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Over the past years, deep learning methods allowed for new state-of-the-artresults in ad-hoc information retrieval. However such methods usually requirelarge amounts of annotated data to be effective. Since most standard ad-hocinformation retrieval datasets publicly available for academic research (e.g.Robust04, ClueWeb09) have at most 250 annotated queries, the recent deeplearning models for information retrieval perform poorly on these datasets.These models (e.g. DUET, Conv-KNRM) are trained and evaluated on data collectedfrom commercial search engines not publicly available for academic researchwhich is a problem for reproducibility and the advancement of research. In thispaper, we propose WIKIR: an open-source toolkit to automatically buildlarge-scale English information retrieval datasets based on Wikipedia. WIKIR ispublicly available on GitHub. We also provide wikIR78k and wikIRS78k: twolarge-scale publicly available datasets that both contain 78,628 queries and3,060,191 (query, relevant documents) pairs.

WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset