Update README.md

README.md

@@ -1,3 +1,46 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-to-image
+- visual-question-answering
+language:
+- en
+---
+# Data statices of M2RAG
+
+Click the links below to view our paper and Github project.
+<a href='https://arxiv.org/abs/2502.17297'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a><a href='https://github.com/NEUIR/M2RAG'><img src="https://img.shields.io/badge/Github-M2RAG-blue?logo=Github"></a>
+
+If you find this work useful, please cite our paper  and give us a shining star 🌟 in Github 
+
+```
+@misc{liu2025benchmarkingretrievalaugmentedgenerationmultimodal,
+      title={Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts}, 
+      author={Zhenghao Liu and Xingsheng Zhu and Tianshuo Zhou and Xinyi Zhang and Xiaoyuan Yi and Yukun Yan and Yu Gu and Ge Yu and Maosong Sun},
+      year={2025},
+      eprint={2502.17297},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2502.17297}, 
+}
+```
+## 🎃 Overview
+
+The **M²RAG** benchmark evaluates Multi-modal Large Language Models (MLLMs) by using multi-modal retrieved documents to answer questions. It includes four tasks: image captioning, multi-modal QA, fact verification, and image reranking, assessing MLLMs’ ability to leverage knowledge from multi-modal contexts. 
+
+<p align="center">
+  <img align="middle" src="https://raw.githubusercontent.com/NEUIR/M2RAG/main/assets/m2rag.png" style="width: 600px;" alt="m2rag"/>
+</p>
+
+## 🎃 Data Storage Structure
+The data storage structure of M2RAG is as follows：
+```
+M2RAG/
+    ├──fact_verify/
+    ├──image_cap/
+    ├──image_rerank/
+    ├──mmqa/
+    ├──imgs.lineidx.new
+    └──imgs.tsv
+```
+❗️Note: To obtain the ```imgs.tsv```, you can follow the instructions in the [WebQA](https://github.com/WebQnA/WebQA?tab=readme-ov-file#download-data) project. Specifically, you need to first download all the data from the folder [WebQA_imgs_7z_chunks](https://drive.google.com/drive/folders/19ApkbD5w0I5sV1IeQ9EofJRyAjKnA7tb), and then run the command ``` 7z x imgs.7z.001```to unzip and merge all chunks to get the imgs.tsv.