File size: 1,336 Bytes
c653f2b 51ffb0e c653f2b ce83ed4 3a11f84 ce83ed4 3a11f84 ce83ed4 d2e168a ce83ed4 51ffb0e |
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
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
@article{rohanian2023effectiveness,
title={On the effectiveness of compact biomedical transformers},
author={Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Clifton, David A},
journal={Bioinformatics},
volume={39},
number={3},
pages={btad103},
year={2023},
publisher={Oxford University Press}
}
``` |