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 trained pipelines 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. Trained pipelines 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 v3.x, you need to download the new pipeline packages. If you've trained your own pipelines, you need to retrain them after updating spaCy.

Quickstart

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

Usage note

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 -U %%SPACY_PKG_NAME[lookups]%%SPACY_PKG_FLAGS

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

from spacy.lang.yo import Yoruba
nlp = Yoruba()  # use directly
nlp = spacy.blank("yo")  # blank instance

A blank pipeline is typically just a tokenizer. You might want to create a blank pipeline when you only need a tokenizer, when you want to add more components from scratch, or for testing purposes. Initializing the language object directly yields the same result as generating it using spacy.blank(). In both cases the default configuration for the chosen language is loaded, and no pretrained components will be available.

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 development. Also see the training documentation for how to train your own pipelines on your data.

import Languages from 'widgets/languages.js'

Multi-language support

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

# With lazy-loading
nlp = spacy.blank("xx")

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

To train a pipeline using the neutral multi-language class, you can set lang = "xx" in your training config. You can also import the MultiLanguage class directly, or call spacy.blank("xx") for lazy-loading.

Chinese language support

The Chinese language class supports three word segmentation options, char, jieba and pkuseg.

Manual setup

from spacy.lang.zh import Chinese

# Character segmentation (default)
nlp = Chinese()
# Jieba
cfg = {"segmenter": "jieba"}
nlp = Chinese.from_config({"nlp": {"tokenizer": cfg}})
# PKUSeg with "mixed" model provided by pkuseg
cfg = {"segmenter": "pkuseg"}
nlp = Chinese.from_config({"nlp": {"tokenizer": cfg}})
nlp.tokenizer.initialize(pkuseg_model="mixed")

### config.cfg
[nlp.tokenizer]
@tokenizers = "spacy.zh.ChineseTokenizer"
segmenter = "char"

Segmenter	Description
char	Character segmentation: Character segmentation is the default segmentation option. It's enabled when you create a new Chinese language class or call spacy.blank("zh").
jieba	Jieba: to use Jieba for word segmentation, you can set the option segmenter to "jieba".
pkuseg	PKUSeg: As of spaCy v2.3.0, support for PKUSeg has been added to support better segmentation for Chinese OntoNotes and the provided Chinese pipelines. Enable PKUSeg by setting tokenizer option segmenter to "pkuseg".

In v3.0, the default word segmenter has switched from Jieba to character segmentation. Because the pkuseg segmenter depends on a model that can be loaded from a file, the model is loaded on initialization (typically before training). This ensures that your packaged Chinese model doesn't depend on a local path at runtime.

The initialize method for the Chinese tokenizer class supports the following config settings for loading pkuseg models:

Name	Description
pkuseg_model	Name of a model provided by spacy-pkuseg or the path to a local model directory. str
pkuseg_user_dict	Optional path to a file with one word per line which overrides the default pkuseg user dictionary. Defaults to "default", the default provided dictionary. str

The initialization settings are typically provided in the training config and the data is loaded in before training and serialized with the model. This allows you to load the data from a local path and save out your pipeline and config, without requiring the same local path at runtime. See the usage guide on the config lifecycle for more background on this.

### config.cfg
[initialize]

[initialize.tokenizer]
pkuseg_model = "/path/to/model"
pkuseg_user_dict = "default"

You can also initialize the tokenizer for a blank language class by calling its initialize method:

### Examples
# Initialize the pkuseg tokenizer
cfg = {"segmenter": "pkuseg"}
nlp = Chinese.from_config({"nlp": {"tokenizer": cfg}})

# Load spaCy's OntoNotes model
nlp.tokenizer.initialize(pkuseg_model="spacy_ontonotes")

# Load pkuseg's "news" model
nlp.tokenizer.initialize(pkuseg_model="news")

# Load local model
nlp.tokenizer.initialize(pkuseg_model="/path/to/pkuseg_model")

# Override the user directory
nlp.tokenizer.initialize(pkuseg_model="spacy_ontonotes", pkuseg_user_dict="/path/to/user_dict")

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 pipelines 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 ("mixed" (equivalent to "default" from pkuseg packages), "news" "web", "medicine", "tourism") and for other uses, pkuseg provides a simple training API:

import spacy_pkuseg as 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
cfg = {"segmenter": "pkuseg"}
nlp = Chinese.from_config({"nlp": {"tokenizer": cfg}})
nlp.tokenizer.initialize(pkuseg_model="/path/to/pkuseg_model")

Japanese language support

Manual setup

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.from_config({"nlp": {"tokenizer": cfg}})

The Japanese language class uses SudachiPy for word segmentation and part-of-speech tagging. The default Japanese language class and the provided Japanese pipelines use SudachiPy split mode A. The tokenizer config can be used to configure the split mode to A, B or C.

### config.cfg
[nlp.tokenizer]
@tokenizers = "spacy.ja.JapaneseTokenizer"
split_mode = "A"

Extra information, such as reading, inflection form, and the SudachiPy normalized form, is available in Token.morph. For B or C split modes, subtokens are stored in Doc.user_data["sub_tokens"].

If you run into errors related to sudachipy, which is currently under active development, we suggest downgrading to sudachipy==0.4.9, which is the version used for training the current Japanese pipelines.

Korean language support

There are currently three built-in options for Korean tokenization, two based on mecab-ko and one using the rule-based tokenizer.

Default mecab-ko tokenizer

# uses mecab-ko-dic
nlp = spacy.blank("ko")

# with custom mecab args
mecab_args = "-d /path/to/dicdir -u /path/to/userdic"
config = {"nlp": {"tokenizer": {"mecab_args": mecab_args}}}
nlp = spacy.blank("ko", config=config)

The default MeCab-based Korean tokenizer requires the python package mecab-ko and no further system requirements.

The natto-py MeCab-based tokenizer (the previous default for spaCy v3.4 and earlier) is available as spacy.KoreanNattoTokenizer.v1. It requires:

• mecab-ko
• mecab-ko-dic
• natto-py

To use this tokenizer, edit [nlp.tokenizer] in your config:

natto-py MeCab-ko tokenizer

config = {"nlp": {"tokenizer": {"@tokenizers": "spacy.KoreanNattoTokenizer.v1"}}}
nlp = spacy.blank("ko", config=config)

### config.cfg
[nlp]
lang = "ko"
tokenizer = {"@tokenizers" = "spacy.KoreanNattoTokenizer.v1"}

For some Korean datasets and tasks, the rule-based tokenizer is better-suited than MeCab. To configure a Korean pipeline with the rule-based tokenizer:

Rule-based tokenizer

config = {"nlp": {"tokenizer": {"@tokenizers": "spacy.Tokenizer.v1"}}}
nlp = spacy.blank("ko", config=config)

### config.cfg
[nlp]
lang = "ko"
tokenizer = {"@tokenizers" = "spacy.Tokenizer.v1"}

The Korean trained pipelines use the rule-based tokenizer, so no additional dependencies are required.

Installing and using trained pipelines

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

Important note for v3.0

Note that as of spaCy v3.0, shortcut links like en that create (potentially brittle) symlinks in your spaCy installation are deprecated. To download and load an installed pipeline package, use its full name:

- python -m spacy download en
+ python -m spacy download en_core_web_sm

- nlp = spacy.load("en")
+ nlp = spacy.load("en_core_web_sm")

# Download best-matching version of a package for your spaCy installation
$ python -m spacy download en_core_web_sm

# Download exact package version
$ python -m spacy download en_core_web_sm-3.0.0 --direct

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

$ pip install -U %%SPACY_PKG_NAME%%SPACY_PKG_FLAGS
$ 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 in a Jupyter notebook or similar environment, you can use the ! prefix to execute commands. Make sure to restart your kernel or runtime after installation (just like you would when installing other Python packages) to make sure that the installed pipeline package can be found.

!python -m spacy download en_core_web_sm

Installation via pip

To download a trained pipeline directly using pip, point pip install to the URL or local path of the wheel file or archive. Installing the wheel is usually more efficient. To find the direct link to a package, head over to the 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-3.0.0/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

# With local file
$ pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
$ pip install /Users/you/en_core_web_sm-3.0.0.tar.gz

By default, this will install the pipeline package into your site-packages directory. You can then use spacy.load to load it via its package name or import it explicitly as a module. If you need to download pipeline packages 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 pipeline packages 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 package 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 package directory that contains another directory with the pipeline data.

### Directory structure {highlight="6"}
└── en_core_web_md-3.0.0.tar.gz       # downloaded archive
    ├── setup.py                      # setup file for pip installation
    ├── meta.json                     # copy of pipeline meta
    └── en_core_web_md                # 📦 pipeline package
        ├── __init__.py               # init for pip installation
        └── en_core_web_md-3.0.0      # pipeline data
            ├── config.cfg            # pipeline config
            ├── meta.json             # pipeline meta
            └── ...                   # directories with component data

You can place the pipeline package directory anywhere on your local file system.

Installation from Python

Since the spacy download command installs the pipeline as a Python package, we always recommend running it from the command line, just like you install other Python packages with pip install. However, if you need to, or if you want to integrate the download process into another CLI command, you can also import and call the download function used by the CLI via Python.

Keep in mind that the download command installs a Python package into your environment. In order for it to be found after installation, you will need to restart or reload your Python process so that new packages are recognized.

import spacy
spacy.cli.download("en_core_web_sm")

Using trained pipelines with spaCy

To load a pipeline package, use spacy.load with the package name or a path to the data directory:

Important note for v3.0

Note that as of spaCy v3.0, shortcut links like en that create (potentially brittle) symlinks in your spaCy installation are deprecated. To download and load an installed pipeline package, use its full name:

- python -m spacy download en
+ python -m spacy download en_core_web_sm

import spacy
nlp = spacy.load("en_core_web_sm")           # load package "en_core_web_sm"
nlp = spacy.load("/path/to/en_core_web_sm")  # load package from a directory

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

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

Importing pipeline packages as modules

If you've installed a trained pipeline via spacy download 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 trained pipelines ultimately depends on personal preference. However, for larger code bases, we usually recommend native imports, as this will make it easier to integrate pipeline packages with your existing build process, continuous integration workflow and testing framework. It'll also prevent you from ever trying to load a package 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 pipeline packages in production.

Using trained pipelines in production

If your application depends on one or more trained pipeline packages, 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 and loading pipeline packages, the underlying functionality is entirely based on native Python packaging. This allows your application to handle a spaCy pipeline like any other package dependency.

Downloading and requiring package 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 pipeline packages as part of an automated build process, this only adds an unnecessary layer of complexity. If you know which packages your application needs, you should be specifying them directly.

Because pipeline packages 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 pipeline packages there. pip's requirements file format supports both package names to download via a PyPi server, as well as direct URLs.

### requirements.txt
spacy>=3.0.0,<4.0.0
https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.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 pipeline packages are versioned and specify their spaCy dependency. This ensures cross-compatibility and lets you specify exact version requirements for each pipeline. If you've trained your own pipeline, you can use the spacy package command to generate the required meta data and turn it into a loadable package.

Loading and testing pipeline packages

Pipeline packages are regular Python packages, so you can also import them as a package using Python's native import syntax, and then call the load method to load the data and return an nlp object:

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 rely on spaCy's loader to resolve string names to packages. If a package can't be imported, Python will raise an ImportError immediately. And if a package is imported but not used, any linter will catch that.

Similarly, it'll give you more flexibility when writing tests that require loading pipelines. 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 pipeline package or version is installed. Each pipeline package exposes a __version__ attribute which you can also use to perform your own version compatibility checks before loading it.