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We present NaturalCC, an efficient and extensible toolkit to bridge the gapbetween natural language and programming language, and facilitate the researchon big code analysis. Using NaturalCC, researchers both from natural languageor programming language communities can quickly and easily reproduce thestate-of-the-art baselines and implement their approach. NaturalCC is builtupon Fairseq and PyTorch, providing (1) an efficient computation with multi-GPUand mixed-precision data processing for fast model training, (2) a modular andextensible framework that makes it easy to reproduce or implement an approachfor big code analysis, and (3) a command line interface and a graphical userinterface to demonstrate each model's performance. Currently, we have includedseveral state-of-the-art baselines across different tasks (e.g., codecompletion, code comment generation, and code retrieval) for demonstration. Thevideo of this demo is available athttps://www.youtube.com/watch?v=q4W5VSI-u3E&t=25s.

NaturalCC: A Toolkit to Naturalize the Source Code Corpus