| --- |
| title: README |
| emoji: 🏃 |
| colorFrom: gray |
| colorTo: purple |
| sdk: static |
| pinned: false |
| license: mit |
| tags: |
| - oxford-legacy |
| --- |
| |
| # 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 |
| @article{rohanian2023lightweight, |
| title={Lightweight transformers for clinical natural language processing}, |
| author={Rohanian, Omid and Nouriborji, Mohammadmahdi and Jauncey, Hannah and Kouchaki, Samaneh and Nooralahzadeh, Farhad and Clifton, Lei and Merson, Laura and Clifton, David A and ISARIC Clinical Characterisation Group and others}, |
| journal={Natural Language Engineering}, |
| pages={1--28}, |
| year={2023}, |
| publisher={Cambridge University Press} |
| } |
| ``` |
|
|
| # Support |
| If this model helps your work, you can keep the project running with a one-off or monthly contribution: |
| https://github.com/sponsors/nlpie-research |
