Text Classification
Transformers
Safetensors
English
HHEMv2Config
hallucination-detection
factual-consistency
rag
custom_code
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)# pip install -U transformers accelerate # 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
Download config.json from vectara/hallucination_evaluation_model: direct link, hf CLI and curl.
- Browser
- Download file 389 Bytes
-
https://huggingface.co/vectara/hallucination_evaluation_model/resolve/main/config.json
- Command line
-
hf download hf://vectara/hallucination_evaluation_model/config.json
-
curl -L -o config.json https://huggingface.co/vectara/hallucination_evaluation_model/resolve/main/config.json
389 Bytes
| { | |
| "architectures": [ | |
| "HHEMv2ForSequenceClassification" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_hhem_v2.HHEMv2Config", | |
| "AutoModelForSequenceClassification": "modeling_hhem_v2.HHEMv2ForSequenceClassification" | |
| }, | |
| "model_type": "HHEMv2Config", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.3", | |
| "id2label": {"0": "hallucinated", "1": "consistent"} | |
| } | |