nlpcloud

Node.js Client For NLP Cloud

This is a Node.js client for the NLP Cloud API: https://docs.nlpcloud.io

NLP Cloud serves all the spaCy pre-trained models, and your own custom models, through a RESTful API, so it's easy for you to use them in production.

If you face an issue, don't hesitate to raise it as a Github issue. Thanks!

Installation

Install via npm.

npm install nlpcloud

Examples

Here is a full example that uses the en_core_web_sm model, with a fake token:

import NLPCloud from 'nlpcloud'

var client = new NLPCloud('en_core_web_sm','4eC39HqLyjWDarjtT1zdp7dc')

client.entities("John Doe is a Go Developer at Google")
.then((data) => {
  console.log(data);
})
.catch((err) => {
  console.error(err);
});

And a full example that uses your own custom model 7894:

import nlpcloud

client = nlpcloud.Client("custom_model/7894", "4eC39HqLyjWDarjtT1zdp7dc")
client.entities("John Doe is a Go Developer at Google")

A json object is returned. Here is what it could look like:

[
  {
    "end": 8,
    "start": 0,
    "text": "John Doe",
    "type": "PERSON"
  },
  {
    "end": 25,
    "start": 13,
    "text": "Go Developer",
    "type": "POSITION"
  },
  {
    "end": 35,
    "start": 30,
    "text": "Google",
    "type": "ORG"
  },
]

Usage

Client Initialization

Pass the spaCy model you want to use and the NLP Cloud token to the client during initialization.

The spaCy model can either be a spaCy pretrained model like en_core_web_sm, fr_core_news_lg... but also one of your custom spaCy models using custom_model/<model id> (e.g. custom_model/2568).

Your token can be retrieved from your NLP Cloud dashboard.

import nlpcloud

client = nlpcloud.Client("<model>", "<your token>")

Entities Endpoint

Call the entities() method and pass the text you want to perform named entity recognition (NER) on.

client.entities("<Your block of text>")

The above command returns a JSON object.

Dependencies Endpoint

Call the dependencies() method and pass the text you want to perform part of speech tagging (POS) + arcs on.

client.dependencies("<Your block of text>")

The above command returns a JSON object.

Sentence Dependencies Endpoint

Call the sentence_dependencies() method and pass a block of text made up of several sentencies you want to perform POS + arcs on.

client.sentence_dependencies("<Your block of text>")

The above command returns a JSON object.

Library Versions Endpoint

Call the lib_versions() method to know the versions of the libraries used behind the hood with the model (for example the spaCy version used).

client.lib_versions()

The above command returns a JSON object.