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---
title: README
emoji: 🏃
colorFrom: gray
colorTo: purple
sdk: static
pinned: false
license: mit
---
# 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
@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}
}
``` |