| | --- |
| | 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 |
