Update README.md

README.md

@@ -42,25 +42,8 @@ BioHiCL aligns:
 
 - Embedding similarity (SimE): cosine similarity between document embeddings  
 - Label similarity (SimL): cosine similarity over weighted MeSH multi-label vectors  
-
-### Training Objective
-- Mean Squared Error (MSE): aligns SimE with SimL  
-- Hierarchical contrastive loss: separates unrelated documents while preserving ontology structure  
-
 ---
 
-## 📖 Citation
-
-If you use this model, please cite:
-
-```bibtex
-@article{lan2026biohicl,
-  title={BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels},
-  author={Lan, Mengfei and Zheng, Lecheng and Kilicoglu, Halil},
-  booktitle={ACL 2026},
-  year={2026}
-}
-
 ---
 
 ## 🚀 Usage (BEIR Evaluation)
@@ -90,4 +73,20 @@ results = retriever.search(corpus, queries, top_k=10, score_function="cos_sim")
 # Evaluation
 ndcg, _map, recall, precision = EvaluateRetrieval.evaluate(
     qrels, results, k_values=[1, 3, 5, 10]
-)
+)
+
+```
+
+## 📖 Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{lan2026biohicl,
+  title={BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels},
+  author={Lan, Mengfei and Zheng, Lecheng and Kilicoglu, Halil},
+  booktitle={ACL 2026},
+  year={2026}
+}
+
+```