README.md

metadata

tags:
  - flair
  - token-classification
  - sequence-tagger-model
language: sv
datasets:
  - SUC 3.0
widget:
  - text: Hampus bor i Skåne och har levererat denna model idag.

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.

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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("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.".

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Please mention londogard if using this models.