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.
%0 Journal Article
%1 Akiki2023-hb
%A Akiki, Christopher
%A Ogundepo, Odunayo
%A Piktus, Aleksandra
%A Zhang, Xinyu
%A Oladipo, Akintunde
%A Lin, Jimmy
%A Potthast, Martin
%D 2023
%I arXiv
%K
%T Spacerini: Plug-and-play search engines with Pyserini and Hugging Face
%X 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.
@article{Akiki2023-hb,
abstract = {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.},
added-at = {2024-09-10T10:41:24.000+0200},
author = {Akiki, Christopher and Ogundepo, Odunayo and Piktus, Aleksandra and Zhang, Xinyu and Oladipo, Akintunde and Lin, Jimmy and Potthast, Martin},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/26271bac0559ece34e92c4e5e0ee4dc39/scadsfct},
interhash = {743bb99a7509568c49f1f5b2eabf8df5},
intrahash = {6271bac0559ece34e92c4e5e0ee4dc39},
keywords = {},
publisher = {arXiv},
timestamp = {2024-09-10T10:47:32.000+0200},
title = {Spacerini: Plug-and-play search engines with Pyserini and Hugging Face},
year = 2023
}
