Node.js Client For NLP Cloud
This is the Node.js client for the NLP Cloud API. See the documentation for more details.
NLP Cloud serves high performance pre-trained for NER, sentiment-analysis, classification, summarization, text generation, question answering, machine translation, language detection, tokenization, lemmatization, POS tagging, and dependency parsing. It is ready for production, served through a REST API.
You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own 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 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 true
as the 3rd argument.
const NLPCloudClient = require('nlpcloud');
const client = new NLPCloudClient('<model>', '<your token>', true)
If you want to use the multilingual add-on in order to process non-English texts, set lang='<your language code>'
as the 4th argument. For example, if you want to process French text, you should set lang='fr'
as the 4th argument.
const NLPCloudClient = require('nlpcloud');
const client = new NLPCloudClient('<model>', '<your token>', false, '<your language code>')
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 the following arguments:
- The text you want to classify, as a string
- The candidate labels for your text, as an array of strings
- (Optional)
multi_class
Whether the classification should be multi-class or not, as a boolean
client.classification("<Your block of text>", ["label 1", "label 2", "..."])
Text Generation Endpoint
Call the generation()
method and pass the following arguments:
- The block of text that starts the generated text, as a string. 1200 tokens maximum.
- (Optional)
minLength
: The minimum number of tokens that the generated text should contain, as an integer. The size of the generated text should not exceed 256 tokens on a CPU plan and 1024 tokens on GPU plan. If lengthNoInput
is false, the size of the generated text is the difference between minLength
and the length of your input text. If lengthNoInput
is true, the size of the generated text simply is minLength
. Defaults to 10. - (Optional)
maxLength
: The maximum number of tokens that the generated text should contain, as an integer. The size of the generated text should not exceed 256 tokens on a CPU plan and 1024 tokens on GPU plan. If lengthNoInput
is false, the size of the generated text is the difference between maxLength
and the length of your input text. If lengthNoInput
is true, the size of the generated text simply is maxLength
. Defaults to 50. - (Optional)
lengthNoInput
: Whether minLength
and maxLength
should not include the length of the input text, as a boolean. If false, minLength
and maxLength
include the length of the input text. If true, min_length and maxLength
don't include the length of the input text. Defaults to false. - (Optional)
endSequence
: A specific token that should be the end of the generated sequence, as a string. For example if could be .
or \n
or ###
or anything else below 10 characters. - (Optional)
removeEndSequence
: Whether you want to remove the end sequence form the result, as a boolean. Defaults to false. - (Optional)
removeInput
: Whether you want to remove the input text form the result, as a boolean. Defaults to false. - (Optional)
doSample
: Whether or not to use sampling ; use greedy decoding otherwise, as a boolean. Defaults to true. - (Optional)
numBeams
: Number of beams for beam search. 1 means no beam search. This is an integer. Defaults to 1. - (Optional)
earlyStopping
: Whether to stop the beam search when at least num_beams sentences are finished per batch or not, as a boolean. Defaults to false. - (Optional)
noRepeatNgramSize
: If set to int > 0, all ngrams of that size can only occur once. This is an integer. Defaults to 0. - (Optional)
numReturnSequences
: The number of independently computed returned sequences for each element in the batch, as an integer. Defaults to 1. - (Optional)
topK
: The number of highest probability vocabulary tokens to keep for top-k-filtering, as an integer. Maximum 1000 tokens. Defaults to 0. - (Optional)
topP
: If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. This is a float. Should be between 0 and 1. Defaults to 0.7. - (Optional)
temperature
: The value used to module the next token probabilities, as a float. Should be between 0 and 1. Defaults to 1. - (Optional)
repetitionPenalty
: The parameter for repetition penalty, as a float. 1.0 means no penalty. Defaults to 1.0. - (Optional)
lengthPenalty
: Exponential penalty to the length, as a float. 1.0 means no penalty. Set to values < 1.0 in order to encourage the model to generate shorter sequences, or to a value > 1.0 in order to encourage the model to produce longer sequences. Defaults to 1.0. - (Optional)
badWords
: List of tokens that are not allowed to be generated, as a list of strings. Defaults to null.
client.generation("<Your input text>")
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:
- Your question
- A context that the model will use to try to answer your question
client.question("<Your question>","<Your context>")
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>")
Semantic Similarity Endpoint
Call the semanticSimilarity()
method and pass an array made up of 2 blocks of text that you want to compare.
client.semanticSimilarity(["<Block of text 1>", "<Block of text 2>"])
The above command returns a JSON object.
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>")
Embeddings Endpoint
Call the embeddings()
method and pass an array of blocks of text that you want to extract embeddings from.
client.embeddings(["<Text 1>", "<Text 2>", "<Tex 3>", ...])
The above command returns a JSON object.
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()