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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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TinyClinicalBERT is a distilled version of the [BioClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) which is distilled for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset. |
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# Distillation Procedure |
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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. |
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# Architecture and Initialisation |
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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. |
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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{rohanian2023lightweight, |
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title={Lightweight transformers for clinical natural language processing}, |
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author={Rohanian, Omid and Nouriborji, Mohammadmahdi and Jauncey, Hannah and Kouchaki, Samaneh and Nooralahzadeh, Farhad and Clifton, Lei and Merson, Laura and Clifton, David A and ISARIC Clinical Characterisation Group and others}, |
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journal={Natural Language Engineering}, |
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pages={1--28}, |
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year={2023}, |
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publisher={Cambridge University Press} |
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} |
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``` |
