README.md

---
license: mit
language:
- en
base_model:
- microsoft/deberta-v3-large
pipeline_tag: text-classification
---

# FactCG for Large Language Model Ungrounded Hallucination Detection

This is a fact-checking model from our work:

📃 [**FactCG: Enhancing Fact Checkers with Graph-Based
Multi-Hop Data**](https://arxiv.org/pdf/2501.17144) (NAACL2025, [GitHub Repo](https://github.com/derenlei/FactCG))


You can load our model with the following example code:

```python
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
config = AutoConfig.from_pretrained("yaxili96/FactCG-DeBERTa-v3-Large", num_labels=2, finetuning_task="text-classification", revision='main', token=None, cache_dir="./cache")
config.problem_type = "single_label_classification"
tokenizer = AutoTokenizer.from_pretrained("yaxili96/FactCG-DeBERTa-v3-Large", use_fast=True, revision='main', token=None, cache_dir="./cache")
model = AutoModelForSequenceClassification.from_pretrained(
            "yaxili96/FactCG-DeBERTa-v3-Large", config=config, revision='main', token=None, ignore_mismatched_sizes=False, cache_dir="./cache")
```


If you find the repository or FactCG helpful, please cite the following paper
```bibtex
@inproceedings{lei2025factcg,
  title={FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data},
  author={Lei, Deren and Li, Yaxi and Li, Siyao and Hu, Mengya and Xu, Rui and Archer, Ken and Wang, Mingyu and Ching, Emily and Deng, Alex},
  journal={NAACL},
  year={2025}
}
```