SINA-BERT: A Pre-trained Language Model for Analysis of Medical Texts in Persian
SINA-BERT is the first Persian medical language model pre-trained on BERT (Devlin et al.,2018). SINA-BERT utilizes pre-training on a large-scale corpus of medical contents including formal and informal texts collected from a variety of online resources in order to improve the performance on health-care related tasks.
Model Evaluation
SINA-BERT can be used for any Persian medical representative task. In our paper we have examined the followings:
categorization of medical questions,
medical sentiment analysis,
and medical question retrieval.
For each task, we have developed Persian annotated data sets, and learnt a representation for the data of each task especially complex and long medical questions. With the same architecture being used across tasks, SINA-BERT outperforms BERT-based models that were previously made available in the Persian language.
To read about the datasets and results, please refer to SINA-BERT paper: arXiv:2104.07613v1
from transformers import AutoConfig, AutoTokenizer, AutoModel
config = AutoConfig.from_pretrained("hooshafzar/SINA-BERT")
tokenizer = AutoTokenizer.from_pretrained("hooshafzar/SINA-BERT")
model = AutoModel.from_pretrained("hooshafzar/SINA-BERT")
Citation
bibtex
1@article{taghizadeh2021sina,
2 title={SINA-BERT: a pre-trained language model for analysis of medical texts in Persian},
3 author={Taghizadeh, Nasrin and Doostmohammadi, Ehsan and Seifossadat, Elham and Rabiee, Hamid R and Tahaei, Maedeh S},
4 journal={arXiv preprint arXiv:2104.07613},
5 year={2021}
6}
