Update README.md

README.md

@@ -100,7 +100,7 @@ datasets:
 
 # ML-Embed-0.6B
 
-**ML-Embed-0.6B** is a multilingual text embedding model developed by CodeFuse AI and trained from **Qwen3-0.6B**. It is part of the **ML-Embed** family introduced in the ICML 2026 paper [**ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World**](https://arxiv.org/abs/2605.15081).
+**ML-Embed-0.6B** is a multilingual text embedding model developed by CodeFuse AI and trained from Qwen3-0.6B. It is part of the ML-Embed family introduced in the ICML 2026 paper [**ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World**](https://arxiv.org/abs/2605.15081).
 
 This model is designed to be:
 
@@ -379,21 +379,6 @@ The model is fully open:
 - Training data is available at: [`codefuse-ai/F2LLM-v2`](https://huggingface.co/datasets/codefuse-ai/F2LLM-v2)
 - Training code is available at: https://github.com/codefuse-ai/CodeFuse-Embeddings/tree/main/ML-Embed
 
-### Citation (to be updated after the conference)
-
-```bibtex
-@misc{zhang2026mlembedinclusiveefficientembeddings,
-      title={ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World}, 
-      author={Ziyin Zhang and Zihan Liao and Hang Yu and Peng Di and Rui Wang},
-      year={2026},
-      eprint={2605.15081},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2605.15081}, 
-}
-```
-
-
 ## Intended Uses
 
 ML-Embed-0.6B can be used for:
@@ -415,6 +400,20 @@ ML-Embed-0.6B can be used for:
 - For best embedding quality, use appropriate prompts for queries.
 - Efficient modes such as fewer-layer deployment and low-rank factorized embeddings involve trade-offs between quality and efficiency.
 
+### Citation (to be updated after the conference)
+
+```bibtex
+@misc{zhang2026mlembedinclusiveefficientembeddings,
+      title={ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World}, 
+      author={Ziyin Zhang and Zihan Liao and Hang Yu and Peng Di and Rui Wang},
+      year={2026},
+      eprint={2605.15081},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2605.15081}, 
+}
+```
+
 ## Related models
 
 The baseline models are released and submitted to the MTEB leaderboard under the name [`F2LLM-v2`](https://huggingface.co/collections/codefuse-ai/f2llm):