SEACrowd/wit

Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. There are more than 12k examples in each of 108 languages, with 53 languages having 100k image-text pairs. Nine languages are spoken in the Southeast Asian region. Since the dataset contains multiple references, following Section 3.2 of the dataset's paper, the seacrowd_imtext subsets specify which reference is used for each data instance's texts via context in metadata.

Languages

ceb, fil, ind, jav, zlm, mya, tha, vie, war

Supported Tasks

Image Captioning

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/wit", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("wit", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("wit"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")

More details on how to load the seacrowd library can be found here.

Dataset Homepage

https://github.com/google-research-datasets/wit

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Attribution Share Alike 3.0 (cc-by-sa-3.0)

Citation

If you are using the Wit dataloader in your work, please cite the following:

@inproceedings{10.1145/3404835.3463257,
    author = {Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
    title = {WIT: Wikipedia-Based Image Text Dataset for Multimodal Multilingual Machine Learning},
    year = {2021},
    isbn = {9781450380379},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3404835.3463257},
    doi = {10.1145/3404835.3463257},
    booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    pages = {2443–2449},
    numpages = {7},
    keywords = {dataset, multimodal, machine learning, wikipedia, multilingual, image-text retrieval, neural networks},
    location = {Virtual Event, Canada},
    series = {SIGIR '21}
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}