WangA/roberta-base-finetuned-ctrip

TextAttack Model Card

This bert model was fine-tuned using TextAttack. The model was fine-tuned for 3 epochs with a batch size of 8, a maximum sequence length of 512, and an initial learning rate of 3e-05. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9713333333333334, as measured by the eval set accuracy, found after 3 epochs.

For more information, check out TextAttack on Github.