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/af6e5f9d3e45f8ed024af6e90f5e23aecb230371/README.md
- Command line
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hf download hf://textattack/facebook-bart-base-RTE@af6e5f9d3e45f8ed024af6e90f5e23aecb230371/README.md
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curl -L -o README.md https://huggingface.co/textattack/facebook-bart-base-RTE/resolve/af6e5f9d3e45f8ed024af6e90f5e23aecb230371/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](https://github.com/QData/TextAttack). | |