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 high performance pre-trained for NER, sentiment-analysis, classification, summarization, text generation, question answering, machine translation, language detection, tokenization, POS tagging, and dependency parsing. It is ready for production, served through a REST API.

Pre-trained models are the spaCy models and some transformers-based models from Hugging Face. You can also deploy your own transformers-based models, or spaCy models.

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

Installation

Install via npm.

npm install nlpcloud --save

Returned Objects

All objects returned by the library are Axios promises.

In case of success, results are contained in response.data. In case of failure, you can retrieve the status code in err.response.status and the error message in err.response.data.detail.

Examples

Here is a full example that performs Named Entity Recognition (NER) using spaCy's en_core_web_lg model, with a fake token:

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('en_core_web_lg','4eC39HqLyjWDarjtT1zdp7dc')

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

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

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('custom_model/7894','4eC39HqLyjWDarjtT1zdp7dc')

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

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

The model can either be a pretrained model like en_core_web_lg, bart-large-mnli... but also one of your custom transformers-based models, or spaCy models, using custom_model/<model id> (e.g. custom_model/2568).

Your token can be retrieved from your NLP Cloud dashboard.

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model>','<your token>')

If you want to use a GPU, pass gpu = true.

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient("<model>", "<your token>", gpu = true)

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>")

Classification Endpoint

Call the classification() method and pass 3 arguments:

1. The text you want to classify, as a string
2. The candidate labels for your text, as an array of strings
3. Whether the classification should be multi-class or not, as a boolean

client.classification("<Your block of text>", ["label 1", "label 2", "..."], true|false)

Sentiment Analysis Endpoint

Call the sentiment() method and pass the text you want to analyze the sentiment of:

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

Question Answering Endpoint

Call the question() method and pass the following:

1. A context that the model will use to try to answer your question
2. Your question

client.question("<Your context>", "<Your question>")

Summarization Endpoint

Call the summarization() method and pass the text you want to summarize.

client.summarization("<Your text to summarize>")

Translation Endpoint

Call the translation() method and pass the text you want to translate.

client.translation("<Your text to translate>")

Language Detection Endpoint

Call the langdetection() method and pass the text you want to analyze in order to detect the languages.

client.langdetection("<The text you want to analyze>")

Tokenization Endpoint

Call the tokens() method and pass the text you want to tokenize.

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

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>")

Sentence Dependencies Endpoint

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

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

Library Versions Endpoint

Call the libVersions() method to know the versions of the libraries used behind the hood with the model (for example the PyTorch, TensorFlow, or spaCy version used).

client.libVersions()