license: mit
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, formatted as a JSON object with the following fields:
- Query ID (
qid): A unique identifier formatted as{dataset_id}:{query_id}. - Query Text (
query_txt): The text component of the query. - Query Image Path (
query_img_path): The file path to the associated query image. - Query Modality (
query_modality): The modality type of the query (text, image or text,image) - Query Source Content (
query_src_content): Additional content from the original dataset, presented as a string by json.dumps(). - Positive Candidates List (
pos_cand_list): A list of positive candidate documents, where each entry includes:- Document ID (
did): A unique identifier formatted as{dataset_id}:{doc_id}.
- Document ID (
- Negative Candidates List (
neg_cand_list): A list of negative candidate documents, where each entry includes:- Document ID (
did): A unique identifier formatted as{dataset_id}:{doc_id}.
- Document ID (
Candidate Pool (JSONL File) The Candidate Pool contains potential matching documents for the queries. Each line in this file is a JSON object representing a candidate document with these fields:
- Document ID (
did): A unique identifier for the document, formatted as{dataset_id}:{doc_id}. - Candidate Text (
txt): The text content of the candidate document. - 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:
# 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}
}