Instructions to use textattack/facebook-bart-large-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/facebook-bart-large-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/facebook-bart-large-RTE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/facebook-bart-large-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/facebook-bart-large-RTE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from textattack/facebook-bart-large-RTE: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/textattack/facebook-bart-large-RTE/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://textattack/facebook-bart-large-RTE/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/textattack/facebook-bart-large-RTE/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 72fab02f822a54c009d6f795c588aab242a3dcd304bf54677b67ad1234938660
- Size of remote file:
- 1.63 GB
- SHA256:
- 635961745e1d6314faf52d16fd1b48d4008220ef7381e87b03631baeed7d0880
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