readme: ---
  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](https://github.com/flairNLP/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](https://www.aclweb.org/anthology/C18-1139/) and Pars-Bert.
  
  ---
  
  ### Demo: How to use in Flair
  
  Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
  
  ```python
  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
                
  ```