---
language: en
license: cc-by-4.0
tags:
- Clinical notes
- Discharge summaries
- longformer
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 | 4096 |
| Batch size | 8 |
| Learning rate | 5e-5 |
| Training epochs | 6 |
| Training time | 130 GPU-hours |