| # 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 the 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} | |
| } | |
| ``` | |