xlnet-base-cased model was fine-tuned for sequence classification using TextAttack
and the glue 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 128.
Since this was a regression task, the model was trained with a mean squared error loss function.
The best score the model achieved on this task was 0.8892630070017784, as measured by the
eval set pearson correlation, found after 4 epochs.
For more information, check out TextAttack on Github.
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