python -m pip install -U virtualenvvirtualenv .envpython -m venv .envsource .env/bin/activatesource .env/bin/activate.env\Scripts\activatepip install -U spacyconda install -c conda-forge spacygit clone https://github.com/explosion/spaCycd spaCyexport PYTHONPATH=`pwd`set PYTHONPATH=/path/to/spaCypip install -r requirements.txtpip install -U spacy-lookups-datapip install -U spacy-lookups-dataconda install -c conda-forge spacy-lookups-datapython setup.py build_ext --inplacepython -m spacy download en_core_web_smpython -m spacy download de_core_news_smpython -m spacy download fr_core_news_smpython -m spacy download es_core_news_smpython -m spacy download pt_core_news_smpython -m spacy download it_core_news_smpython -m spacy download nl_core_news_smpython -m spacy download el_core_news_smpython -m spacy download nb_core_news_smpython -m spacy download lt_core_news_smpython -m spacy download xx_ent_wiki_sm
Using pip, spaCy releases are available as source packages and binary wheels (as of v2.0.13).
pip install -U spacy
When using pip it is generally recommended to install packages in a virtual environment to avoid modifying system state:
python -m venv .env source .env/bin/activate pip install spacy
Thanks to our great community, we’ve been able to re-add conda support. You can
also install spaCy via
conda install -c conda-forge spacy
For the feedstock including the build recipe and configuration, check out this repository. Improvements and pull requests to the recipe and setup are always appreciated.
When updating to a newer version of spaCy, it’s generally recommended to start with a clean virtual environment. If you’re upgrading to a new major version, make sure you have the latest compatible models installed, and that there are no old shortcut links or incompatible model packages left over in your environment, as this can often lead to unexpected results and errors. If you’ve trained your own models, keep in mind that your train and runtime inputs must match. This means you’ll have to retrain your models with the new version.
As of v2.0, spaCy also provides a
validate command, which
lets you verify that all installed models are compatible with your spaCy
version. If incompatible models are found, tips and installation instructions
are printed. The command is also useful to detect out-of-sync model links
resulting from links created in different virtual environments. It’s recommended
to run the command with
python -m to make sure you’re executing the correct
version of spaCy.
pip install -U spacy python -m spacy validate
Run spaCy with GPU v2.0.14
As of v2.0, spaCy comes with neural network models that are implemented in our machine learning library, Thinc. For GPU support, we’ve been grateful to use the work of Chainer’s CuPy module, which provides a numpy-compatible interface for GPU arrays.
spaCy can be installed on GPU by specifying
spacy[cuda100]. If you know your cuda
version, using the more explicit specifier allows cupy to be installed via
wheel, saving some compilation time. The specifiers should install two
pip install -U spacy[cuda92]
Once you have a GPU-enabled installation, the best way to activate it is to call
spacy.require_gpu() somewhere in your
script before any models have been loaded.
require_gpu will raise an error if
no GPU is available.
import spacy spacy.prefer_gpu() nlp = spacy.load("en_core_web_sm")
The other way to install spaCy is to clone its GitHub repository and build it from source. That is the common way if you want to make changes to the code base. You’ll need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, virtualenv and git installed. The compiler part is the trickiest. How to do that depends on your system. See notes on Ubuntu, macOS / OS X and Windows for details.
python -m pip install -U pip # update pip git clone https://github.com/explosion/spaCy # clone spaCy cd spaCy # navigate into directory python -m venv .env # create environment in .env source .env/bin/activate # activate virtual environment export PYTHONPATH=`pwd` # set Python path to spaCy directory pip install -r requirements.txt # install all requirements python setup.py build_ext --inplace # compile spaCy
Compared to regular install via pip, the
additionally installs developer dependencies such as Cython. See the the
quickstart widget to get the right commands for your platform and
Install system-level dependencies via
sudo apt-get install build-essential python-dev git
Install a recent version of XCode, including the so-called “Command Line Tools”. macOS and OS X ship with Python and git preinstalled.
|Python 2.7||Visual Studio 2008|
|Python 3.4||Visual Studio 2010|
|Python 3.5+||Visual Studio 2015|
spaCy comes with an
extensive test suite.
In order to run the tests, you’ll usually want to clone the
build spaCy from source. This will also install the required
development dependencies and test utilities defined in the
Alternatively, you can find out where spaCy is installed and run
that directory. Don’t forget to also install the test utilities via spaCy’s
python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))" pip install -r path/to/requirements.txt python -m pytest [spacy directory]
pytest on the spaCy directory will run only the basic tests. The flag
--slow is optional and enables additional tests that take longer.
# make sure you are using recent pytest version python -m pip install -U pytest python -m pytest [spacy directory] # basic tests python -m pytest [spacy directory] --slow # basic and slow tests
This section collects some of the most common errors you may come across when installing, loading and using spaCy, as well as their solutions.
No compatible model found for [lang] (spaCy vX.X.X).
This usually means that the model you’re trying to download does not exist, or
isn’t available for your version of spaCy. Check the
to see which models are available for your spaCy version. If you’re using an old
version, consider upgrading to the latest release. Note that while spaCy
supports tokenization for a variety of languages, not
all of them come with statistical models. To only use the tokenizer, import the
Language class instead, for example
from spacy.lang.fr import French.
no such option: --no-cache-dir
download command uses pip to install the models and sets the
--no-cache-dir flag to prevent it from requiring too much memory.
requires pip v6.0 or newer. Run
pip install -U pip to upgrade to the latest
version of pip. To see which version you have installed, run
sre_constants.error: bad character range
In v2.1, spaCy changed its implementation of regular expressions for tokenization to make it up to 2-3 times faster. But this also means that it’s very important now that you run spaCy with a wide unicode build of Python. This means that the build has 1114111 unicode characters available, instead of only 65535 in a narrow unicode build. You can check this by running the following command:
python -c "import sys; print(sys.maxunicode)"
If you’re running a narrow unicode build, reinstall Python and use a wide
unicode build instead. You can also rebuild Python and set the
ValueError: unknown locale: UTF-8
This error can sometimes occur on OSX and is likely related to a still
unresolved Python bug. However, it’s easy
to fix: just add the following to your
~/.zshrc and then
source ~/.bash_profile or
source ~/.zshrc. Make sure to add both
export LC_ALL=en_US.UTF-8 export LANG=en_US.UTF-8
Import Error: No module named spacy
This error means that the spaCy module can’t be located on your system, or in
your environment. Make sure you have spaCy installed. If you’re using a virtual
environment, make sure it’s activated and check that spaCy is installed in that
environment – otherwise, you’re trying to load a system installation. You can
which python to find out where your Python executable is located.
ImportError: No module named 'en_core_web_sm'
As of spaCy v1.7, all models can be installed as Python packages. This means
that they’ll become importable modules of your application. When creating
shortcut links, spaCy will also try to import the model
to load its meta data. If this fails, it’s usually a sign that the package is
not installed in the current environment. Run
pip list or
pip freeze to
check which model packages you have installed, and install the
correct models if necessary. If you’re importing a model manually at
the top of a file, make sure to use the name of the package, not the shortcut
link you’ve created.
command not found: spacy
This error may occur when running the
spacy command from the command line.
spaCy does not currently add an entry to your
PATH environment variable, as
this can lead to unexpected results, especially when using a virtual
environment. Instead, spaCy adds an auto-alias that maps
python -m spacy]. If this is not working as expected, run the command with
python -m, yourself – for example
python -m spacy download en_core_web_sm.
For more info on this, see the
AttributeError: 'module' object has no attribute 'load'
While this could technically have many causes, including spaCy being broken, the
most likely one is that your script’s file or directory name is “shadowing” the
module – e.g. your file is called
spacy.py, or a directory you’re importing
from is called
spacy. So, when using spaCy, never call anything else
doc = nlp("They are") print(doc.lemma_) # -PRON-
This is in fact expected behavior and not a bug. Unlike verbs and common nouns,
there’s no clear base form of a personal pronoun. Should the lemma of “me” be
“I”, or should we normalize person as well, giving “it” — or maybe “he”? spaCy’s
solution is to introduce a novel symbol,
-PRON-, which is used as the lemma
for all personal pronouns. For more info on this, see the
If your training data only contained new entities and you didn’t mix in any examples the model previously recognized, it can cause the model to “forget” what it had previously learned. This is also referred to as the “catastrophic forgetting problem”. A solution is to pre-label some text, and mix it with the new text in your updates. You can also do this by running spaCy over some text, extracting a bunch of entities the model previously recognized correctly, and adding them to your training examples.
TypeError: unhashable type: 'list'
If you’re training models, writing them to disk, and versioning them with git,
you might encounter this error when trying to load them in a Windows
environment. This happens because a default install of Git for Windows is
configured to automatically convert Unix-style end-of-line characters (LF) to
Windows-style ones (CRLF) during file checkout (and the reverse when
committing). While that’s mostly fine for text files, a trained model written to
disk has some binary files that should not go through this conversion. When they
do, you get the error above. You can fix it by either changing your
"false", or by committing a
.gitattributes file] to your
repository to tell git on which files or folders it shouldn’t do LF-to-CRLF
conversion, with an entry like
path/to/spacy/model/** -text. After you’ve done
either of these, clone your repository again.