The system that includes 11 million images and their contexts in 108 languages is intended for artificial intelligence training. The data series is available to the public and Google will also hold a WIT-based application competition together with the Wikimedia Foundation and the KEGGLE website
Google is today celebrating its 23rd anniversary. Google AI, one of the company’s junior divisions, announced WIT: a data series linking text images and the Wikipedia context open to the general public for artificial intelligence training.
Research, Google Research has published the details of Google AI’s announcement of WIT – a huge series of images from Wikipedia and their adaptation to text in many languages - for artificial intelligence training.
- the two write: “Modern models of images and descriptions in rich multilingual texts can help to understand the The connection between images and text. “
” Traditionally, these data sets were created by manually adding captions to images, or scanning the web and extracting the alternative text as captions for images. While the previous approach allows Higher quality data, the intensive manual interpretation process limits the amount of data that can be generated. Kim. Another shortcoming of existing data sets is the lack of coverage in non-English languages. The speaker naturally led us to ask: Is it possible to overcome these limitations and create a high-quality, large and multilingual data set with a variety of contents? “
” Today we present the data set Of Wikipedia-based texts and images (WIT), created by extracting multiple texts in image descriptions from Wikipedia articles and image links in Wikipedia. We conducted a rigorous screening that ensured that only high-quality text-image kits would be scanned. As outlined in “WIT: Wikipedia-Based Image Text Data Kit for Multilingual Multilingual Multilingual Machine Learning” presented at SIGIR ’21, the result was a repository of 37.5 million rich text and image examples including 11.5 million Unique images and their descriptions in 108 languages. The WIT dataset is available for download and use under a Creative Commons license. “
The unique advantages of the WIT dataset are:
Size: WIT is the largest multi-modal data set of text-examples Image available to the public. Multilingual: 108 languages WIT has 10 languages more than any other data set. Contextual information: Unlike typical multimodal data systems, which have only one caption per image, WIT includes information that includes page-level and section-level relationships. Entities in the real world: Wikipedia, being a broad knowledge base, is rich in real-world entities represented in WIT.
- Of traditional data sets were around 80 per cent, while the WIT test set gave results around 40 per cent in good resource languages and around 30 per cent for resource-free languages. We hope this in turn can help researchers build stronger and more powerful models.
WIT data set and competition with Wikimedia and Kegel
In addition, we are pleased to announce that we are collaborating with Wikimedia Research and some external collaborators to organize a contest with a kit WIT TESTS . We are hosting this competition in Kegel.
- The competition is a task of retrieving image text. Given a set of images and captions, the task is to retrieve the appropriate captions for each image. -50 for the wide range of database testing training. Kaggle will host all image data in addition to the WIT data set itself. Furthermore, competitors will have access to the Kegel Discussion Forum in order to share code and collaborate. This allows anyone interested in modeling to get started and run experiments easily. We are excited and looking forward to the creation of the WIT database and Wikipedia images on the Kaggle platform.
“For any questions, please contact We’d love to hear how you use the WIT dataset. “The researchers conclude.
FOR SCIENTIFIC RESEARCH
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