--- license: lgpl-3.0 language: - fa base_model: - HooshvareLab/bert-base-parsbert-uncased --- # 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: 1) categorization of medical questions, 2) medical sentiment analysis, 3) 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](https://arxiv.org/pdf/2104.07613) - **Developed by:** HooshAfzar Salamat Team - **Language(s) (NLP):** Persian - **Finetuned from model:** [ParsBert](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) ### Model Sources [optional] - **Repository:** [GitHub](https://github.com/nasrin-taghizadeh/SinaBERT) - **Paper [optional]:** [arXive paper](https://arxiv.org/pdf/2104.07613) ## How to use ``` 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 @article{taghizadeh2021sina, title={SINA-BERT: a pre-trained language model for analysis of medical texts in Persian}, author={Taghizadeh, Nasrin and Doostmohammadi, Ehsan and Seifossadat, Elham and Rabiee, Hamid R and Tahaei, Maedeh S}, journal={arXiv preprint arXiv:2104.07613}, year={2021} } ```