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 pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.

💫 Version 3.0 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 v3.0	New features, backwards incompatibilities and migration guide.
🪐 Project Templates	End-to-end workflows you can clone, modify and run.
🎛 API Reference	The detailed reference for spaCy's API.
📦 Models	Download trained pipelines for spaCy.
🌌 Universe	Plugins, extensions, demos and books from the spaCy ecosystem.
👩‍🏫 Online Course	Learn spaCy in this free and interactive online course.
📺 Videos	Our YouTube channel with video tutorials, talks and more.
🛠 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, @ines, @svlandeg, @adrianeboyd and @polm. 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

Features

• Support for 60+ languages
• Trained pipelines for different languages and tasks
• Multi-task learning with pretrained transformers like BERT
• Support for pretrained word vectors and embeddings
• State-of-the-art speed
• Production-ready training system
• Linguistically-motivated tokenization
• Components for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more
• Easily extensible with custom components and attributes
• Support for custom models in PyTorch, TensorFlow and other frameworks
• Built in visualizers for syntax and NER
• Easy model packaging, deployment and workflow management
• 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 3.6+ (only 64 bit)
• Package managers: pip · conda (via conda-forge)

pip

Using pip, spaCy releases are available as source packages and binary wheels. Before you install spaCy and its dependencies, make sure that your pip, setuptools and wheel are up to date.

pip install -U pip setuptools wheel
pip install spacy

To install additional data tables for lemmatization and normalization you can run pip install spacy[lookups] or install spacy-lookups-data separately. The lookups package is needed to create blank models with lemmatization data, 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:

python -m venv .env
source .env/bin/activate
pip install -U pip setuptools wheel
pip install spacy

conda

You can also install spaCy from conda via the conda-forge channel. For the feedstock including the build recipe and configuration, check out this repository.

conda install -c conda-forge spacy

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:

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 2.x to spaCy 3.x, see the migration guide.

📦 Download model packages

Trained pipelines 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 Pipelines	Detailed pipeline descriptions, accuracy figures and benchmarks.
Models Documentation	Detailed usage and installation instructions.
Training	How to train your own pipelines on your data.

# Download best-matching version of specific model for your spaCy installation
python -m spacy download en_core_web_sm

# pip install .tar.gz archive or .whl from path or URL
pip install /Users/you/en_core_web_sm-3.0.0.tar.gz
pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
pip 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

Loading and using models

To load a model, use spacy.load() with the model name or a path to the model data directory.

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.

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.

⚒ 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.

Platform
Ubuntu	Install system-level dependencies via apt-get: sudo apt-get install build-essential python-dev git .
Mac	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 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.

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 -r requirements.txt
pip install --no-build-isolation --editable .

To install with extras:

pip install --no-build-isolation --editable .[lookups,cuda102]

🚦 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 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:

pip install -r requirements.txt
python -m pytest --pyargs spacy