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---
title: README
emoji: 🏃
colorFrom: gray
colorTo: purple
sdk: static
pinned: false
license: mit
---
# Model Description
TinyBioBERT is a distilled version of the [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2?text=The+goal+of+life+is+%5BMASK%5D.) which is distilled for 100k training steps using a total batch size of 192 on the PubMed 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.2209.03182,
doi = {10.48550/ARXIV.2209.03182},
url = {https://arxiv.org/abs/2209.03182},
author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A.},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, 68T50},
title = {On the Effectiveness of Compact Biomedical Transformers},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` |