
# Model Description
BioTinyBERT is the result of training the [TinyBERT](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) 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 [TinyBERT](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) model available on Huggingface.

# Architecture
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M 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}
}
```
{"license": "mit", "title": "README", "emoji": "\ud83c\udfc3", "colorFrom": "gray", "colorTo": "purple", "sdk": "static", "pinned": false}
transformers pytorch bert fill-mask arxiv:2209.03182 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Model Description
BioTinyBERT is the result of training the TinyBERT 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 TinyBERT model available on Huggingface.

# Architecture
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. 

If you use this model, please consider citing the following paper:


# Model Description
BioTinyBERT is the result of training the TinyBERT 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 TinyBERT model available on Huggingface. # Architecture
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. 

If you use this model, please consider citing the following paper:
TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Model Description
BioTinyBERT is the result of training the TinyBERT 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 TinyBERT model available on Huggingface. # Architecture
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. 

If you use this model, please consider citing the following paper:
passage: TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2209.03182 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Model Description
BioTinyBERT is the result of training the TinyBERT 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 TinyBERT model available on Huggingface.# Architecture
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. 

If you use this model, please consider citing the following paper: