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Pyserini is an easy-to-use Python toolkit that supports replicable IRresearch by providing effective first-stage retrieval in a multi-stage rankingarchitecture. Our toolkit is self-contained as a standard Python package andcomes with queries, relevance judgments, pre-built indexes, and evaluationscripts for many commonly used IR test collections. We aim to support, out ofthe box, the entire research lifecycle of efforts aimed at improving rankingwith modern neural approaches. In particular, Pyserini supports sparseretrieval (e.g., BM25 scoring using bag-of-words representations), denseretrieval (e.g., nearest-neighbor search on transformer-encodedrepresentations), as well as hybrid retrieval that integrates both approaches.This paper provides an overview of toolkit features and presents empiricalresults that illustrate its effectiveness on two popular ranking tasks. We alsodescribe how our group has built a culture of replicability through sharednorms and tools that enable rigorous automated testing.

Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations