File size: 1,098 Bytes
bc737db 35f3b0c 3060464 33aec36 0bd1a97 33aec36 0bd1a97 33aec36 0bd1a97 3060464 0bd1a97 33aec36 0bd1a97 33aec36 0bd1a97 33aec36 0bd1a97 33aec36 0bd1a97 33aec36 0bd1a97 33aec36 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
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']}") |