OpenMatch: An Open-Source Package for Information Retrieval

Zhenghao Liu, Kaitao Zhang, Chenyan Xiong, Zhiyuan Liu

Information Retrieval (IR) is an important task and can be used in many applications. Neural IR (Neu-IR) models overcome the vocabulary mismatch problem of sparse retrievers and thrive on the ranking pipeline with semantic matching. Recent progress in IR mainly focuses on Neu-IR models, including efficient dense retrieval, advanced neural architectures and robustly training for few-shot IR that lacks training data. In order to integrate these advantages for researchers and engineers to utilize and develop, OpenMatch provides various functional neural modules based on PyTorch to maintain sufficient extensibility, making it easy to build customized and higher-capacity IR systems. Besides, OpenMatch consists of complicated optimization tricks, various sparse/dense retrieval methods, and advanced few-shot training methods, liberating users from surplus labor in baseline reimplementation and neural model finetuning. With OpenMatch, we achieve reasonable performance on various ranking datasets, rank first of the automatic group in TREC COVID (Round 2) and rank top on the MS MARCO Document Ranking leaderboard. The library, experimental methodologies and results of OpenMatch are all publicly available at

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