acuvity/flair_en_ner-fast

English NER in Flair

This is the fast and small 4-class NER model for English that ships with Flair.

Model	Size	Type	Language	Dataset	F1 Score (%)
ner-fast	~ 240 mb	NER (4-class)	English	CoNLL-03	92.75

Predicts 4 tags:

tag	meaning
PER	person name
LOC	location name
ORG	organization name
MISC	other name

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Demo: How to use in Flair

Requires: Flair (pip install flair)

from flair.data import Sentence
from flair.models import SequenceTagger

# load tagger
tagger = SequenceTagger.load("acuvity/flair_ner-fast")

# make example sentence
sentence = Sentence("George Washington went to Washington")

# predict NER tags
tagger.predict(sentence)

# print sentence
print(sentence)

# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
    print(entity)

This yields the following output:

Span [1,2]: "George Washington"   [− Labels: PER (1.0)]
Span [5]: "Washington"   [− Labels: LOC (1.0)]

So, the entities "George Washington" (labeled as a person) and "Washington" (labeled as a location) are found in the sentence "George Washington went to Washington".