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bert-based-Turkish-NER
Gorengoz/bert-based-Turkish-NER
bert-based-Turkish-NER is a token classification model from Gorengoz. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
Bu model "https://github.com/stefan-it/turkish-bert" base alınarak geliştirilmiş bir NER(Varlık ismi tanıma) modelidir.
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jul 25, 2024
- Updated
- Aug 8, 2024
- SHA
- ebd05ce3
Model card
From the Hugging Face model README
Bu model "https://github.com/stefan-it/turkish-bert" base alınarak geliştirilmiş bir NER(Varlık ismi tanıma) modelidir.
Eğitim ve validasyon verisi
Fine-tune işlemi için TDD-NER-202112-CC-001 veri seti kullanılmıştır.
@article{tur-etal-2003-milliyet, title={A statistical information extraction system for Turkish}, volume={9}, DOI={10.1017/S135132490200284X}, number={2}, journal={Natural Language Engineering}, publisher={Cambridge University Press}, author={Tür, Gökhan and Hakkani-Tür, Dilek and Oflazer, Kemal}, year={2003}, pages={181–210} }
Hiperparametreler
custom_labels = ["O","B-LOC","I-LOC","B-ORG","I-ORG","B-PER","I-PER"]
model_args = { "train_batch_size": 32, "eval_batch_size": 32, "num_train_epochs": 3, "seed":1, "save_steps": 625, "overwrite_output_dir": True, "output_dir": "/content/Model" }
Nasıl Kullanılacağı
from transformers import pipeline
pipe = pipeline("token-classification", model="Gorengoz/bert-based-Turkish-NER")
pipe("Entity X'in müşteri hizmetleri hızlı ve etkili, Entity Y'nin ürün kalitesi çok kötü.",aggregation_strategy = "simple"")
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Languages
- tr
- Created
- Jul 25, 2024
- Updated
- Aug 8, 2024