Biogpt Large

🚀 BioGPT

预训练语言模型在通用自然语言领域取得巨大成功后，在生物医学领域也受到了越来越多的关注。在通用语言领域的预训练语言模型的两大主要分支中，即BERT（及其变体）和GPT（及其变体），前者在生物医学领域得到了广泛研究，如BioBERT和PubMedBERT。虽然它们在各种判别式下游生物医学任务中取得了巨大成功，但缺乏生成能力限制了它们的应用范围。本文提出了BioGPT，这是一种在大规模生物医学文献上预训练的特定领域生成式Transformer语言模型。我们在六项生物医学自然语言处理任务上对BioGPT进行了评估，结果表明我们的模型在大多数任务上优于以往的模型。特别是，我们在BC5CDR、KD - DTI和DDI端到端关系提取任务中分别获得了44.98%、38.42%和40.76%的F1分数，在PubMedQA上获得了78.2%的准确率，创造了新的记录。我们在文本生成方面的案例研究进一步证明了BioGPT在生物医学文献方面的优势，能够为生物医学术语生成流畅的描述。

🚀 快速开始

文档中未提及快速开始相关的具体内容，若有需要可根据实际情况补充。

✨ 主要特性

• 特定领域生成式Transformer语言模型，预训练于大规模生物医学文献。
• 在六项生物医学自然语言处理任务上进行评估，多数任务表现优于以往模型。
• 在BC5CDR、KD - DTI和DDI端到端关系提取任务及PubMedQA上取得优异成绩。
• 能够为生物医学术语生成流畅描述。

📚 详细文档

模型信息

推理参数

推理时可设置的参数如下：

• max_new_tokens：最大新生成的词元数量，默认设置为50。

小工具示例

可输入文本进行推理，例如输入："COVID - 19 is" 。

📄 许可证

本项目采用MIT许可证。

📚 引用

如果您在研究中发现BioGPT很有用，请引用以下论文：

@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},
}