Document Ranking

CLUE is a Chinese Language Understanding Evaluation benchmark. It consists of different NLU datasets. It is a community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text.

The MQ2008 dataset is a dataset for Learning to Rank. It contains 800 queries with labelled documents.

A dataset on asking Questions for Lack of Clarity in open-domain information-seeking conversations. Qulac presents the first dataset and offline evaluation framework for studying clarifying questions in open-domain information-seeking conversational search systems. Source: https://github.com/aliannejadi/qulac Image Source: https://github.com/aliannejadi/qulac

The datasets are machine learning data, in which queries and urls are represented by IDs. The datasets consist of feature vectors extracted from query-url pairs along with relevance judgment labels: (1) The relevance judgments are obtained from a retired labeling set of a commercial web search engine (Microsoft Bing), which take 5 values from 0 (irrelevant) to 4 (perfectly relevant). (2) The features are basically extracted by us, and are those widely used in the research community. In the data files, each row corresponds to a query-url pair. The first column is relevance label of the pair, the second column is query id, and the following columns are features. The larger value the relevance label has, the more relevant the query-url pair is. A query-url pair is represented by a 136-dimensional feature vector.

DareCzech DaReCzech is a dataset for text relevance ranking in Czech. The dataset consists of more than 1.6M annotated query-documents pairs, which makes it one of the largest available datasets for this task. ### Obtaining the Annotated Data Please, first read a disclaimer that contains the terms of use. If you comply with them, send an email to srch.vyzkum@firma.seznam.cz and the link to the dataset will be sent to you.

Government fiscal policies, particularly annual union budgets, exert significant influence on financial markets. However, real-time analysis of budgetary impacts on sector-specific equity performance remains methodologically challenging and largely unexplored. This study proposes a framework to systematically identify and rank sectors poised to benefit from India's Union Budget announcements. The framework addresses two core tasks: (1) multi-label classification of excerpts from budget transcripts into 81 predefined economic sectors, and (2) performance ranking of these sectors. Leveraging a comprehensive corpus of Indian Union Budget transcripts from 1947 to 2025, we introduce BASIR (Budget-Assisted Sectoral Impact Ranking), an annotated dataset mapping excerpts from budgetary transcripts to sectoral impacts.

The Istella LETOR full dataset is composed of 33,018 queries and 220 features representing each query-document pair. It consists of 10,454,629 examples labeled with relevance judgments ranging from 0 (irrelevant) to 4 (perfectly relevant). The average number of per-query examples is 316. It has been splitted in train and test sets according to a 80%-20% scheme.