mlsanityData to 14 Oct 2026

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

arXiv 2302.14534 ↗First posted 28 Feb 2023cs.IR cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

17 of 33 references.

References · 17

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5 more
  1. 2022Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamicscited by 26
  2. 2016Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Communitycited by 21
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Citing papers per year: 2023: 3.

3 papers.