# spaCy: Industrial-strength NLP spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with [pretrained statistical models](https://spacy.io/models) and word vectors, and currently supports tokenization for **60+ languages**. It features state-of-the-art speed, convolutional **neural network models** for tagging, parsing and **named entity recognition** and easy **deep learning** integration. It's commercial open-source software, released under the MIT license. 💫 **Version 2.3 out now!** [Check out the release notes here.](https://github.com/explosion/spaCy/releases) [![Azure Pipelines]()](https://dev.azure.com/explosion-ai/public/_build?definitionId=8) [![Travis Build Status]()](https://travis-ci.org/explosion/spaCy) [](https://github.com/explosion/spaCy/releases) [](https://pypi.org/project/spacy/) [](https://anaconda.org/conda-forge/spacy) [](https://github.com/explosion/wheelwright/releases) [](https://pypi.org/project/spacy/) [](https://anaconda.org/conda-forge/spacy) [](https://github.com/explosion/spacy-models/releases) [](https://github.com/ambv/black) [](https://twitter.com/spacy_io) ## 📖 Documentation | Documentation | | | --------------- | -------------------------------------------------------------- | | [spaCy 101] | New to spaCy? Here's everything you need to know! | | [Usage Guides] | How to use spaCy and its features. | | [New in v2.3] | New features, backwards incompatibilities and migration guide. | | [API Reference] | The detailed reference for spaCy's API. | | [Models] | Download statistical language models for spaCy. | | [Universe] | Libraries, extensions, demos, books and courses. | | [Changelog] | Changes and version history. | | [Contribute] | How to contribute to the spaCy project and code base. | [spacy 101]: https://spacy.io/usage/spacy-101 [new in v2.3]: https://spacy.io/usage/v2-3 [usage guides]: https://spacy.io/usage/ [api reference]: https://spacy.io/api/ [models]: https://spacy.io/models [universe]: https://spacy.io/universe [changelog]: https://spacy.io/usage#changelog [contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md ## 💬 Where to ask questions The spaCy project is maintained by [@honnibal](https://github.com/honnibal) and [@ines](https://github.com/ines), along with core contributors [@svlandeg](https://github.com/svlandeg) and [@adrianeboyd](https://github.com/adrianeboyd). Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly, so that more people can benefit from it. | Type | Platforms | | ------------------------------- | --------------------------------------- | | 🚨 **Bug Reports** | [GitHub Issue Tracker] | | 🎁 **Feature Requests & Ideas** | [GitHub Discussions] | | 👩‍💻 **Usage Questions** | [GitHub Discussions] · [Stack Overflow] | | 🗯 **General Discussion** | [GitHub Discussions] | [github issue tracker]: https://github.com/explosion/spaCy/issues [github discussions]: https://github.com/explosion/spaCy/discussions [stack overflow]: https://stackoverflow.com/questions/tagged/spacy ## Features - Non-destructive **tokenization** - **Named entity** recognition - Support for **50+ languages** - pretrained [statistical models](https://spacy.io/models) and word vectors - State-of-the-art speed - Easy **deep learning** integration - Part-of-speech tagging - Labelled dependency parsing - Syntax-driven sentence segmentation - Built in **visualizers** for syntax and NER - Convenient string-to-hash mapping - Export to numpy data arrays - Efficient binary serialization - Easy **model packaging** and deployment - Robust, rigorously evaluated accuracy 📖 **For more details, see the [facts, figures and benchmarks](https://spacy.io/usage/facts-figures).** ## Install spaCy For detailed installation instructions, see the [documentation](https://spacy.io/usage). - **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual Studio) - **Python version**: Python 2.7, 3.5+ (only 64 bit) - **Package managers**: [pip] · [conda] (via `conda-forge`) [pip]: https://pypi.org/project/spacy/ [conda]: https://anaconda.org/conda-forge/spacy ### pip Using pip, spaCy releases are available as source packages and binary wheels (as of `v2.0.13`). Before you install spaCy and its dependencies, make sure that `pip`, `setuptools` and `wheel` are up to date. ```bash pip install -U pip setuptools wheel pip install spacy ``` For installation on python 2.7 or 3.5 where binary wheels are not provided for the most recent versions of the dependencies, you can prefer older binary wheels over newer source packages with `--prefer-binary`: ```bash pip install spacy --prefer-binary ``` To install additional data tables for lemmatization and normalization in **spaCy v2.2+** you can run `pip install spacy[lookups]` or install [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) separately. The lookups package is needed to create blank models with lemmatization data for v2.2+ plus normalization data for v2.3+, and to lemmatize in languages that don't yet come with pretrained models and aren't powered by third-party libraries. When using pip it is generally recommended to install packages in a virtual environment to avoid modifying system state: ```bash python -m venv .env source .env/bin/activate pip install -U pip setuptools wheel pip install spacy ``` ### conda Thanks to our great community, we've finally re-added conda support. You can now install spaCy via `conda-forge`: ```bash conda install -c conda-forge spacy ``` For the feedstock including the build recipe and configuration, check out [this repository](https://github.com/conda-forge/spacy-feedstock). Improvements and pull requests to the recipe and setup are always appreciated. ### Updating spaCy Some updates to spaCy may require downloading new statistical models. If you're running spaCy v2.0 or higher, you can use the `validate` command to check if your installed models are compatible and if not, print details on how to update them: ```bash pip install -U spacy python -m spacy validate ``` If you've trained your own models, keep in mind that your training and runtime inputs must match. After updating spaCy, we recommend **retraining your models** with the new version. 📖 **For details on upgrading from spaCy 1.x to spaCy 2.x, see the [migration guide](https://spacy.io/usage/v2#migrating).** ## Download models As of v1.7.0, models for spaCy can be installed as **Python packages**. This means that they're a component of your application, just like any other module. Models can be installed using spaCy's `download` command, or manually by pointing pip to a path or URL. | Documentation | | | ---------------------- | ------------------------------------------------------------- | | [Available Models] | Detailed model descriptions, accuracy figures and benchmarks. | | [Models Documentation] | Detailed usage instructions. | [available models]: https://spacy.io/models [models documentation]: https://spacy.io/docs/usage/models ```bash # download best-matching version of specific model for your spaCy installation python -m spacy download en_core_web_sm # pip install .tar.gz archive from path or URL pip install /Users/you/en_core_web_sm-2.2.0.tar.gz pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz ``` ### Loading and using models To load a model, use `spacy.load()` with the model name, a shortcut link or a path to the model data directory. ```python import spacy nlp = spacy.load("en_core_web_sm") doc = nlp("This is a sentence.") ``` You can also `import` a model directly via its full name and then call its `load()` method with no arguments. ```python import spacy import en_core_web_sm nlp = en_core_web_sm.load() doc = nlp("This is a sentence.") ``` 📖 **For more info and examples, check out the [models documentation](https://spacy.io/docs/usage/models).** ## Compile from source The other way to install spaCy is to clone its [GitHub repository](https://github.com/explosion/spaCy) and build it from source. That is the common way if you want to make changes to the code base. You'll need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, [pip](https://pip.pypa.io/en/latest/installing/), [virtualenv](https://virtualenv.pypa.io/en/latest/) and [git](https://git-scm.com) installed. The compiler part is the trickiest. How to do that depends on your system. See notes on Ubuntu, OS X and Windows for details. ```bash git clone https://github.com/explosion/spaCy cd spaCy python -m venv .env source .env/bin/activate # make sure you are using the latest pip python -m pip install -U pip setuptools wheel pip install . ``` To install with extras: ```bash pip install .[lookups,cuda102] ``` To install all dependencies required for development: ```bash pip install -r requirements.txt ``` Compared to regular install via pip, [requirements.txt](requirements.txt) additionally installs developer dependencies such as Cython. For more details and instructions, see the documentation on [compiling spaCy from source](https://spacy.io/usage#source) and the [quickstart widget](https://spacy.io/usage#section-quickstart) to get the right commands for your platform and Python version. ### Ubuntu Install system-level dependencies via `apt-get`: ```bash sudo apt-get install build-essential python-dev git ``` ### macOS / OS X 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. ### 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. For official distributions these are VS 2008 (Python 2.7), VS 2010 (Python 3.4) and VS 2015 (Python 3.5). ## Run tests spaCy comes with an [extensive test suite](spacy/tests). In order to run the tests, you'll usually want to clone the repository and build spaCy from source. This will also install the required development dependencies and test utilities defined in the `requirements.txt`. Alternatively, you can run `pytest` on the tests from within the installed `spacy` package. Don't forget to also install the test utilities via spaCy's `requirements.txt`: ```bash pip install -r requirements.txt python -m pytest --pyargs spacy ``` See [the documentation](https://spacy.io/usage#tests) for more details and examples.