Instructions to use tum-nlp/Deberta_Human_Value_Detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tum-nlp/Deberta_Human_Value_Detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tum-nlp/Deberta_Human_Value_Detector", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/Deberta_Human_Value_Detector", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b38908e33f27eb537468ea6840cfc9a5a10698f3570b2ea3d34e030b896f3ba
- Size of remote file:
- 1.62 GB
- SHA256:
- f563b917be492d981f2b7f518d2b9e5db5f2dda7be1f94ee0f77ff083723a3d4
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