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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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ClinicalDistilBERT was developed by training the [BioDistilBERT-cased](https://huggingface.co/nlpie/bio-distilbert-cased?text=The+goal+of+life+is+%5BMASK%5D.) model in a continual learning fashion for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset. |
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# Initialisation |
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We initialise our model with the pre-trained checkpoints of the [BioDistilBERT-cased](https://huggingface.co/nlpie/bio-distilbert-cased?text=The+goal+of+life+is+%5BMASK%5D.) model available on Huggingface. |
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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{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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``` |
