textattack/albert-base-v2-WNLI

TextAttack Model Card

This albert-base-v2 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 2e-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 0.5915492957746479, as measured by the eval set accuracy, found after 0 epoch.

For more information, check out TextAttack on Github.