Revisiting Transformer-based Models for Long Document Classification
Paper • 2204.06683 • Published
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("xdai/mimic_longformer_base")
model = AutoModelForMaskedLM.from_pretrained("xdai/mimic_longformer_base")Quick Links
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 |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="xdai/mimic_longformer_base")