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GuiGel/
beto-uncased-flert-context-we-lstm-crf-meddocan

Name: beto-uncased-flert-context-we-lstm-crf-meddocan
Availability: InStock

flair

pytorch

token-classification

sequence-tagger-model

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Demo: How to use in Flair

Requires:

Flair (pip install flair)

python

from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("GuiGel/beto-uncased-flert-context-we-lstm-crf-meddocan")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# 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)