--- title: README emoji: 🏃 colorFrom: gray colorTo: purple sdk: static pinned: false license: mit --- # Model Description 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. # Initialisation 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. # Architecture 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. # Citation If you use this model, please consider citing the following paper: ```bibtex @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} } ```