  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.
