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 70+ 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 3.8 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. |
| ⏩ GPU Processing |
Use spaCy with CUDA-compatible GPU processing. |
| 📦 Models |
Download trained pipelines for spaCy. |
| 🦙 Large Language Models |
Integrate LLMs into spaCy pipelines. |
| 🌌 Universe |
Plugins, extensions, demos and books from the spaCy ecosystem. |
| ⚙️ spaCy VS Code Extension |
Additional tooling and features for working with spaCy's config files. |
| 👩🏫 Online Course |
Learn spaCy in this free and interactive online course. |
| 📰 Blog |
Read about current spaCy and Prodigy development, releases, talks and more from Explosion. |
| 📺 Videos |
Our YouTube channel with video tutorials, talks and more. |
| 🔴 Live Stream |
Join Matt as he works on spaCy and chat about NLP. |
| 🛠 Changelog |
Changes and version history. |
| 💝 Contribute |
How to contribute to the spaCy project and code base. |
| 👕 Swag |
Support us and our work with unique, custom-designed swag! |
 |
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 → |
💬 Where to ask questions
The spaCy project is maintained by the spaCy team.
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.
Features
- Support for 70+ 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 >=3.7, <3.13 (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.
# 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 loa
spaCy 是一个用于 Python 和 Cython 的高级自然语言处理库,从设计之初就面向真实产品。它自带预训练流水线,支持 70 多种语言的分词和训练。