Guides

Visualizers

Visualize dependencies and entities in your browser or in a notebook

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

import spacy from spacy import displacy nlp = spacy.load("en_core_web_sm") doc = nlp("This is a sentence.") displacy.serve(doc, style="dep")
displaCy visualizer

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

ArgumentDescription
compact“Compact mode” with square arrows that takes up less space. Defaults to False. bool
colorText color (HEX, RGB or color names). Defaults to "#000000". str
bgBackground color (HEX, RGB or color names). Defaults to "#ffffff". str
fontFont name or font family for all text. Defaults to "Arial". str

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

displaCy visualizer (compact mode)

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.

import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")
text = """In ancient Rome, some neighbors live in three adjacent houses. In the center is the house of Senex, who lives there with wife Domina, son Hero, and several slaves, including head slave Hysterium and the musical's main character Pseudolus. A slave belonging to Hero, Pseudolus wishes to buy, win, or steal his freedom. One of the neighboring houses is owned by Marcus Lycus, who is a buyer and seller of beautiful women; the other belongs to the ancient Erronius, who is abroad searching for his long-lost children (stolen in infancy by pirates). One day, Senex and Domina go on a trip and leave Pseudolus in charge of Hero. Hero confides in Pseudolus that he is in love with the lovely Philia, one of the courtesans in the House of Lycus (albeit still a virgin)."""
doc = nlp(text)
sentence_spans = list(doc.sents)
displacy.serve(sentence_spans, style="dep")

Visualizing the entity recognizer

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

Named Entity example

import spacy from spacy import displacy text = "When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously." nlp = spacy.load("en_core_web_sm") doc = nlp(text) displacy.serve(doc, style="ent")

The entity visualizer lets you customize the following options:

ArgumentDescription
entsEntity types to highlight (None for all types). Defaults to None. Optional[List[str]]
colorsColor overrides. Entity types should be mapped to color names or values. Defaults to {}. Dict[str, str]

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.

doc = nlp("This is a sentence about Google.")
doc.user_data["title"] = "This is a title"
displacy.serve(doc, style="ent")

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.

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

# Don't forget to install a trained pipeline, e.g.: python -m spacy download en # In[1]: import spacy from spacy import displacy # In[2]: doc = nlp("Rats are various medium-sized, long-tailed rodents.") displacy.render(doc, style="dep") # In[3]: doc2 = nlp(LONG_NEWS_ARTICLE) displacy.render(doc2, style="ent")

displaCy visualizer in a Jupyter notebook

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:

from IPython.core.display import display, HTML

html = displacy.render(doc, style="dep")
display(HTML(html))

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

import spacy from spacy import displacy nlp = spacy.load("en_core_web_sm") doc1 = nlp("This is a sentence.") doc2 = nlp("This is another sentence.") html = displacy.render([doc1, doc2], style="dep", page=True)

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.

svg = displacy.render(doc, style="dep")
output_path = Path("/images/sentence.svg")
output_path.open("w", encoding="utf-8").write(svg)

Example: Export SVG graphics of dependency parses

Example

import spacy from spacy import displacy from pathlib import Path nlp = spacy.load("en_core_web_sm") sentences = ["This is an example.", "This is another one."] for sent in sentences: doc = nlp(sent) svg = displacy.render(doc, style="dep", jupyter=False) 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)

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 (instead of Doc objects). When setting ents manually, make sure to supply them in the right order, i.e. starting with the lowest start position.

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"} ] }

ENT input

{ "text": "But Google is starting from behind.", "ents": [{"start": 4, "end": 10, "label": "ORG"}], "title": None }

ENT input with knowledge base links

{ "text": "But Google is starting from behind.", "ents": [{"start": 4, "end": 10, "label": "ORG", "kb_id": "Q95", "kb_url": "https://www.wikidata.org/entity/Q95"}], "title": None }

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.

spacy streamlit