey
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	dataset_size: 475348
	- config_name: X-CSQA-hi
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	- name: id
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	- name: lang
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	- name: question
	struct:
	- name: stem
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	- name: choices
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	- name: validation
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	num_examples: 1000
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	dataset_size: 811329
	- config_name: X-CSQA-it
	features:
	- name: id
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	- name: lang
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	- name: question
	struct:
	- name: stem
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	- name: choices
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	- name: validation
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	num_examples: 1000
	download_size: 236130
	dataset_size: 453506
	- config_name: X-CSQA-jap
	features:
	- name: id
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	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
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	dataset_size: 491250
	- config_name: X-CSQA-nl
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	- name: id
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	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: text
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	- name: answerKey
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	- name: validation
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	dataset_size: 443143
	- config_name: X-CSQA-pl
	features:
	- name: id
	dtype: string
	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- config_name: X-CSQA-pt
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	- name: id
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	- name: lang
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	- name: question
	struct:
	- name: stem
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	- name: choices
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	- config_name: X-CSQA-ru
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	- name: id
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	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: text
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	- name: answerKey
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	dataset_size: 665473
	- config_name: X-CSQA-sw
	features:
	- name: id
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	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- name: answerKey
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	dataset_size: 433641
	- config_name: X-CSQA-ur
	features:
	- name: id
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	- name: lang
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	- name: question
	struct:
	- name: stem
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	- name: choices
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	- config_name: X-CSQA-vi
	features:
	- name: id
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	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	- config_name: X-CSQA-zh
	features:
	- name: id
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	- name: lang
	dtype: string
	- name: question
	struct:
	- name: stem
	dtype: string
	- name: choices
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	configs:
	- config_name: X-CODAH-ar
	data_files:
	- split: test
	path: X-CODAH-ar/test-*
	- split: validation
	path: X-CODAH-ar/validation-*
	- config_name: X-CODAH-de
	data_files:
	- split: test
	path: X-CODAH-de/test-*
	- split: validation
	path: X-CODAH-de/validation-*
	- config_name: X-CODAH-en
	data_files:
	- split: test
	path: X-CODAH-en/test-*
	- split: validation
	path: X-CODAH-en/validation-*
	- config_name: X-CODAH-es
	data_files:
	- split: test
	path: X-CODAH-es/test-*
	- split: validation
	path: X-CODAH-es/validation-*
	- config_name: X-CODAH-fr
	data_files:
	- split: test
	path: X-CODAH-fr/test-*
	- split: validation
	path: X-CODAH-fr/validation-*
	- config_name: X-CODAH-hi
	data_files:
	- split: test
	path: X-CODAH-hi/test-*
	- split: validation
	path: X-CODAH-hi/validation-*
	- config_name: X-CODAH-it
	data_files:
	- split: test
	path: X-CODAH-it/test-*
	- split: validation
	path: X-CODAH-it/validation-*
	- config_name: X-CODAH-jap
	data_files:
	- split: test
	path: X-CODAH-jap/test-*
	- split: validation
	path: X-CODAH-jap/validation-*
	- config_name: X-CODAH-nl
	data_files:
	- split: test
	path: X-CODAH-nl/test-*
	- split: validation
	path: X-CODAH-nl/validation-*
	- config_name: X-CODAH-pl
	data_files:
	- split: test
	path: X-CODAH-pl/test-*
	- split: validation
	path: X-CODAH-pl/validation-*
	- config_name: X-CODAH-pt
	data_files:
	- split: test
	path: X-CODAH-pt/test-*
	- split: validation
	path: X-CODAH-pt/validation-*
	- config_name: X-CODAH-ru
	data_files:
	- split: test
	path: X-CODAH-ru/test-*
	- split: validation
	path: X-CODAH-ru/validation-*
	- config_name: X-CODAH-sw
	data_files:
	- split: test
	path: X-CODAH-sw/test-*
	- split: validation
	path: X-CODAH-sw/validation-*
	- config_name: X-CODAH-ur
	data_files:
	- split: test
	path: X-CODAH-ur/test-*
	- split: validation
	path: X-CODAH-ur/validation-*
	- config_name: X-CODAH-vi
	data_files:
	- split: test
	path: X-CODAH-vi/test-*
	- split: validation
	path: X-CODAH-vi/validation-*
	- config_name: X-CODAH-zh
	data_files:
	- split: test
	path: X-CODAH-zh/test-*
	- split: validation
	path: X-CODAH-zh/validation-*
	- config_name: X-CSQA-ar
	data_files:
	- split: test
	path: X-CSQA-ar/test-*
	- split: validation
	path: X-CSQA-ar/validation-*
	- config_name: X-CSQA-de
	data_files:
	- split: test
	path: X-CSQA-de/test-*
	- split: validation
	path: X-CSQA-de/validation-*
	- config_name: X-CSQA-en
	data_files:
	- split: test
	path: X-CSQA-en/test-*
	- split: validation
	path: X-CSQA-en/validation-*
	- config_name: X-CSQA-es
	data_files:
	- split: test
	path: X-CSQA-es/test-*
	- split: validation
	path: X-CSQA-es/validation-*
	- config_name: X-CSQA-fr
	data_files:
	- split: test
	path: X-CSQA-fr/test-*
	- split: validation
	path: X-CSQA-fr/validation-*
	- config_name: X-CSQA-hi
	data_files:
	- split: test
	path: X-CSQA-hi/test-*
	- split: validation
	path: X-CSQA-hi/validation-*
	- config_name: X-CSQA-it
	data_files:
	- split: test
	path: X-CSQA-it/test-*
	- split: validation
	path: X-CSQA-it/validation-*
	- config_name: X-CSQA-jap
	data_files:
	- split: test
	path: X-CSQA-jap/test-*
	- split: validation
	path: X-CSQA-jap/validation-*
	- config_name: X-CSQA-nl
	data_files:
	- split: test
	path: X-CSQA-nl/test-*
	- split: validation
	path: X-CSQA-nl/validation-*
	- config_name: X-CSQA-pl
	data_files:
	- split: test
	path: X-CSQA-pl/test-*
	- split: validation
	path: X-CSQA-pl/validation-*
	- config_name: X-CSQA-pt
	data_files:
	- split: test
	path: X-CSQA-pt/test-*
	- split: validation
	path: X-CSQA-pt/validation-*
	- config_name: X-CSQA-ru
	data_files:
	- split: test
	path: X-CSQA-ru/test-*
	- split: validation
	path: X-CSQA-ru/validation-*
	- config_name: X-CSQA-sw
	data_files:
	- split: test
	path: X-CSQA-sw/test-*
	- split: validation
	path: X-CSQA-sw/validation-*
	- config_name: X-CSQA-ur
	data_files:
	- split: test
	path: X-CSQA-ur/test-*
	- split: validation
	path: X-CSQA-ur/validation-*
	- config_name: X-CSQA-vi
	data_files:
	- split: test
	path: X-CSQA-vi/test-*
	- split: validation
	path: X-CSQA-vi/validation-*
	- config_name: X-CSQA-zh
	data_files:
	- split: test
	path: X-CSQA-zh/test-*
	- split: validation
	path: X-CSQA-zh/validation-*
	---

	# Dataset Card for X-CSR

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)

	## Dataset Description

	- Homepage: https://inklab.usc.edu//XCSR/
	- Repository: https://github.com/INK-USC/XCSR
	- Paper: https://arxiv.org/abs/2106.06937
	- Leaderboard: https://inklab.usc.edu//XCSR/leaderboard
	- Point of Contact: https://yuchenlin.xyz/

	### Dataset Summary

	To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future.


	### Supported Tasks and Leaderboards

	https://inklab.usc.edu//XCSR/leaderboard

	### Languages

	The total 16 languages for X-CSR: {en, zh, de, es, fr, it, jap, nl, pl, pt, ru, ar, vi, hi, sw, ur}.


	## Dataset Structure

	### Data Instances

	An example of the X-CSQA dataset:
	```
	{
	"id": "be1920f7ba5454ad", # an id shared by all languages
	"lang": "en", # one of the 16 language codes.
	"question": {
	"stem": "What will happen to your knowledge with more learning?", # question text
	"choices": [
	{"label": "A", "text": "headaches" },
	{"label": "B", "text": "bigger brain" },
	{"label": "C", "text": "education" },
	{"label": "D", "text": "growth" },
	{"label": "E", "text": "knowing more" }
	] },
	"answerKey": "D" # hidden for test data.
	}
	```

	An example of the X-CODAH dataset:
	```
	{
	"id": "b8eeef4a823fcd4b", # an id shared by all languages
	"lang": "en", # one of the 16 language codes.
	"question_tag": "o", # one of 6 question types
	"question": {
	"stem": " ", # always a blank as a dummy question
	"choices": [
	{"label": "A",
	"text": "Jennifer loves her school very much, she plans to drop every courses."},
	{"label": "B",
	"text": "Jennifer loves her school very much, she is never absent even when she's sick."},
	{"label": "C",
	"text": "Jennifer loves her school very much, she wants to get a part-time job."},
	{"label": "D",
	"text": "Jennifer loves her school very much, she quits school happily."}
	]
	},
	"answerKey": "B" # hidden for test data.
	}
	```

	### Data Fields

	- id: an id shared by all languages
	- lang: one of the 16 language codes.
	- question_tag: one of 6 question types
	- stem: always a blank as a dummy question
	- choices: a list of answers, each answer has:
	- label: a string answer identifier for each answer
	- text: the answer text

	### Data Splits

	- X-CSQA: There are 8,888 examples for training in English, 1,000 for development in each language, and 1,074 examples for testing in each language.
	- X-CODAH: There are 8,476 examples for training in English, 300 for development in each language, and 1,000 examples for testing in each language.

	## Dataset Creation

	### Curation Rationale

	To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH.

	The details of the dataset construction, especially the translation procedures, can be found in section A of the appendix of the [paper](https://inklab.usc.edu//XCSR/XCSR_paper.pdf).

	### 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

	```
	# X-CSR
	@inproceedings{lin-etal-2021-common,
	title = "Common Sense Beyond {E}nglish: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning",
	author = "Lin, Bill Yuchen and
	Lee, Seyeon and
	Qiao, Xiaoyang and
	Ren, Xiang",
	booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
	month = aug,
	year = "2021",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2021.acl-long.102",
	doi = "10.18653/v1/2021.acl-long.102",
	pages = "1274--1287",
	abstract = "Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-general probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 14 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method {---} multilingual contrastive pretraining (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks (e.g., +2.7{\%} accuracy for X-CSQA over XLM-R{\_}L).",
	}

	# CSQA
	@inproceedings{Talmor2019commonsenseqaaq,
	address = {Minneapolis, Minnesota},
	author = {Talmor, Alon and Herzig, Jonathan and Lourie, Nicholas and Berant, Jonathan},
	booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
	doi = {10.18653/v1/N19-1421},
	pages = {4149--4158},
	publisher = {Association for Computational Linguistics},
	title = {CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge},
	url = {https://www.aclweb.org/anthology/N19-1421},
	year = {2019}
	}

	# CODAH
	@inproceedings{Chen2019CODAHAA,
	address = {Minneapolis, USA},
	author = {Chen, Michael and D{'}Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},
	booktitle = {Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}},
	doi = {10.18653/v1/W19-2008},
	pages = {63--69},
	publisher = {Association for Computational Linguistics},
	title = {CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
	url = {https://www.aclweb.org/anthology/W19-2008},
	year = {2019}
	}
	```

	### Contributions

	Thanks to [Bill Yuchen Lin](https://yuchenlin.xyz/), [Seyeon Lee](https://seyeon-lee.github.io/), [Xiaoyang Qiao](https://www.linkedin.com/in/xiaoyang-qiao/), [Xiang Ren](http://www-bcf.usc.edu/~xiangren/) for adding this dataset.