{"id":13418377,"url":"https://github.com/explosion/spaCy","last_synced_at":"2025-03-15T03:31:05.835Z","repository":{"id":18294140,"uuid":"21467110","full_name":"explosion/spaCy","owner":"explosion","description":"💫 Industrial-strength Natural Language Processing (NLP) in Python","archived":false,"fork":false,"pushed_at":"2025-02-03T17:32:33.000Z","size":202890,"stargazers_count":30942,"open_issues_count":183,"forks_count":4447,"subscribers_count":562,"default_branch":"master","last_synced_at":"2025-02-19T09:20:05.622Z","etag":null,"topics":["ai","artificial-intelligence","cython","data-science","deep-learning","entity-linking","machine-learning","named-entity-recognition","natural-language-processing","neural-network","neural-networks","nlp","nlp-library","python","spacy","text-classification","tokenization"],"latest_commit_sha":null,"homepage":"https://spacy.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/explosion.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"custom":["https://explosion.ai/merch","https://explosion.ai/tailored-solutions"]}},"created_at":"2014-07-03T15:15:40.000Z","updated_at":"2025-02-19T08:19:41.000Z","dependencies_parsed_at":"2023-12-21T12:20:15.149Z","dependency_job_id":"8d116942-ecdf-4fe1-aad6-4a8868331d75","html_url":"https://github.com/explosion/spaCy","commit_stats":{"total_commits":14069,"total_committers":773,"mean_commits":18.20051746442432,"dds":0.7287653706731111,"last_synced_commit":"2f1e7ed09aae2dcb86a2f36c2df93ad6b4cb3d38"},"previous_names":["spacy-io/spacy"],"tags_count":177,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/explosion%2FspaCy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/explosion%2FspaCy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/explosion%2FspaCy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/explosion%2FspaCy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/explosion","download_url":"https://codeload.github.com/explosion/spaCy/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242926265,"owners_count":20207754,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","artificial-intelligence","cython","data-science","deep-learning","entity-linking","machine-learning","named-entity-recognition","natural-language-processing","neural-network","neural-networks","nlp","nlp-library","python","spacy","text-classification","tokenization"],"created_at":"2024-07-30T22:01:01.604Z","updated_at":"2025-03-15T03:31:05.828Z","avatar_url":"https://github.com/explosion.png","language":"Python","readme":"\u003ca href=\"https://explosion.ai\"\u003e\u003cimg src=\"https://explosion.ai/assets/img/logo.svg\" width=\"125\" height=\"125\" align=\"right\" /\u003e\u003c/a\u003e\n\n# spaCy: Industrial-strength NLP\n\nspaCy is a library for **advanced Natural Language Processing** in Python and\nCython. It's built on the very latest research, and was designed from day one to\nbe used in real products.\n\nspaCy comes with [pretrained pipelines](https://spacy.io/models) and currently\nsupports tokenization and training for **70+ languages**. It features\nstate-of-the-art speed and **neural network models** for tagging, parsing,\n**named entity recognition**, **text classification** and more, multi-task\nlearning with pretrained **transformers** like BERT, as well as a\nproduction-ready [**training system**](https://spacy.io/usage/training) and easy\nmodel packaging, deployment and workflow management. spaCy is commercial\nopen-source software, released under the\n[MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).\n\n💫 **Version 3.8 out now!**\n[Check out the release notes here.](https://github.com/explosion/spaCy/releases)\n\n[![tests](https://github.com/explosion/spaCy/actions/workflows/tests.yml/badge.svg)](https://github.com/explosion/spaCy/actions/workflows/tests.yml)\n[![Current Release Version](https://img.shields.io/github/release/explosion/spacy.svg?style=flat-square\u0026logo=github)](https://github.com/explosion/spaCy/releases)\n[![pypi Version](https://img.shields.io/pypi/v/spacy.svg?style=flat-square\u0026logo=pypi\u0026logoColor=white)](https://pypi.org/project/spacy/)\n[![conda Version](https://img.shields.io/conda/vn/conda-forge/spacy.svg?style=flat-square\u0026logo=conda-forge\u0026logoColor=white)](https://anaconda.org/conda-forge/spacy)\n[![Python wheels](https://img.shields.io/badge/wheels-%E2%9C%93-4c1.svg?longCache=true\u0026style=flat-square\u0026logo=python\u0026logoColor=white)](https://github.com/explosion/wheelwright/releases)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)\n\u003cbr /\u003e\n[![PyPi downloads](https://static.pepy.tech/personalized-badge/spacy?period=total\u0026units=international_system\u0026left_color=grey\u0026right_color=orange\u0026left_text=pip%20downloads)](https://pypi.org/project/spacy/)\n[![Conda downloads](https://img.shields.io/conda/dn/conda-forge/spacy?label=conda%20downloads)](https://anaconda.org/conda-forge/spacy)\n\n## 📖 Documentation\n\n| Documentation | |\n| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| ⭐️ **[spaCy 101]** | New to spaCy? Here's everything you need to know! |\n| 📚 **[Usage Guides]** | How to use spaCy and its features. |\n| 🚀 **[New in v3.0]** | New features, backwards incompatibilities and migration guide. |\n| 🪐 **[Project Templates]** | End-to-end workflows you can clone, modify and run. |\n| 🎛 **[API Reference]** | The detailed reference for spaCy's API. |\n| ⏩ **[GPU Processing]** | Use spaCy with CUDA-compatible GPU processing. |\n| 📦 **[Models]** | Download trained pipelines for spaCy. |\n| 🦙 **[Large Language Models]** | Integrate LLMs into spaCy pipelines. |\n| 🌌 **[Universe]** | Plugins, extensions, demos and books from the spaCy ecosystem. |\n| ⚙️ **[spaCy VS Code Extension]** | Additional tooling and features for working with spaCy's config files. |\n| 👩‍🏫 **[Online Course]** | Learn spaCy in this free and interactive online course. |\n| 📰 **[Blog]** | Read about current spaCy and Prodigy development, releases, talks and more from Explosion. |\n| 📺 **[Videos]** | Our YouTube channel with video tutorials, talks and more. |\n| 🔴 **[Live Stream]** | Join Matt as he works on spaCy and chat about NLP, live every week. |\n| 🛠 **[Changelog]** | Changes and version history. |\n| 💝 **[Contribute]** | How to contribute to the spaCy project and code base. |\n| 👕 **[Swag]** | Support us and our work with unique, custom-designed swag! |\n| \u003ca href=\"https://explosion.ai/tailored-solutions\"\u003e\u003cimg src=\"https://github.com/explosion/spaCy/assets/13643239/36d2a42e-98c0-4599-90e1-788ef75181be\" width=\"150\" alt=\"Tailored Solutions\"/\u003e\u003c/a\u003e | Custom NLP consulting, implementation and strategic advice by spaCy’s core development team. Streamlined, production-ready, predictable and maintainable. Send us an email or take our 5-minute questionnaire, and well'be in touch! **[Learn more \u0026rarr;](https://explosion.ai/tailored-solutions)** |\n\n[spacy 101]: https://spacy.io/usage/spacy-101\n[new in v3.0]: https://spacy.io/usage/v3\n[usage guides]: https://spacy.io/usage/\n[api reference]: https://spacy.io/api/\n[gpu processing]: https://spacy.io/usage#gpu\n[models]: https://spacy.io/models\n[large language models]: https://spacy.io/usage/large-language-models\n[universe]: https://spacy.io/universe\n[spacy vs code extension]: https://github.com/explosion/spacy-vscode\n[videos]: https://www.youtube.com/c/ExplosionAI\n[live stream]: https://www.youtube.com/playlist?list=PLBmcuObd5An5_iAxNYLJa_xWmNzsYce8c\n[online course]: https://course.spacy.io\n[blog]: https://explosion.ai\n[project templates]: https://github.com/explosion/projects\n[changelog]: https://spacy.io/usage#changelog\n[contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md\n[swag]: https://explosion.ai/merch\n\n## 💬 Where to ask questions\n\nThe spaCy project is maintained by the [spaCy team](https://explosion.ai/about).\nPlease understand that we won't be able to provide individual support via email.\nWe also believe that help is much more valuable if it's shared publicly, so that\nmore people can benefit from it.\n\n| Type | Platforms |\n| ------------------------------- | --------------------------------------- |\n| 🚨 **Bug Reports** | [GitHub Issue Tracker] |\n| 🎁 **Feature Requests \u0026 Ideas** | [GitHub Discussions] · [Live Stream] |\n| 👩‍💻 **Usage Questions** | [GitHub Discussions] · [Stack Overflow] |\n| 🗯 **General Discussion** | [GitHub Discussions] · [Live Stream] |\n\n[github issue tracker]: https://github.com/explosion/spaCy/issues\n[github discussions]: https://github.com/explosion/spaCy/discussions\n[stack overflow]: https://stackoverflow.com/questions/tagged/spacy\n[live stream]: https://www.youtube.com/playlist?list=PLBmcuObd5An5_iAxNYLJa_xWmNzsYce8c\n\n## Features\n\n- Support for **70+ languages**\n- **Trained pipelines** for different languages and tasks\n- Multi-task learning with pretrained **transformers** like BERT\n- Support for pretrained **word vectors** and embeddings\n- State-of-the-art speed\n- Production-ready **training system**\n- Linguistically-motivated **tokenization**\n- Components for named **entity recognition**, part-of-speech-tagging,\n dependency parsing, sentence segmentation, **text classification**,\n lemmatization, morphological analysis, entity linking and more\n- Easily extensible with **custom components** and attributes\n- Support for custom models in **PyTorch**, **TensorFlow** and other frameworks\n- Built in **visualizers** for syntax and NER\n- Easy **model packaging**, deployment and workflow management\n- Robust, rigorously evaluated accuracy\n\n📖 **For more details, see the\n[facts, figures and benchmarks](https://spacy.io/usage/facts-figures).**\n\n## ⏳ Install spaCy\n\nFor detailed installation instructions, see the\n[documentation](https://spacy.io/usage).\n\n- **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual\n Studio)\n- **Python version**: Python 3.7+ (only 64 bit)\n- **Package managers**: [pip] · [conda] (via `conda-forge`)\n\n[pip]: https://pypi.org/project/spacy/\n[conda]: https://anaconda.org/conda-forge/spacy\n\n### pip\n\nUsing pip, spaCy releases are available as source packages and binary wheels.\nBefore you install spaCy and its dependencies, make sure that your `pip`,\n`setuptools` and `wheel` are up to date.\n\n```bash\npip install -U pip setuptools wheel\npip install spacy\n```\n\nTo install additional data tables for lemmatization and normalization you can\nrun `pip install spacy[lookups]` or install\n[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)\nseparately. The lookups package is needed to create blank models with\nlemmatization data, and to lemmatize in languages that don't yet come with\npretrained models and aren't powered by third-party libraries.\n\nWhen using pip it is generally recommended to install packages in a virtual\nenvironment to avoid modifying system state:\n\n```bash\npython -m venv .env\nsource .env/bin/activate\npip install -U pip setuptools wheel\npip install spacy\n```\n\n### conda\n\nYou can also install spaCy from `conda` via the `conda-forge` channel. For the\nfeedstock including the build recipe and configuration, check out\n[this repository](https://github.com/conda-forge/spacy-feedstock).\n\n```bash\nconda install -c conda-forge spacy\n```\n\n### Updating spaCy\n\nSome updates to spaCy may require downloading new statistical models. If you're\nrunning spaCy v2.0 or higher, you can use the `validate` command to check if\nyour installed models are compatible and if not, print details on how to update\nthem:\n\n```bash\npip install -U spacy\npython -m spacy validate\n```\n\nIf you've trained your own models, keep in mind that your training and runtime\ninputs must match. After updating spaCy, we recommend **retraining your models**\nwith the new version.\n\n📖 **For details on upgrading from spaCy 2.x to spaCy 3.x, see the\n[migration guide](https://spacy.io/usage/v3#migrating).**\n\n## 📦 Download model packages\n\nTrained pipelines for spaCy can be installed as **Python packages**. This means\nthat they're a component of your application, just like any other module. Models\ncan be installed using spaCy's [`download`](https://spacy.io/api/cli#download)\ncommand, or manually by pointing pip to a path or URL.\n\n| Documentation | |\n| -------------------------- | ---------------------------------------------------------------- |\n| **[Available Pipelines]** | Detailed pipeline descriptions, accuracy figures and benchmarks. |\n| **[Models Documentation]** | Detailed usage and installation instructions. |\n| **[Training]** | How to train your own pipelines on your data. |\n\n[available pipelines]: https://spacy.io/models\n[models documentation]: https://spacy.io/usage/models\n[training]: https://spacy.io/usage/training\n\n```bash\n# Download best-matching version of specific model for your spaCy installation\npython -m spacy download en_core_web_sm\n\n# pip install .tar.gz archive or .whl from path or URL\npip install /Users/you/en_core_web_sm-3.0.0.tar.gz\npip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl\npip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz\n```\n\n### Loading and using models\n\nTo load a model, use [`spacy.load()`](https://spacy.io/api/top-level#spacy.load)\nwith the model name or a path to the model data directory.\n\n```python\nimport spacy\nnlp = spacy.load(\"en_core_web_sm\")\ndoc = nlp(\"This is a sentence.\")\n```\n\nYou can also `import` a model directly via its full name and then call its\n`load()` method with no arguments.\n\n```python\nimport spacy\nimport en_core_web_sm\n\nnlp = en_core_web_sm.load()\ndoc = nlp(\"This is a sentence.\")\n```\n\n📖 **For more info and examples, check out the\n[models documentation](https://spacy.io/docs/usage/models).**\n\n## ⚒ Compile from source\n\nThe other way to install spaCy is to clone its\n[GitHub repository](https://github.com/explosion/spaCy) and build it from\nsource. That is the common way if you want to make changes to the code base.\nYou'll need to make sure that you have a development environment consisting of a\nPython distribution including header files, a compiler,\n[pip](https://pip.pypa.io/en/latest/installing/),\n[virtualenv](https://virtualenv.pypa.io/en/latest/) and\n[git](https://git-scm.com) installed. The compiler part is the trickiest. How to\ndo that depends on your system.\n\n| Platform | |\n| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| **Ubuntu** | Install system-level dependencies via `apt-get`: `sudo apt-get install build-essential python-dev git` . |\n| **Mac** | Install a recent version of [XCode](https://developer.apple.com/xcode/), including the so-called \"Command Line Tools\". macOS and OS X ship with Python and git preinstalled. |\n| **Windows** | Install a version of the [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that matches the version that was used to compile your Python interpreter. |\n\nFor more details and instructions, see the documentation on\n[compiling spaCy from source](https://spacy.io/usage#source) and the\n[quickstart widget](https://spacy.io/usage#section-quickstart) to get the right\ncommands for your platform and Python version.\n\n```bash\ngit clone https://github.com/explosion/spaCy\ncd spaCy\n\npython -m venv .env\nsource .env/bin/activate\n\n# make sure you are using the latest pip\npython -m pip install -U pip setuptools wheel\n\npip install -r requirements.txt\npip install --no-build-isolation --editable .\n```\n\nTo install with extras:\n\n```bash\npip install --no-build-isolation --editable .[lookups,cuda102]\n```\n\n## 🚦 Run tests\n\nspaCy comes with an [extensive test suite](spacy/tests). In order to run the\ntests, you'll usually want to clone the repository and build spaCy from source.\nThis will also install the required development dependencies and test utilities\ndefined in the [`requirements.txt`](requirements.txt).\n\nAlternatively, you can run `pytest` on the tests from within the installed\n`spacy` package. Don't forget to also install the test utilities via spaCy's\n[`requirements.txt`](requirements.txt):\n\n```bash\npip install -r requirements.txt\npython -m pytest --pyargs spacy\n```\n","funding_links":["https://explosion.ai/merch","https://explosion.ai/tailored-solutions"],"categories":["Python","Natural Language Processing","Python Frameworks and Tools","🏆 State-of-the-Art Repositories","Resources and Frameworks","Industrial Strength NLP","Python (144)","Libraries","AutoML NLP","[Python](#python)","Python Frameworks, Libraries, and Tools","其他_NLP自然语言处理","Software","HarmonyOS","Uncategorized","语言资源库","Industry Strength Natural Language Processing","artificial-intelligence","Application Recommendation","文本数据和NLP","函式庫","Tools","Python Learning Resources","📚 فهرست","Speech recognition","Table of Contents","Developer Resources","Machine Learning","\u003ca name=\"Python\"\u003e\u003c/a\u003ePython","Text Processing"],"sub_categories":["In-memory data grids","🥇 Must-Star Repositories","Videos and Online Courses","General Purpose NLP","其他_文本生成、文本对话","Interfaces","Some projects with more that 10 000 lines of Cython code","Windows Manager","Uncategorized","python","General-Purpose Machine Learning","🧠 AI Applications","E-Books","VS Code Extensions for Developer Productivity","Objective-C Tools, Libraries, and Frameworks","viii. 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