spacy

spaCy JS

JavaScript interface for accessing linguistic annotations provided by spaCy. This project is mostly experimental and was developed for fun to play around with different ways of mimicking spaCy's Python API.

The results will still be computed in Python and made available via a REST API. The JavaScript API resembles spaCy's Python API as closely as possible (with a few exceptions, as the values are all pre-computed and it's tricky to express complex recursive relationships).

const spacy = require('spacy');

(async function() {
    const nlp = spacy.load('en_core_web_sm');
    const doc = await nlp('This is a text about Facebook.');
    for (let ent of doc.ents) {
        console.log(ent.text, ent.label);
    }
    for (let token of doc) {
        console.log(token.text, token.pos, token.head.text);
    }
})();

⌛️ Installation

Installing the JavaScript library

You can install the JavaScript package via npm:

npm install spacy

Setting up the Python server

First, clone this repo and install the requirements. If you've installed the package via npm, you can also use the api/server.py and requirements.txt in your ./node_modules/spacy directory. It's recommended to use a virtual environment.

pip install -r requirements.txt

You can then run the REST API. By default, this will serve the API via 0.0.0.0:8080:

python api/server.py

If you like, you can install more models and specify a comma-separated list of models to load as the first argument when you run the server. All models need to be installed in the same environment.

python api/server.py en_core_web_sm,de_core_news_sm

Argument	Type	Description	Default
models	positional (str)	Comma-separated list of models to load and make available.	en_core_web_sm
--host, -ho	option (str)	Host to serve the API.	0.0.0.0
--port, -p	option (int)	Port to server the API.	8080

🎛 API

spacy.load

"Load" a spaCy model. This method mostly exists for consistency with the Python API. It sets up the REST API and nlp object, but doesn't actually load anything, since the models are already available via the REST API.

const nlp = spacy.load('en_core_web_sm');

Argument	Type	Description
model	String	Name of model to load, e.g. 'en_core_web_sm'. Needs to be available via the REST API.
api	String	Alternative URL of REST API. Defaults to http://0.0.0.0:8080.
RETURNS	Language	The nlp object.

nlp async

The nlp object created by spacy.load can be called on a string of text and makes a request to the REST API. The easiest way to use it is to wrap the call in an async function and use await:

async function() {
    const nlp = spacy.load('en_core_web_sm');
    const doc = await nlp('This is a text.');
}

Argument	Type	Description
text	String	The text to process.
RETURNS	Doc	The processed Doc.

Doc

Just like in the original API, the Doc object can be constructed with an array of words and spaces. It also takes an additional attrs object, which corresponds to the JSON-serialized linguistic annotations created in doc2json in api/server.py.

The Doc behaves just like the regular spaCy Doc – you can iterate over its tokens, index into individual tokens, access the Doc attributes and properties and also use native JavaScript methods like map and slice (since there's no real way to make Python's slice notation like doc[2:4] work).

Construction

import { Doc } from 'spacy';

const words = ['Hello', 'world', '!'];
const spaces = [true, false, false];
const doc = Doc(words, spaces)
console.log(doc.text) // 'Hello world!'

Argument	Type	Description
words	Array	The individual token texts.
spaces	Array	Whether the token at this position is followed by a space or not.
attrs	Object	JSON-serialized attributes, see doc2json.
RETURNS	Doc	The newly constructed Doc.

Symbol iterator and token indexing

async function() {
    const nlp = spacy.load('en_core_web_sm');
    const doc = await nlp('Hello world');

    for (let token of doc) {
        console.log(token.text);
    }
    // Hello
    // world

    const token1 = doc[0];
    console.log(token1.text);
    // Hello
}

Properties and Attributes

Name	Type	Description
text	String	The Doc text.
length	Number	The number of tokens in the Doc.
ents	Array	A list of Span objects, describing the named entities in the Doc.
sents	Array	A list of Span objects, describing the sentences in the Doc.
nounChunks	Array	A list of Span objects, describing the base noun phrases in the Doc.
cats	Object	The document categories predicted by the text classifier, if available in the model.
isTagged	Boolean	Whether the part-of-speech tagger has been applied to the Doc.
isParsed	Boolean	Whether the dependency parser has been applied to the Doc.
isSentenced	Boolean	Whether the sentence boundary detector has been applied to the Doc.

Span

A Span object is a slice of a Doc and contains of one or more tokens. Just like in the original API, it can be constructed from a Doc, a start and end index and an optional label, or by slicing a Doc.

Construction

import { Doc, Span } from 'spacy';

const doc = Doc(['Hello', 'world', '!'], [true, false, false]);
const span = Span(doc, 1, 3);
console.log(span.text) // 'world!'

Argument	Type	Description
doc	Doc	The reference document.
start	Number	The start token index.
end	Number	The end token index. This is exclusive, i.e. "up to token X".
label	String	Optional label.
RETURNS	Span	The newly constructed Span.

Properties and Attributes

Name	Type	Description
text	String	The Span text.
length	Number	The number of tokens in the Span.
doc	Doc	The parent Doc.
start	Number	The Span's start index in the parent document.
end	Number	The Span's end index in the parent document.
label	String	The Span's label, if available.

Token

For token attributes that exist as string and ID versions (e.g. Token.pos vs. Token.pos_), only the string versions are exposed.

Usage Examples

async function() {
    const nlp = spacy.load('en_core_web_sm');
    const doc = await nlp('Hello world');

    for (let token of doc) {
        console.log(token.text, token.pos, token.isLower);
    }
    // Hello INTJ false
    // world NOUN true
}

Properties and Attributes

Name	Type	Description
text	String	The token text.
whitespace	String	Whitespace character following the token, if available.
textWithWs	String	Token text with training whitespace.
orth	Number	ID of the token text.
doc	Doc	The parent Doc.
head	Token	The syntactic parent, or "governor", of this token.
i	Number	Index of the token in the parent document.
entType	String	The token's named entity type.
entIob	String	IOB code of the token's named entity tag.
lemma	String	The token's lemma, i.e. the base form.
norm	String	The normalised form of the token.
lower	String	The lowercase form of the token.
shape	String	Transform of the tokens's string, to show orthographic features. For example, "Xxxx" or "dd".
prefix	String	A length-N substring from the start of the token. Defaults to N=1.
suffix	String	Length-N substring from the end of the token. Defaults to N=3.
pos	String	The token's coarse-grained part-of-speech tag.
tag	String	The token's fine-grained part-of-speech tag.
isAlpha	Boolean	Does the token consist of alphabetic characters?
isAscii	Boolean	Does the token consist of ASCII characters?
isDigit	Boolean	Does the token consist of digits?
isLower	Boolean	Is the token lowercase?
isUpper	Boolean	Is the token uppercase?
isTitle	Boolean	Is the token titlecase?
isPunct	Boolean	Is the token punctuation?
isLeftPunct	Boolean	Is the token left punctuation?
isRightPunct	Boolean	Is the token right punctuation?
isSpace	Boolean	Is the token a whitespace character?
isBracket	Boolean	Is the token a bracket?
isCurrency	Boolean	Is the token a currency symbol?
likeUrl	Boolean	Does the token resemble a URL?
likeNum	Boolean	Does the token resemble a number?
likeEmail	Boolean	Does the token resemble an email address?
isOov	Boolean	Is the token out-of-vocabulary?
isStop	Boolean	Is the token a stop word?
isSentStart	Boolean	Does the token start a sentence?

🔔 Run Tests

Python

First, make sure you have pytest and all dependencies installed. You can then run the tests by pointing pytest to /tests:

python -m pytest tests

JavaScript

This project uses Jest for testing. Make sure you have all dependencies and development dependencies installed. You can then run:

npm run test

To allow testing the code without a REST API providing the data, the test suite currently uses a mock of the Language class, which returns static data located in tests/util.js.

✅ Ideas and Todos

• Add Travis CI integration.
• Improve JavaScript tests.
• Experiment with NodeJS bindings to make Python integration easier. To be fair, running a separate API in an environment controlled by the user and not hiding it a few levels deep is often much easier. But maybe there are some modern Node tricks that this project could benefit from.