Add pipeline tag and paper link

README.md

@@ -1,5 +1,8 @@
 ---
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+library_name: transformers
 license: apache-2.0
+pipeline_tag: feature-extraction
 tags:
 - embedding
 - multimodal
@@ -7,15 +10,13 @@ tags:
 - feature-extraction
 - mmeb
 - mveb
-base_model: Qwen/Qwen2.5-VL-7B-Instruct
-library_name: transformers
 ---
 
 # VisME (Qwen2.5-VL-7B)
 
 English | [简体中文](README.md)
 
-**VisME** is a universal multimodal embedding model from our CVPR 2026 paper [*Illuminating Visual Identity in Universal Multimodal Embeddings*](https://openaccess.thecvf.com/content/CVPR2026/html/Cao_Illuminating_Visual_Identity_in_Universal_Multimodal_Embeddings_CVPR_2026_paper.html). Built on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), VisME produces dense embeddings for images, text, and image–text pairs, with strong performance on both general multimodal benchmarks (**MMEB**) and identity-centric retrieval (**MVEB**).
+**VisME** is a universal multimodal embedding model from our CVPR 2026 paper [*Illuminating Visual Identity in Universal Multimodal Embeddings*](https://huggingface.co/papers/2608.01794). Built on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), VisME produces dense embeddings for images, text, and image–text pairs, with strong performance on both general multimodal benchmarks (**MMEB**) and identity-centric retrieval (**MVEB**).
 
 <p align="center">
   <img src="assets/teaser.jpg" alt="VisME teaser" width="95%">
@@ -111,4 +112,4 @@ If you find VisME or MVEB useful, please cite:
 
 ## License
 
-This model is built upon [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). Please follow the license terms of the base model and the respective datasets used during training.
+This model is built upon [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). Please follow the license terms of the base model and the respective datasets used during training.