rerankers: A Lightweight Python Library to Unify Ranking Methods

Benjamin Clavié
8/30/2024
cs.IRcs.AI

Official code: AnswerDotAI/rerankers (1,637 stars, Apache-2.0, last commit 2025-12-20).

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 https://github.com/answerdotai/rerankers.

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Code Implementations(1)

AnswerDotAI/rerankersOfficial100%
Apache-2.0

A lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.

1,637105Python, Jupyter NotebookMar 6, 202410 months agoApache-2.0

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Cite this paper

@article{clavi2024rerankers,
  title  = {rerankers: A Lightweight Python Library to Unify Ranking Methods},
  author = {Benjamin Clavié},
  year   = {2024},
  eprint = {2408.17344},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.17344v2}
}

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