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πŸŽ€ JavaScript API for spaCy with Python REST API

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spaCy JS

travis npm GitHub unpkg

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);
    }
})();

βŒ›οΈ 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

πŸŽ› 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 Β 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.
length Number The length of the token text.
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 Β 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?

πŸ”” 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.

βœ… Ideas and Todos

  • 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.