Update README.md

README.md

@@ -1,11 +1,10 @@
-## xlnet-base-cased fine-tuned with TextAttack on the rotten_tomatoes dataset
-
+## 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 8, a learning 
+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 0.9033771106941839, as measured by the 
+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).