ENTUM-AI/FactGuard

🛡️ FactGuard

Lightweight hallucination and grounding detection model. Checks whether a claim is supported by the given context.

Built on ModernBERT-base (149M params), fine-tuned on 1M+ NLI pairs from SNLI, MultiNLI, and ANLI.

Classes: Supported, Not Supported

🚀 Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="ENTUM-AI/FactGuard")

result = classifier({
    "text": "Apple reported revenue of $94.8 billion in Q1 2024.",
    "text_pair": "Apple's Q1 2024 revenue was $94.8 billion."
})
# [{'label': 'Supported', 'score': 0.99}]

result = classifier({
    "text": "Apple reported revenue of $94.8 billion in Q1 2024.",
    "text_pair": "Apple's revenue exceeded $100 billion."
})
# [{'label': 'Not Supported', 'score': 0.97}]

📊 Training Data

1M+ NLI pairs mapped to binary grounding labels.

🔍 Use Cases

• —RAG pipelines — verify LLM responses against source documents
• —Fact-checking — detect unsupported claims in generated text
• —Content moderation — flag hallucinated content before publishing

⚠️ Limitations

• —English only
• —Designed for single claim verification against a given context