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rerankers: A Lightweight Python Library to Unify Ranking Methods

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arxiv 2408.17344 v2 pith:K5EQMINF submitted 2024-08-30 cs.IR cs.AI

classification cs.IRcs.AI
keywords rerankersmethodsinterfacepythonapproachescodedifferentimplementation
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SAGE-HLS: Syntax-Aware AST-Guided LLM for High-Level Synthesis Code Generation

    cs.PL 2025-08 unverdicted novelty 6.0 of 10

    A fine-tuned 7B LLM with AST-guided prompting generates HLS code that is near 100% synthesizable and 75% functionally correct, according to the abstract.

  2. DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact Verification

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Longer context windows and right-to-left number tokenization do not improve numerical fact verification; evidence quality is the main bottleneck.

  3. DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval

    cs.IR 2025-07 conditional novelty 4.0 of 10

    On the LongEval 2025 French Qwant dataset, BM25 plus a cross-encoder reranker beats variants with Gemini query expansion, with the best system averaging 0.296 NDCG@10.

  4. DS@GT at CheckThat! 2025: Exploring Retrieval and Reranking Pipelines for Scientific Claim Source Retrieval on Social Media Discourse

    cs.IR 2025-07 conditional novelty 4.0 of 10

    A BM25-Pytorch plus T5 reranking pipeline achieved MRR@5 of 0.58 on CheckThat! 2025 Task 4b, a 0.15 improvement over the BM25 baseline of 0.43.

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