Additional Information

To load the dataset,

import datasets
ds = datasets.load_dataset("AmazonScience/MultilingualMultiModalClassification", data_dir="wiki-doc-ar-merged")
print(ds)
DatasetDict({
    train: Dataset({
        features: ['image', 'filename', 'words', 'ocr_bboxes', 'label'],
        num_rows: 8129
    })
    validation: Dataset({
        features: ['image', 'filename', 'words', 'ocr_bboxes', 'label'],
        num_rows: 1742
    })
    test: Dataset({
        features: ['image', 'filename', 'words', 'ocr_bboxes', 'label'],
        num_rows: 1743
    })
})

# In case you encountered `NonMatchingSplitsSizesError`, try out the following:
# from datasets import Image, Value, Sequence, ClassLabel, Features
# features = Features({'image': Image(mode=None, decode=True, id=None), 'filename': Value(dtype='string', id=None), 'words': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ocr_bboxes': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'label': ClassLabel(names=['AcademicJournal', 'AdultActor', 'Album', 'AmateurBoxer', 'Ambassador', 'AmericanFootballPlayer', 'Amphibian', 'AnimangaCharacter', 'Anime', 'Arachnid', 'Baronet', 'BasketballTeam', 'BeautyQueen', 'BroadcastNetwork', 'BusCompany', 'BusinessPerson', 'CanadianFootballTeam', 'Canal', 'Cardinal', 'Cave', 'ChristianBishop', 'ClassicalMusicArtist', 'ClassicalMusicComposition', 'CollegeCoach', 'Comedian', 'ComicsCreator', 'Congressman', 'Conifer', 'Convention', 'Cricketer', 'Crustacean', 'CultivatedVariety', 'Cycad', 'Dam', 'Economist', 'Engineer', 'Entomologist', 'EurovisionSongContestEntry', 'Fern', 'FilmFestival', 'Fish', 'FootballMatch', 'Glacier', 'GolfTournament', 'Governor', 'Gymnast', 'Historian', 'IceHockeyLeague', 'Insect', 'Journalist', 'Judge', 'Lighthouse', 'Magazine', 'Mayor', 'Medician', 'MemberOfParliament', 'MilitaryPerson', 'Model', 'Mollusca', 'Monarch', 'Moss', 'Mountain', 'MountainPass', 'MountainRange', 'MusicFestival', 'Musical', 'MythologicalFigure', 'Newspaper', 'Noble', 'OfficeHolder', 'Other', 'Philosopher', 'Photographer', 'PlayboyPlaymate', 'Poem', 'Poet', 'Pope', 'President', 'PrimeMinister', 'PublicTransitSystem', 'Racecourse', 'RadioHost', 'RadioStation', 'Religious', 'Reptile', 'Restaurant', 'Road', 'RoadTunnel', 'RollerCoaster', 'RugbyClub', 'RugbyLeague', 'Saint', 'School', 'ScreenWriter', 'Senator', 'ShoppingMall', 'Skater', 'SoccerLeague', 'SoccerManager', 'SoccerPlayer', 'SoccerTournament', 'SportsTeamMember', 'SumoWrestler', 'TelevisionStation', 'TennisTournament', 'TradeUnion', 'University', 'Village', 'VoiceActor', 'Volcano', 'WrestlingEvent'], id=None)})
# ds = datasets.load_dataset("AmazonScience/MultilingualMultiModalClassification", data_dir="wiki-doc-ar-merged", features=features, verification_mode="no_checks")

Licensing Information

Wiki

Each image is licensed under original provider.

Any additional work provided by current work is provided under CC-BY-SA-4.0 following the Wikipedia license.

MultiEURLEX

We provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0):

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Citation Information

@inproceedings{fujinuma-etal-2023-multi,
    title = "A Multi-Modal Multilingual Benchmark for Document Image Classification",
    author = "Fujinuma, Yoshinari  and
      Varia, Siddharth  and
      Sankaran, Nishant  and
      Appalaraju, Srikar  and
      Min, Bonan  and
      Vyas, Yogarshi",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.958",
    doi = "10.18653/v1/2023.findings-emnlp.958",
    pages = "14361--14376",
    abstract = "Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents. We show that the only existing dataset for this task (Lewis et al., 2006) has several limitations and we introduce two newly curated multilingual datasets WIKI-DOC and MULTIEURLEX-DOC that overcome these limitations. We further undertake a comprehensive study of popular visually-rich document understanding or Document AI models in previously untested setting in document image classification such as 1) multi-label classification, and 2) zero-shot cross-lingual transfer setup. Experimental results show limitations of multilingual Document AI models on cross-lingual transfer across typologically distant languages. Our datasets and findings open the door for future research into improving Document AI models.",
}