NLPclass/Named_entity_recognition_persian

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']}")