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Upload dataset_infos.json
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dataset_infos.json
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{
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"default": {
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"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.",
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"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\"}",
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"homepage": "https://github.com/Franck-Dernoncourt/pubmed-rct",
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"license": "",
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"features": {
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"text": {
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"dtype": "string",
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"id": null,
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"_type": "Value"
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},
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"label": {
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"num_classes": 2,
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"names": [
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"bac",
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"obj",
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"met",
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"res",
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"con"
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],
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"names_file": null,
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"id": null,
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"_type": "ClassLabel"
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}
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},
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"task_templates": [
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{
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"task": "text-classification",
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"text_column": "text",
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"label_column": "label",
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"labels": [
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"bac",
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"obj",
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"met",
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"res",
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"con"
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]
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}
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],
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"version": {
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"version_str": "1.0.0",
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"description": null,
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"major": 1,
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"minor": 0,
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"patch": 0
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}
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}
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}
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