Named Entity Recognition
Collection
The dataset in CONLL format that can easily be used in the HF Token Classification Course.
•
4 items
•
Updated
id
stringlengths 1
4
| words
sequencelengths 1
94
| ner_tags
sequencelengths 1
94
|
---|---|---|
0 | [
"The",
"back",
"of",
"my",
"jersey",
"says",
"\"",
"The",
"Poet",
"'",
"because",
"growing",
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"the",
"most",
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"they",
"do",
"provide",
"air-conditionting",
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"Henga",
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3 | [
"It",
"costs",
"us",
"so",
"much",
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"to",
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"to",
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"to",
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"Tulsa",
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5 | [
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6 | [
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"cheesecloth",
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"or",
"an",
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"clean",
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7 | [
"At",
"the",
"moment",
"Shalev",
"is",
"in",
"Levico",
"Terme",
",",
"Trento",
",",
"Italy",
"for",
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8 | [
"The",
"big",
"hit",
"of",
"the",
"evening",
",",
"Wikinews",
"observed",
"from",
"the",
"long",
"line",
",",
"was",
"the",
"Polaroid-room",
"where",
"attendees",
"could",
"wear",
"a",
"Warhol-like",
"wig",
"or",
"don",
"crazy",
"glasses",
"and",
"have",
"their",
"own",
"Polaroid",
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9 | [
"This",
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"many",
"ways",
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"even",
"sometimes",
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"initial",
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10 | [
"Apply",
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"oil",
"."
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11 | [
"What",
"about",
"the",
"adage",
",",
"“",
"Those",
"who",
"forget",
"history",
"are",
"doomed",
"to",
"repeat",
"it",
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12 | [
"In",
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"past",
"few",
"days",
"in",
"Italy",
",",
"several",
"municipalities",
"have",
"started",
"asking",
"religious",
"schools",
"to",
"pay",
"taxes",
"for",
"property",
"and",
"local",
"services",
",",
"despite",
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"of",
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13 | [
"And",
"for",
"me",
",",
"what",
"I",
"want",
"the",
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"to",
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14 | [
"They",
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"not",
"run",
"that",
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"especially",
"to",
"the",
"outer",
"towns",
"like",
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"Arrow",
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15 | [
"No",
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"in",
"2010",
"."
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16 | [
"Getting",
"the",
"Material",
"Right"
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17 | [
"Also",
"see",
"where",
"all",
"the",
"other",
"teams",
"are",
"at",
"as",
"well",
"."
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18 | [
"Re-evaluate",
"your",
"list",
"halfway",
"through",
"your",
"day",
"to",
"rank",
"your",
"items",
"based",
"on",
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19 | [
"therefore",
",",
"it",
"may",
"be",
"helpful",
"to",
"focus",
"your",
"efforts",
"on",
"opening",
"your",
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20 | [
"What",
"inspired",
"you",
"to",
"create",
"a",
"cocktail",
"after",
"the",
"santorum",
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21 | [
"The",
"only",
"way",
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"to",
"travel",
"to",
"the",
"Chathams",
"is",
"via",
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22 | [
"Built",
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"by",
"Shah",
"Abbas",
"I.=",
"and",
"designed",
"by",
"his",
"chief",
"architect",
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"Sheikh",
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23 | [
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"or",
"air-port/train",
"station",
"services",
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"there",
"are",
"several",
"local",
"taxi",
"companies",
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24 | [
"It",
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"so",
"that",
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"be",
"easier",
"to",
"learn",
"and",
"speak",
"with",
"[",
"Example",
":",
"Do",
"n't",
"use",
"oh",
"as",
"e",
",",
"hello",
"as",
"llo",
",",
"and",
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"c",
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25 | [
"If",
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"own",
"a",
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26 | [
"Tips"
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27 | [
"As",
"you",
"mark",
"things",
"off",
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"you",
"will",
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"motivated",
"to",
"continue",
"doing",
"so",
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"and",
"seeing",
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"on",
"a",
"regular",
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28 | [
"\"",
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"happened",
"an",
"hour",
"later",
"it",
"would",
"have",
"been",
"much",
"worse",
",",
"\"",
"Khaled",
"Al-Maeena",
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"editor",
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"large",
"at",
"the",
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29 | [
"\"",
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"hanging",
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"the",
"celebrities",
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"and",
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"the",
"same",
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30 | [
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31 | [
"Name",
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"Mississippi",
"River",
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32 | [
"You",
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"you",
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34 | [
"From",
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"Oklahoma",
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"\"",
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"try",
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36 | [
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"the",
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"of",
"dedicated",
"men",
"and",
"women",
"at",
"Johnson",
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"Center",
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"Texas",
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"and",
"I",
"m",
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"disappointed",
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37 | [
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"We",
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40 | [
"\"",
"One",
"of",
"the",
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"that",
"iodine",
"is",
"going",
"down",
"is",
"because",
"people",
"are",
"taking",
"that",
"good",
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"eating",
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41 | [
"Be",
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"to",
"your",
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43 | [
"Like",
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"in",
"the",
"so-called",
"\"",
"rust",
"belt",
"“",
",",
"Cleveland",
"has",
"endured",
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"pains",
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"it",
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44 | [
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45 | [
"Australian",
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46 | [
"\"",
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"found",
"all",
"around",
"the",
"main",
"plaza",
"."
] | [
13,
14,
14,
14,
0,
13,
14,
14,
0,
0,
0,
0,
13,
14,
14,
0
] |
99 | [
"Bos",
"obtained",
"his",
"doctorate",
"from",
"the",
"University",
"of",
"Copenhagen",
"."
] | [
11,
0,
1,
2,
0,
9,
10,
10,
10,
0
] |
The GUM corpus was collected and annotated at Georgetown University. For more information, see the LICENSE.
['O',
'B-abstract', 'I-abstract',
'B-animal', 'I-animal',
'B-event', 'I-event',
'B-object', 'I-object',
'B-organization', 'I-organization',
'B-person', 'I-person',
'B-place', 'I-place',
'B-plant', 'I-plant',
'B-quantity', 'I-quantity',
'B-substance', 'I-substance',
'B-time', 'I-time']
Number of sentences in the train set: 2495
Label count in train set:
Label | Count |
---|---|
O | 20460 |
I-abstract | 4687 |
I-event | 2707 |
I-place | 2212 |
B-abstract | 2002 |
B-person | 1920 |
I-person | 1866 |
I-object | 1732 |
B-place | 1150 |
B-object | 1017 |
B-event | 738 |
I-time | 663 |
I-organization | 552 |
I-substance | 458 |
B-time | 401 |
B-organization | 397 |
B-substance | 278 |
I-quantity | 203 |
I-plant | 166 |
B-plant | 144 |
B-animal | 141 |
I-animal | 120 |
B-quantity | 97 |
Number of sentences in the test set: 1000
Label count in test set:
Label | Count |
---|---|
O | 8543 |
I-abstract | 2048 |
I-event | 934 |
I-place | 926 |
B-person | 823 |
B-abstract | 798 |
I-object | 782 |
I-person | 685 |
B-place | 469 |
B-object | 420 |
B-event | 315 |
I-organization | 278 |
I-time | 242 |
B-organization | 192 |
I-substance | 183 |
B-time | 179 |
B-substance | 95 |
I-quantity | 77 |
B-plant | 62 |
I-plant | 56 |
B-quantity | 44 |
I-animal | 43 |
B-animal | 42 |
@Article{Zeldes2017,
author = {Amir Zeldes},
title = {The {GUM} Corpus: Creating Multilayer Resources in the Classroom},
journal = {Language Resources and Evaluation},
year = {2017},
volume = {51},
number = {3},
pages = {581--612},
doi = {http://dx.doi.org/10.1007/s10579-016-9343-x}
}