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---
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}
}
```
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