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 tokenizer_config.json from textattack/facebook-bart-large-RTE: direct link, hf CLI and curl.
- Browser
- Download file 26 Bytes
-
https://huggingface.co/textattack/facebook-bart-large-RTE/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://textattack/facebook-bart-large-RTE/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/textattack/facebook-bart-large-RTE/resolve/main/tokenizer_config.json
26 Bytes
| {"model_max_length": 1024} |