|
--- |
|
title: README |
|
emoji: 🏃 |
|
colorFrom: gray |
|
colorTo: purple |
|
sdk: static |
|
pinned: false |
|
license: mit |
|
--- |
|
|
|
# Model Description |
|
BioDistilBERT-cased was developed by training the [DistilBERT-cased](https://huggingface.co/distilbert-base-cased?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-cased](https://huggingface.co/distilbert-base-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 |
|
@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} |
|
} |
|
``` |
