readme: # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
  
  # Abstract
  
  
  The impact of design choices on the performance
  of biomedical language models recently
  has been a subject for investigation. In
  this paper, we empirically study biomedical
  domain adaptation with large transformer models
  using different design choices. We evaluate
  the performance of our pretrained models
  against other existing biomedical language
  models in the literature. Our results show that
  we achieve state-of-the-art results on several
  biomedical domain tasks despite using similar
  or less computational cost compared to other
  models in the literature. Our findings highlight
  the significant effect of design choices on
  improving the performance of biomedical language
  models.
  
  # Model Description
  
  This model was pre-trained on PubMed Abstracts only with biomedical domain vocabulary for 500k steps with a batch size of 1024 on TPUv3-32 unit.
  
  Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.
  
  # Acknowledgment
  
  We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
  
  # Citation
  
  ```bibtex
  @inproceedings{alrowili-shanker-2021-biom,
  title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
  author = "Alrowili, Sultan and
  Shanker, Vijay",
  booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
  month = jun,
  year = "2021",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
  pages = "221--227",
  abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
  }
  ```