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---
license: cc-by-4.0
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'80': Racecourse
'81': RadioHost
'82': RadioStation
'83': Religious
'84': Reptile
'85': Restaurant
'86': Road
'87': RoadTunnel
'88': RollerCoaster
'89': RugbyClub
'90': RugbyLeague
'91': Saint
'92': School
'93': ScreenWriter
'94': Senator
'95': ShoppingMall
'96': Skater
'97': SoccerLeague
'98': SoccerManager
'99': SoccerPlayer
'100': SoccerTournament
'101': SportsTeamMember
'102': SumoWrestler
'103': TelevisionStation
'104': TennisTournament
'105': TradeUnion
'106': University
'107': Village
'108': VoiceActor
'109': Volcano
'110': WrestlingEvent
splits:
- name: train
num_bytes: 30248140475.625
num_examples: 23099
- name: test
num_bytes: 6471322916.25
num_examples: 4950
- name: validation
num_bytes: 6507120137.25
num_examples: 4950
download_size: 42958276266
dataset_size: 43226583529.125
- config_name: wiki-doc-zh-merged
features:
- name: image
dtype: image
- name: filename
dtype: string
- name: words
sequence: string
- name: ocr_bboxes
sequence:
sequence: int64
- name: label
dtype:
class_label:
names:
'0': AcademicJournal
'1': AdultActor
'2': Album
'3': AmateurBoxer
'4': Ambassador
'5': AmericanFootballPlayer
'6': Amphibian
'7': AnimangaCharacter
'8': Anime
'9': Arachnid
'10': Baronet
'11': BasketballTeam
'12': BeautyQueen
'13': BroadcastNetwork
'14': BusCompany
'15': BusinessPerson
'16': CanadianFootballTeam
'17': Canal
'18': Cardinal
'19': Cave
'20': ChristianBishop
'21': ClassicalMusicArtist
'22': ClassicalMusicComposition
'23': CollegeCoach
'24': Comedian
'25': ComicsCreator
'26': Congressman
'27': Conifer
'28': Convention
'29': Cricketer
'30': Crustacean
'31': CultivatedVariety
'32': Cycad
'33': Dam
'34': Economist
'35': Engineer
'36': Entomologist
'37': EurovisionSongContestEntry
'38': Fern
'39': FilmFestival
'40': Fish
'41': FootballMatch
'42': Glacier
'43': GolfTournament
'44': Governor
'45': Gymnast
'46': Historian
'47': IceHockeyLeague
'48': Insect
'49': Journalist
'50': Judge
'51': Lighthouse
'52': Magazine
'53': Mayor
'54': Medician
'55': MemberOfParliament
'56': MilitaryPerson
'57': Model
'58': Mollusca
'59': Monarch
'60': Moss
'61': Mountain
'62': MountainPass
'63': MountainRange
'64': MusicFestival
'65': Musical
'66': MythologicalFigure
'67': Newspaper
'68': Noble
'69': OfficeHolder
'70': Other
'71': Philosopher
'72': Photographer
'73': PlayboyPlaymate
'74': Poem
'75': Poet
'76': Pope
'77': President
'78': PrimeMinister
'79': PublicTransitSystem
'80': Racecourse
'81': RadioHost
'82': RadioStation
'83': Religious
'84': Reptile
'85': Restaurant
'86': Road
'87': RoadTunnel
'88': RollerCoaster
'89': RugbyClub
'90': RugbyLeague
'91': Saint
'92': School
'93': ScreenWriter
'94': Senator
'95': ShoppingMall
'96': Skater
'97': SoccerLeague
'98': SoccerManager
'99': SoccerPlayer
'100': SoccerTournament
'101': SportsTeamMember
'102': SumoWrestler
'103': TelevisionStation
'104': TennisTournament
'105': TradeUnion
'106': University
'107': Village
'108': VoiceActor
'109': Volcano
'110': WrestlingEvent
splits:
- name: train
num_bytes: 30382212749.625
num_examples: 23099
- name: test
num_bytes: 6499933446.25
num_examples: 4950
- name: validation
num_bytes: 6536010774.25
num_examples: 4950
download_size: 43027961181
dataset_size: 43418156970.125
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: multieurlex-doc-bg
data_files:
- split: train
path: multieurlex-doc-bg/train-*
- split: validation
path: multieurlex-doc-bg/validation-*
- split: test
path: multieurlex-doc-bg/test-*
- config_name: multieurlex-doc-cs
data_files:
- split: train
path: multieurlex-doc-cs/train-*
- split: validation
path: multieurlex-doc-cs/validation-*
- split: test
path: multieurlex-doc-cs/test-*
- config_name: multieurlex-doc-da
data_files:
- split: train
path: multieurlex-doc-da/train-*
- split: validation
path: multieurlex-doc-da/validation-*
- split: test
path: multieurlex-doc-da/test-*
- config_name: multieurlex-doc-de
data_files:
- split: train
path: multieurlex-doc-de/train-*
- split: test
path: multieurlex-doc-de/test-*
- split: validation
path: multieurlex-doc-de/validation-*
- config_name: multieurlex-doc-el
data_files:
- split: train
path: multieurlex-doc-el/train-*
- split: validation
path: multieurlex-doc-el/validation-*
- split: test
path: multieurlex-doc-el/test-*
- config_name: multieurlex-doc-en
data_files:
- split: train
path: multieurlex-doc-en/train-*
- split: test
path: multieurlex-doc-en/test-*
- split: validation
path: multieurlex-doc-en/validation-*
- config_name: multieurlex-doc-es
data_files:
- split: train
path: multieurlex-doc-es/train-*
- split: test
path: multieurlex-doc-es/test-*
- split: validation
path: multieurlex-doc-es/validation-*
- config_name: multieurlex-doc-et
data_files:
- split: train
path: multieurlex-doc-et/train-*
- split: validation
path: multieurlex-doc-et/validation-*
- split: test
path: multieurlex-doc-et/test-*
- config_name: multieurlex-doc-fi
data_files:
- split: train
path: multieurlex-doc-fi/train-*
- split: validation
path: multieurlex-doc-fi/validation-*
- split: test
path: multieurlex-doc-fi/test-*
- config_name: multieurlex-doc-fr
data_files:
- split: train
path: multieurlex-doc-fr/train-*
- split: validation
path: multieurlex-doc-fr/validation-*
- split: test
path: multieurlex-doc-fr/test-*
- config_name: multieurlex-doc-hr
data_files:
- split: train
path: multieurlex-doc-hr/train-*
- split: validation
path: multieurlex-doc-hr/validation-*
- split: test
path: multieurlex-doc-hr/test-*
- config_name: multieurlex-doc-hu
data_files:
- split: train
path: multieurlex-doc-hu/train-*
- split: validation
path: multieurlex-doc-hu/validation-*
- split: test
path: multieurlex-doc-hu/test-*
- config_name: multieurlex-doc-it
data_files:
- split: train
path: multieurlex-doc-it/train-*
- split: validation
path: multieurlex-doc-it/validation-*
- split: test
path: multieurlex-doc-it/test-*
- config_name: multieurlex-doc-nl
data_files:
- split: train
path: multieurlex-doc-nl/train-*
- split: validation
path: multieurlex-doc-nl/validation-*
- split: test
path: multieurlex-doc-nl/test-*
- config_name: multieurlex-doc-pl
data_files:
- split: train
path: multieurlex-doc-pl/train-*
- split: validation
path: multieurlex-doc-pl/validation-*
- split: test
path: multieurlex-doc-pl/test-*
- config_name: multieurlex-doc-pt
data_files:
- split: train
path: multieurlex-doc-pt/train-*
- split: validation
path: multieurlex-doc-pt/validation-*
- split: test
path: multieurlex-doc-pt/test-*
- config_name: multieurlex-doc-ro
data_files:
- split: train
path: multieurlex-doc-ro/train-*
- split: validation
path: multieurlex-doc-ro/validation-*
- split: test
path: multieurlex-doc-ro/test-*
- config_name: multieurlex-doc-sv
data_files:
- split: train
path: multieurlex-doc-sv/train-*
- split: validation
path: multieurlex-doc-sv/validation-*
- split: test
path: multieurlex-doc-sv/test-*
- config_name: wiki-doc-ar-img
data_files:
- split: train
path: wiki-doc-ar-img/train-*
- split: test
path: wiki-doc-ar-img/test-*
- split: validation
path: wiki-doc-ar-img/validation-*
- config_name: wiki-doc-ar-merged
data_files:
- split: train
path: wiki-doc-ar-merged/train-*
- split: test
path: wiki-doc-ar-merged/test-*
- split: validation
path: wiki-doc-ar-merged/validation-*
- config_name: wiki-doc-de-merged
data_files:
- split: train
path: wiki-doc-de-merged/train-*
- split: validation
path: wiki-doc-de-merged/validation-*
- split: test
path: wiki-doc-de-merged/test-*
- config_name: wiki-doc-en-merged
data_files:
- split: train
path: wiki-doc-en-merged/train-*
- split: validation
path: wiki-doc-en-merged/validation-*
- split: test
path: wiki-doc-en-merged/test-*
- config_name: wiki-doc-es-merged
data_files:
- split: train
path: wiki-doc-es-merged/train-*
- split: validation
path: wiki-doc-es-merged/validation-*
- split: test
path: wiki-doc-es-merged/test-*
- config_name: wiki-doc-fr-merged
data_files:
- split: train
path: wiki-doc-fr-merged/train-*
- split: validation
path: wiki-doc-fr-merged/validation-*
- split: test
path: wiki-doc-fr-merged/test-*
- config_name: wiki-doc-it-merged
data_files:
- split: train
path: wiki-doc-it-merged/train-*
- split: validation
path: wiki-doc-it-merged/validation-*
- split: test
path: wiki-doc-it-merged/test-*
- config_name: wiki-doc-ja-img
data_files:
- split: train
path: wiki-doc-ja-img/train-*
- split: test
path: wiki-doc-ja-img/test-*
- split: validation
path: wiki-doc-ja-img/validation-*
- config_name: wiki-doc-ja-merged
data_files:
- split: train
path: wiki-doc-ja-merged/train-*
- split: validation
path: wiki-doc-ja-merged/validation-*
- split: test
path: wiki-doc-ja-merged/test-*
- config_name: wiki-doc-pt-img
data_files:
- split: train
path: wiki-doc-pt-img/train-*
- split: test
path: wiki-doc-pt-img/test-*
- split: validation
path: wiki-doc-pt-img/validation-*
- config_name: wiki-doc-pt-merged
data_files:
- split: train
path: wiki-doc-pt-merged/train-*
- split: validation
path: wiki-doc-pt-merged/validation-*
- split: test
path: wiki-doc-pt-merged/test-*
- config_name: wiki-doc-pt-merged-v2
data_files:
- split: train
path: wiki-doc-pt-merged-v2/train-*
- split: validation
path: wiki-doc-pt-merged-v2/validation-*
- split: test
path: wiki-doc-pt-merged-v2/test-*
- config_name: wiki-doc-zh-img
data_files:
- split: train
path: wiki-doc-zh-img/train-*
- split: test
path: wiki-doc-zh-img/test-*
- split: validation
path: wiki-doc-zh-img/validation-*
- config_name: wiki-doc-zh-merged
data_files:
- split: train
path: wiki-doc-zh-merged/train-*
- split: test
path: wiki-doc-zh-merged/test-*
- split: validation
path: wiki-doc-zh-merged/validation-*
---
## 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):
© European Union, 1998-2021
The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
### 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.",
}
```