Studies

Biomedical pre-trained language models (BioPLMs) have been achieving state-of-the-art results for various biomedical text mining tasks. However, prevailing fine-tuning approaches naively train BioPLMs on targeted datasets without considering the class distributions. This is problematic, especially with dealing with imbalanced biomedical gold-standard datasets for named entity recognition (NER). Regardless of the high-performing SOTA fine-tuned NER models, they are biased towards other (O) tags ...