| | --- |
| | 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`). |
