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
Download pytorch_model.bin from textattack/facebook-bart-base-RTE: direct link, hf CLI and curl.
- Browser
- Download file 560 MB
-
https://huggingface.co/textattack/facebook-bart-base-RTE/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/facebook-bart-base-RTE/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/facebook-bart-base-RTE/resolve/main/pytorch_model.bin
560 MB
- Xet hash:
- b7544d22698e410c14be385af0d91cee7d3f37b461db2125b161394397a15a98
- Size of remote file:
- 560 MB
- SHA256:
- 2c603be6a9cf1e108525fa7ab13d239ab7223d36c955b1daef15e3e3bc80a57e
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