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The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2002
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Attention is all you need
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Deep code search
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Retrieval on source code: a neural code search
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When deep learning met code search
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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou · 2020
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Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan · 2020
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Graphcodebert: Pre-training code representations with data flow
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Cosqa: 20,000+ web queries for code search and question answering
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Codexglue: A machine learning benchmark dataset for code understanding and generation
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Syncobert: Syntax-guided multi-modal contrastive pre-training for code representation
Xin Wang, Fei Mi Yasheng Wang, Pingyi Zhou, Yao Wan, Xiao Liu, Li Li, Hao Wu, Jin Liu, and Xin Jiang
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Antoine Miech, Jean-Baptiste Alayrac, Ivan Laptev, Josef Sivic, and Andrew Zisserman · 2021
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Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
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In-ide code generation from natural language: Promise and challenges
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