Ihor/gliner-biomed-base-v1.0
Token Classification • Updated • 662 • 5
metadata dict | tokenized_text listlengths 1 15.2k | ner listlengths 0 11.4k | negatives listlengths 0 100 ⌀ |
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154,
169,
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215,
224,
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404,
416,
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15,
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45,
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{
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43,
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5,
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147,
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154,
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11,
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[
36,
36,
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[
29,
29,
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[
45,
45,
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4,
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25,
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27,
28,
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10,
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241,
241,
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[
244,
246,
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[
249,
254,
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[
267,
270,
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null | [
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12,
14,
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[
29,
32,
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null | [
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29,
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17,
18,
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53,
54,
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26,
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17,
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26,
26,
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53,
53,
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[
63,
63,
... | null |
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... | [
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6,
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27,
34,
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[
39,
42,
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[
54,
57,
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[
178,
181,
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[
66,
68,
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... | null |
null | [
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",",
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[
7,
17,
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],
[
18,
31,
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null | [
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"\n",
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",",
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[
26,
26,
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[
26,
26,
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null | [
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] | [
[
0,
0,
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null | [
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"-",
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"-",
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"-",
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",",
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... | [
[
28,
29,
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[
103,
104,
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[
31,
32,
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[
24,
26,
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],
[
135,
136,
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[
147,
148,
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],
[
158,
159,
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],
[
47,
47,
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],
[
... | null |
null | [
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",",
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] | [
[
5,
6,
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] | null |
null | [
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",",
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11,
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14,
15,
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null | [
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",",
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[
45,
67,
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],
[
68,
119,
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null | [
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",",
"dates",
",",
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... | [
[
57,
71,
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],
[
76,
82,
"match"
],
[
109,
124,
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]
] | null |
null | [
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",",
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[
48,
49,
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[
0,
0,
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[
17,
17,
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[
56,
56,
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[
90,
90,
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[
192,
192,
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],
[
251,
251,
"brand"
],
[
64,
74,
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[
108,
... | null |
null | [
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"–",
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",",
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",",
"Hal",
"Naragon",
",",
"had",
"grounded",
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"."
] | [
[
22,
23,
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] | null |
null | [
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",",
"Jingdezhen",
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] | [
[
3,
4,
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[
6,
7,
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] | null |
null | [
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",",
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",",
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[
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[
100,
104,
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[
5,
5,
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[
26,
26,
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[
11,
12,
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],
[
23,
24,
"health condition"
],
[
286,
287,
"health condition"
],
[
28,
28,
"role"
]... | null |
{
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45,
45,
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[
91,
92,
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[
104,
105,
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null | [
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",",
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] | [
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19,
22,
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null | [
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"THIRTEENTH",
"COURT",
"OF",
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"Suleiman",
"Qalawi",
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33,
37,
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53,
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[
62,
66,
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14,
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[
52,
53,
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[
65,
66,
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[
23,
24,
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[
... | null |
null | [
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"wife",
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"(",
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"."
] | [
[
3,
4,
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null | [
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28,
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[
27,
28,
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[
30,
34,
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null | [
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"do",
"when",
"you",
"get",
"an",
"extra",
"day",
"in",
"February",
"?",
"Bake",
"kale",
"chips",
"of",
"course",
"... | [
[
26,
26,
"match"
],
[
57,
57,
"match"
],
[
99,
99,
"match"
],
[
108,
108,
"match"
],
[
180,
180,
"match"
],
[
185,
185,
"match"
],
[
194,
194,
"match"
],
[
219,
219,
"match"
],
[
235,... | null |
null | [
"What",
"is",
"the",
"population",
"change",
"between",
"the",
"2010",
"and",
"2000",
"censuses",
"in",
"East",
"Flat",
"Rock",
"?",
"East",
"Flat",
"Rock",
"is",
"an",
"unincorporated",
"area",
"and",
"census",
"-",
"designated",
"place",
"(",
"CDP",
")",... | [
[
50,
52,
"match"
]
] | null |
null | [
"Decided",
"to",
"edit",
"something",
"a",
"little",
"different",
"from",
"my",
"normal",
"pixiv",
"art",
"so",
"I",
"did",
"this",
".",
"I",
"really",
"wanted",
"to",
"make",
"it",
"darker",
"and",
"bring",
"out",
"all",
"the",
"shadows",
".",
"MH",
"... | [
[
10,
10,
"website"
],
[
31,
31,
"character"
],
[
41,
41,
"event"
],
[
67,
68,
"artist"
],
[
70,
70,
"song"
]
] | null |
null | [
"A",
"re-posted",
"video",
"of",
"Petone",
"College",
"from",
"early",
"2013",
".",
"This",
"former",
"high",
"school",
"-",
"abandoned",
",",
"vandalized",
"and",
"the",
"victim",
"of",
"numerous",
"arson",
"attacks",
"-",
"has",
"finally",
"been",
"euthani... | [
[
4,
5,
"school"
],
[
7,
8,
"year"
],
[
11,
13,
"school"
],
[
15,
15,
"condition"
],
[
17,
17,
"condition"
],
[
22,
24,
"number"
],
[
35,
36,
"development"
]
] | null |
This dataset, used for the post-training stage of the GLiNER-BioMed models, was introduced in the paper GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition.
If you use the GLiNER-BioMed models or datasets in your work, please cite:
@article{yazdani2026gliner,
author = {Yazdani, Anthony and Stepanov, Ihor and Teodoro, Douglas},
title = {{GLiNER-BioMed}: a suite of efficient models for open biomedical named entity recognition},
journal = {Bioinformatics},
volume = {42},
number = {6},
pages = {btag322},
year = {2026},
month = jun,
issn = {1367-4811},
doi = {10.1093/bioinformatics/btag322},
url = {https://doi.org/10.1093/bioinformatics/btag322}
}