spaCy/website/docs/usage/models.md

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Models & Languages	usage/facts-figures	Quickstart quickstart Language Support languages Installation & Usage download Production Use production

spaCy's models can be installed as Python packages. This means that they're a component of your application, just like any other module. They're versioned and can be defined as a dependency in your requirements.txt. Models can be installed from a download URL or a local directory, manually or via pip. Their data can be located anywhere on your file system.

Important note

If you're upgrading to spaCy v1.7.x or v2.x, you need to download the new models. If you've trained statistical models that use spaCy's annotations, you should retrain your models after updating spaCy. If you don't retrain, you may suffer train/test skew, which might decrease your accuracy.

Quickstart

import QuickstartModels from 'widgets/quickstart-models.js'

Language support

spaCy currently provides support for the following languages. You can help by improving the existing language data and extending the tokenization patterns. See here for details on how to contribute to model development.

Usage note

If a model is available for a language, you can download it using the spacy download command. In order to use languages that don't yet come with a model, you have to import them directly, or use spacy.blank:

from spacy.lang.fi import Finnish
nlp = Finnish()  # use directly
nlp = spacy.blank("fi")  # blank instance

If lemmatization rules are available for your language, make sure to install spaCy with the lookups option, or install spacy-lookups-data separately in the same environment:

$ pip install spacy[lookups]

import Languages from 'widgets/languages.js'

Multi-language support

# Standard import
from spacy.lang.xx import MultiLanguage
nlp = MultiLanguage()

# With lazy-loading
from spacy.util import get_lang_class
nlp = get_lang_class('xx')

As of v2.0, spaCy supports models trained on more than one language. This is especially useful for named entity recognition. The language ID used for multi-language or language-neutral models is xx. The language class, a generic subclass containing only the base language data, can be found in lang/xx.

To load your model with the neutral, multi-language class, simply set "language": "xx" in your model package's meta.json. You can also import the class directly, or call util.get_lang_class() for lazy-loading.

Chinese language support

The Chinese language class supports three word segmentation options:

from spacy.lang.zh import Chinese

# Disable jieba to use character segmentation
Chinese.Defaults.use_jieba = False
nlp = Chinese()

# Disable jieba through tokenizer config options
cfg = {"use_jieba": False}
nlp = Chinese(meta={"tokenizer": {"config": cfg}})

# Load with "default" model provided by pkuseg
cfg = {"pkuseg_model": "default", "require_pkuseg": True}
nlp = Chinese(meta={"tokenizer": {"config": cfg}})

1. Jieba: Chinese uses Jieba for word segmentation by default. It's enabled when you create a new Chinese language class or call spacy.blank("zh").
2. Character segmentation: Character segmentation is supported by disabling jieba and setting Chinese.Defaults.use_jieba = False before initializing the language class. As of spaCy v2.3.0, the meta tokenizer config options can be used to configure use_jieba.
3. PKUSeg: In spaCy v2.3.0, support for PKUSeg has been added to support better segmentation for Chinese OntoNotes and the new Chinese models.

The meta argument of the Chinese language class supports the following following tokenizer config settings:

Name	Type	Description
pkuseg_model	unicode	Required: Name of a model provided by pkuseg or the path to a local model directory.
pkuseg_user_dict	unicode	Optional path to a file with one word per line which overrides the default pkuseg user dictionary.
require_pkuseg	bool	Overrides all jieba settings (optional but strongly recommended).

### Examples
# Load "default" model
cfg = {"pkuseg_model": "default", "require_pkuseg": True}
nlp = Chinese(meta={"tokenizer": {"config": cfg}})

# Load local model
cfg = {"pkuseg_model": "/path/to/pkuseg_model", "require_pkuseg": True}
nlp = Chinese(meta={"tokenizer": {"config": cfg}})

# Override the user directory
cfg = {"pkuseg_model": "default", "require_pkuseg": True, "pkuseg_user_dict": "/path"}
nlp = Chinese(meta={"tokenizer": {"config": cfg}})

You can also modify the user dictionary on-the-fly:

# Append words to user dict
nlp.tokenizer.pkuseg_update_user_dict(["中国", "ABC"])

# Remove all words from user dict and replace with new words
nlp.tokenizer.pkuseg_update_user_dict(["中国"], reset=True)

# Remove all words from user dict
nlp.tokenizer.pkuseg_update_user_dict([], reset=True)

The Chinese models provided by spaCy include a custom pkuseg model trained only on Chinese OntoNotes 5.0, since the models provided by pkuseg include data restricted to research use. For research use, pkuseg provides models for several different domains ("default", "news" "web", "medicine", "tourism") and for other uses, pkuseg provides a simple training API:

import pkuseg
from spacy.lang.zh import Chinese

# Train pkuseg model
pkuseg.train("train.utf8", "test.utf8", "/path/to/pkuseg_model")
# Load pkuseg model in spaCy Chinese tokenizer
nlp = Chinese(meta={"tokenizer": {"config": {"pkuseg_model": "/path/to/pkuseg_model", "require_pkuseg": True}}})

Japanese language support

from spacy.lang.ja import Japanese

# Load SudachiPy with split mode A (default)
nlp = Japanese()

# Load SudachiPy with split mode B
cfg = {"split_mode": "B"}
nlp = Japanese(meta={"tokenizer": {"config": cfg}})

The Japanese language class uses SudachiPy for word segmentation and part-of-speech tagging. The default Japanese language class and the provided Japanese models use SudachiPy split mode A.

The meta argument of the Japanese language class can be used to configure the split mode to A, B or C.

If you run into errors related to sudachipy, which is currently under active development, we suggest downgrading to sudachipy==0.4.5, which is the version used for training the current Japanese models.

Installing and using models

Downloading models in spaCy < v1.7

In older versions of spaCy, you can still use the old download commands. This will download and install the models into the spacy/data directory.

 python -m spacy.en.download all
 python -m spacy.de.download all
 python -m spacy.en.download glove

The old models are also attached to the v1.6.0 release. To download and install them manually, unpack the archive, drop the contained directory into spacy/data.

The easiest way to download a model is via spaCy's download command. It takes care of finding the best-matching model compatible with your spaCy installation.

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

# Out-of-the-box: download best-matching default model and create shortcut link
python -m spacy download en

# Download exact model version (doesn't create shortcut link)
python -m spacy download en_core_web_sm-2.2.0 --direct

The download command will install the model via pip and place the package in your site-packages directory.

pip install spacy
python -m spacy download en_core_web_sm

import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a sentence.")

If you're downloading the models using a shortcut like "en", spaCy will create a symlink within the spacy/data directory. This means that your user needs the required permissions. If you've installed spaCy to a system directory and don't have admin privileges, the model linking may fail. The easiest solution is to re-run the command as admin, set the --user flag or use a virtual environment. For more info on this, see the troubleshooting guide.

Installation via pip

To download a model directly using pip, point pip install to the URL or local path of the archive file. To find the direct link to a model, head over to the model releases, right click on the archive link and copy it to your clipboard.

# With external URL
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

# With local file
pip install /Users/you/en_core_web_sm-2.2.0.tar.gz

By default, this will install the model into your site-packages directory. You can then use spacy.load() to load it via its package name, create a shortcut link to assign it a custom name, or import it explicitly as a module. If you need to download models as part of an automated process, we recommend using pip with a direct link, instead of relying on spaCy's download command.

You can also add the direct download link to your application's requirements.txt. For more details, see the section on working with models in production.

Manual download and installation

In some cases, you might prefer downloading the data manually, for example to place it into a custom directory. You can download the model via your browser from the latest releases, or configure your own download script using the URL of the archive file. The archive consists of a model directory that contains another directory with the model data.

### Directory structure {highlight="7"}
└── en_core_web_md-2.2.0.tar.gz       # downloaded archive
    ├── meta.json                     # model meta data
    ├── setup.py                      # setup file for pip installation
    └── en_core_web_md                # 📦 model package
        ├── __init__.py               # init for pip installation
        ├── meta.json                 # model meta data
        └── en_core_web_md-2.2.0      # model data

You can place the model package directory anywhere on your local file system. To use it with spaCy, assign it a name by creating a shortcut link for the data directory.

Using models with spaCy

To load a model, use spacy.load with the model's shortcut link, package name or a path to the data directory:

import spacy
nlp = spacy.load("en_core_web_sm")           # load model package "en_core_web_sm"
nlp = spacy.load("/path/to/en_core_web_sm")  # load package from a directory
nlp = spacy.load("en")                       # load model with shortcut link "en"

doc = nlp("This is a sentence.")

You can use the info command or spacy.info() method to print a model's meta data before loading it. Each Language object with a loaded model also exposes the model's meta data as the attribute meta. For example, nlp.meta['version'] will return the model's version.

Using custom shortcut links

While previous versions of spaCy required you to maintain a data directory containing the models for each installation, you can now choose how and where you want to keep your data. For example, you could download all models manually and put them into a local directory. Whenever your spaCy projects need a model, you create a shortcut link to tell spaCy to load it from there. This means you'll never end up with duplicate data.

The link command will create a symlink in the spacy/data directory.

Why does spaCy use symlinks?

Symlinks were originally introduced to maintain backwards compatibility, as older versions expected model data to live within spacy/data. However, we decided to keep using them in v2.0 instead of opting for a config file. There'll always be a need for assigning and saving custom model names or IDs. And your system already comes with a native solution to mapping unicode aliases to file paths: symbolic links.

$ python -m spacy link [package name or path] [shortcut] [--force]

The first argument is the package name (if the model was installed via pip), or a local path to the the model package. The second argument is the internal name you want to use for the model. Setting the --force flag will overwrite any existing links.

### Examples
# set up shortcut link to load installed package as "en_default"
python -m spacy link en_core_web_md en_default

# set up shortcut link to load local model as "my_amazing_model"
python -m spacy link /Users/you/model my_amazing_model

In order to create a symlink, your user needs the required permissions. If you've installed spaCy to a system directory and don't have admin privileges, the spacy link command may fail. The easiest solution is to re-run the command as admin, set the --user flag or use a virtual environment. For more info on this, see the troubleshooting guide.

Importing models as modules

If you've installed a model via spaCy's downloader, or directly via pip, you can also import it and then call its load() method with no arguments:

### {executable="true"}
import en_core_web_sm

nlp = en_core_web_sm.load()
doc = nlp("This is a sentence.")

How you choose to load your models ultimately depends on personal preference. However, for larger code bases, we usually recommend native imports, as this will make it easier to integrate models with your existing build process, continuous integration workflow and testing framework. It'll also prevent you from ever trying to load a model that is not installed, as your code will raise an ImportError immediately, instead of failing somewhere down the line when calling spacy.load().

For more details, see the section on working with models in production.

Using your own models

If you've trained your own model, for example for additional languages or custom named entities, you can save its state using the Language.to_disk() method. To make the model more convenient to deploy, we recommend wrapping it as a Python package.

For more information and a detailed guide on how to package your model, see the documentation on saving and loading models.

Using models in production

If your application depends on one or more models, you'll usually want to integrate them into your continuous integration workflow and build process. While spaCy provides a range of useful helpers for downloading, linking and loading models, the underlying functionality is entirely based on native Python packages. This allows your application to handle a model like any other package dependency.

For an example of an automated model training and build process, see this overview of how we're training and packaging our models for spaCy.

Downloading and requiring model dependencies

spaCy's built-in download command is mostly intended as a convenient, interactive wrapper. It performs compatibility checks and prints detailed error messages and warnings. However, if you're downloading models as part of an automated build process, this only adds an unnecessary layer of complexity. If you know which models your application needs, you should be specifying them directly.

Because all models are valid Python packages, you can add them to your application's requirements.txt. If you're running your own internal PyPi installation, you can upload the models there. pip's requirements file format supports both package names to download via a PyPi server, as well as direct URLs.

### requirements.txt
spacy>=2.2.0,<3.0.0
https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz#egg=en_core_web_sm

Specifying #egg= with the package name tells pip which package to expect from the download URL. This way, the package won't be re-downloaded and overwritten if it's already installed - just like when you're downloading a package from PyPi.

All models are versioned and specify their spaCy dependency. This ensures cross-compatibility and lets you specify exact version requirements for each model. If you've trained your own model, you can use the package command to generate the required meta data and turn it into a loadable package.

Loading and testing models

Downloading models directly via pip won't call spaCy's link package command, which creates symlinks for model shortcuts. This means that you'll have to run this command separately, or use the native import syntax to load the models:

import en_core_web_sm
nlp = en_core_web_sm.load()

In general, this approach is recommended for larger code bases, as it's more "native", and doesn't depend on symlinks or rely on spaCy's loader to resolve string names to model packages. If a model can't be imported, Python will raise an ImportError immediately. And if a model is imported but not used, any linter will catch that.

Similarly, it'll give you more flexibility when writing tests that require loading models. For example, instead of writing your own try and except logic around spaCy's loader, you can use pytest's importorskip() method to only run a test if a specific model or model version is installed. Each model package exposes a __version__ attribute which you can also use to perform your own version compatibility checks before loading a model.