sschet/bert-base-uncased_clinical-ner
Token Classification
•
Updated
•
1.06k
•
1
tokens
sequence
| tags
sequence
|
---|---|
[
"Naloxone",
"reverses",
"the",
"antihypertensive",
"effect",
"of",
"clonidine",
"."
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1,
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[
"In",
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",",
"spontaneously",
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"rats",
"the",
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"and",
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"clonidine",
",",
"5",
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"20",
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",",
"0",
"."
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[
"2",
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"kg",
"."
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[
"The",
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"effect",
"of",
"100",
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"alpha-methyldopa",
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"partially",
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"."
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[
"Naloxone",
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"."
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[
"In",
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"membranes",
"from",
"spontaneously",
"hypertensive",
"rats",
"clonidine",
",",
"10",
"(-",
"8",
")",
"to",
"10",
"(-",
"5",
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"M",
",",
"did",
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"8",
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"),",
"and",
"naloxone",
",",
"10",
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"8",
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"10",
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"4",
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"M",
",",
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"clonidine",
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"suppressible",
"binding",
"of",
"[3H]-dihydroergocryptine",
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"1",
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").",
"These",
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"that",
"in",
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"rats",
"the",
"effects",
"of",
"central",
"alpha",
"-",
"adrenoceptor",
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"."
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[
"As",
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"clonidine",
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",",
"the",
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[
"Lidocaine",
"-",
"induced",
"cardiac",
"asystole",
"."
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1,
0,
0,
2,
3,
0
] |
[
"Intravenous",
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"a",
"single",
"50",
"-",
"mg",
"bolus",
"of",
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"in",
"a",
"67",
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"year",
"-",
"old",
"man",
"resulted",
"in",
"profound",
"depression",
"of",
"the",
"activity",
"of",
"the",
"sinoatrial",
"and",
"atrioventricular",
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"pacemakers",
"."
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[
"The",
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"which",
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"the",
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";",
"and",
",",
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",",
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[
"Suxamethonium",
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"."
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[
"A",
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"-",
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"study",
"."
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[
"Suxamethonium",
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"(",
"Sch",
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"i",
"."
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0,
1,
0,
0,
0,
0,
0
] |
[
"v",
"."
] | [
0,
0
] |
[
"to",
"36",
"adult",
"males",
"at",
"six",
"rates",
":",
"0",
"."
] | [
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0,
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0,
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0,
0,
0,
0
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[
"25",
"mg",
"s",
"-",
"1",
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"20",
"mg",
"s",
"-",
"1",
"."
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[
"The",
"infusion",
"was",
"discontinued",
"either",
"when",
"there",
"was",
"no",
"muscular",
"response",
"to",
"tetanic",
"stimulation",
"of",
"the",
"ulnar",
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"or",
"when",
"Sch",
"120",
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"."
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[
"Six",
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"a",
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"."
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[
"v",
"."
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[
"bolus",
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"."
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[
"Fasciculations",
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[
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[
"Total",
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[
"Galanthamine",
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",",
"a",
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[
"It",
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[
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[
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[
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[
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[
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[
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[
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] | [
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3,
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[
"Treatment",
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"."
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"cell",
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[
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BioCreative V CDR NER dataset formatted in a part of TNER project. The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.
Chemical
, Disease
An example of train
looks as follows.
{
'tags': [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],
'tokens': ['Fasciculations', 'in', 'six', 'areas', 'of', 'the', 'body', 'were', 'scored', 'from', '0', 'to', '3', 'and', 'summated', 'as', 'a', 'total', 'fasciculation', 'score', '.']
}
The label2id dictionary can be found at here.
{
"O": 0,
"B-Chemical": 1,
"B-Disease": 2,
"I-Disease": 3,
"I-Chemical": 4
}
name | train | validation | test |
---|---|---|---|
bc5cdr | 5228 | 5330 | 5865 |
@article{wei2016assessing,
title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task},
author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and Lu, Zhiyong},
journal={Database},
volume={2016},
year={2016},
publisher={Oxford Academic}
}