Instructions to use pfr/conditional-utilitarian-deberta-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pfr/conditional-utilitarian-deberta-01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pfr/conditional-utilitarian-deberta-01")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pfr/conditional-utilitarian-deberta-01") model = AutoModelForSequenceClassification.from_pretrained("pfr/conditional-utilitarian-deberta-01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b6dcd50d95ac800ecf826d5e40d727fb1eb3d99322c31f585769d7d0c537d5f
- Size of remote file:
- 1.74 GB
- SHA256:
- 210f6b1ca000627d56223fcf81926cdfcb0279e0a7a9a2aca73a2f440772eeca
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