BiomedBERT - Domain-Specific Biomedical Language Model

About Model

BiomedBERT (formerly PubMedBERT (abstracts + full text)) is a domain-specific biomedical language model developed by Microsoft. Unlike general NLP models that start with broad-domain corpora, BiomedBERT is pre-trained from scratch using PubMed abstracts and full-text articles from PubMed Central, enabling superior performance on biomedical NLP tasks. Key capabilities of BiomedBERT include: 1. Biomedical text classification 2. Named entity recognition (NER) for medical terminology 3. Question answering in the medical domain 4. Biomedical language inference and reasoning This model outperforms general-domain language models on various benchmarks and currently holds the top score on the Biomedical Language Understanding and Reasoning Benchmark. BiomedBERT is intended for research purposes only and should not be used for clinical decision-making. It serves as a valuable tool for biomedical AI researchers, medical text mining, and healthcare-related NLP applications.