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).
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