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MS MARCO Web Search is a large-scale information-rich Web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and query distribution, provides rich information for various kinds of downstream tasks. It incorporates the largest open web document dataset, ClueWeb22, as our document corpus. ClueWeb22 includes about 10 billion high-quality web pages, sufficiently large to serve as representative web-scale data. It also contains rich information from the web pages, such as visual representation rendered by web browsers, raw HTML structure, clean text, semantic annotations, language and topic tags labeled by industry document understanding systems, etc. MS MARCO Web Search further contains 10 million unique queries from 93 languages with millions of relevant labeled query-document pairs collected from the search log of the Microsoft Bing search engine to serve as the query set.

It offers a retrieval benchmark on 100 million document set with three web retrieval challenge tasks that demands innovations in both machine learning and information retrieval system research domains: embedding model, embedding retrieval, and end-to-end retrieval challenges. The main goal of the leaderboard is to study what retrieval methods work best and what retrieval methods are cost-efficient when a large amount of data is available.

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