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3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="feedback-dialog-title"> <div data-view-component="true" class="Overlay-body"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="lwKH5110D93bcVjmS5Fk32AQzFMvoBvGAQyX1UiTvCrILLMXo0HTD/9mFJOqsumgGalGx7CZbqh2ly/VkJ5rlg==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> 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src=\"https://camo.githubusercontent.com/1c8a2f07ec87ee3d5a2e3a4157cffc6441af7f55a65d517c7d1f6980322a2e8f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f456469736f6e4c65656565652f477261706847616c6c657279\" alt=\"license\" data-canonical-src=\"https://img.shields.io/github/license/EdisonLeeeee/GraphGallery\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://xcodemind.github.io/papers/icse22_naturalcc_camera_submitted.pdf\" rel=\"nofollow\"\u003ePaper\u003c/a\u003e,\n\u003ca href=\"http://121.43.116.104:3000\" rel=\"nofollow\"\u003eDemo\u003c/a\u003e,\n\u003ca href=\"https://xcodemind.github.io/team\" rel=\"nofollow\"\u003eAbout us-XCodeMind\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNaturalCC - Natural Code Comprehension\u003c/h1\u003e\u003ca id=\"user-content-naturalcc---natural-code-comprehension\" class=\"anchor\" aria-label=\"Permalink: NaturalCC - Natural Code Comprehension\" href=\"#naturalcc---natural-code-comprehension\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📖 Vision\u003c/h2\u003e\u003ca id=\"user-content--vision\" class=\"anchor\" aria-label=\"Permalink: 📖 Vision\" href=\"#-vision\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNaturalCC is a sequence modeling toolkit designed to bridge the gap between programming and natural languages through advanced machine learning techniques. It allows researchers and developers to train custom models for a variety of software engineering tasks, e.g., code generation, code completion, code summarization, code retrieval, code clone detection, and type inference.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🌟 Key Features:\u003c/h2\u003e\u003ca id=\"user-content--key-features\" class=\"anchor\" aria-label=\"Permalink: 🌟 Key Features:\" href=\"#-key-features\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eModular and Extensible Framework:\u003c/strong\u003e Built on the robust Fairseq's registry mechanism, allowing for easy adaptation and extension to diverse software engineering tasks.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDatasets and Preprocessing Tools+:\u003c/strong\u003e Offers access to a variety of clean, preprocessed benchmarks such as Human-Eval, CodeSearchNet, Python-Doc, and Py150. Comes equipped with scripts for feature extraction using compiler tools like LLVM.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSupport for Large Code Models:\u003c/strong\u003e Incorporates state-of-the-art large code models like Code Llama, CodeT5, CodeGen, and StarCoder.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBenchmarking and Evaluation\u003c/strong\u003e: Benchmarks multiple downstream tasks (including code generation and code completion), with evaluation capabilities on well-known benchmarks using popular metrics like pass@k.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOptimized for Efficiency:\u003c/strong\u003e Employs the \u003ccode\u003eNCCL\u003c/code\u003e library and \u003ccode\u003etorch.distributed\u003c/code\u003e for high-efficiency model training across multiple GPUs. Supports both full-precision (\u003ccode\u003eFP32\u003c/code\u003e) and half-precision (\u003ccode\u003eFP16\u003c/code\u003e) computations to accelerate training and inference processes.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEnhanced Logging for Improved Debugging:\u003c/strong\u003e Advanced logging features to provide clear, detailed feedback during model training and operation, aiding in debugging and performance optimization.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e✨ Latest News\u003c/h2\u003e\u003ca id=\"user-content--latest-news\" class=\"anchor\" aria-label=\"Permalink: ✨ Latest News\" href=\"#-latest-news\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e[Nov 25, 2023]\u003c/strong\u003e \u003cstrong\u003eNaturalCC 2.0 Released!\u003c/strong\u003e Now compatible with \u003ca href=\"https://github.com/huggingface/transformers\"\u003eTransformers\u003c/a\u003e and supporting popular large code models like Code Llama, CodeT5, CodeGen, and StarCoder from \u003ca href=\"https://huggingface.co\" rel=\"nofollow\"\u003eHugging Face\u003c/a\u003e. Access the previous version in the \u003ca href=\"/CGCL-codes/naturalcc/blob/main\"\u003encc1\u003c/a\u003e branch.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e[Apr 19, 2023]\u003c/strong\u003e Integrated the source code of \"You See What I Want You to See: Poisoning Vulnerabilities in Neural Code Search\" into NaturalCC.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e[Jan 25, 2022]\u003c/strong\u003e Our paper introducing the NaturalCC toolkit was accepted at the ICSE 2022 Demo Track.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e[May 10, 2022]\u003c/strong\u003e Merged the source code of \"What Do They Capture? - A Structural Analysis of Pre-Trained Language Models for Source Code\" into NaturalCC.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🛠️ Installation Guide\u003c/h2\u003e\u003ca id=\"user-content-️-installation-guide\" class=\"anchor\" aria-label=\"Permalink: 🛠️ Installation Guide\" href=\"#️-installation-guide\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo get started with NaturalCC, ensure your system meets the following requirements:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGCC/G++ version 5.0 or higher\u003c/li\u003e\n\u003cli\u003eNVIDIA GPU, NCCL, and the Cuda Toolkit for training new models (optional but recommended)\u003c/li\u003e\n\u003cli\u003eNVIDIA's apex library for faster training (optional)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eFollow these steps to set up the environment.\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e(Optional) Creating conda environment\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda create -n naturalcc python=3.6\nconda activate naturalcc\"\u003e\u003cpre\u003econda create -n naturalcc python=3.6\nconda activate naturalcc\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eBuilding NaturalCC from source\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/CGCL-codes/naturalcc \u0026amp;\u0026amp; cd naturalcc\npip install -r requirements.txt\ncd src\npip install --editable ./\"\u003e\u003cpre\u003egit clone https://github.com/CGCL-codes/naturalcc \u003cspan class=\"pl-k\"\u003e\u0026amp;\u0026amp;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e naturalcc\npip install -r requirements.txt\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e src\npip install --editable ./\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eInstalling Additional Dependencies\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda install conda-forge::libsndfile\npip install -q -U git+https://github.com/huggingface/transformers.git\npip install -q -U git+https://github.com/huggingface/accelerate.git\"\u003e\u003cpre\u003econda install conda-forge::libsndfile\npip install -q -U git+https://github.com/huggingface/transformers.git\npip install -q -U git+https://github.com/huggingface/accelerate.git\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eHuggingFace Token for Certain Models\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor models like \u003ca href=\"https://github.com/bigcode-project/starcoder\"\u003eStarCoder\u003c/a\u003e, a HuggingFace token is required. Log in to HuggingFace using:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"huggingface-cli login\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ehuggingface-cli login\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🚀 Quick Start\u003c/h2\u003e\u003ca id=\"user-content--quick-start\" class=\"anchor\" aria-label=\"Permalink: 🚀 Quick Start\" href=\"#-quick-start\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample 1: Code Generation\u003c/h3\u003e\u003ca id=\"user-content-example-1-code-generation\" class=\"anchor\" aria-label=\"Permalink: Example 1: Code Generation\" href=\"#example-1-code-generation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eDownload the model checkpoint\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFirst, download the checkpoint of a specific large code model. For this example, we use \u003ca href=\"https://huggingface.co/camenduru/CodeLlama-7b\" rel=\"nofollow\"\u003eCodellama-7B\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003ePrepare the testing dataset\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCreate a JSON file containing your test cases in the following format:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-json notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"[\n {\u0026quot;input\u0026quot;: \u0026quot;this is a\u0026quot;},\n {\u0026quot;input\u0026quot;: \u0026quot;from tqdm import\u0026quot;},\n {\u0026quot;input\u0026quot;: \u0026quot;def calculate(\u0026quot;},\n {\u0026quot;input\u0026quot;: \u0026quot;a = b**2\u0026quot;},\n {\u0026quot;input\u0026quot;: \u0026quot;torch.randint\u0026quot;},\n {\u0026quot;input\u0026quot;: \u0026quot;x = [1,2\u0026quot;}\n]\"\u003e\u003cpre\u003e[\n {\u003cspan class=\"pl-ent\"\u003e\"input\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ethis is a\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e},\n {\u003cspan class=\"pl-ent\"\u003e\"input\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003efrom tqdm import\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e},\n {\u003cspan class=\"pl-ent\"\u003e\"input\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edef calculate(\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e},\n {\u003cspan class=\"pl-ent\"\u003e\"input\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ea = b**2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e},\n {\u003cspan class=\"pl-ent\"\u003e\"input\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etorch.randint\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e},\n {\u003cspan class=\"pl-ent\"\u003e\"input\"\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ex = [1,2\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e}\n]\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRunning the code generation scripts\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eInitialize the task with the specific model and GPU device:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"print('Initializing GenerationTask')\ntask = GenerationTask(task_name=\u0026quot;codellama_7b_code\u0026quot;, device=\u0026quot;cuda:0\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'Initializing GenerationTask'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003etask\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eGenerationTask\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etask_name\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"codellama_7b_code\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"cuda:0\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eLoad the downloaded checkpoint into the task. Replace \u003ccode\u003eckpt_path\u003c/code\u003e with the path to your downloaded checkpoint:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"print('Loading model weights [{}]'.format(ckpt_path))\ntask.from_pretrained(ckpt_path)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'Loading model weights [{}]'\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eckpt_path\u003c/span\u003e))\n\u003cspan class=\"pl-s1\"\u003etask\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eckpt_path\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eLoad your dataset. Replace \u003ccode\u003edataset_path\u003c/code\u003e with the path to your dataset file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"print('Processing dataset [{}]'.format(dataset_path))\ntask.load_dataset(dataset_path)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'Processing dataset [{}]'\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edataset_path\u003c/span\u003e))\n\u003cspan class=\"pl-s1\"\u003etask\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eload_dataset\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edataset_path\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRun the model and output the results. Replace \u003ccode\u003eoutput_path\u003c/code\u003e with your desired output file path:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"task.run(output_path=output_path, batch_size=1, max_length=50)\nprint('Output file: {}'.format(output_path))\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003etask\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutput_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eoutput_path\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ebatch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emax_length\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'Output file: {}'\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eformat\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutput_path\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample 2: Code Summarization\u003c/h3\u003e\u003ca id=\"user-content-example-2-code-summarization\" class=\"anchor\" aria-label=\"Permalink: Example 2: Code Summarization\" href=\"#example-2-code-summarization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eDownload and process a dataset from \u003ccode\u003edatasets\u003c/code\u003e, and follow the instructions from the README.md file.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# ref: dataset/python_wan/README.md\n# download dataset\nbash dataset/python_wan/download.sh\n# clean data\npython -m dataset.python_wan.clean\n# cast data attributes into different files\npython -m dataset.python_wan.attributes_cast\n\n# ref: dataset/python_wan/summarization/README.md\n# save code tokens and docstirng tokens into MMAP format\npython -m dataset.python_wan.summarization.preprocess\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ref: dataset/python_wan/README.md\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e download dataset\u003c/span\u003e\nbash dataset/python_wan/download.sh\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e clean data\u003c/span\u003e\npython -m dataset.python_wan.clean\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e cast data attributes into different files\u003c/span\u003e\npython -m dataset.python_wan.attributes_cast\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ref: dataset/python_wan/summarization/README.md\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e save code tokens and docstirng tokens into MMAP format\u003c/span\u003e\npython -m dataset.python_wan.summarization.preprocess\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eRegister your self-defined models\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIf you want to create a new model, please add your model at \u003ccode\u003encc/models\u003c/code\u003e and \u003ccode\u003encc/modules\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIf your training policy are more complex than we thought, you should update your criterions and training procedure at \u003ccode\u003encc/criterions\u003c/code\u003e and \u003ccode\u003encc/trainers\u003c/code\u003e, respectively.\n\u003cbr\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDo not forget to update your self defined module at \u003ccode\u003encc/XX/__init__.py\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTraining and inference.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSelect a task and a model from \u003ca href=\"/CGCL-codes/naturalcc/blob/main/run\"\u003etask list\u003c/a\u003e and follow the instructions in its README.md to start your learning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# ref: run/summarization/transformer/README.md\n# train\nCUDA_VISIBLE_DEVICES=0,1,2,3 nohup python -m run.summarization.transformer.train -f config/python_wan/python \u0026gt; run/summarization/transformer/config/python_wan/python.log 2\u0026gt;\u0026amp;1 \u0026amp;\n# inference\nCUDA_VISIBLE_DEVICES=0 python -m run.summarization.transformer.eval -f config/python_wan/python -o run/summarization/transformer/config/python_wan/python.txt\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ref: run/summarization/transformer/README.md\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e train\u003c/span\u003e\nCUDA_VISIBLE_DEVICES=0,1,2,3 nohup python -m run.summarization.transformer.train -f config/python_wan/python \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e run/summarization/transformer/config/python_wan/python.log \u003cspan class=\"pl-k\"\u003e2\u0026gt;\u0026amp;1\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e inference\u003c/span\u003e\nCUDA_VISIBLE_DEVICES=0 python -m run.summarization.transformer.eval -f config/python_wan/python -o run/summarization/transformer/config/python_wan/python.txt\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eWe also have more detailed \u003ca href=\"/CGCL-codes/naturalcc/blob/main/examples\"\u003eREADMEs\u003c/a\u003e to start your tutorial of NaturalCC.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📚 Dataset\u003c/h2\u003e\u003ca id=\"user-content--dataset\" class=\"anchor\" aria-label=\"Permalink: 📚 Dataset\" href=\"#-dataset\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNaturalCC supports a diverse range of datasets, catering to various aspects of code analysis and processing. These datasets include:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/python_wan/README.md\"\u003ePython (Wan et al.)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/codesearchnet/README.md\"\u003eCodeSearchNet (Husain et al.)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/codexglue/code_to_text/README.md\"\u003eCodeXGlue (Feng et al.)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/py150/README.md\"\u003ePy150 (official processed)\u003c/a\u003e \u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/raw_py150/README.md\"\u003e(raw)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/opencl/README.md\"\u003eOpenCL (Grewe et al.)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/java_hu/README.md\"\u003eJava (Hu et, al.)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/stackoverflow/README.md\"\u003eStack Overflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/deepcs\"\u003eDeepCS (Gu et al.)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/avatar\"\u003eAVATAR (Ahmad et al.)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/CGCL-codes/naturalcc/blob/main/preprocessing/stackoverflow\"\u003eStackOverflow (Iyer et al.)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🤝 Contributor\u003c/h2\u003e\u003ca id=\"user-content--contributor\" class=\"anchor\" aria-label=\"Permalink: 🤝 Contributor\" href=\"#-contributor\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe warmly welcome contributions to NaturalCC! Your involvement is essential for keeping NaturalCC innovative and accessible.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe're grateful to all our amazing contributors who have made this project what it is today!\u003c/p\u003e\n\u003ca href=\"https://github.com/CGCL-codes/naturalcc/graphs/contributors\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/ddeca73ea59ffc8f3777eeb20e53ac8d148c1e48d0dd8466ba5f7baa1f7aa459/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d4347434c2d636f6465732f6e61747572616c636326723d\" width=\"800px\" data-canonical-src=\"https://contrib.rocks/image?repo=CGCL-codes/naturalcc\u0026amp;r=\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e💡 FAQ\u003c/h2\u003e\u003ca id=\"user-content--faq\" class=\"anchor\" aria-label=\"Permalink: 💡 FAQ\" href=\"#-faq\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you have any questions or encounter issues, please feel free to reach out. For quick queries, you can also check our \u003ccode\u003eIssues\u003c/code\u003e page for common questions and solutions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e😘 License and Acknowledgement\u003c/h2\u003e\u003ca id=\"user-content--license-and-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: 😘 License and Acknowledgement\" href=\"#-license-and-acknowledgement\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLicense:\u003c/strong\u003e NaturalCC is open-sourced under the \u003ca href=\"https://github.com/CGCL-codes/naturalcc/blob/master/LICENSE.txt\"\u003eMIT-licensed\u003c/a\u003e. This permissive license applies not only to the toolkit itself but also to the pre-trained models provided within.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We extend our heartfelt gratitude to the broader open-source community, particularly drawing inspiration from projects like \u003ca href=\"https://github.com/pytorch/fairseq\"\u003eFairseq\u003c/a\u003e for their advanced sequence-to-sequence models, and \u003ca href=\"https://allennlp.org\" rel=\"nofollow\"\u003eAllenNLP\u003c/a\u003e for their robust NLP components. Their groundbreaking work has been instrumental in shaping the development of NaturalCC.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e📄 Citation\u003c/h2\u003e\u003ca id=\"user-content--citation\" class=\"anchor\" aria-label=\"Permalink: 📄 Citation\" href=\"#-citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe're thrilled that you're interested in using NaturalCC for your research or applications! Citing our work helps us to grow and continue improving this toolkit. You can find more in-depth details about NaturalCC in our \u003ca href=\"https://xcodemind.github.io/papers/icse22_naturalcc_camera_submitted.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you use NaturalCC in your research, please consider citing our paper. Below is the BibTex format for citation:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{wan2022naturalcc,\n title={NaturalCC: An Open-Source Toolkit for Code Intelligence},\n author={Yao Wan and Yang He and Zhangqian Bi and Jianguo Zhang and Yulei Sui and Hongyu Zhang and Kazuma Hashimoto and Hai Jin and Guandong Xu and Caiming Xiong and Philip S. Yu},\n booktitle={Proceedings of 44th International Conference on Software Engineering, Companion Volume},\n publisher=ACM,\n year={2022}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{wan2022naturalcc,\n title={NaturalCC: An Open-Source Toolkit for Code Intelligence},\n author={Yao Wan and Yang He and Zhangqian Bi and Jianguo Zhang and Yulei Sui and Hongyu Zhang and Kazuma Hashimoto and Hai Jin and Guandong Xu and Caiming Xiong and Philip S. Yu},\n booktitle={Proceedings of 44th International Conference on Software Engineering, Companion Volume},\n publisher=ACM,\n year={2022}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"NaturalCC - Natural Code Comprehension","anchor":"naturalcc---natural-code-comprehension","htmlText":"NaturalCC - Natural Code Comprehension"},{"level":2,"text":"📖 Vision","anchor":"-vision","htmlText":"📖 Vision"},{"level":2,"text":"🌟 Key Features:","anchor":"-key-features","htmlText":"🌟 Key Features:"},{"level":2,"text":"✨ Latest News","anchor":"-latest-news","htmlText":"✨ Latest News"},{"level":2,"text":"🛠️ Installation Guide","anchor":"️-installation-guide","htmlText":"🛠️ Installation Guide"},{"level":2,"text":"🚀 Quick Start","anchor":"-quick-start","htmlText":"🚀 Quick Start"},{"level":3,"text":"Example 1: Code Generation","anchor":"example-1-code-generation","htmlText":"Example 1: Code Generation"},{"level":3,"text":"Example 2: Code Summarization","anchor":"example-2-code-summarization","htmlText":"Example 2: Code Summarization"},{"level":2,"text":"📚 Dataset","anchor":"-dataset","htmlText":"📚 Dataset"},{"level":2,"text":"🤝 Contributor","anchor":"-contributor","htmlText":"🤝 Contributor"},{"level":2,"text":"💡 FAQ","anchor":"-faq","htmlText":"💡 FAQ"},{"level":2,"text":"😘 License and Acknowledgement","anchor":"-license-and-acknowledgement","htmlText":"😘 License and Acknowledgement"},{"level":2,"text":"📄 Citation","anchor":"-citation","htmlText":"📄 Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FCGCL-codes%2Fnaturalcc"}},{"displayName":"LICENSE","repoName":"naturalcc","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"MIT","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FCGCL-codes%2Fnaturalcc"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> <div data-target="react-partial.reactRoot"><style 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js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><p align="center" dir="auto"> <br> <a target="_blank" rel="noopener noreferrer" href="/CGCL-codes/naturalcc/blob/main/docs/naturalcc_logo.png"><img src="/CGCL-codes/naturalcc/raw/main/docs/naturalcc_logo.png" width="400" style="max-width: 100%;"></a> <br> </p> <div align="center" dir="auto"> <a href="https://xcodemind.github.io/" rel="nofollow"> <img src="https://camo.githubusercontent.com/0c22b6688c061c9b71d1103cf2c7798cad90ae6af6d4e2a03b2d920e998a2d16/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4e61747572616c43432d302e362e302d677265656e" alt="Version" data-canonical-src="https://img.shields.io/badge/NaturalCC-0.6.0-green" style="max-width: 100%;"> </a> <a href="https://www.python.org/downloads/release/python-360/" rel="nofollow"> <img src="https://camo.githubusercontent.com/837108ceef7b5e6ac20eab1645be102df3949acd79274f374cbc2b830b31891b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f507974686f6e2d3e3d332e362d3337373641423f6c6f676f3d707974686f6e" alt="Python" data-canonical-src="https://img.shields.io/badge/Python->=3.6-3776AB?logo=python" style="max-width: 100%;"> </a> <a href="https://github.com/pytorch/pytorch"> <img src="https://camo.githubusercontent.com/99601e9889744bafad6837220c755459e03e07bfc0646383c8ab09eeaa991473/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5079546f7263682d3e3d312e342d4646364630303f6c6f676f3d7079746f726368" alt="pytorch" data-canonical-src="https://img.shields.io/badge/PyTorch->=1.4-FF6F00?logo=pytorch" style="max-width: 100%;"> </a> <a href="https://github.com/EdisonLeeeee/GraphGallery/blob/master/LICENSE"> <img src="https://camo.githubusercontent.com/1c8a2f07ec87ee3d5a2e3a4157cffc6441af7f55a65d517c7d1f6980322a2e8f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f456469736f6e4c65656565652f477261706847616c6c657279" alt="license" data-canonical-src="https://img.shields.io/github/license/EdisonLeeeee/GraphGallery" style="max-width: 100%;"> </a> <p dir="auto"><a href="https://xcodemind.github.io/papers/icse22_naturalcc_camera_submitted.pdf" rel="nofollow">Paper</a>, <a href="http://121.43.116.104:3000" rel="nofollow">Demo</a>, <a href="https://xcodemind.github.io/team" rel="nofollow">About us-XCodeMind</a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">NaturalCC - Natural Code Comprehension</h1><a id="user-content-naturalcc---natural-code-comprehension" class="anchor" aria-label="Permalink: NaturalCC - Natural Code Comprehension" href="#naturalcc---natural-code-comprehension"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📖 Vision</h2><a id="user-content--vision" class="anchor" aria-label="Permalink: 📖 Vision" href="#-vision"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NaturalCC is a sequence modeling toolkit designed to bridge the gap between programming and natural languages through advanced machine learning techniques. It allows researchers and developers to train custom models for a variety of software engineering tasks, e.g., code generation, code completion, code summarization, code retrieval, code clone detection, and type inference.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🌟 Key Features:</h2><a id="user-content--key-features" class="anchor" aria-label="Permalink: 🌟 Key Features:" href="#-key-features"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Modular and Extensible Framework:</strong> Built on the robust Fairseq's registry mechanism, allowing for easy adaptation and extension to diverse software engineering tasks.</li> <li><strong>Datasets and Preprocessing Tools+:</strong> Offers access to a variety of clean, preprocessed benchmarks such as Human-Eval, CodeSearchNet, Python-Doc, and Py150. Comes equipped with scripts for feature extraction using compiler tools like LLVM.</li> <li><strong>Support for Large Code Models:</strong> Incorporates state-of-the-art large code models like Code Llama, CodeT5, CodeGen, and StarCoder.</li> <li><strong>Benchmarking and Evaluation</strong>: Benchmarks multiple downstream tasks (including code generation and code completion), with evaluation capabilities on well-known benchmarks using popular metrics like pass@k.</li> <li><strong>Optimized for Efficiency:</strong> Employs the <code>NCCL</code> library and <code>torch.distributed</code> for high-efficiency model training across multiple GPUs. Supports both full-precision (<code>FP32</code>) and half-precision (<code>FP16</code>) computations to accelerate training and inference processes.</li> <li><strong>Enhanced Logging for Improved Debugging:</strong> Advanced logging features to provide clear, detailed feedback during model training and operation, aiding in debugging and performance optimization.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">✨ Latest News</h2><a id="user-content--latest-news" class="anchor" aria-label="Permalink: ✨ Latest News" href="#-latest-news"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>[Nov 25, 2023]</strong> <strong>NaturalCC 2.0 Released!</strong> Now compatible with <a href="https://github.com/huggingface/transformers">Transformers</a> and supporting popular large code models like Code Llama, CodeT5, CodeGen, and StarCoder from <a href="https://huggingface.co" rel="nofollow">Hugging Face</a>. Access the previous version in the <a href="/CGCL-codes/naturalcc/blob/main">ncc1</a> branch.</li> <li><strong>[Apr 19, 2023]</strong> Integrated the source code of "You See What I Want You to See: Poisoning Vulnerabilities in Neural Code Search" into NaturalCC.</li> <li><strong>[Jan 25, 2022]</strong> Our paper introducing the NaturalCC toolkit was accepted at the ICSE 2022 Demo Track.</li> <li><strong>[May 10, 2022]</strong> Merged the source code of "What Do They Capture? - A Structural Analysis of Pre-Trained Language Models for Source Code" into NaturalCC.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🛠️ Installation Guide</h2><a id="user-content-️-installation-guide" class="anchor" aria-label="Permalink: 🛠️ Installation Guide" href="#️-installation-guide"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To get started with NaturalCC, ensure your system meets the following requirements:</p> <ul dir="auto"> <li>GCC/G++ version 5.0 or higher</li> <li>NVIDIA GPU, NCCL, and the Cuda Toolkit for training new models (optional but recommended)</li> <li>NVIDIA's apex library for faster training (optional)</li> </ul> <p dir="auto">Follow these steps to set up the environment.</p> <ol dir="auto"> <li> <p dir="auto">(Optional) Creating conda environment</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda create -n naturalcc python=3.6 conda activate naturalcc"><pre>conda create -n naturalcc python=3.6 conda activate naturalcc</pre></div> </li> <li> <p dir="auto">Building NaturalCC from source</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/CGCL-codes/naturalcc && cd naturalcc pip install -r requirements.txt cd src pip install --editable ./"><pre>git clone https://github.com/CGCL-codes/naturalcc <span class="pl-k">&&</span> <span class="pl-c1">cd</span> naturalcc pip install -r requirements.txt <span class="pl-c1">cd</span> src pip install --editable ./</pre></div> </li> <li> <p dir="auto">Installing Additional Dependencies</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda install conda-forge::libsndfile pip install -q -U git+https://github.com/huggingface/transformers.git pip install -q -U git+https://github.com/huggingface/accelerate.git"><pre>conda install conda-forge::libsndfile pip install -q -U git+https://github.com/huggingface/transformers.git pip install -q -U git+https://github.com/huggingface/accelerate.git</pre></div> </li> <li> <p dir="auto">HuggingFace Token for Certain Models</p> <p dir="auto">For models like <a href="https://github.com/bigcode-project/starcoder">StarCoder</a>, a HuggingFace token is required. Log in to HuggingFace using:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="huggingface-cli login"><pre class="notranslate"><code>huggingface-cli login </code></pre></div> </li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🚀 Quick Start</h2><a id="user-content--quick-start" class="anchor" aria-label="Permalink: 🚀 Quick Start" href="#-quick-start"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Example 1: Code Generation</h3><a id="user-content-example-1-code-generation" class="anchor" aria-label="Permalink: Example 1: Code Generation" href="#example-1-code-generation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Download the model checkpoint</p> <p dir="auto">First, download the checkpoint of a specific large code model. For this example, we use <a href="https://huggingface.co/camenduru/CodeLlama-7b" rel="nofollow">Codellama-7B</a>.</p> </li> <li> <p dir="auto">Prepare the testing dataset</p> <p dir="auto">Create a JSON file containing your test cases in the following format:</p> <div class="highlight highlight-source-json notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="[ {"input": "this is a"}, {"input": "from tqdm import"}, {"input": "def calculate("}, {"input": "a = b**2"}, {"input": "torch.randint"}, {"input": "x = [1,2"} ]"><pre>[ {<span class="pl-ent">"input"</span>: <span class="pl-s"><span class="pl-pds">"</span>this is a<span class="pl-pds">"</span></span>}, {<span class="pl-ent">"input"</span>: <span class="pl-s"><span class="pl-pds">"</span>from tqdm import<span class="pl-pds">"</span></span>}, {<span class="pl-ent">"input"</span>: <span class="pl-s"><span class="pl-pds">"</span>def calculate(<span class="pl-pds">"</span></span>}, {<span class="pl-ent">"input"</span>: <span class="pl-s"><span class="pl-pds">"</span>a = b**2<span class="pl-pds">"</span></span>}, {<span class="pl-ent">"input"</span>: <span class="pl-s"><span class="pl-pds">"</span>torch.randint<span class="pl-pds">"</span></span>}, {<span class="pl-ent">"input"</span>: <span class="pl-s"><span class="pl-pds">"</span>x = [1,2<span class="pl-pds">"</span></span>} ]</pre></div> </li> <li> <p dir="auto">Running the code generation scripts</p> <ol dir="auto"> <li> <p dir="auto">Initialize the task with the specific model and GPU device:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="print('Initializing GenerationTask') task = GenerationTask(task_name="codellama_7b_code", device="cuda:0")"><pre><span class="pl-en">print</span>(<span class="pl-s">'Initializing GenerationTask'</span>) <span class="pl-s1">task</span> <span class="pl-c1">=</span> <span class="pl-v">GenerationTask</span>(<span class="pl-s1">task_name</span><span class="pl-c1">=</span><span class="pl-s">"codellama_7b_code"</span>, <span class="pl-s1">device</span><span class="pl-c1">=</span><span class="pl-s">"cuda:0"</span>)</pre></div> </li> <li> <p dir="auto">Load the downloaded checkpoint into the task. Replace <code>ckpt_path</code> with the path to your downloaded checkpoint:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="print('Loading model weights [{}]'.format(ckpt_path)) task.from_pretrained(ckpt_path)"><pre><span class="pl-en">print</span>(<span class="pl-s">'Loading model weights [{}]'</span>.<span class="pl-en">format</span>(<span class="pl-s1">ckpt_path</span>)) <span class="pl-s1">task</span>.<span class="pl-en">from_pretrained</span>(<span class="pl-s1">ckpt_path</span>)</pre></div> </li> <li> <p dir="auto">Load your dataset. Replace <code>dataset_path</code> with the path to your dataset file:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="print('Processing dataset [{}]'.format(dataset_path)) task.load_dataset(dataset_path)"><pre><span class="pl-en">print</span>(<span class="pl-s">'Processing dataset [{}]'</span>.<span class="pl-en">format</span>(<span class="pl-s1">dataset_path</span>)) <span class="pl-s1">task</span>.<span class="pl-en">load_dataset</span>(<span class="pl-s1">dataset_path</span>)</pre></div> </li> <li> <p dir="auto">Run the model and output the results. Replace <code>output_path</code> with your desired output file path:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="task.run(output_path=output_path, batch_size=1, max_length=50) print('Output file: {}'.format(output_path))"><pre><span class="pl-s1">task</span>.<span class="pl-en">run</span>(<span class="pl-s1">output_path</span><span class="pl-c1">=</span><span class="pl-s1">output_path</span>, <span class="pl-s1">batch_size</span><span class="pl-c1">=</span><span class="pl-c1">1</span>, <span class="pl-s1">max_length</span><span class="pl-c1">=</span><span class="pl-c1">50</span>) <span class="pl-en">print</span>(<span class="pl-s">'Output file: {}'</span>.<span class="pl-en">format</span>(<span class="pl-s1">output_path</span>))</pre></div> </li> </ol> </li> </ol> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Example 2: Code Summarization</h3><a id="user-content-example-2-code-summarization" class="anchor" aria-label="Permalink: Example 2: Code Summarization" href="#example-2-code-summarization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li> <p dir="auto">Download and process a dataset from <code>datasets</code>, and follow the instructions from the README.md file.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# ref: dataset/python_wan/README.md # download dataset bash dataset/python_wan/download.sh # clean data python -m dataset.python_wan.clean # cast data attributes into different files python -m dataset.python_wan.attributes_cast # ref: dataset/python_wan/summarization/README.md # save code tokens and docstirng tokens into MMAP format python -m dataset.python_wan.summarization.preprocess"><pre><span class="pl-c"><span class="pl-c">#</span> ref: dataset/python_wan/README.md</span> <span class="pl-c"><span class="pl-c">#</span> download dataset</span> bash dataset/python_wan/download.sh <span class="pl-c"><span class="pl-c">#</span> clean data</span> python -m dataset.python_wan.clean <span class="pl-c"><span class="pl-c">#</span> cast data attributes into different files</span> python -m dataset.python_wan.attributes_cast <span class="pl-c"><span class="pl-c">#</span> ref: dataset/python_wan/summarization/README.md</span> <span class="pl-c"><span class="pl-c">#</span> save code tokens and docstirng tokens into MMAP format</span> python -m dataset.python_wan.summarization.preprocess</pre></div> </li> <li> <p dir="auto">Register your self-defined models</p> <ul dir="auto"> <li> <p dir="auto">If you want to create a new model, please add your model at <code>ncc/models</code> and <code>ncc/modules</code>.</p> </li> <li> <p dir="auto">If your training policy are more complex than we thought, you should update your criterions and training procedure at <code>ncc/criterions</code> and <code>ncc/trainers</code>, respectively. <br></p> <p dir="auto">Do not forget to update your self defined module at <code>ncc/XX/__init__.py</code>.</p> </li> </ul> </li> <li> <p dir="auto">Training and inference.</p> <ul dir="auto"> <li>Select a task and a model from <a href="/CGCL-codes/naturalcc/blob/main/run">task list</a> and follow the instructions in its README.md to start your learning.</li> </ul> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# ref: run/summarization/transformer/README.md # train CUDA_VISIBLE_DEVICES=0,1,2,3 nohup python -m run.summarization.transformer.train -f config/python_wan/python > run/summarization/transformer/config/python_wan/python.log 2>&1 & # inference CUDA_VISIBLE_DEVICES=0 python -m run.summarization.transformer.eval -f config/python_wan/python -o run/summarization/transformer/config/python_wan/python.txt"><pre><span class="pl-c"><span class="pl-c">#</span> ref: run/summarization/transformer/README.md</span> <span class="pl-c"><span class="pl-c">#</span> train</span> CUDA_VISIBLE_DEVICES=0,1,2,3 nohup python -m run.summarization.transformer.train -f config/python_wan/python <span class="pl-k">></span> run/summarization/transformer/config/python_wan/python.log <span class="pl-k">2>&1</span> <span class="pl-k">&</span> <span class="pl-c"><span class="pl-c">#</span> inference</span> CUDA_VISIBLE_DEVICES=0 python -m run.summarization.transformer.eval -f config/python_wan/python -o run/summarization/transformer/config/python_wan/python.txt</pre></div> </li> </ol> <blockquote> <p dir="auto">We also have more detailed <a href="/CGCL-codes/naturalcc/blob/main/examples">READMEs</a> to start your tutorial of NaturalCC.</p> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📚 Dataset</h2><a id="user-content--dataset" class="anchor" aria-label="Permalink: 📚 Dataset" href="#-dataset"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NaturalCC supports a diverse range of datasets, catering to various aspects of code analysis and processing. These datasets include:</p> <ul dir="auto"> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/python_wan/README.md">Python (Wan et al.)</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/codesearchnet/README.md">CodeSearchNet (Husain et al.)</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/codexglue/code_to_text/README.md">CodeXGlue (Feng et al.)</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/py150/README.md">Py150 (official processed)</a> <a href="/CGCL-codes/naturalcc/blob/main/preprocessing/raw_py150/README.md">(raw)</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/opencl/README.md">OpenCL (Grewe et al.)</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/java_hu/README.md">Java (Hu et, al.)</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/stackoverflow/README.md">Stack Overflow</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/deepcs">DeepCS (Gu et al.)</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/avatar">AVATAR (Ahmad et al.)</a></li> <li><a href="/CGCL-codes/naturalcc/blob/main/preprocessing/stackoverflow">StackOverflow (Iyer et al.)</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🤝 Contributor</h2><a id="user-content--contributor" class="anchor" aria-label="Permalink: 🤝 Contributor" href="#-contributor"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We warmly welcome contributions to NaturalCC! Your involvement is essential for keeping NaturalCC innovative and accessible.</p> <p dir="auto">We're grateful to all our amazing contributors who have made this project what it is today!</p> <a href="https://github.com/CGCL-codes/naturalcc/graphs/contributors"> <img src="https://camo.githubusercontent.com/ddeca73ea59ffc8f3777eeb20e53ac8d148c1e48d0dd8466ba5f7baa1f7aa459/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d4347434c2d636f6465732f6e61747572616c636326723d" width="800px" data-canonical-src="https://contrib.rocks/image?repo=CGCL-codes/naturalcc&r=" style="max-width: 100%;"> </a> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">💡 FAQ</h2><a id="user-content--faq" class="anchor" aria-label="Permalink: 💡 FAQ" href="#-faq"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you have any questions or encounter issues, please feel free to reach out. For quick queries, you can also check our <code>Issues</code> page for common questions and solutions.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">😘 License and Acknowledgement</h2><a id="user-content--license-and-acknowledgement" class="anchor" aria-label="Permalink: 😘 License and Acknowledgement" href="#-license-and-acknowledgement"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>License:</strong> NaturalCC is open-sourced under the <a href="https://github.com/CGCL-codes/naturalcc/blob/master/LICENSE.txt">MIT-licensed</a>. This permissive license applies not only to the toolkit itself but also to the pre-trained models provided within.</p> <p dir="auto"><strong>Acknowledgements:</strong> We extend our heartfelt gratitude to the broader open-source community, particularly drawing inspiration from projects like <a href="https://github.com/pytorch/fairseq">Fairseq</a> for their advanced sequence-to-sequence models, and <a href="https://allennlp.org" rel="nofollow">AllenNLP</a> for their robust NLP components. Their groundbreaking work has been instrumental in shaping the development of NaturalCC.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">📄 Citation</h2><a id="user-content--citation" class="anchor" aria-label="Permalink: 📄 Citation" href="#-citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We're thrilled that you're interested in using NaturalCC for your research or applications! Citing our work helps us to grow and continue improving this toolkit. You can find more in-depth details about NaturalCC in our <a href="https://xcodemind.github.io/papers/icse22_naturalcc_camera_submitted.pdf" rel="nofollow">paper</a>.</p> <p dir="auto">If you use NaturalCC in your research, please consider citing our paper. Below is the BibTex format for citation:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{wan2022naturalcc, title={NaturalCC: An Open-Source Toolkit for Code Intelligence}, author={Yao Wan and Yang He and Zhangqian Bi and Jianguo Zhang and Yulei Sui and Hongyu Zhang and Kazuma Hashimoto and Hai Jin and Guandong Xu and Caiming Xiong and Philip S. Yu}, booktitle={Proceedings of 44th International Conference on Software Engineering, Companion Volume}, publisher=ACM, year={2022} }"><pre class="notranslate"><code>@inproceedings{wan2022naturalcc, title={NaturalCC: An Open-Source Toolkit for Code Intelligence}, author={Yao Wan and Yang He and Zhangqian Bi and Jianguo Zhang and Yulei Sui and Hongyu Zhang and Kazuma Hashimoto and Hai Jin and Guandong Xu and Caiming Xiong and Philip S. Yu}, booktitle={Proceedings of 44th International Conference on Software Engineering, Companion Volume}, publisher=ACM, year={2022} } </code></pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="6e4O41zlDZKo7gO10jPfvbCOWsLbwvfTm/lZQ3ob6YPmDs/YyeVqJkrv6GIgRqwqEnZuXrjMg4RKI5zwOr+P7A==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> NaturalCC: An Open-Source Toolkit for Code Intelligence </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path 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