The advancement of natural language processing (NLP) in biology hinges on models' ability to interpret intricate biomedical literature. Traditional models often struggle with the complex and domain-specific language in this field. In this paper, we present BioMamba, a pre-trained model specifically designed for biomedical text mining. BioMamba builds upon the Mamba architecture and is pre-trained on an extensive corpus of biomedical literature. Our empirical studies demonstrate that BioMamba significantly outperforms models like BioBERT and general-domain Mamba across various biomedical tasks. For instance, BioMamba achieves a 100 times reduction in perplexity and a 4 times reduction in cross-entropy loss on the BioASQ test set. We provide an overview of the model architecture, pre-training process, and fine-tuning techniques. Additionally, we release the code and trained model to facilitate further research.

本研究解决了生物医学文献中复杂语言解读的难题。提出的BioMamba模型专为生物医学文本挖掘设计，通过在广泛的生物医学文献上进行预训练，显著超越了现有模型如BioBERT。实验结果表明，BioMamba在BioASQ测试集上实现了100倍的困惑度降低和4倍的交叉熵损失降低。

BioMamba：一种利用Mamba的预训练生物医学语言表示模型