OpenMatch: An Open Source Library for Neu-IR Research
Not on the map. Why?
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jan 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 3; 2023: 2; 2024: 1.
6 papers.
- 2022COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learningcited by 20
- 2024Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challengescited by 20
- 2023Text Matching Improves Sequential Recommendation by Reducing Popularity Biasescited by 10
- 2022Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negativescited by 5
- 2022SCAI-QReCC Shared Task on Conversational Question Answeringcited by 4
- 2023Improving Multitask Retrieval by Promoting Task Specializationcited by 1
- 2024Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges18 Feb 2024
- 2023Text Matching Improves Sequential Recommendation by Reducing Popularity Biases27 Aug 2023
- 2023Improving Multitask Retrieval by Promoting Task Specialization1 Jul 2023
- 2022Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives31 Oct 2022
- 2022COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning27 Oct 2022
- 2022SCAI-QReCC Shared Task on Conversational Question Answering26 Jan 2022