m-elio/madove-multilingual-train

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The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at 4ca2fb3
Licence files: No licence file at the top level of the repositoryEvidence: file list at 4ca2fb3
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

If you use this dataset, please cite the following work.

```bibtex
@inproceedings{musacchio-etal-2026-madove,
    title = "{M}a{DOVE}: a Dataset for Multilingual Document {VQA} Evaluation",
    author = "Musacchio, Elio  and
      Siciliani, Lucia  and
      Basile, Pierpaolo  and
      Semeraro, Giovanni  and
      Zampedroni, Mark Federico  and
      Uboldi, Asia Beatrice",
    editor = "T.Y.S.S., Santosh  and
      Agarwal, Amit  and
      Gupta, Vivek  and
      Kementchedjhieva, Yova  and
      Garimella, Aparna  and
      Hou, Yufang",
    booktitle = "The Proceedings of Workshop on Document Intelligence and Understanding",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.docinsights-1.28/",
    pages = "409--427",
    ISBN = "979-8-89176-477-4",
    abstract = "Visual Question Answering (VQA) is one of the most popular tasks in the fields of Computer Vision and Natural Language Processing. The task is to provide an answer to a question related to the contents of a given image. VQA has been expanded to several domains. One of these is document VQA, where the image is a page of a document and the question is related to the textual contents of the page. In this case, the system that is being evaluated has to understand textual contents that are written inside the image, differently from typical VQA where images are natural scenes. However, document

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dataset_licence({ "id": "m-elio/madove-multilingual-train" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0