| --- |
| 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 |
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| 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 |
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|
