sschet/biomedical-ner-all
Token Classification
•
Updated
•
71
•
3
tokens
sequence | tags
sequence |
---|---|
[
"Naloxone",
"reverses",
"the",
"antihypertensive",
"effect",
"of",
"clonidine",
"."
] | [
1,
0,
0,
0,
0,
0,
1,
0
] |
[
"In",
"unanesthetized",
",",
"spontaneously",
"hypertensive",
"rats",
"the",
"decrease",
"in",
"blood",
"pressure",
"and",
"heart",
"rate",
"produced",
"by",
"intravenous",
"clonidine",
",",
"5",
"to",
"20",
"micrograms",
"/",
"kg",
",",
"was",
"inhibited",
"or",
"reversed",
"by",
"nalozone",
",",
"0",
"."
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[
"2",
"to",
"2",
"mg",
"/",
"kg",
"."
] | [
0,
0,
0,
0,
0,
0,
0
] |
[
"The",
"hypotensive",
"effect",
"of",
"100",
"mg",
"/",
"kg",
"alpha-methyldopa",
"was",
"also",
"partially",
"reversed",
"by",
"naloxone",
"."
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[
"Naloxone",
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"not",
"affect",
"either",
"blood",
"pressure",
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"heart",
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"."
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1,
0,
0,
0,
0,
0,
0,
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[
"In",
"brain",
"membranes",
"from",
"spontaneously",
"hypertensive",
"rats",
"clonidine",
",",
"10",
"(-",
"8",
")",
"to",
"10",
"(-",
"5",
")",
"M",
",",
"did",
"not",
"influence",
"stereoselective",
"binding",
"of",
"[3H]-naloxone",
"(",
"8",
"nM",
"),",
"and",
"naloxone",
",",
"10",
"(-",
"8",
")",
"to",
"10",
"(-",
"4",
")",
"M",
",",
"did",
"not",
"influence",
"clonidine",
"-",
"suppressible",
"binding",
"of",
"[3H]-dihydroergocryptine",
"(",
"1",
"nM",
").",
"These",
"findings",
"indicate",
"that",
"in",
"spontaneously",
"hypertensive",
"rats",
"the",
"effects",
"of",
"central",
"alpha",
"-",
"adrenoceptor",
"stimulation",
"involve",
"activation",
"of",
"opiate",
"receptors",
"."
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[
"As",
"naloxone",
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"clonidine",
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"not",
"appear",
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"with",
"the",
"same",
"receptor",
"site",
",",
"the",
"observed",
"functional",
"antagonism",
"suggests",
"the",
"release",
"of",
"an",
"endogenous",
"opiate",
"by",
"clonidine",
"or",
"alpha-methyldopa"
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0,
0,
0,
0,
0,
0,
0,
0,
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1,
0,
1
] |
[
"Lidocaine",
"-",
"induced",
"cardiac",
"asystole",
"."
] | [
1,
0,
0,
2,
3,
0
] |
[
"Intravenous",
"administration",
"of",
"a",
"single",
"50",
"-",
"mg",
"bolus",
"of",
"lidocaine",
"in",
"a",
"67",
"-",
"year",
"-",
"old",
"man",
"resulted",
"in",
"profound",
"depression",
"of",
"the",
"activity",
"of",
"the",
"sinoatrial",
"and",
"atrioventricular",
"nodal",
"pacemakers",
"."
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[
"The",
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"had",
"no",
"apparent",
"associated",
"conditions",
"which",
"might",
"have",
"predisposed",
"him",
"to",
"the",
"development",
"of",
"bradyarrhythmias",
";",
"and",
",",
"thus",
",",
"this",
"probably",
"represented",
"a",
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0,
0,
0,
0,
0,
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1
] |
[
"Suxamethonium",
"infusion",
"rate",
"and",
"observed",
"fasciculations",
"."
] | [
1,
0,
0,
0,
0,
2,
0
] |
[
"A",
"dose",
"-",
"response",
"study",
"."
] | [
0,
0,
0,
0,
0,
0
] |
[
"Suxamethonium",
"chloride",
"(",
"Sch",
")",
"was",
"administered",
"i",
"."
] | [
1,
4,
0,
1,
0,
0,
0,
0,
0
] |
[
"v",
"."
] | [
0,
0
] |
[
"to",
"36",
"adult",
"males",
"at",
"six",
"rates",
":",
"0",
"."
] | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] |
[
"25",
"mg",
"s",
"-",
"1",
"to",
"20",
"mg",
"s",
"-",
"1",
"."
] | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
] |
[
"The",
"infusion",
"was",
"discontinued",
"either",
"when",
"there",
"was",
"no",
"muscular",
"response",
"to",
"tetanic",
"stimulation",
"of",
"the",
"ulnar",
"nerve",
"or",
"when",
"Sch",
"120",
"mg",
"was",
"exceeded",
"."
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[
"Six",
"additional",
"patients",
"received",
"a",
"30",
"-",
"mg",
"i",
"."
] | [
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0,
0,
0,
0,
0,
0,
0,
0,
0
] |
[
"v",
"."
] | [
0,
0
] |
[
"bolus",
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"."
] | [
0,
0,
0
] |
[
"Fasciculations",
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"areas",
"of",
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"were",
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"from",
"0",
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"3",
"and",
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"a",
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"."
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[
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"twitch",
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"tetanus",
"suppression",
"were",
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"the",
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"."
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[
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"the",
"total",
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[
"Total",
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0,
2
] |
[
"Galanthamine",
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",",
"a",
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",",
"in",
"the",
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"the",
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"a",
"patient",
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"scopolamine",
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"."
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[
"It",
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"anaesthesia",
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"the",
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[
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[
"Effects",
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"."
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[
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"a",
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[
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[
"19",
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[
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"."
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[
"When",
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"treated",
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"as",
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"a",
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"."
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[
"In",
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"renal",
"failure"
] | [
0,
0,
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2,
3,
3
] |
[
"Treatment",
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"with",
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"acid",
":",
"an",
"antibiotic",
"with",
"immunosuppressive",
"properties",
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"cyclosporin",
"."
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[
"Fusidic",
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"cell",
"specific",
"immunosuppressive",
"effects",
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"those",
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"."
] | [
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[
"Because",
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",",
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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}
}