README.md

metadata

license: apache-2.0

Cross-Encoder for Hallucination Detection

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base.

Training Data

The model was trained on the NLI data and a variety of datasets evaluating summarization accuracy for factual consistency, including FEVER, Vitamin C and PAWS.

Performance

TODO

Usage

Pre-trained models can be used like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('vectara/hallucination_evaluation_model')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])

Usage with Transformers AutoModel

You can use the model also directly with Transformers library (without SentenceTransformers library):

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('vectara/hallucination_evaluation_model')
tokenizer = AutoTokenizer.from_pretrained('vectara/hallucination_evaluation_model')

features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    scores = model(**features)