
# Model Description
ClinicalMobileBERT is the result of training the [BioMobileBERT](https://huggingface.co/nlpie/bio-mobilebert) model in a continual learning scenario for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset. 

# Initialisation
We initialise our model with the pre-trained checkpoints of the [BioMobileBERT](https://huggingface.co/nlpie/bio-mobilebert) model available on Huggingface.

# Architecture
MobileBERT uses a 128-dimensional embedding layer followed by 1D convolutions to up-project its output to the desired hidden dimension expected by the transformer blocks. For each of these blocks, MobileBERT uses linear down-projection at the beginning of the transformer block and up-projection at its end, followed by a residual connection originating from the input of the block before down-projection. Because of these linear projections, MobileBERT can reduce the hidden size and hence the computational cost of multi-head attention and feed-forward blocks. This model additionally incorporates up to four feed-forward blocks in order to enhance its representation learning capabilities. Thanks to the strategically placed linear projections, a 24-layer MobileBERT (which is used in this work) has around 25M parameters.

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

```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.04725,
  doi = {10.48550/ARXIV.2302.04725},
  url = {https://arxiv.org/abs/2302.04725},
  author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Jauncey, Hannah and Kouchaki, Samaneh and Group, ISARIC Clinical Characterisation and Clifton, Lei and Merson, Laura and Clifton, David A.},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7, 68T50},
  title = {Lightweight Transformers for Clinical Natural Language Processing},
  publisher = {arXiv},
  year = {2023},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```
{"license": "mit", "title": "README", "emoji": "\ud83c\udfc3", "colorFrom": "gray", "colorTo": "purple", "sdk": "static", "pinned": false}
transformers pytorch mobilebert fill-mask arxiv:2302.04725 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #mobilebert #fill-mask #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Description
ClinicalMobileBERT is the result of training the BioMobileBERT model in a continual learning scenario for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset. 

# Initialisation
We initialise our model with the pre-trained checkpoints of the BioMobileBERT model available on Huggingface.

# Architecture
MobileBERT uses a 128-dimensional embedding layer followed by 1D convolutions to up-project its output to the desired hidden dimension expected by the transformer blocks. For each of these blocks, MobileBERT uses linear down-projection at the beginning of the transformer block and up-projection at its end, followed by a residual connection originating from the input of the block before down-projection. Because of these linear projections, MobileBERT can reduce the hidden size and hence the computational cost of multi-head attention and feed-forward blocks. This model additionally incorporates up to four feed-forward blocks in order to enhance its representation learning capabilities. Thanks to the strategically placed linear projections, a 24-layer MobileBERT (which is used in this work) has around 25M parameters.

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


# Model Description
ClinicalMobileBERT is the result of training the BioMobileBERT model in a continual learning scenario for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset. # Initialisation
We initialise our model with the pre-trained checkpoints of the BioMobileBERT model available on Huggingface. # Architecture
MobileBERT uses a 128-dimensional embedding layer followed by 1D convolutions to up-project its output to the desired hidden dimension expected by the transformer blocks. For each of these blocks, MobileBERT uses linear down-projection at the beginning of the transformer block and up-projection at its end, followed by a residual connection originating from the input of the block before down-projection. Because of these linear projections, MobileBERT can reduce the hidden size and hence the computational cost of multi-head attention and feed-forward blocks. This model additionally incorporates up to four feed-forward blocks in order to enhance its representation learning capabilities. Thanks to the strategically placed linear projections, a 24-layer MobileBERT (which is used in this work) has around 25M parameters.

If you use this model, please consider citing the following paper:
TAGS
#transformers #pytorch #mobilebert #fill-mask #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Description
ClinicalMobileBERT is the result of training the BioMobileBERT model in a continual learning scenario for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset. # Initialisation
We initialise our model with the pre-trained checkpoints of the BioMobileBERT model available on Huggingface. # Architecture
MobileBERT uses a 128-dimensional embedding layer followed by 1D convolutions to up-project its output to the desired hidden dimension expected by the transformer blocks. For each of these blocks, MobileBERT uses linear down-projection at the beginning of the transformer block and up-projection at its end, followed by a residual connection originating from the input of the block before down-projection. Because of these linear projections, MobileBERT can reduce the hidden size and hence the computational cost of multi-head attention and feed-forward blocks. This model additionally incorporates up to four feed-forward blocks in order to enhance its representation learning capabilities. Thanks to the strategically placed linear projections, a 24-layer MobileBERT (which is used in this work) has around 25M parameters.

If you use this model, please consider citing the following paper:
TAGS
#transformers #pytorch #mobilebert #fill-mask #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Description
ClinicalMobileBERT is the result of training the BioMobileBERT model in a continual learning scenario for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset.# Initialisation
We initialise our model with the pre-trained checkpoints of the BioMobileBERT model available on Huggingface.# Architecture
MobileBERT uses a 128-dimensional embedding layer followed by 1D convolutions to up-project its output to the desired hidden dimension expected by the transformer blocks. For each of these blocks, MobileBERT uses linear down-projection at the beginning of the transformer block and up-projection at its end, followed by a residual connection originating from the input of the block before down-projection. Because of these linear projections, MobileBERT can reduce the hidden size and hence the computational cost of multi-head attention and feed-forward blocks. This model additionally incorporates up to four feed-forward blocks in order to enhance its representation learning capabilities. Thanks to the strategically placed linear projections, a 24-layer MobileBERT (which is used in this work) has around 25M parameters.

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