| | --- |
| | |
| | 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 | |
