//- 💫 DOCS > USAGE > MODELS > INSTALLATION +aside("Downloading models in spaCy < v1.7") | In older versions of spaCy, you can still use the old download commands. | This will download and install the models into the #[code spacy/data] | directory. +code.o-no-block. python -m spacy.en.download all python -m spacy.de.download all python -m spacy.en.download glove | The old models are also #[+a(gh("spacy") + "/tree/v1.6.0") attached to the v1.6.0 release]. | To download and install them manually, unpack the archive, drop the | contained directory into #[code spacy/data]. include _install-basics +h(3, "download-pip") Installation via pip p | To download a model directly using #[+a("https://pypi.python.org/pypi/pip") pip], | simply point #[code pip install] to the URL or local path of the archive | file. To find the direct link to a model, head over to the | #[+a(gh("spacy-models") + "/releases") model releases], right click on the archive | link and copy it to your clipboard. +code(false, "bash"). # with external URL pip install #{gh("spacy-models")}/releases/download/en_core_web_md-1.2.0/en_core_web_md-1.2.0.tar.gz # with local file pip install /Users/you/en_core_web_md-1.2.0.tar.gz p | By default, this will install the model into your #[code site-packages] | directory. You can then use #[code spacy.load()] to load it via its | package name, create a #[+a("#usage-link") shortcut link] to assign it a | custom name, or #[+a("usage-import") import it] explicitly as a module. | If you need to download models as part of an automated process, we | recommend using pip with a direct link, instead of relying on spaCy's | #[+api("cli#download") #[code download]] command. +infobox | You can also add the direct download link to your application's | #[code requirements.txt]. For more details, | see the section on | #[+a("/models/#production") working with models in production]. +h(3, "download-manual") Manual download and installation p | 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 #[+a(gh("spacy-models")) 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. +code("Directory structure", "yaml"). └── en_core_web_md-1.2.0.tar.gz # downloaded archive ├── meta.json # model meta data ├── setup.py # setup file for pip installation └── en_core_web_md # 📦 model package ├── __init__.py # init for pip installation ├── meta.json # model meta data └── en_core_web_md-1.2.0 # model data p | You can place the #[strong model package directory] anywhere on your | local file system. To use it with spaCy, simply assign it a name by | creating a #[+a("#usage") shortcut link] for the data directory. +h(3, "usage") Using models with spaCy p | To load a model, use #[+api("spacy#load") #[code spacy.load()]] with the | model's shortcut link, package name or a path to the data directory: +code. import spacy nlp = spacy.load('en') # load model with shortcut link "en" nlp = spacy.load('en_core_web_sm') # load model package "en_core_web_sm" nlp = spacy.load('/path/to/en_core_web_sm') # load package from a directory doc = nlp(u'This is a sentence.') +infobox("Tip: Preview model info") | You can use the #[+api("cli#info") #[code info]] command or | #[+api("spacy#info") #[code spacy.info()]] method to print a model's meta data | before loading it. Each #[code Language] object with a loaded model also | exposes the model's meta data as the attribute #[code meta]. For example, | #[code nlp.meta['version']] will return the model's version. +h(3, "usage-link") Using custom shortcut links p | While previous versions of spaCy required you to maintain a data directory | containing the models for each installation, you can now choose | #[strong how and where you want to keep your data]. For example, you could | download all models manually and put them into a local directory. | Whenever your spaCy projects need a models, you create a shortcut link to | tell spaCy to load it from there. This means you'll never end up with | duplicate data. p | The #[+api("cli#link") #[code link]] command will create a symlink | in the #[code spacy/data] directory. +aside("Why does spaCy use symlinks?") | Symlinks were originally introduced to maintain backwards compatibility, | as older versions expected model data to live within #[code spacy/data]. | However, we decided to keep using them in v2.0 instead of opting for | a config file. There'll always be a need for assigning and saving custom | model names or IDs. And your system already comes with a native solution | to mapping unicode aliases to file paths: symbolic links. +code(false, "bash", "$"). spacy link [package name or path] [shortcut] [--force] p | The first argument is the #[strong package name] (if the model was | installed via pip), or a local path to the the #[strong model package]. | The second argument is the internal name you want to use for the model. | Setting the #[code --force] flag will overwrite any existing links. +code("Examples", "bash"). # set up shortcut link to load installed package as "en_default" spacy link en_core_web_md en_default # set up shortcut link to load local model as "my_amazing_model" spacy link /Users/you/model my_amazing_model +infobox("Important note") | In order to create a symlink, your user needs the #[strong required permissions]. | If you've installed spaCy to a system directory and don't have admin | privileges, the #[code spacy link] command may fail. The easiest solution | is to re-run the command as admin, or use a #[code virtualenv]. For more | info on this, see the | #[+a("/usage/#symlink-privilege") troubleshooting guide]. +h(3, "usage-import") Importing models as modules p | If you've installed a model via spaCy's downloader, or directly via pip, | you can also #[code import] it and then call its #[code load()] method | with no arguments: +code. import en_core_web_md nlp = en_core_web_md.load() doc = nlp(u'This is a sentence.') p | How you choose to load your models ultimately depends on personal | preference. However, #[strong for larger code bases], we usually recommend | native imports, as this will make it easier to integrate models with your | existing build process, continuous integration workflow and testing | framework. It'll also prevent you from ever trying to load a model that | is not installed, as your code will raise an #[code ImportError] | immediately, instead of failing somewhere down the line when calling | #[code spacy.load()]. +infobox | For more details, see the section on | #[+a("/models/#production") working with models in production]. +h(3, "own-models") Using your own models p | If you've trained your own model, for example for | #[+a("/usage/adding-languages") additional languages] or | #[+a("/usage/training#ner") custom named entities], you can save its | state using the #[+api("language#to_disk") #[code Language.to_disk()]] | method. To make the model more convenient to deploy, we recommend | wrapping it as a Python package. +infobox("Saving and loading models") | For more information and a detailed guide on how to package your model, | see the documentation on | #[+a("/usage/training#saving-loading") saving and loading models].