WangA/roberta-base-finetuned-jd

TextAttack Model Card

        This `bert` model was fine-tuned using TextAttack. The model was fine-tuned
        for 3 epochs with a batch size of 8,
         a maximum sequence length of 512, and an initial learning rate of 3e-05.
        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 0.9556666666666667, as measured by the
        eval set accuracy, found after 3 epochs.

        For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).