OpenMatch: An Open Source Library for Neu-IR Research
arXiv:2102.00166
Abstract
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.
arXiv comment4 pages
References in corpus (6)
- HuggingFace's Transformers: State-of-the-art Natural Language Processing
- End-to-End Neural Ad-hoc Ranking with Kernel Pooling
- Deeper Text Understanding for IR with Contextual Neural Language Modeling
- Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
- Understanding the Behaviors of BERT in Ranking
- Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations