{
    "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
        }
    }
}