Instructions to use vectara/hallucination_evaluation_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vectara/hallucination_evaluation_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vectara/hallucination_evaluation_model", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("vectara/hallucination_evaluation_model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d27034679a1988f2ab307b7939c817807fc64e1aed2233f4f4adca33029efa69
- Size of remote file:
- 738 MB
- SHA256:
- eb3789996c599ab27a5506005f03229a0a4600fe3634d121c48731ae95cde40e
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