metadata
tags:
- flair
- token-classification
- sequence-tagger-model
language: fa
dataset:
- NSURL-2019
widget:
- text: >-
اولین نمایش این فیلمها روز دوشنبه 13 اردیبهشت و از ساعت 21 در موزه
سینماست.
metrics:
- f1
Persian NER Using Flair
This is the 7-class Named-entity recognition model for Persian that ships with Flair.
F1-Score: 90.33 (NSURL-2019)
Predicts NER tags:
| tag | meaning |
|---|---|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| DAT | date |
| TIM | time |
| PCT | percent |
| MON | Money |
Based on Flair embeddings and Pars-Bert.
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("PooryaPiroozfar/Flair-Persian-NER")
# make example sentence
sentence = Sentence("اولین نمایش این فیلمها روز دوشنبه 13 اردیبهشت و از ساعت 21 در موزه سینماست.")
# 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[4:8]: "روز دوشنبه 13 اردیبهشت" → DAT (1.0)
Span[10:12]: "ساعت 21" → TIM (1.0)
Span[13:15]: "موزه سینماست" → LOC (0.9999)
Results
- F-score (micro) 0.9033
- F-score (macro) 0.8976
- Accuracy 0.8277
By class:
precision recall f1-score support
ORG 0.9016 0.8667 0.8838 1523
LOC 0.9113 0.9305 0.9208 1425
PER 0.9216 0.9322 0.9269 1224
DAT 0.8623 0.7958 0.8277 480
MON 0.9665 0.9558 0.9611 181
PCT 0.9375 0.9740 0.9554 77
TIM 0.8235 0.7925 0.8077 53
micro avg 0.9081 0.8984 0.9033 4963
macro avg 0.9035 0.8925 0.8976 4963
weighted avg 0.9076 0.8984 0.9028 4963
samples avg 0.8277 0.8277 0.8277 4963