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292 lines
12 KiB
Markdown
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
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# spaCy: Industrial-strength NLP
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spaCy is a library for advanced Natural Language Processing in Python and
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Cython. It's built on the very latest research, and was designed from day one to
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be used in real products. spaCy comes with
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[pretrained statistical models](https://spacy.io/models) and word vectors, and
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currently supports tokenization for **60+ languages**. It features
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state-of-the-art speed, convolutional **neural network models** for tagging,
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parsing and **named entity recognition** and easy **deep learning** integration.
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It's commercial open-source software, released under the MIT license.
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💫 **Version 2.3 out now!**
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[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
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[>)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
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[](https://github.com/explosion/spaCy/releases)
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[](https://pypi.org/project/spacy/)
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[](https://anaconda.org/conda-forge/spacy)
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[](https://github.com/explosion/wheelwright/releases)
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[](https://pypi.org/project/spacy/)
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[](https://anaconda.org/conda-forge/spacy)
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[](https://github.com/explosion/spacy-models/releases)
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[](https://github.com/ambv/black)
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[](https://twitter.com/spacy_io)
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## 📖 Documentation
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| Documentation | |
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| --------------- | -------------------------------------------------------------- |
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| [spaCy 101] | New to spaCy? Here's everything you need to know! |
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| [Usage Guides] | How to use spaCy and its features. |
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| [New in v2.3] | New features, backwards incompatibilities and migration guide. |
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| [API Reference] | The detailed reference for spaCy's API. |
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| [Models] | Download statistical language models for spaCy. |
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| [Universe] | Libraries, extensions, demos, books and courses. |
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| [Changelog] | Changes and version history. |
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| [Contribute] | How to contribute to the spaCy project and code base. |
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[spacy 101]: https://spacy.io/usage/spacy-101
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[new in v2.3]: https://spacy.io/usage/v2-3
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[usage guides]: https://spacy.io/usage/
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[api reference]: https://spacy.io/api/
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[models]: https://spacy.io/models
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[universe]: https://spacy.io/universe
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[changelog]: https://spacy.io/usage#changelog
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[contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
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## 💬 Where to ask questions
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The spaCy project is maintained by [@honnibal](https://github.com/honnibal),
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[@ines](https://github.com/ines), [@svlandeg](https://github.com/svlandeg) and
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[@adrianeboyd](https://github.com/adrianeboyd). Please understand that we won't
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be able to provide individual support via email. We also believe that help is
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much more valuable if it's shared publicly, so that more people can benefit from
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it.
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| Type | Platforms |
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| ------------------------ | ------------------------------------------------------ |
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| 🚨 **Bug Reports** | [GitHub Issue Tracker] |
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| 🎁 **Feature Requests** | [GitHub Issue Tracker] |
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| 👩💻 **Usage Questions** | [Stack Overflow] · [Gitter Chat] · [Reddit User Group] |
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| 🗯 **General Discussion** | [Gitter Chat] · [Reddit User Group] |
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[github issue tracker]: https://github.com/explosion/spaCy/issues
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[stack overflow]: https://stackoverflow.com/questions/tagged/spacy
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[gitter chat]: https://gitter.im/explosion/spaCy
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[reddit user group]: https://www.reddit.com/r/spacynlp
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## Features
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- Non-destructive **tokenization**
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- **Named entity** recognition
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- Support for **50+ languages**
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- pretrained [statistical models](https://spacy.io/models) and word vectors
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- State-of-the-art speed
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- Easy **deep learning** integration
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- Part-of-speech tagging
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- Labelled dependency parsing
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- Syntax-driven sentence segmentation
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- Built in **visualizers** for syntax and NER
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- Convenient string-to-hash mapping
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- Export to numpy data arrays
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- Efficient binary serialization
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- Easy **model packaging** and deployment
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- Robust, rigorously evaluated accuracy
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📖 **For more details, see the
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[facts, figures and benchmarks](https://spacy.io/usage/facts-figures).**
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## Install spaCy
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For detailed installation instructions, see the
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[documentation](https://spacy.io/usage).
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- **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
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Studio)
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- **Python version**: Python 3.6+ (only 64 bit)
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- **Package managers**: [pip] · [conda] (via `conda-forge`)
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[pip]: https://pypi.org/project/spacy/
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[conda]: https://anaconda.org/conda-forge/spacy
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> ⚠️ **Important note for Python 3.8:** We can't yet ship pre-compiled binary
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> wheels for spaCy that work on Python 3.8, as we're still waiting for our CI
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> providers and other tooling to support it. This means that in order to run
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> spaCy on Python 3.8, you'll need [a compiler installed](#source) and compile
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> the library and its Cython dependencies locally. If this is causing problems
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> for you, the easiest solution is to **use Python 3.7** in the meantime.
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### pip
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Using pip, spaCy releases are available as source packages and binary wheels (as
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of `v2.0.13`).
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```bash
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pip install spacy
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```
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To install additional data tables for lemmatization and normalization in
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**spaCy v2.2+** you can run `pip install spacy[lookups]` or install
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[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
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separately. The lookups package is needed to create blank models with
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lemmatization data for v2.2+ plus normalization data for v2.3+, and to
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lemmatize in languages that don't yet come with pretrained models and aren't
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powered by third-party libraries.
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When using pip it is generally recommended to install packages in a virtual
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environment to avoid modifying system state:
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```bash
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python -m venv .env
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source .env/bin/activate
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pip install spacy
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```
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### conda
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Thanks to our great community, we've finally re-added conda support. You can now
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install spaCy via `conda-forge`:
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```bash
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conda install -c conda-forge spacy
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```
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For the feedstock including the build recipe and configuration, check out
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[this repository](https://github.com/conda-forge/spacy-feedstock). Improvements
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and pull requests to the recipe and setup are always appreciated.
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### Updating spaCy
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Some updates to spaCy may require downloading new statistical models. If you're
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running spaCy v2.0 or higher, you can use the `validate` command to check if
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your installed models are compatible and if not, print details on how to update
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them:
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```bash
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pip install -U spacy
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python -m spacy validate
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```
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If you've trained your own models, keep in mind that your training and runtime
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inputs must match. After updating spaCy, we recommend **retraining your models**
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with the new version.
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📖 **For details on upgrading from spaCy 1.x to spaCy 2.x, see the
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[migration guide](https://spacy.io/usage/v2#migrating).**
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## Download models
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As of v1.7.0, models for spaCy can be installed as **Python packages**. This
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means that they're a component of your application, just like any other module.
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Models can be installed using spaCy's `download` command, or manually by
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pointing pip to a path or URL.
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| Documentation | |
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| ---------------------- | ------------------------------------------------------------- |
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| [Available Models] | Detailed model descriptions, accuracy figures and benchmarks. |
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| [Models Documentation] | Detailed usage instructions. |
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[available models]: https://spacy.io/models
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[models documentation]: https://spacy.io/docs/usage/models
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```bash
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# download best-matching version of specific model for your spaCy installation
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python -m spacy download en_core_web_sm
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# pip install .tar.gz archive from path or URL
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pip install /Users/you/en_core_web_sm-2.2.0.tar.gz
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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
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```
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### Loading and using models
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To load a model, use `spacy.load()` with the model name or a
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path to the model data directory.
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```python
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import spacy
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nlp = spacy.load("en_core_web_sm")
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doc = nlp("This is a sentence.")
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```
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You can also `import` a model directly via its full name and then call its
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`load()` method with no arguments.
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```python
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import spacy
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import en_core_web_sm
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nlp = en_core_web_sm.load()
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doc = nlp("This is a sentence.")
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```
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📖 **For more info and examples, check out the
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[models documentation](https://spacy.io/docs/usage/models).**
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## Compile from source
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The other way to install spaCy is to clone its
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[GitHub repository](https://github.com/explosion/spaCy) and build it from
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source. That is the common way if you want to make changes to the code base.
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You'll need to make sure that you have a development environment consisting of a
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Python distribution including header files, a compiler,
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[pip](https://pip.pypa.io/en/latest/installing/),
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[virtualenv](https://virtualenv.pypa.io/en/latest/) and
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[git](https://git-scm.com) installed. The compiler part is the trickiest. How to
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do that depends on your system. See notes on Ubuntu, OS X and Windows for
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details.
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```bash
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# make sure you are using the latest pip
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python -m pip install -U pip
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git clone https://github.com/explosion/spaCy
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cd spaCy
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python -m venv .env
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source .env/bin/activate
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export PYTHONPATH=`pwd`
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pip install -r requirements.txt
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python setup.py build_ext --inplace
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```
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Compared to regular install via pip, [requirements.txt](requirements.txt)
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additionally installs developer dependencies such as Cython. For more details
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and instructions, see the documentation on
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[compiling spaCy from source](https://spacy.io/usage#source) and the
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[quickstart widget](https://spacy.io/usage#section-quickstart) to get the right
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commands for your platform and Python version.
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### Ubuntu
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Install system-level dependencies via `apt-get`:
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```bash
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sudo apt-get install build-essential python-dev git
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```
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### macOS / OS X
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Install a recent version of [XCode](https://developer.apple.com/xcode/),
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including the so-called "Command Line Tools". macOS and OS X ship with Python
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and git preinstalled.
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### Windows
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Install a version of the
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[Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
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or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that
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matches the version that was used to compile your Python interpreter.
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## Run tests
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spaCy comes with an [extensive test suite](spacy/tests). In order to run the
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tests, you'll usually want to clone the repository and build spaCy from source.
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This will also install the required development dependencies and test utilities
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defined in the `requirements.txt`.
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Alternatively, you can find out where spaCy is installed and run `pytest` on
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that directory. Don't forget to also install the test utilities via spaCy's
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`requirements.txt`:
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```bash
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python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
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pip install -r path/to/requirements.txt
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python -m pytest <spacy-directory>
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```
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See [the documentation](https://spacy.io/usage#tests) for more details and
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examples.
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