| --- |
| title: README |
| emoji: 🏃 |
| colorFrom: gray |
| colorTo: purple |
| sdk: static |
| pinned: false |
| license: mit |
| tags: |
| - oxford-legacy |
| --- |
| |
| # 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} |
| } |
| ``` |
| # Support |
| If this model helps your work, you can keep the project running with a one-off or monthly contribution: |
| https://github.com/sponsors/nlpie-research |
