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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| Main Authors: | , , |
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| 格式: | text |
| 語言: | English |
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Institutional Knowledge at Singapore Management University
2021
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| 主題: | |
| 在線閱讀: | 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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