Instructions to use textattack/facebook-bart-base-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/facebook-bart-base-RTE with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("textattack/facebook-bart-base-RTE") model = AutoModelForSeq2SeqLM.from_pretrained("textattack/facebook-bart-base-RTE") - Notebooks
- Google Colab
- Kaggle
| ## TextAttack Model CardSince 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 0.7256317689530686, as measured by the | |
| eval set accuracy, found after 4 epochs. | |
| For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack). | |