# textattack /bert-base-uncased-RTE 0

## 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 8, a learning rate of 2e-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 0.7256317689530686, as measured by the eval set accuracy, found after 2 epochs.

For more information, check out TextAttack on Github.

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Text Classification
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