@inproceedings{zhao-etal-2023-tencentpretrain,
title = "{T}encent{P}retrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities",
author = "Zhao, Zhe and
Li, Yudong and
Hou, Cheng and
Zhao, Jing and
Tian, Rong and
Liu, Weijie and
Chen, Yiren and
Sun, Ningyuan and
Liu, Haoyan and
Mao, Weiquan and
Guo, Han and
Gou, Weigang and
Wu, Taiqiang and
Zhu, Tao and
Shi, Wenhang and
Chen, Chen and
Huang, Shan and
Chen, Sihong and
Liu, Liqun and
Li, Feifei and
Chen, Xiaoshuai and
Sun, Xingwu and
Kang, Zhanhui and
Du, Xiaoyong and
Shen, Linlin and
Yan, Kimmo",
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.20",
doi = "10.18653/v1/2023.acl-demo.20",
pages = "217--225",
abstract = "Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.",
}
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<abstract>Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.</abstract>
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%0 Conference Proceedings
%T TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
%A Zhao, Zhe
%A Li, Yudong
%A Hou, Cheng
%A Zhao, Jing
%A Tian, Rong
%A Liu, Weijie
%A Chen, Yiren
%A Sun, Ningyuan
%A Liu, Haoyan
%A Mao, Weiquan
%A Guo, Han
%A Gou, Weigang
%A Wu, Taiqiang
%A Zhu, Tao
%A Shi, Wenhang
%A Chen, Chen
%A Huang, Shan
%A Chen, Sihong
%A Liu, Liqun
%A Li, Feifei
%A Chen, Xiaoshuai
%A Sun, Xingwu
%A Kang, Zhanhui
%A Du, Xiaoyong
%A Shen, Linlin
%A Yan, Kimmo
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhao-etal-2023-tencentpretrain
%X Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.
%R 10.18653/v1/2023.acl-demo.20
%U https://aclanthology.org/2023.acl-demo.20
%U https://doi.org/10.18653/v1/2023.acl-demo.20
%P 217-225
Markdown (Informal)
[TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities](https://aclanthology.org/2023.acl-demo.20) (Zhao et al., ACL 2023)
ACL
- Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, et al.. 2023. TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 217–225, Toronto, Canada. Association for Computational Linguistics.