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* Update README.md to include links for GPU processing, LLM, and spaCy's blog. * Create ojo4f3.md * corrected README to most current version with links to GPU processing, LLM's, and the spaCy blog. * Delete .github/contributors/ojo4f3.md * changed LLM icon Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Apply suggestions from code review --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
291 lines
23 KiB
Markdown
291 lines
23 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.
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spaCy comes with [pretrained pipelines](https://spacy.io/models) and currently
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supports tokenization and training for **70+ languages**. It features
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state-of-the-art speed and **neural network models** for tagging, parsing,
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**named entity recognition**, **text classification** and more, multi-task
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learning with pretrained **transformers** like BERT, as well as a
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production-ready [**training system**](https://spacy.io/usage/training) and easy
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model packaging, deployment and workflow management. spaCy is commercial
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open-source software, released under the
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[MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
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💫 **Version 3.7 out now!**
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[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
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[](https://github.com/explosion/spaCy/actions/workflows/tests.yml)
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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://github.com/ambv/black)
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<br />
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[](https://pypi.org/project/spacy/)
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[](https://anaconda.org/conda-forge/spacy)
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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 v3.0]** | New features, backwards incompatibilities and migration guide. |
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| 🪐 **[Project Templates]** | End-to-end workflows you can clone, modify and run. |
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| 🎛 **[API Reference]** | The detailed reference for spaCy's API. |
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| ⏩ **[GPU Processing]** | Use spaCy with CUDA-compatible GPU processing. |
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| 📦 **[Models]** | Download trained pipelines for spaCy. |
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| 🦙 **[Large Language Models]** | Integrate LLMs into spaCy pipelines. |
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| 🌌 **[Universe]** | Plugins, extensions, demos and books from the spaCy ecosystem. |
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| ⚙️ **[spaCy VS Code Extension]** | Additional tooling and features for working with spaCy's config files. |
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| 👩🏫 **[Online Course]** | Learn spaCy in this free and interactive online course. |
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| 📰 **[Blog]** | Read about current spaCy and Prodigy development, releases, talks and more from Explosion. |
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| 📺 **[Videos]** | Our YouTube channel with video tutorials, talks and more. |
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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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| 👕 **[Swag]** | Support us and our work with unique, custom-designed swag! |
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| <a href="https://explosion.ai/tailored-solutions"><img src="https://github.com/explosion/spaCy/assets/13643239/36d2a42e-98c0-4599-90e1-788ef75181be" width="150" alt="Tailored Solutions"/></a> | 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 →](https://explosion.ai/tailored-solutions)** |
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[spacy 101]: https://spacy.io/usage/spacy-101
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[new in v3.0]: https://spacy.io/usage/v3
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[usage guides]: https://spacy.io/usage/
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[api reference]: https://spacy.io/api/
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[gpu processing]: https://spacy.io/usage#gpu
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[models]: https://spacy.io/models
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[large language models]: https://spacy.io/usage/large-language-models
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[universe]: https://spacy.io/universe
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[spacy vs code extension]: https://github.com/explosion/spacy-vscode
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[videos]: https://www.youtube.com/c/ExplosionAI
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[online course]: https://course.spacy.io
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[blog]: https://explosion.ai
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[project templates]: https://github.com/explosion/projects
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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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[swag]: https://explosion.ai/merch
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## 💬 Where to ask questions
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The spaCy project is maintained by the [spaCy team](https://explosion.ai/about).
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Please understand that we won't be able to provide individual support via email.
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We also believe that help is much more valuable if it's shared publicly, so that
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more people can benefit from 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 & Ideas** | [GitHub Discussions] |
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| 👩💻 **Usage Questions** | [GitHub Discussions] · [Stack Overflow] |
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| 🗯 **General Discussion** | [GitHub Discussions] |
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[github issue tracker]: https://github.com/explosion/spaCy/issues
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[github discussions]: https://github.com/explosion/spaCy/discussions
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[stack overflow]: https://stackoverflow.com/questions/tagged/spacy
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## Features
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- Support for **70+ languages**
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- **Trained pipelines** for different languages and tasks
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- Multi-task learning with pretrained **transformers** like BERT
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- Support for pretrained **word vectors** and embeddings
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- State-of-the-art speed
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- Production-ready **training system**
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- Linguistically-motivated **tokenization**
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- Components for named **entity recognition**, part-of-speech-tagging,
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dependency parsing, sentence segmentation, **text classification**,
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lemmatization, morphological analysis, entity linking and more
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- Easily extensible with **custom components** and attributes
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- Support for custom models in **PyTorch**, **TensorFlow** and other frameworks
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- Built in **visualizers** for syntax and NER
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- Easy **model packaging**, deployment and workflow management
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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 3.7+ (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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### pip
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Using pip, spaCy releases are available as source packages and binary wheels.
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Before you install spaCy and its dependencies, make sure that your `pip`,
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`setuptools` and `wheel` are up to date.
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```bash
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pip install -U pip setuptools wheel
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pip install spacy
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```
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To install additional data tables for lemmatization and normalization you can
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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, and to lemmatize in languages that don't yet come with
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pretrained models and aren't 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 -U pip setuptools wheel
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pip install spacy
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```
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### conda
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You can also install spaCy from `conda` via the `conda-forge` channel. For the
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feedstock including the build recipe and configuration, check out
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[this repository](https://github.com/conda-forge/spacy-feedstock).
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```bash
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conda install -c conda-forge spacy
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```
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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 2.x to spaCy 3.x, see the
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[migration guide](https://spacy.io/usage/v3#migrating).**
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## 📦 Download model packages
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Trained pipelines for spaCy can be installed as **Python packages**. This means
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that they're a component of your application, just like any other module. Models
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can be installed using spaCy's [`download`](https://spacy.io/api/cli#download)
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command, or manually by pointing pip to a path or URL.
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| Documentation | |
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| -------------------------- | ---------------------------------------------------------------- |
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| **[Available Pipelines]** | Detailed pipeline descriptions, accuracy figures and benchmarks. |
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| **[Models Documentation]** | Detailed usage and installation instructions. |
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| **[Training]** | How to train your own pipelines on your data. |
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[available pipelines]: https://spacy.io/models
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[models documentation]: https://spacy.io/usage/models
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[training]: https://spacy.io/usage/training
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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 or .whl from path or URL
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pip install /Users/you/en_core_web_sm-3.0.0.tar.gz
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pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
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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
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```
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### Loading and using models
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To load a model, use [`spacy.load()`](https://spacy.io/api/top-level#spacy.load)
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with the model name or a 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.
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| Platform | |
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| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| **Ubuntu** | Install system-level dependencies via `apt-get`: `sudo apt-get install build-essential python-dev git` . |
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| **Mac** | 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. |
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| **Windows** | Install a version of the [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that matches the version that was used to compile your Python interpreter. |
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For more details 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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```bash
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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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# make sure you are using the latest pip
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python -m pip install -U pip setuptools wheel
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pip install -r requirements.txt
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pip install --no-build-isolation --editable .
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```
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To install with extras:
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```bash
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pip install --no-build-isolation --editable .[lookups,cuda102]
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```
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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`](requirements.txt).
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Alternatively, you can run `pytest` on the tests from within the installed
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`spacy` package. Don't forget to also install the test utilities via spaCy's
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[`requirements.txt`](requirements.txt):
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```bash
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pip install -r requirements.txt
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python -m pytest --pyargs spacy
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```
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