displacy

displaCy

Serve one or more spaCy models and extract syntactic dependencies, part-of-speech tags and/or entities. For usage examples, see the displaCy and displaCy ENT demos.

Installation

pip install -r requirements.txt
python app.py

API

GET /models

Get a list of available models and their human-readable name, keyed by model name.

Example response

{
    "en_core_web_sm": "English - en_core_web_sm (v2.0.0)",
    "de_core_news_sm": "German - de_core_news_sm (v2.0.0)",
    "es_core_news_sm": "Spanish - es_core_news_sm (v2.0.0)"
}

POST /dep

Example request

{
    "text": "They ate the pizza with anchovies",
    "model":"en_core_web_sm",
    "collapse_punctuation": false,
    "collapse_phrases": true
}

Name	Type	Description
text	string	Text to be parsed.
model	string	Name of the model used.
collapse_punctuation	boolean	Merge punctuation onto the preceding token?
collapse_phrases	boolean	Merge noun chunks and named entities into single tokens?

Example response

{
    "arcs": [
        { "dir": "left", "start": 0, "end": 1, "label": "nsubj" },
        { "dir": "right", "start": 1, "end": 2, "label": "dobj" },
        { "dir": "right", "start": 1, "end": 3, "label": "prep" },
        { "dir": "right", "start": 3, "end": 4, "label": "pobj" },
        { "dir": "left", "start": 2, "end": 3, "label": "prep" }
    ],
    "words": [
        { "tag": "PRP", "text": "They" },
        { "tag": "VBD", "text": "ate" },
        { "tag": "NN", "text": "the pizza" },
        { "tag": "IN", "text": "with" },
        { "tag": "NNS", "text": "anchovies" }
    ]
}

Name	Type	Description
arcs	array	Data to generate the arrows.
dir	string	Direction of arrow ("left" or "right").
start	number	Offset of word the arrow starts on.
end	number	Offset of word the arrow ends on.
label	string	Dependency label.
words	array	Data to generate the words.
tag	string	Part-of-speech tag.
text	string	Token text.

POST /ent

Example request

{
    "text": "When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously.",
    "model": "en"
}

Name	Type	Description
text	string	Text to be parsed.
model	string	Name of model to use.

Example response

[
    { "end": 20, "start": 5,  "label": "PERSON" },
    { "end": 67, "start": 61, "label": "ORG" },
    { "end": 75, "start": 71, "label": "DATE" }
]

Name	Type	Description
start	number	Character offset the entity starts on.
end	number	Character offset the entity ends after.
label	string	Entity label.

Usage Example (JavaScript)

function getEntities(text, model) {
    const options = {
        method: 'POST',
        headers: { 'Accept': 'application/json', 'Content-Type': 'application/json' },
        credentials: 'same-origin',
        body: JSON.stringify({ text, model })
    };
    fetch('/ent', options)
        .then(res => res.json())
        .then(entities => {
            console.log(entities);
        });
}