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TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned for 10 epochs with a batch size of 16, 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 0.875234521575985, as measured by the eval set accuracy, found after 4 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
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