Datasets:
Tasks:
Multiple Choice
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10M - 100M
ArXiv:
License:
File size: 2,468 Bytes
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{
"default": {
"description": "ASNQ is a dataset for answer sentence selection derived from\nGoogle's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).\n\nEach example contains a question, candidate sentence, label indicating whether or not\nthe sentence answers the question, and two additional features --\nsentence_in_long_answer and short_answer_in_sentence indicating whether ot not the\ncandidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.\n\nFor more details please see\nhttps://arxiv.org/pdf/1911.04118.pdf\n\nand\n\nhttps://research.google/pubs/pub47761/\n",
"citation": "@article{garg2019tanda,\n title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},\n author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},\n year={2019},\n eprint={1911.04118},\n}\n",
"homepage": "https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq",
"license": "",
"features": {
"question": {
"dtype": "string",
"_type": "Value"
},
"sentence": {
"dtype": "string",
"_type": "Value"
},
"label": {
"names": [
"neg",
"pos"
],
"_type": "ClassLabel"
},
"sentence_in_long_answer": {
"dtype": "bool",
"_type": "Value"
},
"short_answer_in_sentence": {
"dtype": "bool",
"_type": "Value"
}
},
"builder_name": "parquet",
"dataset_name": "asnq",
"config_name": "default",
"version": {
"version_str": "1.0.0",
"major": 1,
"minor": 0,
"patch": 0
},
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"dataset_name": null
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"dataset_name": null
}
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}
} |