A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning IR, an easy-to-use PyTorch Lightning-based framework for applying transformer-based language models in retrieval scenarios. Lightning IR provides a modular and extensible architecture that supports all stages of a retrieval pipeline: from fine-tuning and indexing to searching and re-ranking. Designed to be scalable and reproducible, Lightning IR is available as open-source: https://github.com/webis-de/lightning-ir.
@article{arxiv.2411.04677,
title = {Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval},
author = {Ferdinand Schlatt and Maik Fröbe and Matthias Hagen},
journal= {arXiv preprint arXiv:2411.04677},
year = {2025}
}