Datasets:

emrekuruu
/

MP-DocVQA

task_categories:
  - text-retrieval
dataset_info:
  features:
    - name: query
      dtype: string
    - name: image_filename
      dtype: string
    - name: image
      dtype: image
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 2917901918
      num_examples: 6122
  download_size: 2867797655
  dataset_size: 2917901918
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

MP-DocVQA

MP-DocVQA is one of the 11 retrieval benchmarks used in RetrievalRouter: Joint Modality and Architecture Selection for Document Retrieval (EMNLP 2026). Each record pairs a rendered page image, a query, and the page's extracted text, supporting both text-based and multimodal retrieval evaluation.

📄 Paper: https://arxiv.org/pdf/2608.25625
💻 Code: https://github.com/emrekuruu/retrieval-router
🤗 Collection: https://huggingface.co/collections/emrekuruu/retrieval-router

Source benchmark: MMDocIR (Dong et al., 2025). This repository repackages that benchmark for the RetrievalRouter experiments; if you use it, please cite the original source as well.

Citation

@misc{kuru2026retrievalrouterjointmodalityarchitecture,
      title={RetrievalRouter: Joint Modality and Architecture Selection for Document Retrieval}, 
      author={Emre Kuru and Mehmet Onur Keskin and Reza Farahbakhsh and Noel Crespi},
      year={2026},
      eprint={2608.25625},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2608.25625}, 
}

@misc{dong2025mmdocir,
  title={MMDocIR: Benchmarking Multi-Modal Retrieval for Long Documents},
  author={Dong, Kuicai and Chang, Yujing and Goh, Xin Deik and Li, Dexun and Tang, Ruiming and Liu, Yong},
  year={2025},
  eprint={2501.08828},
  archivePrefix={arXiv},
  primaryClass={cs.IR}
}