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# Model Description
BioDistilBERT-uncased is the result of training the [DistilBERT-uncased](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.) model in a continual learning fashion for 200k training steps using a total batch size of 192 on the PubMed dataset.
# Initialisation
We initialise our model with the pre-trained checkpoints of the [DistilBERT-uncased](https://huggingface.co/distilbert-base-uncased?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 30522. 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.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}
}
```
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