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textattack/albert-base-v2-QQP
albert-base-v2-QQP is a text classification model from textattack. Use it when you need a label for a piece of text. It is set up for transformers.
This albert-base-v2 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 32, a learning rate o…
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From the Hugging Face model README
This albert-base-v2 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 32, a learning
rate of 5e-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 0.9073707642839476, as measured by the
eval set accuracy, found after 3 epochs.
For more information, check out TextAttack on Github.