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Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets.
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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 |