# OpenMatch: An Open Source Library for Neu-IR Research

arXiv: https://arxiv.org/abs/2102.00166 · HTML: https://mlsanity.com/paper/2102.00166/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2021.

## Built on by (most cited first, 6 of 6)

- [COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning](https://mlsanity.com/paper/2210.15212/) (2022)
- [Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges](https://mlsanity.com/paper/2403.00784/) (2024)
- [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://mlsanity.com/paper/2308.14029/) (2023)
- [Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives](https://mlsanity.com/paper/2210.17167/) (2022)
- [SCAI-QReCC Shared Task on Conversational Question Answering](https://mlsanity.com/paper/2201.11094/) (2022)
- [Improving Multitask Retrieval by Promoting Task Specialization](https://mlsanity.com/paper/2307.00342/) (2023)

## Abstract (arXiv)

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.