File size: 1,648 Bytes
92d2862 41c6323 ec2ca4d 92d2862 41c6323 bdf972d 41c6323 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
---
title: README
emoji: 🏃
colorFrom: gray
colorTo: purple
sdk: static
pinned: false
license: mit
---
# Model Description
TinyClinicalBERT is a distilled version of the [BioClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) which is distilled for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset.
# Distillation Procedure
This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the student to align the attention maps and the hidden states of the student with those of the teacher.
# Architecture and Initialisation
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. Due to the model's small hidden dimension size, it uses random initialisation.
# 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}
}
``` |