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