{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/18"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Rax provides ranking losses and metrics for learning-to-rank problems in JAX, enabling optimization of relative ordering for lists of items given a context.","skillfed_tags":["learning-to-rank","jax-ecosystem","ranking-metrics"],"use_cases":["Ranking documents in search engines by relevance to a query using differentiable ranking losses.","Building recommendation systems that rank items for a user and optimize for ranking metrics like MRR.","Training question-answering systems to rank candidate answers by relevance.","Optimizing dialogue systems to select the best response from a list of candidates.","Implementing pairwise or listwise ranking losses in JAX-based machine learning pipelines."],"what_it_does":"Rax is a Learning-to-Rank library built on JAX that solves the problem of optimizing the relative order of items in a list given a context. Unlike classification or regression, ranking problems care about correctness of ordering rather than absolute predictions. Rax provides off-the-shelf ranking losses (softmax_loss, pairwise_logistic_loss), ranking metrics (mrr_metric, ndcg_metric), and transformations (approx_t12n, gumbel_t12n) that compose with standard JAX transformations.\n\nThe library is designed for search, recommendation, question-answering, and dialogue systems where you need to score and rank items. All losses and metrics are purely functional and differentiable, allowing you to use them directly in neural network training pipelines. You can also transform non-differentiable ranking metrics into differentiable losses, enabling gradient-based optimization of metrics like NDCG.","worth_installing":"Yes. Rax fills a clear gap for learning-to-rank in the JAX ecosystem with low install friction, active maintenance, no known vulnerabilities, and permissive licensing. Install it if you're building ranking systems in JAX or need to optimize ranking metrics directly."},"id":"rax","links":{"html":"https://skillfed.io/packages/rax","md":"https://skillfed.io/packages/rax.md","pypi":"https://pypi.org/project/rax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-01-03","license_spdx":null,"license_treatment":"permissive","name":"rax","python_support":"supports_current","summary":"Learning-to-Rank using JAX."},"popularity":{"monthly_downloads":146035,"position":11110,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.0"}