# textattack / bert-base-uncased-rotten_tomatoes

## bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset

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 64, a learning
rate of 5e-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).
Mask token: [MASK]