Clinical ModernBERT: An efficient and long context encoder for biomedical text
This work addresses the need for domain-specific language models in biomedical and clinical NLP, though it is incremental as it adapts existing architectural innovations to this domain.
The authors tackled the problem of generating efficient and semantically rich representations for long biomedical and clinical texts by introducing Clinical ModernBERT, a transformer encoder pretrained on diverse biomedical data, which achieved strong performance on clinical NLP benchmarks.
We introduce Clinical ModernBERT, a transformer based encoder pretrained on large scale biomedical literature, clinical notes, and medical ontologies, incorporating PubMed abstracts, MIMIC IV clinical data, and medical codes with their textual descriptions. Building on ModernBERT the current state of the art natural language text encoder featuring architectural upgrades such as rotary positional embeddings (RoPE), Flash Attention, and extended context length up to 8,192 tokens our model adapts these innovations specifically for biomedical and clinical domains. Clinical ModernBERT excels at producing semantically rich representations tailored for long context tasks. We validate this both by analyzing its pretrained weights and through empirical evaluation on a comprehensive suite of clinical NLP benchmarks.