BioMed-RoBERTa-base is a language model based on the RoBERTa-base (Liu et. al, 2019) architecture. We adapt RoBERTa-base to 2.68 million scientific papers from the Semantic Scholar corpus via continued pretraining. This amounts to 7.55B tokens and 47GB of data. We use the full text of the papers in training, not just abstracts.

Specific details of the adaptive pretraining procedure can be found in Gururangan et. al, 2020.


BioMed-RoBERTa achieves competitive performance to state of the art models on a number of NLP tasks in the biomedical domain (numbers are mean (standard deviation) over 3+ random seeds)

Task Task Type RoBERTa-base BioMed-RoBERTa-base
RCT-180K Text Classification 86.4 (0.3) 86.9 (0.2)
ChemProt Relation Extraction 81.1 (1.1) 83.0 (0.7)
JNLPBA NER 74.3 (0.2) 75.2 (0.1)
BC5CDR NER 85.6 (0.1) 87.8 (0.1)
NCBI-Disease NER 86.6 (0.3) 87.1 (0.8)

More evaluations TBD.


If using this model, please cite the following paper:

 author = {Suchin Gururangan and Ana Marasović and Swabha Swayamdipta and Kyle Lo and Iz Beltagy and Doug Downey and Noah A. Smith},
 title = {Don't Stop Pretraining: Adapt Language Models to Domains and Tasks},
 year = {2020},
 booktitle = {Proceedings of ACL},
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