Update README.md

README.md

@@ -42,21 +42,52 @@ configs:
     path: "qrels/test/*.txt"
 ---
 
-## UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
+### **UniIR: Training and Benchmarking Universal Multimodal Information Retrievers**
+[**🌐 Homepage**](https://tiger-ai-lab.github.io/UniIR/) | [**🤗 Paper**](https://huggingface.co/papers/2311.17136) | [**📖 arXiv**](https://arxiv.org/pdf/2311.17136.pdf)  | [**GitHub**](https://github.com/TIGER-AI-Lab/UniIR)
 
-Project Page: [https://tiger-ai-lab.github.io/UniIR/](https://tiger-ai-lab.github.io/UniIR/)
 
-Paper: [https://arxiv.org/pdf/2311.17136.pdf](https://arxiv.org/pdf/2311.17136.pdf)
+## **Dataset Summary**
 
-Code: [https://github.com/TIGER-AI-Lab/UniIR](https://github.com/TIGER-AI-Lab/UniIR)
+M-BEIR, the **M**ultimodal **BE**nchmark for **I**nstructed **R**etrieval, is a comprehensive large-scale retrieval benchmark designed to train and evaluate unified multimodal retrieval models (**UniIR models**).
+The M-BEIR benchmark comprises eight multimodal retrieval tasks and ten datasets from a variety of domains and sources. 
+Each task is accompanied by human-authored instructions, encompassing 1.5 million queries and a pool of 5.6 million retrieval candidates in total.
 
 
 ## **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:
+The M-BEIR dataset is structured into five primary components: Query Data, Candidate Pool, Instructions, Qrels, and Images.
 
-Query Data (JSONL File)
-Each line in the Query Data file represents a unique query. The structure of each query JSON object is as follows::
+### Query Data
+
+Below is the directory structure for the query data:
+```
+query/
+│
+├── train/
+│   ├── mbeir_cirr_train.jsonl
+│   ├── mbeir_edis_train.jsonl
+│   ...
+├── union_train/
+│   └── mbeir_union_up_train.jsonl
+├── val/
+│   ├── mbeir_visualnews_task0_val.jsonl
+│   ├── mbeir_visualnews_task3_val.jsonl
+│   ...
+└── test/
+    ├── mbeir_visualnews_task0_test.jsonl
+    ├── mbeir_visualnews_task3_test.jsonl
+    ...
+```
+`train`: Contains all the training data from 8 different datasets formatted in the M-BEIR style.
+
+`mbeir_union_up_train.jsonl`: This file is the default training data for in-batch contrastive training specifically designed for UniIR models. 
+It aggregates all the data from the train directory and datasets with relatively smaller sizes have been upsampled to balance the training process.
+
+`val`: Contains separate files for validation queries, organized by task.
+
+`test`: Contains separate files for test queries, organized by task.
+
+Every M-BEIR query instance has at least one positive candidate data and possibly no negative candidate data
+Each line in a Query Data file represents a unique query. The structure of each query JSON object is as follows::
 ```json
 {
   "qid": "A unique identifier formatted as {dataset_id}:{query_id}",
@@ -79,8 +110,30 @@ Each line in the Query Data file represents a unique query. The structure of eac
 }
 ```
 
-Candidate Pool (JSONL File)
-The Candidate Pool contains potential matching documents for the queries. The structure of each candidate JSON object in this file is as follows::
+### Candidate Pool
+The Candidate Pool contains potential matching documents for the queries. 
+
+#### M-BEIR_5.6M
+Within the global directory, the default retrieval setting requires models to retrieve positive candidates from a heterogeneous pool encompassing various modalities and domains. 
+The M-BEIR's global candidate pool, comprising 5.6 million candidates, includes the retrieval corpus from all tasks and datasets.
+
+#### M-BEIR_local
+Within the local directory, we provide dataset-task-specific pool as M-BEIR_local. Dataset-task-specific pool contains homogeneous candidates that originate from by the original dataset.
+
+Below is the directory structure for the candidate pool:
+```
+cand_pool/
+│
+├── global/
+│   ├── mbeir_union_val_cand_pool.jsonl
+│   └──mbeir_union_test_cand_pool.jsonl
+│
+└── local/
+    ├── mbeir_visualnews_task0_cand_pool.jsonl
+    ├── mbeir_visualnews_task3_cand_pool.jsonl
+    ...
+```
+The structure of each candidate JSON object in cand_pool file is as follows::
 ```json
 {
   "did": "A unique identifier for the document, formatted as {dataset_id}:{doc_id}",
@@ -91,9 +144,16 @@ The Candidate Pool contains potential matching documents for the queries. The st
 }
 ```
 
+### Instructions
+`query_instructions.tsv` contains human-authorized instructions within the UniIR framework. Each task is accompanied by four human-authored instructions.
+
+### Qrels
+Within the `qrels` directory, you will find qrels for both the validation and test sets. These files serve the purpose of evaluating UniIR models.
+
 ## **How to Use**
 ### Downloading the M-BEIR Dataset
-Download the dataset files directly from the page.
+Clone the M-BEIR repo from the current Page. 
+Ensure that Git LFS (Large File Storage) is installed on your system, as it will download the required data files.
 
 ### Decompressing M-BEIR Images
 After downloading, you will need to decompress the image files. Follow these steps in your terminal:
@@ -107,9 +167,12 @@ sh -c 'cat mbeir_images.tar.gz.part-00 mbeir_images.tar.gz.part-01 mbeir_images.
 # Extract the images from the tar.gz file
 tar -xzf mbeir_images.tar.gz
 ```
+Now, you are ready to use the M-BEIR benchmark.
 
-## **Citation**
+### Dataloader and Evaluation Pipeline
+We offer a dedicated dataloader and evaluation pipeline for the M-BEIR benchmark. Please refer to [**GitHub Repo**](https://github.com/TIGER-AI-Lab/UniIR) for detailed information.
 
+## **Citation**
 Please cite our paper if you use our data, model or code. 
 
 ```