⚠️Important note: Because the models can be very large and consist mostly of binary data, we can't simply provide them as files in a GitHub repository. Instead, we've opted for adding them to releases as
.tar.gzfiles. This allows us to still maintain a public release history.
To install a specific model, run the following command with the model name
python -m spacy download [model]
Model naming conventions
In general, spaCy expects all model packages to follow the naming convention of
[lang]_[name]. For our models, we also chose to divide the name into three
- type: Model capabilities (e.g.
corefor general-purpose model with vocabulary, syntax, entities and word vectors, or
depentfor only vocab, syntax and entities)
- genre: Type of text the model is trained on (e.g.
webfor web text,
newsfor news text)
- size: Model size indicator (
en_depent_web_md is a medium-sized English model trained on
written web text (blogs, news, comments), that includes vocabulary, syntax and
Additionally, the model versioning reflects both the compatibility with spaCy,
as well as the major and minor model version. A model version
a: spaCy major version. For example,
2for spaCy v2.x.
b: Model major version. Models with a different major version can't be loaded by the same code. For example, changing the width of the model, adding hidden layers or changing the activation changes the model major version.
c: Model minor version. Same model structure, but different parameter values, e.g. from being trained on different data, for different numbers of iterations, etc.
For a detailed compatibility overview, see the
This is also the source of spaCy's internal compatibility check, performed when you
Support for older versions
If you're using an older version (v1.6.0 or below), you can still download and
install the old models from within spaCy using
python -m spacy.en.download all
python -m spacy.de.download all. The
.tar.gz archives are also
attached to the v1.6.0 release.
To download and install the models manually, unpack the archive, drop the
contained directory into
spacy/data and load the model via
To increase transparency and make it easier to use spaCy with your own models, all data is now available as direct downloads, organised in individual releases. spaCy 1.7 also supports installing and loading models as Python packages. You can now choose how and where you want to keep the data files, and set up "shortcut links" to load models by name from within spaCy. For more info on this, see the new models documentation.
# out-of-the-box: download best-matching default model python -m spacy download en # download best-matching version of specific model for your spaCy installation python -m spacy download en_core_web_sm # pip install .tar.gz archive from path or URL pip install /Users/you/en_core_web_sm-2.0.0.tar.gz pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz # set up shortcut link to load installed package as "en_default" python -m spacy link en_core_web_sm en_default # set up shortcut link to load local model as "my_amazing_model" python -m spacy link /Users/you/data my_amazing_model
Loading and using models
To load a model, use
spacy.load() with the model's shortcut link:
import spacy nlp = spacy.load('en') doc = nlp(u'This is a sentence.')
If you've installed a model via pip, you can also
import it directly and
then call its
load() method with no arguments. This should also work for
older models in previous versions of spaCy.
import spacy import en_core_web_sm nlp = en_core_web_sm.load() doc = nlp(u'This is a sentence.')
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.
└── en_core_web_sm-2.0.0.tar.gz # downloaded archive ├── meta.json # model meta data ├── setup.py # setup file for pip installation └── en_core_web_md # model directory ├── __init__.py # init for pip installation ├── meta.json # model meta data └── en_core_web_sm-2.0.0 # model data
You can place the model data directory anywhere on your local file system. To use it with spaCy, simply assign it a name by createing a shortcut link for the data directory.
Issues and bug reports
To report an issue with a model, please open an issue on the spaCy issue tracker. Please note that no model is perfect. Because models are statistical, their expected behaviour will always include some errors. However, particular errors can indicate deeper issues with the training feature extraction or optimisation code. If you come across patterns in the model's performance that seem suspicious, please do file a report.