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
File size: 760 Bytes
e2b6d9e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | from transformers import PretrainedConfig
class HHEMv2Config(PretrainedConfig):
model_type = "HHEMv2"
foundation = "google/flan-t5-base"
prompt = "<pad> Determine if the hypothesis is true given the premise?\n\nPremise: {text1}\n\nHypothesis: {text2}"
def __init___(self,
foundation="xyz",
prompt="abc",
**kwargs):
super().__init__(**kwargs)
self.foundation = foundation
self.prompt = prompt
# FIXME: The default values passed to the constructor are not used.
# Instead, the values set as global before the constructor are used.
# To test, run this:
# config = HHEMv2Config()
# print(config.foundation)
# The output will not be xyz but google/flan-t5-base. |