| | --- |
| | title: README |
| | emoji: 🏃 |
| | colorFrom: gray |
| | colorTo: purple |
| | sdk: static |
| | pinned: false |
| | license: mit |
| | tags: |
| | - oxford-legacy |
| | --- |
| | |
| | # 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 model's small hidden dimension size, it uses random initialisation. |
| |
|
| | # 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 |
| |
|
