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
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Check out the documentation for more information.
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.
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