Biomedical Entity Recognition Using a Small Language Model
DOI:
https://doi.org/10.32870/recibe.v14i3.448Keywords:
Small Language Model, Selective State Space Models, Biomedical Named Entity RecognitionAbstract
This research proposes developing a computationally efficient Small Language Model (SLM) for Biomedical Named Entity Recognition (NER). By fine-tuning a general-domain pretrained SLM (based on selective state space models, SSMs) on specialized biomedical texts, we aim to achieve performance comparable to larger models. Our approach addresses limitations of Transformer-based models like BioBERT (110M+ parameters) by combining linear-time sequence processing with biomedical domain adaptation techniques.References
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