research-article

C-Pack: Packed Resources For General Chinese Embeddings

Authors:

Niklas Muennighoff,

Jian-Yun NieAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 641 - 649

https://doi.org/10.1145/3626772.3657878

Published: 11 July 2024 Publication History

Abstract

We introduce C-Pack, a package of resources that significantly advances the field of general text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a massive training dataset for text embedding, which is based on the curation of vast unlabeled corpora and the integration of high-quality labeled corpora. 2) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 3) BGE is a family of embedding models covering multiple sizes. Our models outperform all prior Chinese text embeddings on C-MTEB by more than +10% upon the time of the release. We also integrate and optimize the entire suite of training methods for BGE. Along with our resources on general Chinese embedding, we release our data and models for English text embeddings. The English models also achieve state-of-the-art performance on the MTEB benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. Both Chinese and English datasets are the largest public release of training data for text embeddings. All these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding.

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Cited By

Elliott OClark JOosterhuis HBast HXiong C(2024)The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in Hierarchical Navigable Small WorldsProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672512(25-33)Online publication date: 5-Aug-2024
https://doi.org/10.1145/3664190.3672512
Hendrawan RBrusilovsky PLekshmi Narayanan ABarria-Pineda JBoratto LGena CMarras MGermanakos PPopescus E(2024)Explanations in Open User Models for Personalized Information ExplorationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665188(256-263)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665188
Wang ZWu Y(2024)Scaling Dual Stage Image-Text Retrieval with Multimodal Large Language Models2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651538(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651538

Index Terms

C-Pack: Packed Resources For General Chinese Embeddings
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

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Published: 11 July 2024

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Cited By

Elliott OClark JOosterhuis HBast HXiong C(2024)The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in Hierarchical Navigable Small WorldsProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672512(25-33)Online publication date: 5-Aug-2024
https://doi.org/10.1145/3664190.3672512
Hendrawan RBrusilovsky PLekshmi Narayanan ABarria-Pineda JBoratto LGena CMarras MGermanakos PPopescus E(2024)Explanations in Open User Models for Personalized Information ExplorationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665188(256-263)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665188
Wang ZWu Y(2024)Scaling Dual Stage Image-Text Retrieval with Multimodal Large Language Models2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651538(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651538

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