---
library_name: transformers
license: mit
language:
- fa
pipeline_tag: token-classification
---
Named entity recognition On Persian dataset

traindataset=20484 persian sentense 

valdataset=2561

AutoTokenizer=HooshvareLab/bert-fa-base-uncased

ner_tags=
['O', 'B-pro',
'I-pro',
'B-pers', 
'I-pers', 
'B-org', 
'I-org',
'B-loc',
'I-loc', 
'B-fac',
'I-fac', 
'B-event',
'I-event']

training_args=
    learning_rate=2e-5,
    
    per_device_train_batch_size=16,
    
    per_device_eval_batch_size=16,
    
    num_train_epochs=4,
    
    weight_decay=0.01
    

Training Loss=0.001000

sample1:
  'entity': 'B-loc',
  'score': 0.9998902,
  'index': 2,
  'word': 'تهران',

sample2:
  'entity': 'B-pers',
  'score': 0.99988234,
  'index': 2,
  'word': 'عباس',


for use this model:

    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="NLPclass/Named_entity_recognition_persian")

    sentence = ""

    predicted_ner = pipe(sentence)
    
    for entity in predicted_ner:
    
        print(f"Entity: {entity['word']}, Label: {entity['entity']}")