---
title: Visualizers
teaser: Visualize dependencies and entities in your browser or in a notebook
new: 2
menu:
  - ['Dependencies', 'dep']
  - ['Entities', 'ent']
  - ['Jupyter Notebooks', 'jupyter']
  - ['Rendering HTML', 'html']
---

As of v2.0, our popular visualizers,
[displaCy](https://explosion.ai/demos/displacy) and
[displaCy <sup>ENT</sup>](https://explosion.ai/demos/displacy-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 [Jupyter](https://jupyter.org) notebook, displaCy will
detect this and return the markup in a format
[ready to be rendered and exported](#jupyter).

> #### What about the old visualizers?
>
> Our JavaScript-based visualizers
> [`displacy.js`](https://github.com/explosion/displacy) and
> [`displacy-ent.js`](https://github.com/explosion/displacy-ent) 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 `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.

The quickest way to visualize `Doc` is to use
[`displacy.serve`](/api/top-level#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 model or modifications
you like.

## Visualizing the dependency parse {#dep}

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

```python
### 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](../images/displacy.svg)

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

<Infobox title="Important note" variant="warning">

There's currently a known issue with the `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 `distance` setting in the `options` to allow longer
arcs.

</Infobox>

| Argument  | Type    | Description                                                 | Default     |
| --------- | ------- | ----------------------------------------------------------- | ----------- |
| `compact` | bool    | "Compact mode" with square arrows that takes up less space. | `False`     |
| `color`   | unicode | Text color (HEX, RGB or color names).                       | `"#000000"` |
| `bg`      | unicode | Background color (HEX, RGB or color names).                 | `"#ffffff"` |
| `font`    | unicode | Font name or font family for all text.                      | `"Arial"`   |

For a list of all available options, see the
[`displacy` API documentation](/api/top-level#displacy_options).

> #### Options example
>
> ```python
> options = {"compact": True, "bg": "#09a3d5",
>            "color": "white", "font": "Source Sans Pro"}
> displacy.serve(doc, style="dep", options=options)
> ```

![displaCy visualizer (compact mode)](../images/displacy-compact.svg)

### Visualizing long texts {#dep-long-text new="2.0.12"}

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`](/api/doc) and [`Span`](/api/span) objects, as
well as lists of `Doc`s or `Span`s. 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.

```python
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 {#ent}

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

```python
### 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")
```

import DisplacyEntHtml from 'images/displacy-ent2.html'

<Iframe title="displaCy visualizer for entities" html={DisplacyEntHtml} height={180} />

The entity visualizer lets you customize the following `options`:

| Argument | Type | Description                                                                           | Default |
| -------- | ---- | ------------------------------------------------------------------------------------- | ------- |
| `ents`   | list |  Entity types to highlight (`None` for all types).                                    | `None`  |
| `colors` | dict | Color overrides. Entity types in uppercase should be mapped to color names or values. | `{}`    |

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 supported by spaCy](/api/annotation#named-entities). If you're
using custom entity types, you can use the `colors` setting to add your own
colors for them.

> #### Options example
>
> ```python
> colors = {"ORG": "linear-gradient(90deg, #aa9cfc, #fc9ce7)"}
> options = {"ents": ["ORG"], "colors": colors}
> displacy.serve(doc, style="ent", options=options)
> ```

import DisplacyEntCustomHtml from 'images/displacy-ent-custom.html'

<Iframe title="displaCy visualizer for entities (custom styling)" html={DisplacyEntCustomHtml} height={225} />

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](https://tympanus.net/codrops/css_reference/background/)
or shorthand — including gradients and even images!

### Adding titles to documents {#ent-titles}

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.

```python
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 {#jupyter}

displaCy is able to detect whether you're working in a
[Jupyter](https://jupyter.org) 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.

```python
### Jupyter example
# Don't forget to install a model, 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")
```

<Infobox variant="warning" title="Important note">

To explicitly enable or disable "Jupyter mode", you can use the `jupyter`
keyword argument – e.g. to return raw HTML in a notebook, or to force Jupyter
rendering if auto-detection fails.

</Infobox>

![displaCy visualizer in a Jupyter notebook](../images/displacy_jupyter.jpg)

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:

```python
from IPython.core.display import display, HTML

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

## Rendering HTML {#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`](/api/top-level#displacy.render). It works the same way, but
returns a string containing the markup.

```python
### 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.

> #### What's SVG?
>
> Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML markup
> that's easy to manipulate
> [using CSS](https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/)
> or
> [JavaScript](https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/).
> Essentially, SVG lets you design with code, which makes it a perfect fit for
> visualizing dependency trees. SVGs can be embedded online in an `<img>` tag,
> or inlined in an HTML document. They're also pretty easy to
> [convert](https://convertio.co/image-converter/).

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

<Infobox title="Important note" variant="warning">

Since each visualization is generated as a separate SVG, exporting `.svg` files
only works if you're rendering **one single doc** at a time. (This makes sense –
after all, each visualization should be a standalone graphic.) So instead of
rendering all `Doc`s at one, loop over them and export them separately.

</Infobox>

### Example: Export SVG graphics of dependency parses {#examples-export-svg}

```python
### 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 {#manual-usage}

You can also use displaCy to manually render data. This can be useful if you
want to visualize output from other libraries, like [NLTK](http://www.nltk.org)
or
[SyntaxNet](https://github.com/tensorflow/models/tree/master/research/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.

> #### Example
>
> ```python
> 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)
> ```

```python
### 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"}
    ]
}
```

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

### Using displaCy in a web application {#webapp}

If you want to use the visualizers as part of a web application, for example to
create something like our [online demo](https://explosion.ai/demos/displacy),
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`](https://github.com/explosion/displacy) to render the
JSON-formatted output.

> #### Why not return the HTML by the server?
>
> It's certainly possible to just have your server return the markup. But
> outputting raw, unsanitized HTML is risky and makes your app vulnerable to
> [cross-site scripting](https://en.wikipedia.org/wiki/Cross-site_scripting)
> (XSS). All your user needs to do is find a way to make spaCy return text like
> `<script src="malicious-code.js"></script>`, which is pretty easy in NER mode.
> Instead of relying on the server to render and sanitize HTML, you can do this
> on the client in JavaScript. displaCy.js creates the markup as DOM nodes and
> will never insert raw HTML.

The `parse_deps` function takes a `Doc` object and returns a dictionary in a
format that can be rendered by displaCy.

```python
### Example
import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")

def displacy_service(text):
    doc = nlp(text)
    return displacy.parse_deps(doc)
```

Using a library like [Flask](http://flask.pocoo.org/) or
[Hug](http://www.hug.rest/), 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 [`displacy.js`](https://github.com/explosion/displacy) and
initialize it with the API URL and the ID or query selector of the container to
render the visualization in, e.g. `'#displacy'` for `<div id="displacy">`.

```javascript
/// script.js
var displacy = new displaCy('http://localhost:8080', {
  container: '#displacy',
})

function parse(text) {
  displacy.parse(text)
}
```

When you call `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 `<input>` field.
