albert-base-v2 model was fine-tuned for sequence classification using TextAttack
and the snli dataset loaded using the
nlp library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 64.
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 0.9060150375939849, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out TextAttack on Github.
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