File size: 1,796 Bytes
2f04da5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- text-classification
- hallucination-detection
- grounding
- factual-consistency
- nli
- rag
datasets:
- stanfordnlp/snli
- nyu-mll/multi_nli
- anli
pipeline_tag: text-classification
---
# 🛡️ FactGuard
Lightweight hallucination and grounding detection model. Checks whether a claim is supported by the given context.
Built on [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) (149M params), fine-tuned on 1M+ NLI pairs from SNLI, MultiNLI, and ANLI.
**Classes:** Supported, Not Supported
## 🚀 Usage
```python
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
| Dataset | Samples |
|---------|---------|
| [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) | ~550K |
| [nyu-mll/multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli) | ~393K |
| [anli](https://huggingface.co/datasets/anli) | ~163K |
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
|