---
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
@article{rohanian2023lightweight,
  title={Lightweight transformers for clinical natural language processing},
  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},
  journal={Natural Language Engineering},
  pages={1--28},
  year={2023},
  publisher={Cambridge University Press}
}
```