add qqmm

results.csv

@@ -23,4 +23,5 @@ VLM2Vec (Qwen2-VL-7B-LoRA-HighRes),8.29,TIGER-Lab,65.8,62.6,57.8,69.9,81.7
 VLM2Vec (Qwen2-VL-2B-LoRA-HighRes),2.21,TIGER-Lab,59.3,59.0,49.4,65.4,73.4
 LLaVE-7B,8.03,Self-Reported,70.3,65.7,65.4,70.9,91.9
 LLaVE-2B,1.95,Self-Reported,65.2,62.1,60.2,65.2,84.9
-LLaVE-0.5B,0.894,Self-Reported,59.1,57.4,50.3,59.8,82.9
+LLaVE-0.5B,0.894,Self-Reported,59.1,57.4,50.3,59.8,82.9
+QQMM,8.297,Self-Reported,72.175,70.07,69.52,71.175,87.075

utils.py

@@ -41,6 +41,8 @@ or a combination of both. MMEB is divided into 20 in-distribution datasets, whic
 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.
+Github link: https://github.com/TIGER-AI-Lab/VLM2Vec
+Overview: https://tiger-ai-lab.github.io/VLM2Vec/
 """
 
 TABLE_INTRODUCTION = """"""
@@ -94,6 +96,8 @@ SUBMIT_INTRODUCTION = """# Submit on MMEB Leaderboard Introduction
     }, 
 ]
 ```
+You may refer to the Github page for instructions about evaluating your model. 
+Github link: https://github.com/TIGER-AI-Lab/VLM2Vec
 Please send us an email at [email protected], attaching the JSON file. We will review your submission and update the leaderboard accordingly.
 """