Model Description

BioDistilBERT-cased was developed by training the DistilBERT-cased model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset.

Initialisation

We initialise our model with the pre-trained checkpoints of the DistilBERT-cased model available on Huggingface.

Architecture

In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 28996. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million parameters.

Citation

If you use this model, please consider citing the following paper:

@article{rohanian2023effectiveness,
  title={On the effectiveness of compact biomedical transformers},
  author={Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A},
  journal={Bioinformatics},
  volume={39},
  number={3},
  pages={btad103},
  year={2023},
  publisher={Oxford University Press}
}

Support

If this model helps your work, you can keep the project running with a one-off or monthly contribution:
https://github.com/sponsors/nlpie-research

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Collection including nlpie/bio-distilbert-cased

Compact Biomedical Models

Collection

This collection contains the models from the "On the Effectiveness of Compact Biomedical Transformers" • 9 items • Updated Dec 17, 2025 • 2