title: README
emoji: 🏃
colorFrom: gray
colorTo: purple
sdk: static
pinned: false
license: mit
Model Description
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.
Initialisation
We initialise our model with the pre-trained checkpoints of the BioMobileBERT model available on Huggingface.
Architecture
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.
Citation
If you use this model, please consider citing the following paper:
@misc{https://doi.org/10.48550/arxiv.2302.04725,
doi = {10.48550/ARXIV.2302.04725},
url = {https://arxiv.org/abs/2302.04725},
author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Jauncey, Hannah and Kouchaki, Samaneh and Group, ISARIC Clinical Characterisation and Clifton, Lei and Merson, Laura and Clifton, David A.},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7, 68T50},
title = {Lightweight Transformers for Clinical Natural Language Processing},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}