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Node.js client for the NLP Cloud API. NLP Cloud serves high performance pre-trained and custom models for NER, sentiment-analysis, classification, summarization, question answering, and POS tagging, ready for production, served through a REST API. More d
This is a Node.js client for the NLP Cloud API: https://docs.nlpcloud.io
NLP Cloud serves high performance pre-trained models for NER, sentiment-analysis, classification, summarization, question answering, and POS tagging, 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!
Install via npm.
npm install nlpcloud --save
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
.
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"
},
]
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('en_core_web_sm','4eC39HqLyjWDarjtT1zdp7dc')
Call the entities()
method and pass the text you want to perform named entity recognition (NER) on.
client.entities("<Your block of text>")
Call the classification()
method and pass 3 arguments:
client.classification("<Your block of text>", ["label 1", "label 2", "..."], true|false)
Call the sentiment()
method and pass the text you want to analyze the sentiment of:
client.sentiment("<Your block of text>")
Call the question()
method and pass the following:
client.question("<Your context>", "<Your question>")
Call the summarization()
method and pass the text you want to summarize.
Note that your block of text should not exceed 1024 words, otherwise you will get an error. Also note that this model works best for blocks of text between 56 and 142 words.
client.summarization("<Your text to summarize>")
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>")
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>")
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()
FAQs
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, text generation, image generation, code generation, question answering, automatic speech recognition, machine translat
The npm package nlpcloud receives a total of 595 weekly downloads. As such, nlpcloud popularity was classified as not popular.
We found that nlpcloud demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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