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--- |
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title: README |
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emoji: 🏃 |
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colorFrom: gray |
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colorTo: purple |
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sdk: static |
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pinned: false |
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license: mit |
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--- |
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# Model Description |
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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. |
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# Distillation Procedure |
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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. |
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# Initialisation |
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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. |
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# Architecture |
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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. |
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# Citation |
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If you use this model, please consider citing the following paper: |
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```bibtex |
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@article{rohanian2023effectiveness, |
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title={On the effectiveness of compact biomedical transformers}, |
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author={Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A}, |
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journal={Bioinformatics}, |
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volume={39}, |
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number={3}, |
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pages={btad103}, |
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year={2023}, |
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publisher={Oxford University Press} |
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} |
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``` |
