2021/01/30 by Zhenghao Liu, Liu, Zhenghao, Kaitao Zhang +7 · 1 citation
Computer Science · Materials Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #H.3.3 #Information Retrieval (cs.IR) #Machine Learning in Materials Science #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2102.00166
openalex publication_date 2021/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
OpenMatch is a Python-based library that serves for Neural Information Retrieval (Neu-IR) research. It provides self-contained neural and traditional IR modules, making it easy to build customized and higher-capacity IR systems. In order to develop the advantages of Neu-IR models for users, OpenMatch provides implementations of recent neural IR models, complicated experiment instructions, and advanced few-shot training methods. OpenMatch reproduces corresponding ranking results of previous work on widely-used IR benchmarks, liberating users from surplus labor in baseline reimplementation. Our OpenMatch-based solutions conduct top-ranked empirical results on various ranking tasks, such as ad hoc retrieval and conversational retrieval, illustrating the convenience of OpenMatch to facilitate building an effective IR system. The library, experimental methodologies and results of OpenMatch are all publicly available at https://github.com/thunlp/OpenMatch.