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[ "In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "." ]
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[ "In", "1990", ",", "he", "published", "the", "last", "Rabbit", "novel", ",", "Rabbit", "at", "Rest", ",", "which", "won", "the", "Pulitzer", "Prize", "and", "the", "National", "Book", "Critics", "Circle", "Award", "." ]
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CrossNER LITERATURE Dataset

An NER dataset for cross-domain evaluation, read more.
This split contains labeled data from the LITERATURE domain.

Features

  • tokens: A list of words in the sentence
  • ner_tags: A list of NER labels (as integers) corresponding to each token

Label Mapping

The dataset uses the following 25 labels:

Index Label
0 O
1 B-book
2 I-book
3 B-writer
4 I-writer
5 B-award
6 I-award
7 B-poem
8 I-poem
9 B-event
10 I-event
11 B-magazine
12 I-magazine
13 B-literarygenre
14 I-literarygenre
15 B-country
16 I-country
17 B-person
18 I-person
19 B-location
20 I-location
21 B-organisation
22 I-organisation
23 B-misc
24 I-misc

Usage 

from datasets import load_dataset

dataset = load_dataset("eesuhn/crossner-literature")
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