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.

python -m spacy download en_core_web_smimport spacynlp = spacy.load("en_core_web_sm")import en_core_web_smnlp = en_core_web_sm.load()doc = nlp("This is a sentence.")print([(w.text, w.pos_) for w in doc])

Usage note

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:

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.

Multi-language support

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.

config.cfg

config.cfg

Examples

Japanese language support

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

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"].

Korean language support

The default MeCab-based Korean tokenizer requires:

• mecab-ko
• mecab-ko-dic
• natto-py

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:

config.cfg

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.

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

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.

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.

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

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.

Using trained pipelines with spaCy

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

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:

Editable CodespaCy v3.7 · Python 3 · via Binder
run

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. For instance, you can specify the en_core_web_sm model for spaCy 3.7.x as follows:

requirements.txt

See the list of models for model download links for the current spaCy version.

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:

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.