---
library_name: Transformers PHP
tags:
- onnx
pipeline_tag: text-classification
---

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).
  
---
  
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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).