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README.md

@@ -1,113 +1,113 @@
----
-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  
-
----
-
-## 🌐 Multilingual Performance
-
-Claim2Vec demonstrates strong performance in multilingual settings, where clusters containing multiple languages benefit significantly from fine-tuning, indicating effective cross-lingual semantic transfer.
-
----
-
-## 💡 Use Cases
-
-- Fact-checking systems  
-- Misinformation detection pipelines  
-- Claim deduplication  
-- Evidence grouping for RAG systems  
-- News verification tools  
-- Cross-lingual semantic clustering  
-
----
-
-## 🧬 Usage
-
-```python
-from sentence_transformers import SentenceTransformer
-
-model = SentenceTransformer("your-username/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
+---
+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  
+
+---
+
+## 🌐 Multilingual Performance
+
+Claim2Vec demonstrates strong performance in multilingual settings, where clusters containing multiple languages benefit significantly from fine-tuning, indicating effective cross-lingual semantic transfer.
+
+---
+
+## 💡 Use Cases
+
+- Fact-checking systems  
+- Misinformation detection pipelines  
+- Claim deduplication  
+- Evidence grouping for RAG systems  
+- News verification tools  
+- Cross-lingual semantic clustering  
+
+---
+
+## 🧬 Usage
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("your-username/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