---
title: README
emoji: 🏃
colorFrom: gray
colorTo: purple
sdk: static
pinned: false
license: mit
---

# Model Description
BioMobileBERT is the result of training the [MobileBERT-uncased](https://huggingface.co/google/mobilebert-uncased) model in a continual learning scenario 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 [MobileBERT-uncased](https://huggingface.co/google/mobilebert-uncased) model available on Huggingface.

# Architecture
MobileBERT uses a 128-dimensional embedding layer followed by 1D convolutions to up-project its output to the desired hidden dimension expected by the transformer blocks. For each of these blocks, MobileBERT uses linear down-projection at the beginning of the transformer block and up-projection at its end, followed by a residual connection originating from the input of the block before down-projection. Because of these linear projections, MobileBERT can reduce the hidden size and hence the computational cost of multi-head attention and feed-forward blocks. This model additionally incorporates up to four feed-forward blocks in order to enhance its representation learning capabilities. Thanks to the strategically placed linear projections, a 24-layer MobileBERT (which is used in this work) has around 25M parameters.

# Citation
If you use this model, please consider citing the following paper:

```bibtex
@article{rohanian2023effectiveness,
  title={On the effectiveness of compact biomedical transformers},
  author={Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A},
  journal={Bioinformatics},
  volume={39},
  number={3},
  pages={btad103},
  year={2023},
  publisher={Oxford University Press}
}
```