{"url":"/dataset/mq2007","name":"MQ2007","full_name":null,"description_markdown":"The **MQ2007** dataset consists of queries, corresponding retrieved documents and labels provided by human experts. The possible relevance labels for each document are “relevant”, “partially relevant”, and “not relevant”.\r\n\r\nSource: [ARSM GRADIENT ESTIMATOR FOR SUPERVISED LEARNING TO RANK](https://arxiv.org/abs/1911.00465)","description_withheld":null,"homepage":"https://www.microsoft.com/en-us/research/project/letor-learning-rank-information-retrieval/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/13062597","title":"Introducing LETOR 4.0 Datasets","first_author":"Tao Qin","url":null},"license":{"name":"Custom","url":"https://www.microsoft.com/en-us/research/project/letor-learning-rank-information-retrieval/?from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fum%2Fbeijing%2Fprojects%2Fletor%2Fletor-agreement.txt#!le"},"modalities":[{"name":"Ranking","url":"/datasets/modality/ranking"}],"tasks":[{"name":"Meta-Learning","url":"/task/meta-learning","datasets_with_task":"/datasets/task/meta-learning"},{"name":"Information Retrieval","url":"/task/information-retrieval","datasets_with_task":"/datasets/task/information-retrieval"},{"name":"Learning-To-Rank","url":"/task/learning-to-rank","datasets_with_task":"/datasets/task/learning-to-rank"}],"languages":[],"variants":["MQ2007"],"data_loaders":[],"num_papers_in_archive":32,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}