textattack/bert-base-uncased-WNLI

TextAttack Model Card

This bert-base-uncased model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the nlp library. The model was fine-tuned for 5 epochs with a batch size of 64, a learning rate of 5e-05, and a maximum sequence length of 256. 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 0.5633802816901409, as measured by the eval set accuracy, found after 1 epoch.

For more information, check out TextAttack on Github.