| ## 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). | |