BiomedNLP-BiomedBERT-large-uncased-abstract

Model overview

BiomedNLP-BiomedBERT-large-uncased-abstract is published by microsoft as a fill-mask model. The captured configuration identifies BertForMaskedLM with model type bert. According to the model card, it is MSR BiomedBERT-large trained on abstracts only, and the recorded license is mit.

Recorded capabilities

Use cases in the source record

• Biomedical fill-mask experiments on abstract text through the snapshot-listed inference path, subject to refreshed availability.
• Domain-adaptation research that follows the card's from-scratch biomedical pretraining account and cited fine-tuning paper.

Limitations and unknowns

• No parameter count was extracted from this record.
• No evaluation results were extracted from this record.
• No context-window value was extracted from this record.
• Provider state is historical snapshot data and should be refreshed before being presented as current.

Source and provenance

Source: microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract

Captured: Unknown. Processed: 2026-09-07T19:34:51.715297+00:00.

MSR BiomedBERT-large (abstracts only) This model was previously named "PubMedBERT large (abstracts)" . You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract" or update your transformers library to version 4.22+ if you need to refer to the old name. Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models…