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 and word vectors, and currently supports tokenization for 50+ 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 2.1 out now! Check out the release notes here.

📖 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 v2.1 | 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. |

💬 Where to ask questions

The spaCy project is maintained by @honnibal and @ines, along with core contributors @svlandeg and @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 | GitHub Issue Tracker | | 👩‍💻 Usage Questions | Stack Overflow · Gitter Chat · Reddit User Group | | 🗯 General Discussion | Gitter Chat · Reddit User Group |

Features

• Non-destructive tokenization
• Named entity recognition
• Support for 50+ languages
• Pre-trained statistical 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.

Install spaCy

For detailed installation instructions, see the documentation.

• 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

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. 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.

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. |

```bash

download best-matching version of specific model for your spaCy installation

python -m spacy download encoreweb_sm

out-of-the-box: download best-matching default model

python -m spacy download en

pip install .tar.gz archive from path or URL

pip install /Users/you/encorewebsm-2.2.0.tar.gz pip install https://github.com/explosion/spacy-models/releases/download/encorewebsm-2.2.0/encoreweb_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(u"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 encoreweb_sm

nlp = encoreweb_sm.load() doc = nlp(u"This is a sentence.") ```

📖 For more info and examples, check out the models documentation.

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. 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 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, virtualenv and git 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 additionally installs developer dependencies such as Cython. For more details and instructions, see the documentation on compiling spaCy from source and the quickstart widget 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, 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 or 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. 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 <spacy-directory>

See the documentation for more details and examples.