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.
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.
For more information, check out TextAttack on Github.