readme: ## 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).
  