
# Model Description
TinyClinicalBERT is a distilled version of the [BioClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) which is distilled for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset.

# Distillation Procedure
This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the student to align the attention maps and the hidden states of the student with those of the teacher.

# Architecture and Initialisation
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. Due to the model's small hidden dimension size, it uses random initialisation.

# 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 bert fill-mask arxiv:2302.04725 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Model Description
TinyClinicalBERT is a distilled version of the BioClinicalBERT which is distilled for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset.

# Distillation Procedure
This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the student to align the attention maps and the hidden states of the student with those of the teacher.

# Architecture and Initialisation
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. Due to the model's small hidden dimension size, it uses random initialisation.

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


# Model Description
TinyClinicalBERT is a distilled version of the BioClinicalBERT which is distilled for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset. # Distillation Procedure
This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the student to align the attention maps and the hidden states of the student with those of the teacher. # Architecture and Initialisation
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. Due to the model's small hidden dimension size, it uses random initialisation.

If you use this model, please consider citing the following paper:
TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Model Description
TinyClinicalBERT is a distilled version of the BioClinicalBERT which is distilled for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset. # Distillation Procedure
This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the student to align the attention maps and the hidden states of the student with those of the teacher. # Architecture and Initialisation
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. Due to the model's small hidden dimension size, it uses random initialisation.

If you use this model, please consider citing the following paper:
TAGS
#transformers #pytorch #bert #fill-mask #arxiv-2302.04725 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Model Description
TinyClinicalBERT is a distilled version of the BioClinicalBERT which is distilled for 3 epochs using a total batch size of 192 on the MIMIC-III notes dataset.# Distillation Procedure
This model uses a unique distillation method called ‘transformer-layer distillation’ which is applied on each layer of the student to align the attention maps and the hidden states of the student with those of the teacher.# Architecture and Initialisation
This model uses 4 hidden layers with a hidden dimension size and an embedding size of 768 resulting in a total of 15M parameters. Due to the model's small hidden dimension size, it uses random initialisation.

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