Biogpt Large

🚀 BioGPT

BioGPT is a domain - specific generative Transformer language model pre - trained on large - scale biomedical literature, which shows excellent performance in various biomedical natural language processing tasks.

🚀 Quick Start

This section will be updated if there are any start - up instructions in the future.

✨ Features

Pre - trained language models have gained increasing attention in the biomedical domain, thanks to their remarkable success in the general natural language domain. In the general language domain, there are two main branches of pre - trained language models: BERT (and its variants) and GPT (and its variants). The former has been extensively studied in the biomedical domain, like BioBERT and PubMedBERT. Although they have achieved great success in a variety of discriminative downstream biomedical tasks, their lack of generation ability limits their application scope.

BioGPT, a domain - specific generative Transformer language model, is pre - trained on large - scale biomedical literature. It is evaluated on six biomedical natural language processing tasks and outperforms previous models on most tasks. Specifically, it achieves F1 scores of 44.98%, 38.42%, and 40.76% on BC5CDR, KD - DTI, and DDI end - to - end relation extraction tasks respectively, and an accuracy of 78.2% on PubMedQA, setting a new record. A case study on text generation further demonstrates BioGPT's advantage in generating fluent descriptions for biomedical terms from biomedical literature.

📄 License

This project is licensed under the MIT license.

📚 Documentation

Dataset

• Pubmed

Library

• transformers

Pipeline Tag

• text - generation

Tags

• medical

Inference Parameters

Widget

The widget takes the text "COVID - 19 is" as an input example.

📚 Citation

If you find BioGPT useful in your research, please cite the following paper:

@article{10.1093/bib/bbac409,
    author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie - Yan},
    title = "{BioGPT: generative pre - trained transformer for biomedical text generation and mining}",
    journal = {Briefings in Bioinformatics},
    volume = {23},
    number = {6},
    year = {2022},
    month = {09},
    abstract = "{Pre - trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre - trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain - specific generative Transformer language model pre - trained on large - scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98\%, 38.42\% and 40.76\% F1 score on BC5CDR, KD - DTI and DDI end - to - end relation extraction tasks, respectively, and 78.2\% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.}",
    issn = {1477 - 4054},
    doi = {10.1093/bib/bbac409},
    url = {https://doi.org/10.1093/bib/bbac409},
    note = {bbac409},
    eprint = {https://academic.oup.com/bib/article - pdf/23/6/bbac409/47144271/bbac409.pdf},
}