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include ../_includes/_mixins
| As of v2.0, our popular visualizers, #[+a(DEMOS_URL + "/displacy") displaCy]
| and #[+a(DEMOS_URL + "/displacy-ent") displaCy #[sup ENT]] are finally an
| official part of the library. Visualizing a dependency parse or named
| entities in a text is not only a fun NLP demo – it can also be incredibly
| helpful in speeding up development and debugging your code and training
| process. If you're running a #[+a("") Jupyter] notebook,
| displaCy will detect this and return the markup in a format
| #[+a("#jupyter") ready to be rendered and exported].
+aside("What about the old visualizers?")
| Our JavaScript-based visualizers #[+src(gh("displacy")) #[code displacy.js]] and
| #[+src(gh("displacy-ent")) #[code displacy-ent.js]] will still be available on
| GitHub. If you're looking to implement web-based visualizations, we
| generally recommend using those instead of spaCy's built-in
| #[code displacy] module. It'll allow your application to perform all
| rendering on the client and only rely on the server for the text
| processing. The generated markup is also more compatible with modern web
| standards.
| The quickest way visualize #[code Doc] is to use
| #[+api("displacy#serve") #[code displacy.serve]]. This will spin up a
| simple web server and let you view the result straight from your browser.
| displaCy can either take a single #[code Doc] or a list of #[code Doc]
| objects as its first argument. This lets you construct them however you
| like – using any model or modifications you like.
+h(2, "dep") Visualizing the dependency parse
include _visualizers/_dep
+h(2, "ent") Visualizing the entity recognizer
include _visualizers/_ent
+h(2, "jupyter") Using displaCy in Jupyter notebooks
include _visualizers/_jupyter
+h(2, "html") Rendering HTML
include _visualizers/_html