WGLab/Bioformer

Bioformer: an efficient BERT model for biomedical text mining

Bioformer: an efficient BERT model for biomedical text mining

Bioformer is a lightweight BERT model pretrained from biomedical Literature. We pretrained two Bioformer models, Bioformer-8L and Bioformer-16L. Both models were pretrained on all PubMed abstracts (as of Jan 2021) and 1 million subsampled PubMed Central full-text articles. We used the original implementation of BERT to train the model. Bioformer models have the following features:

• Accurate. Bioformer achieves comparable or even better performance than BioBERT/PubMedBERT on downstream NLP tasks. A detailed evaluation is here.
• Smaller model size. Bioformer-8L and Bioformer-16L reduced the model size by 60% compared with BERT-Base/BioBERT-Base/PubMedBERT.
• Fast and memory efficient. Bioformer-8L is 3X as fast as PubMedBERT, and Bioformer-16L is 2X as fast as PubMedBERT.
• Biomedical vocabulary. Bioformer uses a biomedical vocabulary of 32768 tokens, which was trained from PubMed abstracts and PubMed Central full-text articles. Bioformer is able to encode some special unicode symbols that are not in the original BERT vocabulary.

Download

Pytorch checkpoint

Pretrained model weights of Bioformer-8L and Bioformer-16L are available on HuggingFace (Bioformer-8L, and Bioformer-16L)

You can easily use Bioformer with the transformers library.

Acknowledgment

Pretraining of Bioformer is partly supported by the Google TPU Research Cloud (TRC) program.

Citation

Fang L, Chen Q, Wei C-H, Lu Z, Wang K: Bioformer: an efficient transformer language model for biomedical text mining. arXiv preprint arXiv:2302.01588 (2023). DOI: https://doi.org/10.48550/arXiv.2302.01588

@ARTICLE{fangli2023bioformer,
       author = {{Fang}, Li and {Chen}, Qingyu and {Wei}, Chih-Hsuan and {Lu}, Zhiyong and {Wang}, Kai},
        title = "{Bioformer: an efficient transformer language model for biomedical text mining}",
      journal = {arXiv preprint arXiv:2302.01588},
         year = {2023}
}