# 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 [pre-trained statistical models](https://spacy.io/models) and word vectors, and currently supports tokenization for **45+ languages**. It features the **fastest syntactic parser** in the world, 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 2.0 out now!** [Check out the release notes here.](https://github.com/explosion/spaCy/releases) [](https://dev.azure.com/explosion-ai/public/_build?definitionId=8) [](https://travis-ci.org/explosion/spaCy) [](https://github.com/explosion/spaCy/releases) [](https://pypi.python.org/pypi/spacy) [](https://anaconda.org/conda-forge/spacy) [](https://github.com/explosion/wheelwright/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. | | [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 [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). 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** | [GitHub Issue Tracker] | | 👩‍💻 **Usage Questions** | [Stack Overflow] · [Gitter Chat] · [Reddit User Group] | | 🗯 **General Discussion** | [Gitter Chat] · [Reddit User Group] | [github issue tracker]: https://github.com/explosion/spaCy/issues [stack overflow]: http://stackoverflow.com/questions/tagged/spacy [gitter chat]: https://gitter.im/explosion/spaCy [reddit user group]: https://www.reddit.com/r/spacynlp ## Features - **Fastest syntactic parser** in the world - **Named entity** recognition - Non-destructive **tokenization** - Support for **45+ languages** - Pre-trained [statistical models](https://spacy.io/models) and word vectors - 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 - State-of-the-art speed - 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, 3.4+ (only 64 bit) - **Package managers**: [pip] · [conda] (via `conda-forge`) [pip]: https://pypi.python.org/pypi/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`). ```bash pip install spacy ``` 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 spacy ``` ### conda Thanks to our great community, we've finally re-added conda support. You can now install spaCy via `conda-forge`: ```bash conda config --add channels conda-forge conda install 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 # out-of-the-box: download best-matching default model python -m spacy download en # download best-matching version of specific model for your spaCy installation python -m spacy download en_core_web_lg # pip install .tar.gz archive from path or URL pip install /Users/you/en_core_web_sm-2.0.0.tar.gz ``` ### Loading and using models To load a model, use `spacy.load()` with the model's shortcut link: ```python import spacy nlp = spacy.load('en') doc = nlp(u'This is a sentence.') ``` If you've installed a model via pip, you can also `import` it directly and then call its `load()` method: ```python import spacy import en_core_web_sm nlp = en_core_web_sm.load() doc = nlp(u'This is a sentence.') ``` 📖 **For more info and examples, check out the [models documentation](https://spacy.io/docs/usage/models).** ### Support for older versions If you're using an older version (`v1.6.0` or below), you can still download and install the old models from within spaCy using `python -m spacy.en.download all` or `python -m spacy.de.download all`. The `.tar.gz` archives are also [attached to the v1.6.0 release](https://github.com/explosion/spaCy/tree/v1.6.0). To download and install the models manually, unpack the archive, drop the contained directory into `spacy/data` and load the model via `spacy.load('en')` or `spacy.load('de')`. ## 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/) 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 # make sure you are using the latest pip python -m pip install -U pip git clone https://github.com/explosion/spaCy cd spaCy python -m venv .env source .env/bin/activate export PYTHONPATH=`pwd` pip install -r requirements.txt python setup.py build_ext --inplace ``` 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://www.visualstudio.com/vs/visual-studio-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 find out where spaCy is installed and run `pytest` on that directory. Don't forget to also install the test utilities via spaCy's `requirements.txt`: ```bash python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))" pip install -r path/to/requirements.txt python -m pytest ``` See [the documentation](https://spacy.io/usage/#tests) for more details and examples.