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. That’s why our popular visualizers, displaCy and displaCy ^ENT are also an official part of the core library. If you’re running a Jupyter notebook, displaCy will detect this and return the markup in a format ready to be rendered and exported.

The quickest way to visualize Doc is to use 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 Doc or a list of Doc objects as its first argument. This lets you construct them however you like – using any pipeline or modifications you like. If you’re using Streamlit, check out the spacy-streamlit package that helps you integrate spaCy visualizations into your apps!

Visualizing the dependency parse

The dependency visualizer, dep, shows part-of-speech tags and syntactic dependencies.

Dependency example

The argument options lets you specify a dictionary of settings to customize the layout, for example:

For a list of all available options, see the displacy API documentation.

Visualizing long texts

Long texts can become difficult to read when displayed in one row, so it’s often better to visualize them sentence-by-sentence instead. As of v2.0.12, displacy supports rendering both Doc and Span objects, as well as lists of Docs or Spans. Instead of passing the full Doc to displacy.serve, you can also pass in a list doc.sents. This will create one visualization for each sentence.

Visualizing the entity recognizer

The entity visualizer, ent, highlights named entities and their labels in a text.

Named Entity example

The entity visualizer lets you customize the following options:

If you specify a list of ents, only those entity types will be rendered – for example, you can choose to display 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 colors for all entity types used by trained spaCy pipelines. If you’re using custom entity types, you can use the colors setting to add your own colors for them.

The above example uses a little trick: Since the background color values are added as the background style attribute, you can use any valid background value or shorthand – including gradients and even images!

Adding titles to documents

Rendering several large documents on one page can easily become confusing. To add a headline to each visualization, you can add a title to its user_data. User data is never touched or modified by spaCy.

This feature is especially 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.

Visualizing spans

The span visualizer, span, highlights overlapping spans in a text.

Span example

The span visualizer lets you customize the following options:

Because spans can be stored across different keys in doc.spans, you need to specify which one displaCy should use with spans_key (sc is the default).

Using displaCy in Jupyter notebooks

displaCy is able to detect whether you’re working in a 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.

Jupyter example

Internally, displaCy imports display and HTML from IPython.core.display and returns a Jupyter HTML object. If you were doing it manually, it’d look like this:

Rendering HTML

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 displacy.render. It works the same way, but returns a string containing the markup.

Example

page=True renders the markup wrapped as a full HTML page. For minified and more compact HTML markup, you can set minify=True. If you’re rendering a dependency parse, you can also export it as an .svg file.

Example: Export SVG graphics of dependency parses

Example

The above code will generate the dependency visualizations as two files, This-is-an-example.svg and This-is-another-one.svg.

Rendering data manually

You can also use displaCy to manually render data. This can be useful if you want to visualize output from other libraries, like NLTK or SyntaxNet. If you set manual=True on either render() or serve(), you can pass in data in displaCy’s format as a dictionary (instead of Doc objects). There are helper functions for converting Doc objects to displaCy’s format for use with manual=True: displacy.parse_deps, displacy.parse_ents, and displacy.parse_spans.

DEP input

ENT input

ENT input with knowledge base links

SPANS input

Using displaCy in a web application

If you want to use the visualizers as part of a web application, for example to create something like our online demo, it’s not recommended to only wrap and serve the displaCy renderer. Instead, you should only rely on the server to perform spaCy’s processing capabilities, and use a client-side implementation like displaCy.js to render the JSON-formatted output.

Alternatively, if you’re using Streamlit, check out the spacy-streamlit package that helps you integrate spaCy visualizations into your apps. It includes a full embedded visualizer, as well as individual components.