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TextAttack Model Card
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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