| --- |
| title: README |
| emoji: 🏃 |
| colorFrom: gray |
| colorTo: purple |
| sdk: static |
| pinned: false |
| license: mit |
| tags: |
| - oxford-legacy |
| --- |
| |
| # Model Description |
| CompactBioBERT 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.) model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset. |
|
|
| # Distillation Procedure |
| This model has the same overall architecture as [DistilBioBERT](https://huggingface.co/nlpie/distil-biobert) with the difference that here we combine the distillation approaches of DistilBioBERT and [TinyBioBERT](https://huggingface.co/nlpie/tiny-biobert). We utilise the same initialisation technique as in [DistilBioBERT](https://huggingface.co/nlpie/distil-biobert), and apply a layer-to-layer distillation with three major components, namely, MLM, layer, and output distillation. |
|
|
| # Initialisation |
| Following [DistilBERT](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.), we initialise the student model by taking weights from every other layer of the teacher. |
|
|
| # Architecture |
| In this model, the size of the hidden dimension and the embedding layer are both set to 768. The vocabulary size is 28996. The number of transformer layers is 6 and the expansion rate of the feed-forward layer is 4. Overall, this model has around 65 million 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 |
