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TextAttack Model CardThis bert-base-uncased model was fine-tuned for sequence classification using TextAttack

and the ag_news dataset loaded using the nlp library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 3e-05, and a maximum sequence length of 128. 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 0.9514473684210526, as measured by the eval set accuracy, found after 3 epochs.

For more information, check out TextAttack on Github.

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