//- 💫 DOCS > USAGE > VISUALIZERS include ../../_includes/_mixins p | 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. Instead of printing a list of dependency labels or entity spans, | you can simply pass your #[code Doc] objects to #[code displacy] and view | the visualizations in your browser, or export them as HTML files or | vector graphics. displaCy also comes with a #[+a("#jupyter") Jupyter hook] | that returns the markup in a format ready to be rendered in a notebook. +aside("What about the old visualizers?") | Our JavaScript-based visualizers #[+src(gh("displacy")) displacy.js] and | #[+src(gh("displacy-ent")) 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. +h(2, "getting-started") Getting started p | 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(3, "dep") Visualizing the dependency parse p | The dependency visualizer, #[code dep], shows part-of-speech tags | and syntactic dependencies. +code("Dependency example"). import spacy from spacy import displacy nlp = spacy.load('en') doc = nlp(u'This is a sentence.') displacy.serve(doc, style='dep') +codepen("f0e85b64d469d6617251d8241716d55f", 370) p | The argument #[code options] lets you specify a dictionary of settings | to customise the layout, for example: +table(["Name", "Type", "Description", "Default"]) +row +cell #[code compact] +cell bool +cell "Compact mode" with square arrows that takes up less space. +cell #[code False] +row +cell #[code color] +cell unicode +cell Text color (HEX, RGB or color names). +cell #[code '#000000'] +row +cell #[code bg] +cell unicode +cell Background color (HEX, RGB or color names). +cell #[code '#ffffff'] +row +cell #[code font] +cell unicode +cell Font name or font family for all text. +cell #[code 'Arial'] p | For a list of all available options, see the | #[+api("displacy#options") #[code displacy] API documentation]. +aside-code("Options example"). options = {'compact': True, 'bg': '#09a3d5', 'color': 'white', 'font': 'Source Sans Pro'} displacy.serve(doc, style='dep', options=options) +codepen("39c02c893a84794353de77a605d817fd", 360) +h(3, "ent") Visualizing the entity recognizer p | The entity visualizer, #[code ent], highlights named entities and | their labels in a text. +code("Named Entity example"). import spacy from spacy import displacy text = """But Google is starting from behind. The company made a late push into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa software, which runs on its Echo and Dot devices, have clear leads in consumer adoption.""" nlp = spacy.load('custom_ner_model') doc = nlp(text) displacy.serve(doc, style='ent') +codepen("a73f8b68f9af3157855962b283b364e4", 345) p The entity visualizer lets you customise the following #[code options]: +table(["Name", "Type", "Description", "Default"]) +row +cell #[code ents] +cell list +cell | Entity types to highlight (#[code None] for all types). +cell #[code None] +row +cell #[code colors] +cell dict +cell | Color overrides. Entity types in lowercase should be mapped to | color names or values. +cell #[code {}] p | If you specify a list of #[code ents], only those entity types will be | rendered – for example, you can choose to display #[code PERSON] entities. | Internally, the visualizer knows nothing about available entity types and | will render whichever spans and labels it receives. This makes it | especially easy to work with custom entity types. By default, displaCy | comes with colours for all | #[+a("/docs/api/annotation#named-entities") entity types supported by spaCy]. | If you're using custom entity types, you can use the #[code colors] | setting to add your own colours for them. +aside-code("Options example"). colors = {'ORG': 'linear-gradient(90deg, #aa9cfc, #fc9ce7)'} options = {'ents': ['ORG'], 'colors': colors} displacy.serve(doc, style='ent', options=options) +codepen("f42ec690762b6f007022a7acd6d0c7d4", 300) p | The above example uses a little trick: Since the background colour values | are added as the #[code background] style attribute, you can use any | #[+a("https://tympanus.net/codrops/css_reference/background/") valid background value] | or shorthand — including gradients and even images! +h(2, "render") Rendering visualizations p | If you don't need the web server and just want to generate the markup | – for example, to export it to a file or serve it in a custom | way – you can use #[+api("displacy#render") #[code displacy.render]] | instead. It works the same, but returns a string containing the markup. +code("Example"). import spacy from spacy import displacy nlp = spacy.load('en') doc1 = nlp(u'This is a sentence.') doc2 = nlp(u'This is another sentence.') html = displacy.render([doc1, doc2], style='dep', page=True) p | #[code page=True] renders the markup wrapped as a full HTML page. | For minified and more compact HTML markup, you can set #[code minify=True]. | If you're rendering a dependency parse, you can also export it as an | #[code .svg] file. +aside("What's SVG?") | Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML | markup that's easy to manipulate | #[+a("https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/") using CSS] or | #[+a("https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/") JavaScript]. | Essentially, SVG lets you design with code, which makes it a perfect fit | for visualizing dependency trees. SVGs can be embedded online in an | #[code <img>] tag, or inlined in an HTML document. They're also | pretty easy to #[+a("https://convertio.co/image-converter/") convert]. +code. svg = displacy.render(doc, style='dep') output_path = Path('/images/sentence.svg') output_path.open('w', encoding='utf-8').write(svg) +infobox("Important note") | Since each visualization is generated as a separate SVG, exporting | #[code .svg] files only works if you're rendering #[strong one single doc] | at a time. (This makes sense – after all, each visualization should be | a standalone graphic.) So instead of rendering all #[code Doc]s at one, | loop over them and export them separately. +h(2, "jupyter") Using displaCy in Jupyter notebooks p | If you're working with a #[+a("https://jupyter.org") Jupyter] notebook, | you can use displaCy's "Jupyter mode" to return markup that can be | rendered in a cell straight away. When you export your notebook, the | visualizations will be included as HTML. +code("Jupyter Example"). # don't forget to install a model, e.g.: python -m spacy download en import spacy from spacy import displacy doc = nlp(u'Rats are various medium-sized, long-tailed rodents.') displacy.render(doc, style='dep', jupyter=True) doc2 = nlp(LONG_NEWS_ARTICLE) displacy.render(doc2, style='ent', jupyter=True) +image("/assets/img/docs/displacy_jupyter.jpg", 700, false, "Example of using the displaCy dependency and named entity visualizer in a Jupyter notebook") p | Internally, displaCy imports #[code display] and #[code HTML] from | #[code IPython.core.display] and returns a Jupyter HTML object. If you | were doing it manually, it'd look like this: +code. from IPython.core.display import display, HTML html = displacy.render(doc, style='dep') return display(HTML(html)) +h(2, "examples") Usage examples +h(2, "manual-usage") Rendering data manually p | You can also use displaCy to manually render data. This can be useful if | you want to visualize output from other libraries, like | #[+a("http://www.nltk.org") NLTK] or | #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet]. | Simply convert the dependency parse or recognised entities to displaCy's | format and import #[code DependencyRenderer] or #[code EntityRenderer] | from #[code spacy.displacy.render]. A renderer class can be is initialised | with a dictionary of options. To generate the visualization markup, call | the renderer's #[code render()] method on a list of dictionaries (one | per visualization). +aside-code("Example"). from spacy.displacy.render import EntityRenderer ex = [{'text': 'But Google is starting from behind.', 'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}], 'title': None}] renderer = EntityRenderer() html = renderer.render(ex) +code("DependencyRenderer input"). [{ 'words': [ {'text': 'This', 'tag': 'DT'}, {'text': 'is', 'tag': 'VBZ'}, {'text': 'a', 'tag': 'DT'}, {'text': 'sentence', 'tag': 'NN'}], 'arcs': [ {'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'}, {'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'}, {'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}] }] +code("EntityRenderer input"). [{ 'text': 'But Google is starting from behind.', 'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}], 'title': None }]