Paper Info
Title
ranx: A Blazing-Fast Python Library for Ranking Evaluation and Comparison
Abstract
This paper presents ranx, a Python evaluation library for Information Retrieval built on top of Numba. ranx provides a user-friendly interface to the most common ranking evaluation metrics, such as MAP, MRR, and NDCG. Moreover, it offers a convenient way of managing the evaluation results, comparing different runs, performing statistical tests between them, and exporting LATEX tables ready to be used in scientific publications, all in a few lines of code. The efficiency brought by Numba, a just-in-time compiler for Python code, makes the adoption ranx convenient even for industrial applications.
Year
DOI
Venue
2022
10.1007/978-3-030-99739-7_30
ADVANCES IN INFORMATION RETRIEVAL, PT II
Keywords
DocType
Volume
Information Retrieval, Evaluation, Comparison
Conference
13186
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Elias Bassani112.08