add model pipeline info and technical report info

README.md

@@ -1,10 +1,15 @@
 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: text-ranking
+
 base_model:
 - Qwen/Qwen3-VL-2B-Instruct
+
 tags:
 - transformers
 - multimodal rerank
+- text rerank
 ---
 # Qwen3-VL-Reranker-2B
 
@@ -34,7 +39,7 @@ While the Embedding model generates high-dimensional vectors for broad applicati
 - Number of Parameters: 2B
 - Context Length: 32k
 
-For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwen.ai/blog?id=qwen3-vl-embedding), [GitHub](https://github.com/QwenLM/Qwen3-VL-Embedding).
+For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [technical report](https://arxiv.org/abs/2601.04720), [blog](https://qwen.ai/blog?id=qwen3-vl-embedding), [GitHub](https://github.com/QwenLM/Qwen3-VL-Embedding).
 
 ## Qwen3-VL-Embedding and Qwen3-VL-Reranker Model list
 
@@ -214,7 +219,7 @@ If you find our work helpful, feel free to give us a cite.
 @article{qwen3vlembedding,
   title={Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking},
   author={Li, Mingxin and Zhang, Yanzhao and Long, Dingkun and Chen Keqin and Song, Sibo and Bai, Shuai and Yang, Zhibo and Xie, Pengjun and Yang, An and Liu, Dayiheng and Zhou, Jingren and Lin, Junyang},
-  journal={arXiv},
+  journal={arXiv preprint arXiv:2601.04720},
   year={2026}
 }
 ```