SEACrowd/wit

Wit数据集是一个基于维基百科的大型多模态多语言数据集，包含3760万条实体丰富的图像-文本对，涵盖108种维基百科语言。每种语言至少有1.2万个样本，其中53种语言有10万对图像-文本。该数据集特别关注东南亚地区的九种语言，包括ceb、fil、ind、jav、zlm、mya、tha、vie和war。数据集支持图像描述任务，并提供了多种加载方式，包括使用`datasets`库和`seacrowd`库。数据集的主页和版本信息也在README中提供，许可证为Creative Commons Attribution Share Alike 3.0 (cc-by-sa-3.0)。

The Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset 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. The dataset includes nine languages spoken in the Southeast Asian region. It supports the task of image captioning and provides multiple ways to load the dataset, including using the `datasets` library and the `seacrowd` library. The datasets homepage and version information are provided in the README, and it is licensed under Creative Commons Attribution Share Alike 3.0 (cc-by-sa-3.0).

原始信息汇总

Wikipedia-based Image Text (WIT) Dataset

概述

• 名称: Wikipedia-based Image Text (WIT) Dataset
• 类型: 多模态多语言数据集
• 规模: 包含3760万条实体丰富的图像-文本示例，涵盖1150万张独特图像，跨越108种维基百科语言。
• 语言: 108种维基百科语言，其中53种语言拥有超过10万条图像-文本对。
• 区域: 东南亚地区有9种语言。

语言

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

支持的任务

• 图像描述生成 (Image Captioning)

数据集版本

• 源版本: 1.0.0
• SEACrowd版本: 2024.06.20

许可证

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

引用

@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} }