readme: ---
  
  language: 
    - English
  tags:
  - Clinical notes
  - Discharge summaries
  - RoBERTa
  license: "cc-by-4.0"
  datasets:
  - MIMIC-III
  
  ---
  
  * Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets.
  
  * Details can be found in the following paper
  
  > Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (https://arxiv.org/abs/2204.06683)
  
  * Important hyper-parameters 
  
  | | |
  |---|---|
  | Max sequence | 128 |
  | Batch size | 128 |
  | Learning rate | 5e-5 |
  | Training epochs | 15 |
  | Training time | 40 GPU-hours |