Revisiting Transformer-based Models for Long Document Classification
Paper
• 2204.06683 • Published
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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 |
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