readme: ---
  title: README
  emoji: 🏃
  colorFrom: gray
  colorTo: purple
  sdk: static
  pinned: false
  ---
  
  # Model Description
  DistilBioBERT is a distilled version of the [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2?text=The+goal+of+life+is+%5BMASK%5D.) model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset. 
  
  # Distillation Procedure
  This model uses a simple distillation technique, which tries to align the output distribution of the student model with the output distribution of the teacher based on the MLM objective. In addition, it optionally uses another alignment loss for aligning the last hidden state of the student and teacher.
  
  # Initialisation
  Following [DistilBERT](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.), we initialise the student model by taking weights from every other layer of the teacher.
  
  # 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:
  
  ```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}
  }
  ```