readme: ## TextAttack Model Card
  This `xlnet-base-cased` model was fine-tuned for sequence classification using TextAttack 
  and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned 
  for 5 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 0.9071294559099438, as measured by the 
  eval set accuracy, found after 2 epochs.
  
  For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
  