Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face

By Christopher Akiki, Odunayo Ogundepo, Aleksandra Piktus, Xinyu Crystina Zhang, Akintunde Oladipo, Jimmy J. Lin, Martin Potthast. 2023

5 citations recorded by the source corpus.

We present Spacerini, a tool that integrates the Pyserini toolkit for reproducible information retrieval research with Hugging Face to enable the seamless construction and deployment of interactive search engines. Spacerini makes state-of-the-art sparse and dense retrieval models more accessible to non-IR practitioners while minimizing deployment effort. This is useful for NLP researchers who want to better understand and validate their research by performing qualitative analyses of training corpora, for IR researchers who want to demonstrate new retrieval models integrated into the growing Pyserini ecosystem, and for third parties reproducing the work of other researchers. Spacerini is open source and includes utilities for loading, preprocessing, indexing, and deploying search engines locally and remotely. We demonstrate a portfolio of 13 search engines created with Spacerini for different use cases.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

StarCoder: may the source be with you! (2023, background): “This pressing need for understanding data (Mitchell et al., 2022) is being increasingly recognized and operationalized in the form of dataset inspection tools and toolkits (Akiki et al., 2023; Marone & Van Durme, 2023; Piktus et al., 2023).”