nlpcloud

Node.js Client For NLP Cloud

This is the Node.js client (with Typescript types) for the NLP Cloud API. See the documentation for more details.

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, question answering, machine translation, language detection, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. 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>')

Ad Generation and Product Description Generation Endpoint

Call the adGeneration() method and pass the keywords you want to use to generate your ad or product description.

client.adGeneration(["keyword 1", "keyword 2", ...])

Chatbot/Conversational AI Endpoint

Call the chatbot() method and pass the following arguments:

1. Your input
2. (Optional) history The history of your previous exchanges with the model

client.chatbot("<Your input>")

Classification Endpoint

Call the classification() method and pass the following arguments:

1. The text you want to classify, as a string
2. The candidate labels for your text, as an array of strings
3. (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", ...])

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>")

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>", "<Text 3>", ...])

The above command returns a JSON object.

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>")

Generation Endpoint

Call the generation() method and pass the following arguments:

1. The block of text that starts the generated text, as a string. 1200 tokens maximum.
2. (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.
3. (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.
4. (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.
5. (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.
6. (Optional) removeEndSequence: Whether you want to remove the end sequence form the result, as a boolean. Defaults to false.
7. (Optional) removeInput: Whether you want to remove the input text form the result, as a boolean. Defaults to false.
8. (Optional) doSample: Whether or not to use sampling ; use greedy decoding otherwise, as a boolean. Defaults to true.
9. (Optional) numBeams: Number of beams for beam search. 1 means no beam search. This is an integer. Defaults to 1.
10. (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.
11. (Optional) noRepeatNgramSize: If set to int > 0, all ngrams of that size can only occur once. This is an integer. Defaults to 0.
12. (Optional) numReturnSequences: The number of independently computed returned sequences for each element in the batch, as an integer. Defaults to 1.
13. (Optional) topK: The number of highest probability vocabulary tokens to keep for top-k-filtering, as an integer. Maximum 1000 tokens. Defaults to 0.
14. (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.
15. (Optional) temperature: The value used to module the next token probabilities, as a float. Should be between 0 and 1. Defaults to 1.
16. (Optional) repetitionPenalty: The parameter for repetition penalty, as a float. 1.0 means no penalty. Defaults to 1.0.
17. (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.
18. (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>")

Grammar and Spelling Correction Endpoint

Call the gsCorrection() method and pass the text you want to correct.

client.gsCorrection("<The text you want to correct>")

Intent Classification Endpoint

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

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

Keywords and Keyphrases Extraction Endpoint

Call the kwKpExtraction() method and pass the text you want to extract keywords and keyphrases from.

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

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>")

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()

Question Answering Endpoint

Call the question() method and pass the following:

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

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

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.

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>")

Sentiment Analysis Endpoint

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

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

Summarization Endpoint

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

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

Paraphrasing Endpoint

Call the paraphrasing() method and pass the text you want to paraphrase.

client.paraphrasing("<Your text to paraphrase>")

Tokenization Endpoint

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

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

Translation Endpoint

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

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