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:
# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from textattack/facebook-bart-base-RTE: direct link, hf CLI and curl.
- Browser
- Download file 345 Bytes
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https://huggingface.co/textattack/facebook-bart-base-RTE/resolve/bd442fb84746d37b035d697082f968ba45452803/README.md
- Command line
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hf download hf://textattack/facebook-bart-base-RTE@bd442fb84746d37b035d697082f968ba45452803/README.md
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curl -L -o README.md https://huggingface.co/textattack/facebook-bart-base-RTE/resolve/bd442fb84746d37b035d697082f968ba45452803/README.md
345 Bytes
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.