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README.md

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 ## TextAttack Model Card
 This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack 
 and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned 
-for 5 epochs with a batch size of 8, a learning 
-rate of 5e-05, and a maximum sequence length of 512. 
+for 5 epochs with a batch size of 16, a learning 
+rate of 5e-05, and a maximum sequence length of 256. 
 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 0.5000526315789474, as measured by the 
-eval set accuracy, found after 1 epoch.
+The best score the model achieved on this task was 0.9699473684210527, as measured by the 
+eval set accuracy, found after 4 epochs.
 
 For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).