readme: ---
  title: README
  emoji: 🏃
  colorFrom: gray
  colorTo: purple
  sdk: static
  pinned: false
  ---
  
  # Model Description
  BioTinyBERT is the result of training the [TinyBERT](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) 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 [TinyBERT](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) model available on Huggingface.
  
  # Architecture
  This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. 
  
  # Citation
  If you use this model, please consider citing the following paper:
  
  ```bibtex
  @misc{https://doi.org/10.48550/arxiv.2209.03182,
    doi = {10.48550/ARXIV.2209.03182},
    url = {https://arxiv.org/abs/2209.03182},
    author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A.},
    keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, 68T50},
    title = {On the Effectiveness of Compact Biomedical Transformers},
    publisher = {arXiv},
    year = {2022}, 
    copyright = {arXiv.org perpetual, non-exclusive license}
  }
  ```
  