README.md

---
license: "mit"
pretty_name: "M-BEIR"
task_categories:
- text-retrieval
- text-to-image
- image-to-text
- visual-question-answering
language:
- "en"
configs:
- config_name: query
  data_files:
  - split: train
    path: "query/train/*.jsonl"
  - split: union_train
    path: "query/union_train/*.jsonl"
  - split: val
    path: "query/val/*.jsonl"
  - split: union_val
    path: "query/union_val/*.jsonl"
  - split: test
    path: "query/test/*.jsonl"
- config_name: mbeir_local
  data_files:
  - split: mbeir_local
    path: "cand_pool/local/*.jsonl"
- config_name: mbeir_global
  data_files:
  - split: mbeir_global
    path: "cand_pool/global/mbeir_union_test_cand_pool.jsonl"
- config_name: instructions
  sep: "\t"
  data_files:
  - split: instructions
    path: "instructions/*.tsv"
- config_name: qrels
  data_files:
  - split: train
    path: "qrels/train/*.txt"
  - split: val
    path: "qrels/val/*.txt"
  - split: test
    path: "qrels/test/*.txt"
---

## **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:

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::
```json
{
  "qid": "A unique identifier formatted as {dataset_id}:{query_id}",
  "query_txt": "The text component of the query",
  "query_img_path": "The file path to the associated query image",
  "query_modality": "The modality type of the query (text, image or text,image)",
  "query_src_content": "Additional content from the original dataset, presented as a string by json.dumps()",
  "pos_cand_list": [
    {
      "did": "A unique identifier formatted as {dataset_id}:{doc_id}"
    }
    // ... more positive candidates
  ],
  "neg_cand_list": [
    {
      "did": "A unique identifier formatted as {dataset_id}:{doc_id}"
    }
    // ... more negative candidates
  ]
}
```

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::
```json
{
  "did": "A unique identifier for the document, formatted as {dataset_id}:{doc_id}",
  "txt": "The text content of the candidate document",
  "img_path": "The file path to the candidate document's image",
  "modality": "The modality type of the candidate (e.g., text, image or text,image)",
  "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}
}
```