OpenMatch-v2: An All-in-one Multi-Modality PLM-based Information Retrieval Toolkit

被引:3
|
作者
Yu, Shi [1 ]
Liu, Zhenghao [2 ]
Xiong, Chenyan [3 ]
Liu, Zhiyuan [1 ]
机构
[1] Tsinghua Univ, Beijing, Peoples R China
[2] Northeastern Univ, Shenyang, Peoples R China
[3] Microsoft Res, Redmond, WA USA
来源
PROCEEDINGS OF THE 46TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2023 | 2023年
基金
中国国家自然科学基金;
关键词
PLM-based IR; dense retrieval; re-ranking; open-source toolkit;
D O I
10.1145/3539618.3591813
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Pre-trained language models (PLMs) have emerged as the foundation of the most advanced Information Retrieval (IR) models. Powered by PLMs, the latest IR research has proposed novel models, new domain adaptation algorithms as well as enlarged datasets. In this paper, we present a Python-based IR toolkit OpenMatch-v2. As a full upgrade of OpenMatch proposed in 2021, OpenMatch-v2 incorporates the most recent advancements of PLM-based IR research, providing support for new, cross-modality models and enhanced domain adaptation techniques with a streamlined, optimized infrastructure. The code of OpenMatch is publicly available at https://github.com/OpenMatch/OpenMatch.
引用
收藏
页码:3160 / 3164
页数:5
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