README.md

metadata

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::

{
  "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::

{
  "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:

# 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}
}