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Update visualizers docs and add submenu

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ines 2017-10-03 19:40:39 +02:00
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website/usage/_data.json
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@ -167,7 +167,15 @@
"visualizers": {
"title": "Visualizers",
"next": "resources"
"tag_new": 2,
"teaser": "Visualize dependencies and entities in your browser and notebook, or export HTML.",
"next": "resources",
"menu": {
"Dependencies": "dep",
"Entities": "ent",
"Jupyter Notebooks": "jupyter",
"Rendering HTML": "html"
}
},
"resources": {

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//- 💫 DOCS > USAGE > VISUALIZERS > DEPENDENCIES
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:
+aside("Important note")
| There's currently a known issue with the #[code compact] mode for
| sentences with short arrows and long dependency labels, that causes labels
| longer than the arrow to wrap. So if you come across this problem,
| especially when using custom labels, you'll have to increase the
| #[code distance] setting in the #[code options] to allow longer arcs.
+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)

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//- 💫 DOCS > USAGE > VISUALIZERS > ENTITIES
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("/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(3, "ent-titles") Adding titles to documents
p
| Rendering several large documents on one page can easily become confusing.
| To add a headline to each visualization, you can add a #[code title] to
| its #[code user_data]. User data is never touched or modified by spaCy.
+code.
doc = nlp(u'This is a sentence about Google.')
doc.user_data['title'] = 'This is a title'
displacy.serve(doc, style='ent')
p
| This feature is espeically handy if you're using displaCy to compare
| performance at different stages of a process, e.g. during training. Here
| you could use the title for a brief description of the text example and
| the number of iterations.

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//- 💫 DOCS > USAGE > VISUALIZERS > HTML
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]].
| It works the same way, 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(3, "examples-export-svg") Example: Export SVG graphics of dependency parses
+code("Example").
import spacy
from spacy import displacy
from pathlib import Path
nlp = spacy.load('en')
sentences = ["This is an example.", "This is another one."]
for sent in sentences:
doc = nlp(sentence)
svg = displacy.render(doc, style='dep')
file_name = '-'.join([w.text for w in doc if not w.is_punct]) + '.svg'
output_path = Path('/images/' + file_name)
output_path.open('w', encoding='utf-8').write(svg)
p
| The above code will generate the dependency visualizations and them to
| two files, #[code This-is-an-example.svg] and #[code This-is-another-one.svg].
+h(3, "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 set #[code manual=True] on either #[code render()] or
| #[code serve()].
+aside-code("Example").
ex = [{'text': 'But Google is starting from behind.',
'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
'title': None}]
html = displacy.render(ex, style='ent', manual=True)
+code("DEP 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("ENT input").
{
'text': 'But Google is starting from behind.',
'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
'title': None
}
+h(3, "webapp") Using displaCy in a web application
p
| If you want to use the visualizers as part of a web application, for
| example to create something like our
| #[+a(DEMOS_URL + "/displacy") online demo], it's not recommended to
| simply wrap and serve the displaCy renderer. Instead, you should only
| rely on the server to perform spaCy's processing capabilities, and use
| #[+a(gh("displacy")) displaCy.js] to render the JSON-formatted output.
+aside("Why not return the HTML by the server?")
| It's certainly possible to just have your server return the markup.
| But outputting raw, unsanitised HTML is risky and makes your app vulnerable to
| #[+a("https://en.wikipedia.org/wiki/Cross-site_scripting") cross-site scripting]
| (XSS). All your user needs to do is find a way to make spaCy return text
| like #[code <script src="malicious-code.js"><script>], which
| is pretty easy in NER mode. Instead of relying on the server to render
| and sanitise HTML, you can do this on the client in JavaScript.
| displaCy.js creates the markup as DOM nodes and will never insert raw
| HTML.
p
| The #[code parse_deps] function takes a #[code Doc] object and returns
| a dictionary in a format that can be rendered by displaCy.
+code("Example").
import spacy
from spacy import displacy
nlp = spacy.load('en')
def displacy_service(text):
doc = nlp(text)
return displacy.parse_deps(doc)
p
| Using a library like #[+a("https://falconframework.org/") Falcon] or
| #[+a("http://www.hug.rest/") Hug], you can easily turn the above code
| into a simple REST API that receives a text and returns a JSON-formatted
| parse. In your front-end, include #[+a(gh("displacy")) displacy.js] and
| initialise it with the API URL and the ID or query selector of the
| container to render the visualisation in, e.g. #[code '#displacy'] for
| #[code <div id="displacy">].
+code("script.js", "javascript").
var displacy = new displaCy('http://localhost:8080', {
container: '#displacy'
})
function parse(text) {
displacy.parse(text);
}
p
| When you call #[code parse()], it will make a request to your API,
| receive the JSON-formatted parse and render it in your container. To
| create an interactive experience, you could trigger this function by
| a button and read the text from an #[code <input>] field.

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//- 💫 DOCS > USAGE > VISUALIZERS > JUPYTER
p
| displaCy is able to detect whether you're working in a
| #[+a("https://jupyter.org") Jupyter] notebook, and will 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.: 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')
doc2 = nlp(LONG_NEWS_ARTICLE)
displacy.render(doc2, style='ent')
+aside("Enabling or disabling Jupyter mode")
| To explicitly enable or disable "Jupyter mode", you can use the
| #[code jupyter] keyword argument – e.g. to return raw HTML in a notebook,
| or to force Jupyter rendering if auto-detection fails.
+image("/assets/img/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))

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@ -2,383 +2,47 @@
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.
p
| If you're running a #[+a("https://jupyter.org") 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.
+h(2, "getting-started") Getting started
+tag-new(2)
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:
+aside("Important note")
| There's currently a known issue with the #[code compact] mode for
| sentences with short arrows and long dependency labels, that causes labels
| longer than the arrow to wrap. So if you come across this problem,
| especially when using custom labels, you'll have to increase the
| #[code distance] setting in the #[code options] to allow longer arcs.
+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("/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(3, "ent-titles") Adding titles to documents
p
| Rendering several large documents on one page can easily become confusing.
| To add a headline to each visualization, you can add a #[code title] to
| its #[code user_data]. User data is never touched or modified by spaCy.
+code.
doc = nlp(u'This is a sentence about Google.')
doc.user_data['title'] = 'This is a title'
displacy.serve(doc, style='ent')
p
| This feature is espeically handy if you're using displaCy to compare
| performance at different stages of a process, e.g. during training. Here
| you could use the title for a brief description of the text example and
| the number of iterations.
+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]].
| It works the same way, 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(3, "examples-export-svg") Example: Export SVG graphics of dependency parses
+code("Example").
import spacy
from spacy import displacy
from pathlib import Path
nlp = spacy.load('en')
sentences = ["This is an example.", "This is another one."]
for sent in sentences:
doc = nlp(sentence)
svg = displacy.render(doc, style='dep')
file_name = '-'.join([w.text for w in doc if not w.is_punct]) + '.svg'
output_path = Path('/images/' + file_name)
output_path.open('w', encoding='utf-8').write(svg)
p
| The above code will generate the dependency visualizations and them to
| two files, #[code This-is-an-example.svg] and #[code This-is-another-one.svg].
+h(2, "jupyter") Using displaCy in Jupyter notebooks
p
| displaCy is able to detect whether you're working in a
| #[+a("https://jupyter.org") Jupyter] notebook, and will 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.: 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')
doc2 = nlp(LONG_NEWS_ARTICLE)
displacy.render(doc2, style='ent')
+aside("Enabling or disabling Jupyter mode")
| To explicitly enable or disable "Jupyter mode", you can use the
| #[code jupyter] keyword argument – e.g. to return raw HTML in a notebook,
| or to force Jupyter rendering if auto-detection fails.
+image("/assets/img/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, "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 set #[code manual=True] on either #[code render()] or
| #[code serve()].
+aside-code("Example").
ex = [{'text': 'But Google is starting from behind.',
'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
'title': None}]
html = displacy.render(ex, style='ent', manual=True)
+code("DEP 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("ENT input").
{
'text': 'But Google is starting from behind.',
'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
'title': None
}
+h(2, "webapp") Using displaCy in a web application
p
| If you want to use the visualizers as part of a web application, for
| example to create something like our
| #[+a(DEMOS_URL + "/displacy") online demo], it's not recommended to
| simply wrap and serve the displaCy renderer. Instead, you should only
| rely on the server to perform spaCy's processing capabilities, and use
| #[+a(gh("displacy")) displaCy.js] to render the JSON-formatted output.
+aside("Why not return the HTML by the server?")
| It's certainly possible to just have your server return the markup.
| But outputting raw, unsanitised HTML is risky and makes your app vulnerable to
| #[+a("https://en.wikipedia.org/wiki/Cross-site_scripting") cross-site scripting]
| (XSS). All your user needs to do is find a way to make spaCy return text
| like #[code <script src="malicious-code.js"><script>], which
| is pretty easy in NER mode. Instead of relying on the server to render
| and sanitise HTML, you can do this on the client in JavaScript.
| displaCy.js creates the markup as DOM nodes and will never insert raw
| HTML.
p
| The #[code parse_deps] function takes a #[code Doc] object and returns
| a dictionary in a format that can be rendered by displaCy.
+code("Example").
import spacy
from spacy import displacy
nlp = spacy.load('en')
def displacy_service(text):
doc = nlp(text)
return displacy.parse_deps(doc)
p
| Using a library like #[+a("https://falconframework.org/") Falcon] or
| #[+a("http://www.hug.rest/") Hug], you can easily turn the above code
| into a simple REST API that receives a text and returns a JSON-formatted
| parse. In your front-end, include #[+a(gh("displacy")) displacy.js] and
| initialise it with the API URL and the ID or query selector of the
| container to render the visualisation in, e.g. #[code '#displacy'] for
| #[code <div id="displacy">].
+code("script.js", "javascript").
var displacy = new displaCy('http://localhost:8080', {
container: '#displacy'
})
function parse(text) {
displacy.parse(text);
}
p
| When you call #[code parse()], it will make a request to your API,
| receive the JSON-formatted parse and render it in your container. To
| create an interactive experience, you could trigger this function by
| a button and read the text from an #[code <input>] field.
+section
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. If you're running a #[+a("https://jupyter.org") 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.
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.
+section("dep")
+h(2, "dep") Visualizing the dependency parse
include _visualizers/_dep
+section("ent")
+h(2, "ent") Visualizing the entity recognizer
include _visualizers/_ent
+section("jupyter")
+h(2, "jupyter") Using displaCy in Jupyter notebooks
include _visualizers/_jupyter
+section("html")
+h(2, "html") Rendering HTML
include _visualizers/_html