@inproceedings{akiki-etal-2023-spacerini,
title = "Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face",
author = "Akiki, Christopher and
Ogundepo, Odunayo and
Piktus, Aleksandra and
Zhang, Xinyu and
Oladipo, Akintunde and
Lin, Jimmy and
Potthast, Martin",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.12",
doi = "10.18653/v1/2023.emnlp-demo.12",
pages = "140--148",
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.",
}
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%0 Conference Proceedings
%T Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face
%A Akiki, Christopher
%A Ogundepo, Odunayo
%A Piktus, Aleksandra
%A Zhang, Xinyu
%A Oladipo, Akintunde
%A Lin, Jimmy
%A Potthast, Martin
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F akiki-etal-2023-spacerini
%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.
%R 10.18653/v1/2023.emnlp-demo.12
%U https://aclanthology.org/2023.emnlp-demo.12
%U https://doi.org/10.18653/v1/2023.emnlp-demo.12
%P 140-148
Markdown (Informal)
[Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face](https://aclanthology.org/2023.emnlp-demo.12) (Akiki et al., EMNLP 2023)
ACL
- Christopher Akiki, Odunayo Ogundepo, Aleksandra Piktus, Xinyu Zhang, Akintunde Oladipo, Jimmy Lin, and Martin Potthast. 2023. Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 140–148, Singapore. Association for Computational Linguistics.