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Computer Science > Information Retrieval

arXiv:2408.17344 (cs)
[Submitted on 30 Aug 2024 (v1), last revised 3 Sep 2024 (this version, v2)]

Title:rerankers: A Lightweight Python Library to Unify Ranking Methods

Authors:Benjamin Clavié
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Abstract:This paper presents rerankers, a Python library which provides an easy-to-use interface to the most commonly used re-ranking approaches. Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, relying on different implementation methods. rerankers unifies these methods into a single user-friendly interface, allowing practitioners and researchers alike to explore different methods while only changing a single line of Python code. Moreover ,rerankers ensures that its implementations are done with the fewest dependencies possible, and re-uses the original implementation whenever possible, guaranteeing that our simplified interface results in no performance degradation compared to more complex ones. The full source code and list of supported models are updated regularly and available at this https URL.
Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2408.17344 [cs.IR]
  (or arXiv:2408.17344v2 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2408.17344

Submission history

From: Benjamin Clavié [view email]
[v1] Fri, 30 Aug 2024 15:16:52 UTC (247 KB)
[v2] Tue, 3 Sep 2024 10:50:17 UTC (248 KB)
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