ClinicalMobileBERT is the result of training the
BioMobileBERT model in a continual learning scenario for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset.
We initialise our model with the pre-trained checkpoints of the
BioMobileBERT model available on Huggingface.
MobileBERT uses a 128-dimensional embedding layer followed by 1D convolutions to up-project its output to the desired hidden dimension expected by the transformer blocks. For each of these blocks, MobileBERT uses linear down-projection at the beginning of the transformer block and up-projection at its end, followed by a residual connection originating from the input of the block before down-projection. Because of these linear projections, MobileBERT can reduce the hidden size and hence the computational cost of multi-head attention and feed-forward blocks. This model additionally incorporates up to four feed-forward blocks in order to enhance its representation learning capabilities. Thanks to the strategically placed linear projections, a 24-layer MobileBERT (which is used in this work) has around 25M parameters.
1@article{rohanian2023lightweight,
2 title={Lightweight transformers for clinical natural language processing},
3 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},
4 journal={Natural Language Engineering},
5 pages={1--28},
6 year={2023},
7 publisher={Cambridge University Press}
8}
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