readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: 
  -  fa
  datasets:
  - ontonotes
  widget:
  - text: "مقامات مصری به خاطر حفظ ثبات کشور در منطقهای پرآشوب بر خود میبالند ، در حالی که این کشور در طول ۱۶ سال گذشته تنها هشت سال آنرا بدون اعلام وضعیت اضطراری سپری کرده است ."
  ---
  
  ## Persian Universal Part-of-Speech Tagging in Flair
  
  This is the universal part-of-speech tagging model for Persian that ships with [Flair](https://github.com/flairNLP/flair/).
  
  F1-Score: **97,73** (UD_PERSIAN)
  
  Predicts Universal POS tags:
  
  | **tag**                        | **meaning** |
  |:---------------------------------:|:-----------:|
  |ADJ |  adjective |
  |   ADP |  adposition |
  |   ADV |  adverb |
  |   AUX |  auxiliary |
  |   CCONJ |  coordinating conjunction |
  |   DET |  determiner |
  |   INTJ |  interjection |
  |   NOUN |  noun |
  |   NUM |  numeral |
  |   PART |  particle |
  |   PRON |  pronoun |
  |   PUNCT |  punctuation |
  |   SCONJ |  subordinating conjunction |
  |   VERB |  verb |
  |   X |  other |
  
  ---
  
  ### 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("hamedkhaledi/persain-flair-upos")
  
  # make example sentence
  sentence = Sentence("مقامات مصری به خاطر حفظ ثبات کشور در منطقهای پرآشوب بر خود میبالند .")
  
  tagger.predict(sentence)
  #print result
  print(sentence.to_tagged_string())
  ```
  
  This yields the following output:
  ```
  مقامات <NOUN> مصری <ADJ> به <ADP> خاطر <NOUN> حفظ <NOUN> ثبات <NOUN> کشور <NOUN> در <ADP> منطقهای <NOUN> پرآشوب <ADJ> بر <ADP> خود <PRON> میبالند <VERB> . <PUNCT>
  ```
  
  ---
  
  ### Results
  - F-score (micro) 0.9773
  - F-score (macro) 0.9461
  - Accuracy 0.9773
  
  ```
  By class:
                precision    recall  f1-score   support
  
          NOUN     0.9770    0.9849    0.9809      6420
           ADP     0.9947    0.9916    0.9932      1909
           ADJ     0.9342    0.9128    0.9234      1525
         PUNCT     1.0000    1.0000    1.0000      1365
          VERB     0.9840    0.9711    0.9775      1141
         CCONJ     0.9912    0.9937    0.9925       794
           AUX     0.9622    0.9799    0.9710       546
          PRON     0.9751    0.9865    0.9808       517
         SCONJ     0.9797    0.9757    0.9777       494
           NUM     0.9948    1.0000    0.9974       385
           ADV     0.9343    0.9033    0.9185       362
           DET     0.9773    0.9711    0.9742       311
          PART     0.9916    1.0000    0.9958       237
          INTJ     0.8889    0.8000    0.8421        10
             X     0.7143    0.6250    0.6667         8
  
     micro avg     0.9773    0.9773    0.9773     16024
     macro avg     0.9533    0.9397    0.9461     16024
  weighted avg     0.9772    0.9773    0.9772     16024
   samples avg     0.9773    0.9773    0.9773     16024
  
  Loss: 0.12471389770507812
  
  ```