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language:
- multilingual
license: mit
library_name: sentence-transformers
tags:
- claim2vec
- embedding-model
- fact-checking
- claim-clustering
- semantic-search
- misinformation
- contrastive-learning
- multilingual-nlp
---
# 🧠 Claim2Vec
**Claim2Vec** is a multilingual embedding model designed specifically for **fact-checked claim representation and clustering** in misinformation detection systems.
It learns a semantic embedding space where recurrent and semantically equivalent claims are mapped close together, enabling improved grouping and retrieval of fact-checkable information across languages.
---
## 🎯 Motivation
Recurrent claims are a major challenge for automated fact-checking systems, especially in multilingual environments. While existing approaches focus on pairwise claim matching, they often fail to capture global structures of semantically equivalent claims.
Claim2Vec addresses this gap by learning embeddings optimized for **claim clustering**, enabling better organization of repeated misinformation narratives across datasets and languages.
---
## 🚀 Key Features
- 🌍 Multilingual claim representation
- 🔗 Optimized for claim clustering tasks
- 🧠 Contrastive learning with semantically similar claim pairs
- 📊 Improved embedding geometry for cluster separation
- 🔄 Strong cross-lingual knowledge transfer
- ⚡ Designed for scalable fact-checking pipelines
---
## 🧪 Training Objective
Claim2Vec is trained using contrastive learning, encouraging semantically similar claims to have closer embeddings while pushing unrelated claims apart.
---
## 📊 Experimental Results
Evaluation across:
- 3 benchmark datasets
- 14 embedding baselines
- 7 clustering algorithms
shows that Claim2Vec consistently improves:
- Cluster label alignment
- Embedding space structure
- Robustness across clustering configurations
---
## 💡 Use Cases
- Fact-checking systems
- Misinformation detection pipelines
- Claim deduplication
- Cross-lingual semantic clustering
---
## 🧬 Usage
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Rrubaa/claim2vec")
claims = [
"COVID vaccines cause infertility",
"Studies show no link between COVID vaccines and infertility"
]
embeddings = model.encode(claims)
print(embeddings.shape)
```
## 📄 Citation
If you use Claim2Vec in your work, please cite:
```bibtex
@misc{claim2vec2026,
title={Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering},
author={Panchendrarajan, Rrubaa and Zubiaga, Arkaitz},
year={2026},
eprint={2604.09812},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2604.09812}
}
```
📄 arXiv: https://arxiv.org/abs/2604.09812
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