bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset
This bert-base-uncased model was fine-tuned for sequence classificationusing TextAttack and the rotten_tomatoes dataset loaded using the nlp library. The model was fine-tuned for 10 epochs with a batch size of 64, 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 0.875234521575985, as measured by the eval set accuracy, found after 4 epochs.
For more information, check out TextAttack on Github.
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git clone https://www.modelscope.cn/zimuwangnlp/bert-base-uncased-rotten_tomatoes.git
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