Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving transformations

We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model of Corder can be used in two ways: (1) it can produce vector representation of co...

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Bibliographic Details
Main Authors: BUI, Duy Quoc Nghi, Yijun Yu, JIANG, Lingxiao
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/6719
https://ink.library.smu.edu.sg/context/sis_research/article/7722/viewcontent/sigir21corder.pdf
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Institution: Singapore Management University
Language: English

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