Update ReadME

README.md

@@ -1,7 +1,7 @@
 ---
 license: mit
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-## Dataset Structure Overview
+## **Dataset Structure Overview**
 M-BEIR dataset comprises two main components: Query Data and Candidate Pool. 
 Each of these sections consists of structured entries in JSONL format (JSON Lines), meaning each line is a valid JSON object. Below is a detailed breakdown of the components and their respective fields:
 
@@ -26,3 +26,33 @@ The Candidate Pool contains potential matching documents for the queries. Each l
 - Candidate Image Path (`img_path`): The file path to the candidate document's image.
 - Candidate Modality (`modality`): The modality type of the candidate (e.g., text, image or text,image).
 - Source Content (`src_content`): Additional content from the original dataset, presented as a string by json.dumps().
+
+## **How to Use**
+### Downloading the M-BEIR Dataset
+Download the dataset files directly from the page.
+
+### Decompressing M-BEIR Images
+After downloading, you will need to decompress the image files. Follow these steps in your terminal:
+```bash
+# Navigate to the M-BEIR directory
+cd path/to/M-BEIR
+
+# Combine the split tar.gz files into one
+sh -c 'cat mbeir_images.tar.gz.part-00 mbeir_images.tar.gz.part-01 mbeir_images.tar.gz.part-02 mbeir_images.tar.gz.part-03 > mbeir_images.tar.gz'
+
+# Extract the images from the tar.gz file
+tar -xzf mbeir_images.tar.gz
+```
+
+## **Citation**
+
+Please cite our paper if you use our data, model or code. 
+
+```
+@article{wei2023uniir,
+  title={UniIR: Training and Benchmarking Universal Multimodal Information Retrievers},
+  author={Wei, Cong and Chen, Yang and Chen, Haonan and Hu, Hexiang and Zhang, Ge and Fu, Jie and Ritter, Alan and Chen, Wenhu},
+  journal={arXiv preprint arXiv:2311.17136},
+  year={2023}
+}
+```