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--- |
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license: mit |
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--- |
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## **Dataset Structure Overview** |
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M-BEIR dataset comprises two main components: Query Data and Candidate Pool. |
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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: |
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Query Data (JSONL File) |
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Each line in the Query Data file represents a unique query. The structure of each query JSON object is as follows:: |
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```json |
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{ |
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"qid": "A unique identifier formatted as {dataset_id}:{query_id}", |
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"query_txt": "The text component of the query", |
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"query_img_path": "The file path to the associated query image", |
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"query_modality": "The modality type of the query (text, image or text,image)", |
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"query_src_content": "Additional content from the original dataset, presented as a string by json.dumps()", |
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"pos_cand_list": [ |
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{ |
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"did": "A unique identifier formatted as {dataset_id}:{doc_id}" |
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} |
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// ... more positive candidates |
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], |
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"neg_cand_list": [ |
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{ |
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"did": "A unique identifier formatted as {dataset_id}:{doc_id}" |
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} |
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// ... more negative candidates |
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] |
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} |
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``` |
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Candidate Pool (JSONL File) |
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The Candidate Pool contains potential matching documents for the queries. The structure of each candidate JSON object in this file is as follows:: |
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```json |
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{ |
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"did": "A unique identifier for the document, formatted as {dataset_id}:{doc_id}", |
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"txt": "The text content of the candidate document", |
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"img_path": "The file path to the candidate document's image", |
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"modality": "The modality type of the candidate (e.g., text, image or text,image)", |
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"src_content": "Additional content from the original dataset, presented as a string by json.dumps()" |
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} |
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``` |
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## **How to Use** |
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### Downloading the M-BEIR Dataset |
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Download the dataset files directly from the page. |
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### Decompressing M-BEIR Images |
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After downloading, you will need to decompress the image files. Follow these steps in your terminal: |
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```bash |
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# Navigate to the M-BEIR directory |
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cd path/to/M-BEIR |
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# Combine the split tar.gz files into one |
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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' |
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# Extract the images from the tar.gz file |
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tar -xzf mbeir_images.tar.gz |
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``` |
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## **Citation** |
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Please cite our paper if you use our data, model or code. |
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``` |
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@article{wei2023uniir, |
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title={UniIR: Training and Benchmarking Universal Multimodal Information Retrievers}, |
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author={Wei, Cong and Chen, Yang and Chen, Haonan and Hu, Hexiang and Zhang, Ge and Fu, Jie and Ritter, Alan and Chen, Wenhu}, |
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journal={arXiv preprint arXiv:2311.17136}, |
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year={2023} |
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} |
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``` |
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