Persian NER in Flair
This is the universal Named-entity recognition model for Persian that ships with Flair.
F1-Score: 84.03 (NSURL-2019)
Predicts NER tags:
tag | meaning |
---|---|
PER | person name |
LOC | location name |
ORG | organization name |
DAT | date |
TIM | time |
PCT | percent |
MON | Money |
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("hamedkhaledi/persain-flair-ner")
# make example sentence
sentence = Sentence("آخرین مقام برجسته ژاپنی که پس از انقلاب 57 تاکنون به ایران سفر کرده است شینتارو آبه است.")
tagger.predict(sentence)
#print result
print(sentence.to_tagged_string())
This yields the following output:
آخرین مقام برجسته ژاپنی که پس از انقلاب 57 <B-DAT> تاکنون به ایران <B-LOC> سفر کرده است شینتارو <B-PER> آبه <I-PER> است .
Results
- F-score (micro) 0.8403
- F-score (macro) 0.8656
- Accuracy 0.7357
By class:
precision recall f1-score support
LOC 0.8789 0.8589 0.8688 4083
ORG 0.8390 0.7653 0.8005 3166
PER 0.8395 0.8169 0.8280 2741
DAT 0.8648 0.7957 0.8288 1150
MON 0.9758 0.9020 0.9374 357
TIM 0.8500 0.8193 0.8344 166
PCT 0.9615 0.9615 0.9615 156
micro avg 0.8616 0.8200 0.8403 11819
macro avg 0.8871 0.8456 0.8656 11819
weighted avg 0.8613 0.8200 0.8400 11819
samples avg 0.7357 0.7357 0.7357 11819
Loss: 0.06893542408943176'
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