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318 lines
12 KiB
Plaintext
318 lines
12 KiB
Plaintext
//- 💫 DOCS > USAGE > VISUALIZERS
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include ../../_includes/_mixins
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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. Instead of printing a list of dependency labels or entity spans,
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| you can simply pass your #[code Doc] objects to #[code displacy] and view
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| the visualizations in your browser, or export them as HTML files or
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| vector graphics.
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p
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| 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")) displacy.js] and
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| #[+src(gh("displacy-ent")) 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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+h(2, "getting-started") Getting started
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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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+h(3, "dep") Visualizing the dependency parse
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p
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| The dependency visualizer, #[code dep], shows part-of-speech tags
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| and syntactic dependencies.
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+code("Dependency example").
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import spacy
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from spacy import displacy
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nlp = spacy.load('en')
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doc = nlp(u'This is a sentence.')
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displacy.serve(doc, style='dep')
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+codepen("f0e85b64d469d6617251d8241716d55f", 370)
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p
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| The argument #[code options] lets you specify a dictionary of settings
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| to customise the layout, for example:
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+table(["Name", "Type", "Description", "Default"])
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+row
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+cell #[code compact]
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+cell bool
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+cell "Compact mode" with square arrows that takes up less space.
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+cell #[code False]
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+row
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+cell #[code color]
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+cell unicode
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+cell Text color (HEX, RGB or color names).
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+cell #[code '#000000']
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+row
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+cell #[code bg]
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+cell unicode
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+cell Background color (HEX, RGB or color names).
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+cell #[code '#ffffff']
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+row
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+cell #[code font]
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+cell unicode
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+cell Font name or font family for all text.
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+cell #[code 'Arial']
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p
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| For a list of all available options, see the
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| #[+api("displacy#options") #[code displacy] API documentation].
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+aside-code("Options example").
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options = {'compact': True, 'bg': '#09a3d5',
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'color': 'white', 'font': 'Source Sans Pro'}
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displacy.serve(doc, style='dep', options=options)
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+codepen("39c02c893a84794353de77a605d817fd", 360)
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+h(3, "ent") Visualizing the entity recognizer
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p
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| The entity visualizer, #[code ent], highlights named entities and
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| their labels in a text.
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+code("Named Entity example").
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import spacy
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from spacy import displacy
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text = """But Google is starting from behind. The company made a late push
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into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
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software, which runs on its Echo and Dot devices, have clear leads in
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consumer adoption."""
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nlp = spacy.load('custom_ner_model')
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doc = nlp(text)
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displacy.serve(doc, style='ent')
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+codepen("a73f8b68f9af3157855962b283b364e4", 345)
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p The entity visualizer lets you customise the following #[code options]:
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+table(["Name", "Type", "Description", "Default"])
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+row
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+cell #[code ents]
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+cell list
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+cell
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| Entity types to highlight (#[code None] for all types).
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+cell #[code None]
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+row
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+cell #[code colors]
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+cell dict
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+cell
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| Color overrides. Entity types in lowercase should be mapped to
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| color names or values.
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+cell #[code {}]
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p
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| If you specify a list of #[code ents], only those entity types will be
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| rendered – for example, you can choose to display #[code PERSON] entities.
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| Internally, the visualizer knows nothing about available entity types and
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| will render whichever spans and labels it receives. This makes it
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| especially easy to work with custom entity types. By default, displaCy
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| comes with colours for all
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| #[+a("/docs/api/annotation#named-entities") entity types supported by spaCy].
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| If you're using custom entity types, you can use the #[code colors]
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| setting to add your own colours for them.
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+aside-code("Options example").
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colors = {'ORG': 'linear-gradient(90deg, #aa9cfc, #fc9ce7)'}
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options = {'ents': ['ORG'], 'colors': colors}
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displacy.serve(doc, style='ent', options=options)
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+codepen("f42ec690762b6f007022a7acd6d0c7d4", 300)
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p
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| The above example uses a little trick: Since the background colour values
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| are added as the #[code background] style attribute, you can use any
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| #[+a("https://tympanus.net/codrops/css_reference/background/") valid background value]
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| or shorthand — including gradients and even images!
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+h(3, "ent-titles") Adding titles to documents
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p
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| Rendering several large documents on one page can easily become confusing.
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| To add a headline to each visualization, you can add a #[code title] to
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| its #[code user_data]. User data is never touched or modified by spaCy.
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+code.
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doc = nlp(u'This is a sentence about Google.')
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doc.user_data['title'] = 'This is a title'
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displacy.serve(doc, style='ent')
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p
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| This feature is espeically handy if you're using displaCy to compare
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| performance at different stages of a process, e.g. during training. Here
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| you could use the title for a brief description of the text example and
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| the number of iterations.
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+h(2, "render") Rendering visualizations
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p
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| If you don't need the web server and just want to generate the markup
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| – for example, to export it to a file or serve it in a custom
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| way – you can use #[+api("displacy#render") #[code displacy.render]]
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| instead. It works the same, but returns a string containing the markup.
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+code("Example").
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import spacy
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from spacy import displacy
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nlp = spacy.load('en')
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doc1 = nlp(u'This is a sentence.')
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doc2 = nlp(u'This is another sentence.')
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html = displacy.render([doc1, doc2], style='dep', page=True)
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p
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| #[code page=True] renders the markup wrapped as a full HTML page.
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| For minified and more compact HTML markup, you can set #[code minify=True].
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| If you're rendering a dependency parse, you can also export it as an
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| #[code .svg] file.
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+aside("What's SVG?")
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| Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML
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| markup that's easy to manipulate
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| #[+a("https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/") using CSS] or
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| #[+a("https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/") JavaScript].
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| Essentially, SVG lets you design with code, which makes it a perfect fit
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| for visualizing dependency trees. SVGs can be embedded online in an
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| #[code <img>] tag, or inlined in an HTML document. They're also
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| pretty easy to #[+a("https://convertio.co/image-converter/") convert].
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+code.
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svg = displacy.render(doc, style='dep')
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output_path = Path('/images/sentence.svg')
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output_path.open('w', encoding='utf-8').write(svg)
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+infobox("Important note")
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| Since each visualization is generated as a separate SVG, exporting
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| #[code .svg] files only works if you're rendering #[strong one single doc]
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| at a time. (This makes sense – after all, each visualization should be
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| a standalone graphic.) So instead of rendering all #[code Doc]s at one,
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| loop over them and export them separately.
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+h(2, "jupyter") Using displaCy in Jupyter notebooks
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p
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| displaCy is able to detect whether you're within a
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| #[+a("https://jupyter.org") Jupyter] notebook, and will return markup
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| that can be rendered in a cell straight away. When you export your
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| notebook, the visualizations will be included as HTML.
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+code("Jupyter Example").
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# don't forget to install a model, e.g.: python -m spacy download en
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import spacy
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from spacy import displacy
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doc = nlp(u'Rats are various medium-sized, long-tailed rodents.')
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displacy.render(doc, style='dep')
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doc2 = nlp(LONG_NEWS_ARTICLE)
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displacy.render(doc2, style='ent')
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+aside("Enabling or disabling Jupyter mode")
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| To explicitly enable or disable "Jupyter mode", you can use the
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| #[code jupyter] keyword argument – e.g. to return raw HTML in a notebook,
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| or to force Jupyter rendering if auto-detection fails.
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+image("/assets/img/docs/displacy_jupyter.jpg", 700, false, "Example of using the displaCy dependency and named entity visualizer in a Jupyter notebook")
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p
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| Internally, displaCy imports #[code display] and #[code HTML] from
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| #[code IPython.core.display] and returns a Jupyter HTML object. If you
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| were doing it manually, it'd look like this:
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+code.
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from IPython.core.display import display, HTML
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html = displacy.render(doc, style='dep')
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return display(HTML(html))
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+h(2, "examples") Usage examples
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+h(3, "examples-export-svg") Export SVG graphics of dependency parses
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+code("Example").
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import spacy
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from spacy import displacy
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from pathlib import Path
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nlp = spacy.load('en')
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sentences = ["This is an example.", "This is another one."]
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for sent in sentences:
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doc = nlp(sentence)
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svg = displacy.render(doc, style='dep')
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file_name = '-'.join([w.text for w in doc if not w.is_punct]) + '.svg'
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output_path = Path('/images/' + file_name)
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output_path.open('w', encoding='utf-8').write(svg)
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p
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| The above code will generate the dependency visualizations and them to
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| two files, #[code This-is-an-example.svg] and #[code This-is-another-one.svg].
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+h(2, "manual-usage") Rendering data manually
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p
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| You can also use displaCy to manually render data. This can be useful if
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| you want to visualize output from other libraries, like
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| #[+a("http://www.nltk.org") NLTK] or
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| #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet].
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| Simply convert the dependency parse or recognised entities to displaCy's
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| format and set #[code manual=True] on either #[code render()] or
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| #[code serve()].
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+aside-code("Example").
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ex = [{'text': 'But Google is starting from behind.',
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'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
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'title': None}]
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html = displacy.render(ex, style='ent', manual=True)
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+code("DEP input").
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{
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'words': [
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{'text': 'This', 'tag': 'DT'},
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{'text': 'is', 'tag': 'VBZ'},
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{'text': 'a', 'tag': 'DT'},
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{'text': 'sentence', 'tag': 'NN'}],
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'arcs': [
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{'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'},
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{'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'},
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{'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
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}
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+code("ENT input").
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{
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'text': 'But Google is starting from behind.',
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'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
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'title': None
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}
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