allRank
A PyTorch framework for training neural learning-to-rank models with flexible loss functions and scoring architectures.
What is allRank?
allRank is a PyTorch framework for training neural learning-to-rank models, which are used to order items based on relevance. It provides implementations of common ranking loss functions, scoring architectures like Transformers, and evaluation metrics, enabling researchers and engineers to experiment with and deploy custom ranking systems. The framework addresses the need for a flexible, research-friendly tool that also supports industrial applications in areas like search engines and recommendation systems.
Machine learning researchers and engineers working on search ranking, recommendation systems, or information retrieval who need to train and evaluate neural ranking models. It is particularly useful for those experimenting with novel loss functions or architectures in a PyTorch environment.
Developers choose allRank for its comprehensive set of pre-implemented ranking losses, easy configurability via JSON, and support for both traditional and cutting-edge approaches like NeuralNDCG. Its flexibility in adding custom components and seamless integration with PyTorch make it a preferred open-source alternative for prototyping and production ranking models.
Overview
allRank is a framework for training learning-to-rank neural models based on PyTorch.
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