
# Model Description
CompactBioBERT 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.) model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset. 

# Distillation Procedure
This model has the same overall architecture as [DistilBioBERT](https://huggingface.co/nlpie/distil-biobert) with the difference that here we combine the distillation approaches of DistilBioBERT and [TinyBioBERT](https://huggingface.co/nlpie/tiny-biobert). We utilise the same initialisation technique as in [DistilBioBERT](https://huggingface.co/nlpie/distil-biobert), and apply a layer-to-layer distillation with three major components, namely, MLM, layer, and output distillation.

# Initialisation
Following [DistilBERT](https://huggingface.co/distilbert-base-uncased?text=The+goal+of+life+is+%5BMASK%5D.), we initialise the student model by taking weights from every other layer of the teacher.

# 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}
}
```
{"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
CompactBioBERT is a distilled version of the BioBERT model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset. 

# Distillation Procedure
This model has the same overall architecture as DistilBioBERT with the difference that here we combine the distillation approaches of DistilBioBERT and TinyBioBERT. We utilise the same initialisation technique as in DistilBioBERT, and apply a layer-to-layer distillation with three major components, namely, MLM, layer, and output distillation.

# Initialisation
Following DistilBERT, we initialise the student model by taking weights from every other layer of the teacher.

# 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.

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


# Model Description
CompactBioBERT is a distilled version of the BioBERT model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset. # Distillation Procedure
This model has the same overall architecture as DistilBioBERT with the difference that here we combine the distillation approaches of DistilBioBERT and TinyBioBERT. We utilise the same initialisation technique as in DistilBioBERT, and apply a layer-to-layer distillation with three major components, namely, MLM, layer, and output distillation. # Initialisation
Following DistilBERT, we initialise the student model by taking weights from every other layer of the teacher. # 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.

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
CompactBioBERT is a distilled version of the BioBERT model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset. # Distillation Procedure
This model has the same overall architecture as DistilBioBERT with the difference that here we combine the distillation approaches of DistilBioBERT and TinyBioBERT. We utilise the same initialisation technique as in DistilBioBERT, and apply a layer-to-layer distillation with three major components, namely, MLM, layer, and output distillation. # Initialisation
Following DistilBERT, we initialise the student model by taking weights from every other layer of the teacher. # 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.

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
CompactBioBERT is a distilled version of the BioBERT model which is distilled for 100k training steps using a total batch size of 192 on the PubMed dataset.# Distillation Procedure
This model has the same overall architecture as DistilBioBERT with the difference that here we combine the distillation approaches of DistilBioBERT and TinyBioBERT. We utilise the same initialisation technique as in DistilBioBERT, and apply a layer-to-layer distillation with three major components, namely, MLM, layer, and output distillation.# Initialisation
Following DistilBERT, we initialise the student model by taking weights from every other layer of the teacher.# 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.

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