Update utils.py

utils.py

@@ -29,16 +29,20 @@ All tasks are reformulated as ranking tasks, where the model follows instruction
 or a combination of both. MMEB is divided into 20 in-distribution datasets, which can be used for
 training, and 16 out-of-distribution datasets, reserved for evaluation.
 
-The detailed explanation of the benchmark and datasets can be found in our paper: https://doi.org/10.48550/arXiv.2410.05160. \n
-Github link: https://github.com/TIGER-AI-Lab/VLM2Vec. \n
-Overview: https://tiger-ai-lab.github.io/VLM2Vec/. \n
+Building upon on **MMEB**, **MMEB-V2** expands the evaluation scope to include five new tasks: four video-based tasks 
+— Video Retrieval, Moment Retrieval, Video Classification, and Video Question Answering — and one task focused on visual documents, Visual Document Retrieval. 
+This comprehensive suite enables robust evaluation of multimodal embedding models across static, temporal, and structured visual data settings.
+
+| [**Overview**](https://tiger-ai-lab.github.io/VLM2Vec/) | [**Github**](https://github.com/TIGER-AI-Lab/VLM2Vec) 
+| [**📖MMEB-V2/VLM2Vec-V2 Paper (TBA)**](https://arxiv.org/abs/2410.05160) 
+| [**📖MMEB-V1/VLM2Vec-V1 Paper**](https://arxiv.org/abs/2410.05160) 
+| [**Hugging Face**](https://huggingface.co/datasets/TIGER-Lab/MMEB-V2)
 """
 
 TABLE_INTRODUCTION = """"""
 
 LEADERBOARD_INFO = """
 ## Dataset Summary
-<img width="900" alt="abs" src="overview.png">
 """
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"