bert-base-uncased-rotten-tomatoes

https://huggingface.co/textattack/bert-base-uncased-rotten-tomatoes with ONNX weights to be compatible with Transformers PHP

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).

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Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).