pytorch jax bert text-classification transformers
.gitattributes README.md config.json flax_model.msgpack log.txt pytorch_model.bin special_tokens_map.json tokenizer_config.json train_args.json vocab.txt
## TextAttack Model Card    
    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 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 0.875234521575985, as measured by the 
    eval set accuracy, found after 4 epochs.
    
    For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
