Published with ❤️ from londogard.

Swedish NER in Flair (SUC 3.0)

F1-Score: 85.6 (SUC 3.0)

Predicts 8 tags:

Tag Meaning
PRS person name
ORG organisation name
TME time unit
WRK building name
LOC location name
EVN event name
MSR measurement unit
OBJ object (like "Rolls-Royce" is a object in the form of a special car)

Based on Flair embeddings and LSTM-CRF.


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("londogard/flair-swe-ner")
# make example sentence
sentence = Sentence("Hampus bor i Skåne och har levererat denna model idag.")
# 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 [0]: "Hampus"   [− Labels: PRS (1.0)]
Span [3]: "Skåne"   [− Labels: LOC (1.0)]
Span [9]: "idag"   [− Labels: TME(1.0)]

So, the entities "Hampus" (labeled as a PRS), "Skåne" (labeled as a LOC), "idag" (labeled as a TME) are found in the sentence "Hampus bor i Skåne och har levererat denna model idag.".


Please mention londogard if using this models.

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