mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
110 lines
3.9 KiB
Plaintext
110 lines
3.9 KiB
Plaintext
include ../../_includes/_mixins
|
|
|
|
p
|
|
| After training your model, you'll usually want to save its state, and load
|
|
| it back later. You can do this with the
|
|
| #[+api("language#to_disk") #[code Language.to_disk()]]
|
|
| method:
|
|
|
|
+code.
|
|
nlp.to_disk('/home/me/data/en_example_model')
|
|
|
|
p
|
|
| The directory will be created if it doesn't exist, and the whole pipeline
|
|
| will be written out. To make the model more convenient to deploy, we
|
|
| recommend wrapping it as a Python package.
|
|
|
|
+h(2, "generating") Generating a model package
|
|
|
|
+infobox("Important note")
|
|
| The model packages are #[strong not suitable] for the public
|
|
| #[+a("https://pypi.python.org") pypi.python.org] directory, which is not
|
|
| designed for binary data and files over 50 MB. However, if your company
|
|
| is running an internal installation of pypi, publishing your models on
|
|
| there can be a convenient solution to share them with your team.
|
|
|
|
p
|
|
| spaCy comes with a handy CLI command that will create all required files,
|
|
| and walk you through generating the meta data. You can also create the
|
|
| meta.json manually and place it in the model data directory, or supply a
|
|
| path to it using the #[code --meta] flag. For more info on this, see the
|
|
| #[+a("/docs/usage/cli/#package") #[code package] command] documentation.
|
|
|
|
+aside-code("meta.json", "json").
|
|
{
|
|
"name": "example_model",
|
|
"lang": "en",
|
|
"version": "1.0.0",
|
|
"spacy_version": ">=1.7.0,<2.0.0",
|
|
"description": "Example model for spaCy",
|
|
"author": "You",
|
|
"email": "you@example.com",
|
|
"license": "CC BY-SA 3.0"
|
|
}
|
|
|
|
+code(false, "bash").
|
|
python -m spacy package /home/me/data/en_example_model /home/me/my_models
|
|
|
|
p This command will create a model package directory that should look like this:
|
|
|
|
+code("Directory structure", "yaml").
|
|
└── /
|
|
├── MANIFEST.in # to include meta.json
|
|
├── meta.json # model meta data
|
|
├── setup.py # setup file for pip installation
|
|
└── en_example_model # model directory
|
|
├── __init__.py # init for pip installation
|
|
└── en_example_model-1.0.0 # model data
|
|
|
|
p
|
|
| You can also find templates for all files in our
|
|
| #[+a(gh("spacy-dev-resouces", "templates/model")) spaCy dev resources].
|
|
| If you're creating the package manually, keep in mind that the directories
|
|
| need to be named according to the naming conventions of
|
|
| #[code [language]_[name]] and #[code [language]_[name]-[version]]. The
|
|
| #[code lang] setting in the meta.json is also used to create the
|
|
| respective #[code Language] class in spaCy, which will later be returned
|
|
| by the model's #[code load()] method.
|
|
|
|
+h(2, "building") Building a model package
|
|
|
|
p
|
|
| To build the package, run the following command from within the
|
|
| directory. This will create a #[code .tar.gz] archive in a directory
|
|
| #[code /dist].
|
|
|
|
+code(false, "bash").
|
|
python setup.py sdist
|
|
|
|
p
|
|
| For more information on building Python packages, see the
|
|
| #[+a("https://setuptools.readthedocs.io/en/latest/") Python Setuptools documentation].
|
|
|
|
|
|
+h(2, "loading") Loading a model package
|
|
|
|
p
|
|
| Model packages can be installed by pointing pip to the model's
|
|
| #[code .tar.gz] archive:
|
|
|
|
+code(false, "bash").
|
|
pip install /path/to/en_example_model-1.0.0.tar.gz
|
|
|
|
p You'll then be able to load the model as follows:
|
|
|
|
+code.
|
|
import en_example_model
|
|
nlp = en_example_model.load()
|
|
|
|
p
|
|
| To load the model via #[code spacy.load()], you can also
|
|
| create a #[+a("/docs/usage/models#usage") shortcut link] that maps the
|
|
| package name to a custom model name of your choice:
|
|
|
|
+code(false, "bash").
|
|
python -m spacy link en_example_model example
|
|
|
|
+code.
|
|
import spacy
|
|
nlp = spacy.load('example')
|