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 16, 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 0.8774509803921569, as measured by the eval set accuracy, found after 1 epoch. For more information, check out TextAttack on Github.
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