pytorch jax bert text-classification transformers
.gitattributes README.md config.json eval_results_mrpc.txt flax_model.msgpack log.txt pytorch_model.bin special_tokens_map.json tokenizer_config.json train_args.json training_args.bin vocab.txt
## TextAttack Model Card
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 16, a learning 
rate of 2e-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 0.8774509803921569, as measured by the 
eval set accuracy, found after 1 epoch.

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