| # Model Description | |
| TinyBioBERT is a distilled version of the [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2?text=The+goal+of+life+is+%5BMASK%5D.) which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset. | |
| # Distillation Procedure | |
| This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the student to align the attention maps and the hidden states of the student with those of the teacher. | |
| # Architecture and Initialisation | |
| This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. Due to the small hidden dimension size used in this model, it uses a random initialisation. | |
| # 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} | |
| } | |
| ``` |