Update README.md

README.md

@@ -741,9 +741,42 @@ configs:
   data_files:
   - split: test
     path: Wiki-SS-NQ/test-*
+license: mit
+language:
+- en
+tags:
+- ranking
+pretty_name: MMEB
+size_categories:
+- 10K<n<100K
 ---
 
+# Massive Multimodal Embedding Benchmark
 
+We compile a large set of evaluation tasks to understand the capabilities of multimodal embedding models. This benchmark covers 4 meta tasks and 36 datasets meticulously selected for evaluation.
 
-![Leaderboard](leaderboard.jpg)
+The dataset is published in our paper [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://arxiv.org/abs/2410.05160).
 
+## Dataset Usage
+For each dataset, we have 1000 examples for evaluation. Each example contains a query and a set of targets. Both the query and target could be any combination of image and text. The first one in the candidate list is the groundtruth target.  
+
+## Statistics
+We show the statistics of all the datasets as follows:
+<img width="900" alt="abs" src="statistics.png">
+
+## Per-dataset Results
+We list the performance of different embedding models in the following:
+<img width="900" alt="abs" src="leaderboard.png">
+
+## Submission
+We will set a formal leaderboard soon. If you want to add your results to the leaderboard, please send email to us at ruimeng@salesforce.com.
+
+## Cite Us
+```
+@article{jiang2024vlm2vec,
+  title={VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks},
+  author={Jiang, Ziyan and Meng, Rui and Yang, Xinyi and Yavuz, Semih and Zhou, Yingbo and Chen, Wenhu},
+  journal={arXiv preprint arXiv:2410.05160},
+  year={2024}
+}
+```