Methods › Natural Language Processing › Code Generation Transformers › CuBERT
CuBERT
Introduced by Aditya Kanade et al. in Learning and Evaluating Contextual Embedding of Source Code
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
CuBERT, or Code Understanding BERT, is a BERT based model for code understanding. In order to achieve this, the authors curate a massive corpus of Python programs collected from GitHub. GitHub projects are known to contain a large amount of duplicate code. To avoid biasing the model to such duplicated code, authors perform deduplication using the method of Allamanis (2018). The resulting corpus has 7.4 million files with a total of 9.3 billion tokens (16 million unique).
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Intraoperative perfusion assessment by continuous, low-latency hyperspectral light-field imaging: development, methodology, and clinical application 15 Apr 2025 · 0 repositories · arXiv:2504.10953
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SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation 10 Aug 2021 · 0 repositories · arXiv:2108.04556
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Learning and Evaluating Contextual Embedding of Source Code 21 Dec 2019 · 2 repositories · arXiv:2001.00059
Tasks archive 2025-07-28
15 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections