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{
"default": {
"description": "A dataset based on PubMed for sentence classification. The dataset consists of sentences from 20,000 abstracts of abstracts of randomized controlled trials; in total 240k sentences. Sentences are classified into five categories based based on the role they play in the abstract: background, objective, methods, results or conclusions.",
"citation": "@inproceedings{dernoncourt-lee-2017-pubmed,\ntitle = \"{P}ub{M}ed 200k {RCT}: a Dataset for Sequential Sentence Classification in Medical Abstracts\",\nauthor = \"Dernoncourt, Franck and\n Lee, Ji Young\",\nbooktitle = \"Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)\",\nmonth = nov,\nyear = \"2017\",\naddress = \"Taipei, Taiwan\",\npublisher = \"Asian Federation of Natural Language Processing\",\nurl = \"https://aclanthology.org/I17-2052\",\npages = \"308--313\"}",
"homepage": "https://github.com/Franck-Dernoncourt/pubmed-rct",
"license": "",
"features": {
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"label": {
"num_classes": 5,
"names": [
"bac",
"obj",
"met",
"res",
"con"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
},
"task_templates": [
{
"task": "text-classification",
"text_column": "text",
"label_column": "label",
"labels": [
"bac",
"obj",
"met",
"res",
"con"
]
}
],
"version": {
"version_str": "1.0.0",
"description": null,
"major": 1,
"minor": 0,
"patch": 0
}
}
} |