Ranking Python Packages

rank-bm25

A Collection of BM25 Algorithms in Python

flashrank

Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.

openskill

Multiplayer Rating System. No Friction.

tensorflow-ranking

Learning to Rank in TensorFlow

recommenders

Best Practices on Recommendation Systems

evalica

Evalica, your favourite evaluation toolkit

choix

Inference algorithms for models based on Luce's choice axiom

torchsort

Fast, differentiable sorting and ranking in PyTorch

caserecommender

A recommender systems framework for Python

ranking-table-tennis

Ranking system based on rating points for table tennis players.

whole-history-rating

a python convertion from the ruby implementation of Rémi Coulom's Whole-History Rating (WHR) algorithm.

cake-ensemble

Confidence in Assignments via K-partition Ensembles

rexmex

A general purpose recommender metrics library for fair evaluation.

ranklab

Learning-to-rank losses and evaluation

allrank

allRank is a framework for training learning-to-rank neural models

kickscore

Pairwise comparisons with flexible time-dynamics.

discordlevelingcard

A library with leveling cards for your discord bot.

scorio

Bayesian evaluation and ranking toolkit

rstt

Ranking Simulation Testing Tool

bwsample

Sampling algorithm for best-worst scaling sets.

pre-reco-utils

Best Practices on Recommendation Systems

fast-forward-indexes

Efficient interpolation-based ranking on CPUs

pysarplus

Best Practices on Recommendation Systems

mallows-models

Mallows Kendall. A python package to for Mallows Model with top-k and complete rankings using Kendall's-tau distance. By Ahmed Boujaada, Fabien Collas and Ekhine Irurozki. We present methods for inference in the Mallows Model (MM), the best-known distribution for permutations. This is short tutorial for top-k rankings and complete rankings under the Kendall's-tau distance. Theoretical details are given in Fabien Collas and Ekhine Irurozki (2020). Concentric mixtures of Mallows Models for top-k rankings: sampling and identifiability. In: International Conference on Machine Learning (ICML 21). 2021.