This repository contains releases of models for the spaCy NLP library. For more info on how to download, install and use the models, see the models documentation.
โ ๏ธ 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.gz
files. This allows us to still maintain a public release history.
To install a specific model, run the following command with the model name
(for example en_core_web_sm
):
python -m spacy download [model]
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
components:
- type: Model capabilities (e.g.
core
for general-purpose model with vocabulary, syntax, entities and word vectors, ordepent
for only vocab, syntax and entities) - genre: Type of text the model is trained on (e.g.
web
for web text,news
for news text) - size: Model size indicator (
sm
,md
orlg
)
For example, en_depent_web_md
is a medium-sized English model trained on
written web text (blogs, news, comments), that includes vocabulary, syntax and
entities.
Additionally, the model versioning reflects both the compatibility with spaCy,
as well as the major and minor model version. A model version a.b.c
translates to:
a
: spaCy major version. For example,2
for 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 compatibility.json
.
This is also the source of spaCy's internal compatibility check, performed when you
run the download
command.
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
or 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 spacy.load('en')
or spacy.load('de')
.
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_s en_default
# set up shortcut link to load local model as "my_amazing_model"
python -m spacy link /Users/you/data my_amazing_model
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.')
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.
โโโ en_core_web_sm-2.0.0.tar.gz # downloaded archive
โโโ meta.json # model meta data
โโโ setup.py # setup file for pip installation
โโโ en_core_web_md # model directory
โโโ __init__.py # init for pip installation
โโโ meta.json # model meta data
โโโ 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.
๐ For more info and examples, check out the models documentation.
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.