10 дек. 2019 г. · We find that a lower learning rate, such as 2e-5, is necessary to make BERT overcome the catastrophic forgetting problem. With an aggressive ...

27 июн. 2021 г. · I need to finetune BERT model on a multi label sentiment classification task. However, my dataset is really small, 5k. Do you have any suggestions for learning ... [D] I just can't fine tune BERT over 40% accuracy for ... - Reddit What is considered as a small learning rate? - Reddit Другие результаты с сайта www.reddit.com

23 окт. 2020 г. · The best of 20 runs for BERT was 72.2% test-set accuracy. DistilBERT's best of 20 runs was 62.5% accuracy. Both of these RTE scores are slightly ... Setting up the Sweep · BERT: Optimal Hyperparameters

13 янв. 2021 г. · Learning Rate. learning rate, a positive scalar determining the size of the step. we should not use a learning rate that is too large or too ...

The BERT-based model was trained for 20 epochs with data embedding size of 100, batch size (BS)=16, a learning rate (LR)=(2e-5-1e-5), a warm-up proportion (WP) ...

3 мар. 2021 г. · The BERT authors recommend fine-tuning for 4 epochs over the following hyperparameter options: batch sizes: 8, 16, 32, 64, 128 learning rates: 3e-4, 1e-4, 5e-5 ...

The learning rate is linearly increased from 0 to 2e−5 for the first 10% of iterations—which is known as a warmup—and linearly decreased to 0 afterward. We ...

Reduces learning rate when a metric has stopped improving. Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates.

5 дек. 2019 г. · The purpose of this post is to provide an overview of one class of solutions to this problem: layer-wise adaptive optimizers, such as LARS, LARC, and LAMB.

7 февр. 2020 г. · When should I call scheduler.step() ? If I do after train , the learning rate is zero for the first epoch. Should I call it for each batch?