readme: ---
  language:
  - en
  license: apache-2.0
  ---
  
  
  Bioformer-16L is a lightweight BERT model for biomedical text mining. Bioformer-16L uses a biomedical vocabulary and is pre-trained from scratch only on biomedical domain corpora. Our experiments show that Bioformer-16L is 2x as fast as BERT-base, and achieves comparable or even better performance than BioBERT/PubMedBERT on downstream NLP tasks.
  
  Bioformer-16L has 16 layers (transformer blocks) with a hidden embedding size of 384, and the number of self-attention heads is 8. Its total number of parameters is about 42 million.
  
  **The usage of Bioformer-16L is the same as a standard BERT model. The documentation of BERT can be found [here](https://huggingface.co/docs/transformers/model_doc/bert).**
  
  ## Vocabulary of Bioformer
  Bioformer-16L uses a cased WordPiece vocabulary trained from a biomedical corpus, which included all PubMed abstracts (33 million, as of Feb 1, 2021) and 1 million PMC full-text articles. PMC has 3.6 million articles but we down-sampled them to 1 million such that the total size of PubMed abstracts and PMC full-text articles are approximately equal. To mitigate the out-of-vocabulary issue and include special symbols (e.g. male and female symbols) in biomedical literature, we trained Bioformer’s vocabulary from the Unicode text of the two resources. The vocabulary size of Bioformer-16L is 32768 (2^15), which is similar to that of the original BERT.
  
  ## Pre-training of Bioformer
  Bioformer-16L was pre-trained from scratch on the same corpus as the vocabulary (33 million PubMed abstracts + 1 million PMC full-text articles). For the masked language modeling (MLM) objective, we used whole-word masking with a masking rate of 15%. There are debates on whether the next sentence prediction (NSP) objective could improve the performance on downstream tasks. We include it in our pre-training experiment in case the prediction of the next sentence is needed by end-users. Sentence segmentation of all training text was performed using [SciSpacy](https://allenai.github.io/scispacy/).
  
  Pre-training of Bioformer-16L was performed on a single Cloud TPU device (TPUv2, 8 cores, 8GB memory per core). The maximum input sequence length was fixed to 512, and the batch size was set to 256. We pre-trained Bioformer-16L for 2 million steps, which took about 11 days.
  
  ## Link
  [Bioformer-8L](https://huggingface.co/bioformers/bioformer-8L)
  
  ## Acknowledgment
  Training and evaluation of Bioformer-16L is supported by the Google TPU Research Cloud (TRC) program, the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health (NIH), and NIH/NLM grants LM012895 and 1K99LM014024-01.
  
  ## Questions
  If you have any questions, please submit an issue here: https://github.com/WGLab/bioformer/issues
  
  You may also send an email to Li Fang (fangli9@mail.sysu.edu.cn, https://fangli80.github.io/).