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and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 3e-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 0.9514473684210526, as measured by the eval set accuracy, found after 3 epochs.

Text analysis
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This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the imdb dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-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 0.89088, as measured by the eval set accuracy, found after 4 epochs. For more information, check out TextAttack on Github.

Text analysis
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This `bert-base-uncased` 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 2e-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 0.7256317689530686, as measured by the eval set accuracy, found after 2 epochs.

Text analysis
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This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 5e-05, and a maximum sequence length of 256. 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 0.9699473684210527, as measured by the eval set accuracy, found after 4 epochs.

Text analysis
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for 5 epochs with a batch size of 64, a learning rate of 3e-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 0.8235858101629914, as measured by the eval set accuracy, found after 2 epochs.

Text analysis
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