NAV
cURL Python Ruby Go Node.js PHP C#

Introduction

Get entities using the en_core_web_lg pre-trained model:

curl "https://api.nlpcloud.io/v1/en_core_web_lg/entities" \
  -H "Authorization: Token 4eC39HqLyjWDarjtT1zdp7dc" \
  -X POST \
  -d '{"text":"John Doe has been working for Microsoft in Seattle since 1999."}'
import nlpcloud

client = nlpcloud.Client("en_core_web_lg", "4eC39HqLyjWDarjtT1zdp7dc")
# Returns a json object.
client.entities("John Doe has been working for Microsoft in Seattle since 1999.")
require 'nlpcloud'

client = NLPCloud::Client.new('en_core_web_lg','4eC39HqLyjWDarjtT1zdp7dc')
# Returns a json object.
client.entities("John Doe has been working for Microsoft in Seattle since 1999.")
package main

import "github.com/nlpcloud/nlpcloud-go"

func main() {
    client := nlpcloud.NewClient("en_core_web_lg", "4eC39HqLyjWDarjtT1zdp7dc", false)
    // Returns an Entities struct.
    client.Entities("John Doe has been working for Microsoft in Seattle since 1999.")
}
const NLPCloudClient = require('nlpcloud');

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

// Returns an Axios promise with the results.
// 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`.
client.entities("John Doe has been working for Microsoft in Seattle since 1999.")
  .then(function (response) {
    console.log(response.data);
  })
  .catch(function (err) {
    console.error(err.response.status);
    console.error(err.response.data.detail);
  });
<?php
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('en_core_web_lg','4eC39HqLyjWDarjtT1zdp7dc');
# Returns a json object.
$client->entities('John Doe has been working for Microsoft in Seattle since 1999.);
?>
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Get entities using your own model with ID 7894:

curl "https://api.nlpcloud.io/v1/custom-model/7894/entities" \
  -H "Authorization: Token 4eC39HqLyjWDarjtT1zdp7dc" \
  -X POST \
  -d '{"text":"John Doe has been working for Microsoft in Seattle since 1999."}'
import nlpcloud

client = nlpcloud.Client("custom-model/7894", "4eC39HqLyjWDarjtT1zdp7dc")
# Returns a json object.
client.entities("John Doe has been working for Microsoft in Seattle since 1999.")
require 'nlpcloud'

client = NLPCloud::Client.new('custom-model/7894','4eC39HqLyjWDarjtT1zdp7dc')
# Returns a json object.
client.entities("John Doe has been working for Microsoft in Seattle since 1999.")
package main

import "github.com/nlpcloud/nlpcloud-go"

func main() {
    client := nlpcloud.NewClient("custom-model/7894", "4eC39HqLyjWDarjtT1zdp7dc", false)
    // Returns an Entities struct.
    client.Entities("John Doe has been working for Microsoft in Seattle since 1999.")
}
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('custom-model/7894','4eC39HqLyjWDarjtT1zdp7dc')

client.entities("John Doe has been working for Microsoft in Seattle since 1999.")
  .then(function (response) {
    console.log(response.data);
  })
  .catch(function (err) {
    console.error(err.response.status);
    console.error(err.response.data.detail);
  });
<?php
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('custom-model/7894','4eC39HqLyjWDarjtT1zdp7dc');
# Returns a json object.
$client->entities('John Doe has been working for Microsoft in Seattle since 1999.);
?>
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "entities": [
    {
      "start": 0,
      "end": 8,
      "type": "PERSON",
      "text": "John Doe"
    },
    {
      "start": 30,
      "end": 39,
      "type": "ORG",
      "text": "Microsoft"
    },
    {
      "start": 43,
      "end": 50,
      "type": "GPE",
      "text": "Seattle"
    },
    {
      "start": 57,
      "end": 61,
      "type": "DATE",
      "text": "1999"
    }
  ]
}

Welcome to the NLP Cloud API documentation.

All your Natural Language Processing tasks in one single API, suited for production:

Use Case Model Used
Named Entity Recognition (NER): extract and tag relevant entities from a text, like name, company, country... in many languages (see endpoint) All the large spaCy models are available (15 languages) .
Classification: send a text with possible categories, and let the model categorize the text for you in many languages (see endpoint) We are using the Facebook's Bart Large MNLI and Joe Davison's XLM Roberta Large XNLI models with PyTorch and Hugging Face transformers
Summarization: send a text, and get a smaller text keeping essential information only, in many languages (see endpoint) We are using Facebook's Bart Large CNN, Google's Pegasus XSUM, and Michau's T5 Base EN Generate Headline, with PyTorch and Hugging Face transformers
Question answering: send a piece of text as a context, and ask questions about anything related to this context, in many languages (see endpoint) We are using the Deepset's Roberta Base Squad 2 model with PyTorch and Hugging Face transformers
Sentiment analysis: determine whether a text is rather positive or negative (see endpoint) We are using DistilBERT Base Uncased Finetuned SST-2, Théophile Blard's TF Allociné, Sagorsarker's Codeswitch SpaEng Sentiment Analysis Lince, Daigo's Bert Base Japanese Sentiment, Oliver Guhr's German Sentiment Bert, and Prosus AI's Finbert, with PyTorch, Tensorflow, and Hugging Face transformers
Text generation: start a sentence and let the model generate the rest for you, in many languages (see endpoint) We are using the GPT-J and GPT Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of "OpenAI GPT-3".
Translation: translate text from one language to another (see endpoint) Several Helsinki NLP's Opus MT models are available (7 languages) with PyTorch and Hugging Face transformers
Language Detection: detect one or several languages from a text (see endpoint) We are simply using Python's Langdetect library.
Part-Of-Speech (POS) tagging: assign parts of speech to each word of your text, in many languages (see endpoint) All the large spaCy models are available (15 languages)
Tokenization: extract tokens from a text, in many languages (see endpoint) All the large spaCy models are available (15 languages)
Lemmatization: extract lemmas from a text, in many languages (see endpoint) All the large spaCy models are available (15 languages)

All these models can be used for free with a maximum of 3 requests per minute (except GPT-J and GPT-Neo 2.7B that require a paid plan because of the huge computation costs involved). For more requests, (i.e. for production use), please see the paid plans.

If not done yet, please retrieve a free API token from your dashboard. Also do not hesitate to contact us: [email protected].

We do recommend to subscribe a GPU plan for better performance, especially for computation-intensive models based on Transformers like summarization, classification, and text generation. We do our best to provide affordable GPU prices.

See on the right a full example retrieving entities from a block of text, using both the spaCy pre-trained en_core_web_lg model, and your own custom-model/7894 model. And the same example below using Postman:

Authentication example with Postman

NER example with Postman

You can upload your own spaCy and Hugging Face transformers-based models in your dashboard. You can also fine-tune your own models.

If you have a large batch of requests to process you can also upload them all at once in a CSV file using batch processing.

In addition to this documentation, you can also read this introduction article and watch this introduction video.

We welcome every feedbacks about the API, the documentation, or the client libraries, please let us know!

Set Up

Client Installation

If you are using one of our client libraries, here is how to install them.

Python

Install with pip.

pip install nlpcloud

More details on the source repo: https://github.com/nlpcloud/nlpcloud-python

Ruby

Install with gem.

gem install nlpcloud

More details on the source repo: https://github.com/nlpcloud/nlpcloud-ruby

Go

Install with go get.

go get -u github.com/nlpcloud/nlpcloud-go

More details on the source repo: https://github.com/nlpcloud/nlpcloud-go

Node.js

Install with NPM.

npm install nlpcloud --save

More details on the source repo: https://github.com/nlpcloud/nlpcloud-js

PHP

Install with Composer.

Create a composer.json file containing at least the following:

{"require": {"nlpcloud/nlpcloud-client": "*"}}

Then launch the following:

composer install

More details on the source repo: https://github.com/nlpcloud/nlpcloud-php

C#

This is a client built by the community.

See this repo from DaveCS1 for more details about installation and usage: https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Authentication

Replace with your token:

curl "https://api.nlpcloud.io/v1/<model>/<endpoint>" \
  -H "Authorization: Token <token>"
import nlpcloud

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

client = NLPCloud::Client.new('<model>','<token>')
package main

import "github.com/nlpcloud/nlpcloud-go"

func main() {
    client := nlpcloud.NewClient("<model>", "<token>", false)
}
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model>','<token>')
use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model>','<token>');
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Add your API token after the Token keyword in an Authorization header. You should include this header in all your requests: Authorization: Token <token>. Alternatively you can also use Bearer instead of Token: Authorization: Bearer <token>.

Here is an example using Postman (Postman is automatically adding headers to the requests. You should at least keep the Host header, otherwise you will get a 400 error.):

Authentication example with Postman

If not done yet, please get a free API token in your dashboard.

All API requests must be made over HTTPS. Calls made over plain HTTP will fail. API requests without authentication will also fail.

Versioning

Replace with the right API version:

curl "https://api.nlpcloud.io/<version>/<model>/<endpoint>"
# The latest API version is automatically set by the library.
# The latest API version is automatically set by the library.
// The latest API version is automatically set by the library.
// The latest API version is automatically set by the library.
// The latest API version is automatically set by the library.
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

The latest API version is v1.

The API version comes right after the domain name, and before the model name.

Encoding

POST JSON data:

curl "https://api.nlpcloud.io/v1/<model>/<endpoint>" \
  -X POST \
  -d '{"text":"John Doe has been working for Microsoft in Seattle since 1999."}'
# Encoding is automatically handled by the library.
# Encoding is automatically handled by the library.
// Encoding is automatically handled by the library.
// Encoding is automatically handled by the library.
// Encoding is automatically handled by the library.
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

You should send JSON encoded data in POST requests.

Here is an example using Postman:

Encoding with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

Models

Replace with the right pre-trained model:

curl "https://api.nlpcloud.io/v1/<model>/<endpoint>"
# Set the model during client initialization.
client = nlpcloud.Client("<model>", "<token>")
client = NLPCloud::Client.new('<model>','<token>')
client := nlpcloud.NewClient("<model>", "<token>", false)
const client = new NLPCloudClient('<model>', '<token>')
$client = new \NLPCloud\NLPCloud('<model>','<token>');
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Example: pre-trained spaCy's en_core_web_lg model for Named Entity Recognition (NER):

curl "https://api.nlpcloud.io/v1/en_core_web_lg/entities"
client = nlpcloud.Client("en_core_web_lg", "<token>")
client = NLPCloud::Client.new('en_core_web_lg','<token>')
client := nlpcloud.NewClient("en_core_web_lg", "<token>", false)
const client = new NLPCloudClient('en_core_web_lg', '<token>')
$client = new \NLPCloud\NLPCloud('en_core_web_lg','<your token>');
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Example: your own spaCy model with ID 7894 for Named Entity Recognition (NER):

curl "https://api.nlpcloud.io/v1/custom-model/7894/entities"
client = nlpcloud.Client("custom-model/7894", "<token>")
client = NLPCloud::Client.new('custom-model/7894','<token>')
client := nlpcloud.NewClient("custom-model/7894", "<token>", false)
const client = new NLPCloudClient('custom-model/7894', '<token>')
$client = new \NLPCloud\NLPCloud('custom-model/7894','<your token>');
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

We selected the best state-of-the-art pre-trained models from spaCy and Hugging Face in order to perform Named Entity Recognition (NER), text classification, text summarization, sentiment analysis, question answering, and Part-of-Speech (POS) tagging.

You can also also use your own spaCy and Hugging Face transformers-based models by uploading your models in your dashboard.

The name of the model comes right after the API version, and before the name of the endpoint.

If you are using your own spaCy or transformers-based model, the model name is made up of 2 things: custom-model and the ID of your model. For example if your model ID is 7894, you should use custom-model/7894. Your model ID appears in your dashboard once you upload the model and the instance creation is finished.

Here are examples on the right performing Named Entity Recognition (NER) with spaCy's en_core_web_lg model and another example doing the same thing with your own spaCy model with ID 7894 (ID of the custom model can be retrieved from your dashboard).

Models List

Here is a comprehensive list of all the pre-trained models supported by the NLP Cloud API:

Name Description Libraries
en_core_web_lg: spaCy's English Large See on spaCy spaCy v3
fr_core_news_lg: spaCy's French Large See on spaCy spaCy v3
zh_core_web_lg: spaCy's Chinese Large See on spaCy spaCy v3
da_core_news_lg: spaCy's Danish Large See on spaCy spaCy v3
nl_core_news_lg: spaCy's Dutch Large See on spaCy spaCy v3
de_core_news_lg: spaCy's German Large See on spaCy spaCy v3
el_core_news_lg: spaCy's Greek Large See on spaCy spaCy v3
it_core_news_lg: spaCy's Italian Large See on spaCy spaCy v3
ja_core_news_lg: spaCy's Japanese Large See on spaCy spaCy v3
lt_core_news_lg: spaCy's Lithuanian Large See on spaCy spaCy v3
nb_core_news_lg: spaCy's Norwegian okmål Large See on spaCy spaCy v3
pl_core_news_lg: spaCy's Polish Large See on spaCy spaCy v3
pt_core_news_lg: spaCy's Portuguese Large See on spaCy spaCy v3
ro_core_news_lg: spaCy's Romanian Large See on spaCy spaCy v3
es_core_news_lg: spaCy's Spanish Large See on spaCy spaCy v3
bart-large-mnli: Facebook's Bart Large MNLI See on Hugging Face PyTorch / Transformers
xlm-roberta-large-xnli: Joe Davison's XLM Roberta Large XNLI See on Hugging Face PyTorch / Transformers
bart-large-cnn: Facebook's Bart Large CNN See on Hugging Face PyTorch / Transformers
pegasus-xsum: Google's Pegasus XSUM See on Hugging Face PyTorch / Transformers
t5-base-en-generate-headline: Michau's T5 Base EN Generate Headline See on Hugging Face PyTorch / Transformers
roberta-base-squad2: Deepset's Roberta Base Squad 2 See on Hugging Face PyTorch / Transformers
distilbert-base-uncased-finetuned-sst-2-english: Distilbert Finetuned SST 2 See on Hugging Face PyTorch / Transformers
tf-allocine: Théophile Blard's TF Allociné See on Hugging Face Tensorflow / Transformers
codeswitch-spaeng-sentiment-analysis-lince: Sagorsarker's Codeswitch SpaEng Sentiment Analysis Lince See on Hugging Face PyTorch / Transformers
bert-base-japanese-sentiment: Daigo's Bert Base Japanese Sentiment See on Hugging Face PyTorch / Transformers
german-sentiment-bert: Oliver Guhr's German Sentiment Bert See on Hugging Face PyTorch / Transformers
finbert: Prosus AI's Finbert See on Hugging Face PyTorch / Transformers
gpt-j: GPT-J See on Hugging Face PyTorch / Transformers
gpt-neo-27b: GPT-Neo 2.7B See on Hugging Face PyTorch / Transformers
opus-mt-en-fr: Helsinki NLP's Opus MT English to French See on Hugging Face PyTorch / Transformers
opus-mt-fr-en: Helsinki NLP's Opus MT French to English See on Hugging Face PyTorch / Transformers
opus-mt-en-es: Helsinki NLP's Opus MT English to Spanish See on Hugging Face PyTorch / Transformers
opus-mt-es-en: Helsinki NLP's Opus MT Spanish to English See on Hugging Face PyTorch / Transformers
opus-mt-en-de: Helsinki NLP's Opus MT English to German See on Hugging Face PyTorch / Transformers
opus-mt-de-en: Helsinki NLP's Opus MT German to English See on Hugging Face PyTorch / Transformers
opus-mt-en-nl: Helsinki NLP's Opus MT English to Dutch See on Hugging Face PyTorch / Transformers
opus-mt-nl-en: Helsinki NLP's Opus MT Dutch to English See on Hugging Face PyTorch / Transformers
opus-mt-en-zh: Helsinki NLP's Opus MT English to Chinese See on Hugging Face PyTorch / Transformers
opus-mt-zh-en: Helsinki NLP's Opus MT Chinese to English See on Hugging Face PyTorch / Transformers
opus-mt-en-ru: Helsinki NLP's Opus MT English to Russian See on Hugging Face PyTorch / Transformers
opus-mt-ru-en: Helsinki NLP's Opus MT Russian to English See on Hugging Face PyTorch / Transformers
opus-mt-en-ar: Helsinki NLP's Opus MT English to Arabic See on Hugging Face PyTorch / Transformers
opus-mt-ar-en: Helsinki NLP's Opus MT Arabic to English See on Hugging Face PyTorch / Transformers
python-langdetect: Python LangDetect library See on Pypi LangDetect

Upload Your Transformer-Based Model

Save your model to disk

model.save_pretrained('saved_model')

You can use your own transformers-based models.

Save your model to disk in a saved_model directory using the .save_pretrained method: model.save_pretrained('saved_model').

Then compress the newly created saved_model directory using Zip.

Finally, upload your Zip file in your dashboard.

If you experience difficulties, do not hesitate to contact us, it will be a pleasure to help!

Upload Your spaCy Model

Export in Python script:

nlp.to_disk("/path")

Package:

python -m spacy package /path/to/exported/model /path/to/packaged/model

Archive as .tar.gz:

# Go to /path/to/packaged/model
python setup.py sdist

Or archive as .whl:

# Go to /path/to/packaged/model
python setup.py bdist_wheel

You can use your own spaCy models.

Upload your custom spaCy model in your dashboard, but first you need to export it and package it as a Python module.

Here is what you should do:

  1. Export your model to disk using the spaCy to_disk("/path") command.
  2. Package your exported model using the spacy package command.
  3. Archive your packaged model either as a .tar.gz archive using python setup.py sdist or as a Python wheel using python setup.py bdist_wheel (both formats are accepted).
  4. Retrieve you archive in the newly created dist folder and upload it in your dashboard.

If you experience difficulties, do not hesitate to contact us, it will be a pleasure to help!

GPU

Text classification with Bart Large MNLI on GPU

curl "https://api.nlpcloud.io/v1/gpu/bart-large-mnli/classification" \
  -H "Authorization: Token <token>" \
  -X POST \
  -d '{
    "text":"John Doe is a Go Developer at Google. He has been working there for 10 years and has been awarded employee of the year",
    "labels":["job", "nature", "space"],
    "multi_class": true
  }'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>", gpu=True)
# Returns a json object.
client.classification("""John Doe is a Go Developer at Google. 
  He has been working there for 10 years and has been 
  awarded employee of the year.""",
  ["job", "nature", "space"],
  True)
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>', gpu: true)
# Returns a json object.
client.classification("John Doe is a Go Developer at Google.
  He has been working there for 10 years and has been 
  awarded employee of the year.",
  ["job", "nature", "space"],
  true)
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("<model_name>", "<token>", true)
// Return a Classification struct.
client.Classification(`John Doe is a Go Developer at Google. 
  He has been working there for 10 years and has been 
  awarded employee of the year.`,
  []string{"job", "nature", "space"},
  true)
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', gpu = true)
// Returns an Axios promise with the results.
// 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`.
client.classification(`John Doe is a Go Developer at Google. 
  He has been working there for 10 years and has been 
  awarded employee of the year.`,
  ["job", "nature", "space"],
  true)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>', true);
# Returns a json object.
$client->classification("John Doe is a Go Developer at Google. 
  He has been working there for 10 years and has been 
  awarded employee of the year.",
  array("job", "nature", "space"),
  true)
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

We do recommend to subscribe to a GPU plan for better performance, especially for real-time applications or for computation-intensive models based on Transformers, like summarization, classification, and text generation. We do our best to provide affordable GPU prices.

By default, all models are running on CPUs. In order to use a GPU instead, simply add gpu in the endpoint URL, after the API version, and before the name of the model.

For example if you want to use the Bart Large MNLI classification model on a GPU, you should use the following endpoint:

https://api.nlpcloud.io/v1/gpu/bart-large-mnli/classification

See a full example on the right.

Fine-tuning

This is possible to train/fine-tune your own models on NLP Cloud and have them available in production right away.

The idea is that you should select the base model you want to fine-tune, and then upload your own dataset so we can use it to fine-tune the model. It all happens in your dashboard. You can also upload a validation dataset so we can measure the impact of the fine-tuning on the model accuracy, but this is optional.

Datasets should be in the CSV format (see below for more details about how to build your dataset).

Here are the models you can fine-tune on NLP Cloud (more to come):

GPT-J

You can fine-tune GPT-J for text generation.

Your CSV dataset should contain one column called text.

Each row is a new example you want to teach the model. For example, let's say you want to teach GPT-J how to generate short sentences about specific topics. You could build a dataset like the following (many more examples would be needed of course):

text
love: I went out yesterday with my girlfriend, we spent an amazing moment.
adventure: We stayed one week in the jungle without anything to eat, it was tough...
love: I fell in love with NLP Cloud. My life has changed since I met them!

Don't forget that few-shot learning is also a very good way to get more advanced results from GPT-J, without even fine-tuning the model.

Endpoints

Entities

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/entities" \
  -H "Authorization: Token <token>" \
  -X POST \
  -d '{"text":"John Doe has been working for Microsoft in Seattle since 1999."}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns a json object.
client.entities("John Doe has been working for Microsoft in Seattle since 1999.")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.entities("John Doe has been working for Microsoft in Seattle since 1999.")
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("<model_name>", "<token>", false)
// Return an Entities struct.
client.Entities("John Doe has been working for Microsoft in Seattle since 1999.")
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.entities('John Doe has been working for Microsoft in Seattle since 1999.')
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->entities("John Doe has been working for Microsoft in Seattle since 1999.")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using en_core_web_lg for the example):

{
  "entities": [
    {
      "start": 0,
      "end": 8,
      "type": "PERSON",
      "text": "John Doe"
    },
    {
      "start": 30,
      "end": 39,
      "type": "ORG",
      "text": "Microsoft"
    },
    {
      "start": 43,
      "end": 50,
      "type": "GPE",
      "text": "Seattle"
    },
    {
      "start": 57,
      "end": 61,
      "type": "DATE",
      "text": "1999"
    }
  ]
}

This endpoint uses any spaCy model to perform Named Entity Recognition (NER), in many languages. It can be either a spaCy pre-trained model or your own spaCy or transformers-based custom model. Give a block of text to the model and it will try to extract entitites from it like persons, organizations, countries...

See the spaCy named entity recognition documentation for more details.

Here are all the spaCy pre-trained models you can use for NER in several languages (see the models section for more details) :

Each spaCy pre-trained model has a list of supported built-in entities it is able to extract. For example, the list of entities for the en_core_web_lg model can be found here:

Here is an example using Postman:

NER example with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/entities

POST Values

These values must be encoded as JSON.

Key Type Description
text string The sentence you want to analyze. 1000 characters maximum.

Output

This endpoint returns a JSON array of entities. Each entity is an object made up of the following:

Key Type Description
text string The content of the entity
type string The type of entity (PERSON, ORG, etc.)
start integer The position of the 1st character of the entity (starting at 0)
end integer The position of the 1st character after the entity

Classification

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/classification" \
  -H "Authorization: Token <token>" \
  -X POST \
  -d '{
    "text":"John Doe is a Go Developer at Google. He has been working there for 10 years and has been awarded employee of the year",
    "labels":["job", "nature", "space"],
    "multi_class": true
  }'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns a json object.
client.classification("""John Doe is a Go Developer at Google. 
  He has been working there for 10 years and has been 
  awarded employee of the year.""",
  ["job", "nature", "space"],
  True)
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.classification("John Doe is a Go Developer at Google.
  He has been working there for 10 years and has been 
  awarded employee of the year.",
  ["job", "nature", "space"],
  true)
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("<model_name>", "<token>", false)
// Return a Classification struct.
client.Classification(`John Doe is a Go Developer at Google. 
  He has been working there for 10 years and has been 
  awarded employee of the year.`,
  []string{"job", "nature", "space"},
  true)
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.classification(`John Doe is a Go Developer at Google. 
  He has been working there for 10 years and has been 
  awarded employee of the year.`,
  ["job", "nature", "space"],
  true)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->classification("John Doe is a Go Developer at Google. 
  He has been working there for 10 years and has been 
  awarded employee of the year.",
  array("job", "nature", "space"),
  true)
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using bart-large-mnli for the example):

{
  "labels":["job", "space", "nature"],
  "scores":[0.9258800745010376, 0.1938474327325821, 0.010988450609147549]
}

This endpoint uses Facebook's Bart Large MNLI and Joe Davison's XLM Roberta Large XNLI models to perform classification on a piece of text, in many languages. It can also use your own transformers-based custom model (replace <model_name> with the ID of your model in the URL).

Here are the 2 transformer-based models you can use:

Pass your text along with a list of labels. The model will give a score to each label. The higher the score, the more likely the text is related to this label.

You also need to say if you want more than one label to apply to your text, by passing the multi_class boolean.

Here is an example using Postman:

Classification example with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/classification

POST Values

These values must be encoded as JSON.

Key Type Description
text string The block of text you want to analyze. 10,000 characters maximum.
labels array A list of labels you want to classify your text with
multi_class boolean Optional. Whether multiple labels should be applied to your text, meaning that the model will calculate an independent score for each label. Defaults to true.

Output

This endpoint returns a JSON object containing a list of labels along with a list of scores. Order matters. For example, the second score in the list corresponds to the second label.

Key Type Description
labels array of strings The labels you passed in your request
scores array of floats The scores applied to each label. Each score goes from 0 to 1. The higher the better

Sentiment Analysis

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/sentiment" \
  -H "Authorization: Token <token>" \
  -X POST -d '{"text":"NLP Cloud proposes an amazing service!"}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns a json object.
client.sentiment("NLP Cloud proposes an amazing service!")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.sentiment("NLP Cloud proposes an amazing service!")
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("<model_name>", "<token>", false)
// Return a Sentiment struct.
client.Sentiment("NLP Cloud proposes an amazing service!")
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.sentiment('NLP Cloud proposes an amazing service!')
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->sentiment("NLP Cloud proposes an amazing service!")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using distilbert-base-uncased-finetuned-sst-2-english for the example):

{
  "scored_labels":[
    {
      "label":"POSITIVE",
      "score":0.9996881484985352
    }
  ]
}

This endpoint uses either Distilbert Base Uncased Finetuned SST 2, or Théophile Blard's TF Allociné or Sagorsarker's Codeswitch SpaEng Sentiment Analysis Lince, or Daigo's Bert Base Japanese Sentiment or Oliver Guhr's German Sentiment Bert for sentiment analysis in English, French, Spanish, German, or Japanese.

The endpoint can also use Prosus AI's Finbert for financial sentiment analysis.

It can also use your own transformers-based custom model (replace <model_name> with the ID of your model in the URL).

Here are the 6 transformer-based models you can use:

Pass your text and let the model apply a POSITIVE or NEGATIVE label, with a score. The higher the score, the more accurate the label is.

Here is an example using Postman:

Sentiment analysis example with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/sentiment

POST Values

These values must be encoded as JSON.

Key Type Description
text string The block of text you want to analyze. 512 tokens maximum.

Output

This endpoint returns a JSON object containing a list of labels called scored_labels.

Key Type Description
scored_labels array of objects The returned scored labels. It can be one or two scored labels.

Each score label is an object made up of the following elements:

Key Type Description
label string POSITIVE or NEGATIVE
score float The score applied to the label. It goes from 0 to 1. The higher the score, the more important the sentiment is.

Question Answering

Input:

curl "https://api.nlpcloud.io/v1/roberta-base-squad2/question" \
  -H "Authorization: Token <token>" \
  -X POST -d '{
    "context":"French president Emmanuel Macron said the country was at war with an invisible, elusive enemy, and the measures were unprecedented, but circumstances demanded them.",
    "question":"Who is the French president?"
  }'
import nlpcloud

client = nlpcloud.Client("roberta-base-squad2", "<token>")
# Returns a json object.
client.question("""French president Emmanuel Macron said the country was at war
  with an invisible, elusive enemy, and the measures were unprecedented,
  but circumstances demanded them.""",
  "Who is the French president?")
require 'nlpcloud'

client = NLPCloud::Client.new('roberta-base-squad2','<token>')
# Returns a json object.
client.question("French president Emmanuel Macron said the country was at war
  with an invisible, elusive enemy, and the measures were unprecedented,
  but circumstances demanded them.",
  "Who is the French president?")
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("roberta-base-squad2", "<token>", false)
// Return an Question struct.
client.Question(`French president Emmanuel Macron said the country was at war
  with an invisible, elusive enemy, and the measures were unprecedented,
  but circumstances demanded them.`,
  "Who is the French president?")
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('roberta-base-squad2','<token>')
// Returns an Axios promise with the results.
// 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`.
client.question(`French president Emmanuel Macron said the country was at war
  with an invisible, elusive enemy, and the measures were unprecedented,
  but circumstances demanded them.`,
  `Who is the French president?`)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('roberta-base-squad2','<token>');
# Returns a json object.
$client->question("French president Emmanuel Macron said the country was at war
  with an invisible, elusive enemy, and the measures were unprecedented,
  but circumstances demanded them.",
  "Who is the French president?")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "answer":"Emmanuel Macron",
  "score":0.9595934152603149,
  "start":17,
  "end":32
}

This endpoint uses Deepset's Roberta Base Squad 2 model to answer questions based on a context, in many languages. It can also use your own transformers-based custom model (replace roberta-base-squad2 with the ID of your model in the URL).

Pass your context, and your question, and the model will return the answer along with the score (the higher the score, the more accurate the answer is), and the position of the answer in the context.

Here is an example using Postman:

Question answering example with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

HTTP Request

POST https://api.nlpcloud.io/v1/roberta-base-squad2/question

POST Values

These values must be encoded as JSON.

Key Type Description
context string The block of text that the model will use in order to find an answer to your question. 100,000 characters maximum.
question string The question you want to ask

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
answer string The answer to your question
score float The accuracy of the answer. It goes from 0 to 1. The higher the score, the more accurate the answer is.
start integer Position of the starting character of the response in your context.
end integer Position of the ending character of the response in your context.

Summarization

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/summarization" \
  -H "Authorization: Token <token>" \
  -X POST -d '{"text":"One month after the United States began what has become a 
  troubled rollout of a national COVID vaccination campaign, the effort is finally 
  gathering real steam. Close to a million doses -- over 951,000, to be more exact -- 
  made their way into the arms of Americans in the past 24 hours, the U.S. Centers 
  for Disease Control and Prevention reported Wednesday. That s the largest number 
  of shots given in one day since the rollout began and a big jump from the 
  previous day, when just under 340,000 doses were given, CBS News reported. 
  That number is likely to jump quickly after the federal government on Tuesday 
  gave states the OK to vaccinate anyone over 65 and said it would release all 
  the doses of vaccine it has available for distribution. Meanwhile, a number 
  of states have now opened mass vaccination sites in an effort to get larger 
  numbers of people inoculated, CBS News reported."}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns a json object.
client.summarization("""One month after the United States began what has become a 
  troubled rollout of a national COVID vaccination campaign, the effort is finally 
  gathering real steam. Close to a million doses -- over 951,000, to be more exact -- 
  made their way into the arms of Americans in the past 24 hours, the U.S. Centers 
  for Disease Control and Prevention reported Wednesday. That s the largest number 
  of shots given in one day since the rollout began and a big jump from the 
  previous day, when just under 340,000 doses were given, CBS News reported. 
  That number is likely to jump quickly after the federal government on Tuesday 
  gave states the OK to vaccinate anyone over 65 and said it would release all 
  the doses of vaccine it has available for distribution. Meanwhile, a number 
  of states have now opened mass vaccination sites in an effort to get larger 
  numbers of people inoculated, CBS News reported.""")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.summarization("One month after the United States began what has become a 
  troubled rollout of a national COVID vaccination campaign, the effort is finally 
  gathering real steam. Close to a million doses -- over 951,000, to be more exact -- 
  made their way into the arms of Americans in the past 24 hours, the U.S. Centers 
  for Disease Control and Prevention reported Wednesday. That s the largest number 
  of shots given in one day since the rollout began and a big jump from the 
  previous day, when just under 340,000 doses were given, CBS News reported. 
  That number is likely to jump quickly after the federal government on Tuesday 
  gave states the OK to vaccinate anyone over 65 and said it would release all 
  the doses of vaccine it has available for distribution. Meanwhile, a number 
  of states have now opened mass vaccination sites in an effort to get larger 
  numbers of people inoculated, CBS News reported.")
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("<model_name>", "<token>", false)
// Return a Summarization struct.
client.Summarization(`One month after the United States began what has become a 
  troubled rollout of a national COVID vaccination campaign, the effort is finally 
  gathering real steam. Close to a million doses -- over 951,000, to be more exact -- 
  made their way into the arms of Americans in the past 24 hours, the U.S. Centers 
  for Disease Control and Prevention reported Wednesday. That s the largest number 
  of shots given in one day since the rollout began and a big jump from the 
  previous day, when just under 340,000 doses were given, CBS News reported. 
  That number is likely to jump quickly after the federal government on Tuesday 
  gave states the OK to vaccinate anyone over 65 and said it would release all 
  the doses of vaccine it has available for distribution. Meanwhile, a number 
  of states have now opened mass vaccination sites in an effort to get larger 
  numbers of people inoculated, CBS News reported.`)
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.summarization(`One month after the United States began what has become a 
  troubled rollout of a national COVID vaccination campaign, the effort is finally 
  gathering real steam. Close to a million doses -- over 951,000, to be more exact -- 
  made their way into the arms of Americans in the past 24 hours, the U.S. Centers 
  for Disease Control and Prevention reported Wednesday. That s the largest number 
  of shots given in one day since the rollout began and a big jump from the 
  previous day, when just under 340,000 doses were given, CBS News reported. 
  That number is likely to jump quickly after the federal government on Tuesday 
  gave states the OK to vaccinate anyone over 65 and said it would release all 
  the doses of vaccine it has available for distribution. Meanwhile, a number 
  of states have now opened mass vaccination sites in an effort to get larger 
  numbers of people inoculated, CBS News reported.`)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->summarization("One month after the United States began what has become a 
  troubled rollout of a national COVID vaccination campaign, the effort is finally 
  gathering real steam. Close to a million doses -- over 951,000, to be more exact -- 
  made their way into the arms of Americans in the past 24 hours, the U.S. Centers 
  for Disease Control and Prevention reported Wednesday. That s the largest number 
  of shots given in one day since the rollout began and a big jump from the 
  previous day, when just under 340,000 doses were given, CBS News reported. 
  That number is likely to jump quickly after the federal government on Tuesday 
  gave states the OK to vaccinate anyone over 65 and said it would release all 
  the doses of vaccine it has available for distribution. Meanwhile, a number 
  of states have now opened mass vaccination sites in an effort to get larger 
  numbers of people inoculated, CBS News reported.")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using bart-large-cnn for the example):

{
  "summary_text": "Over 951,000 doses were given in the past 24 hours. 
  That's the largest number of shots given in one day since the rollout began. 
  That number is likely to jump quickly after the federal government 
  gave states the OK to vaccinate anyone over 65. A number of states have 
  now opened mass vaccination sites."
}

This endpoint uses either Facebook's Bart Large CNN model or Google's Pegasus XSUM for text summarization in many languages, or Michau's T5 Base EN Generate Headline for headline generation in English. These are "abstractive" summarizations, which means that some sentences are directly taken from the input text, but also that new sentences might be generated. You can also use your own transformers-based custom model (replace <model_name> with the ID of your model in the URL).

Pass your block of text, and the model will return a summary.

Here are the 3 Transformer-based models you can use:

Here is an example using Postman:

Summarization example with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/summarization

POST Values

These values must be encoded as JSON.

Key Type Description
text string The block of text that you want to summarize. 1024 tokens maximum.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
summary_text string The summary of your text

Generation

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/generation" \
  -H "Authorization: Token <token>" \
  -X POST -d '{
    "text":"GPT-J is a powerful NLP model",
    "min_length":10,
    "max_length":50
}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns a JSON object.
client.generation("GPT-J is a powerful NLP model", min_length=10, max_length=50)
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.generation('GPT-J is a powerful NLP model', min_length: 10, max_length: 50)
// Not released yet
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.generation('GPT-J is a powerful NLP model', minLength=10, maxLength=50)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->generation("GPT-J is a powerful NLP model", 10, 50, null, null, null, null, null, null, null, null)
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using gpt-j for the example):

{
  "generated_text":"GPT-J is a powerful NLP model for text generation. 
  This is the open-source version of GPT-3 by OpenAI. It is the most 
  advanced NLP model created as of today."
}

This endpoint uses either EleutherAI' GPT-J or EleutherAI's GPT-Neo 2.7B models to generate a block of text, in many languages (GPT-J is the most advanced model, trained on 6 billion parameters, so this is the one we recommend). Start a sentence and let the model generate the rest for you. It can also use your own transformers-based custom model (replace <model> with the ID of your model in the URL).

The 2 models available are:

Pass your block of text, and the model will return a generated text. You can pass many optional arguments like min_length, max_length,end_sequence, and more. See below the comprehensive list of optional arguments. If you are looking for additional arguments for more fine-tuning, please don't hesitate to ask us!

You can achieve almost any NLP use case thanks to GPT-J: sentiment analysis, grammar and spelling correction, question answering, code generation, machine translation, semantic similarity, intent classification, paraphrasing... and more! Read our article about few-shot learning to know more.

This is also possible to train/fine-tune your own GPT-J model if few-shot learning is not enough.

For advanced text generation tuning, you can play with many parameters like num_beams, temperature, repetition_penalty, etc. They are sometimes a good way to produce more original and fluent content. See the full list of parameters below. If you are not sure what these parameters do, please read this very good article from Hugging Face.

Here is an example using Postman:

Text generation example with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/generation

POST Values

These values must be encoded as JSON.

Key Type Description
text string The block of text that starts the generated text. 1200 tokens maximum.
min_length int Optional. The minimum number of tokens that the generated text should contain. The size of the generated text should not exceed 256 tokens on a CPU plan and 1024 tokens on GPU plan. If length_no_input is false, the size of the generated text is the difference between min_length and the length of your input text. If length_no_input is true, the size of the generated text simply is min_length. Defaults to 10.
max_length int Optional. The maximum number of tokens that the generated text should contain. The size of the generated text should not exceed 256 tokens on a CPU plan and 1024 tokens on GPU plan. If length_no_input is false, the size of the generated text is the difference between max_length and the length of your input text. If length_no_input is true, the size of the generated text simply is max_length. Defaults to 50.
length_no_input bool Optional. Whether min_length and max_length should not include the length of the input text. If false, min_length and max_length include the length of the input text. If true, min_length and max_length don't include the length of the input text. Defaults to false.
end_sequence string Optional. A specific token that should be the end of the generated sequence. For example if could be . or \n or ### or anything else below 10 characters.
remove_input bool Optional. Whether you want to remove the input text form the result. Defaults to false.
do_sample bool Optional. Whether or not to use sampling ; use greedy decoding otherwise. Defaults to true.
num_beams int Optional. Number of beams for beam search. 1 means no beam search. Defaults to 1.
early_stopping bool Optional. Whether to stop the beam search when at least num_beams sentences are finished per batch or not. Defaults to false.
no_repeat_ngram_size int Optional. If set to int > 0, all ngrams of that size can only occur once. Defaults to 0.
num_return_sequences int Optional. The number of independently computed returned sequences for each element in the batch. Defaults to 1.
top_k int Optional. The number of highest probability vocabulary tokens to keep for top-k-filtering. Maximum 1000 tokens. Defaults to 0.
top_p float Optional. If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. Should be between 0 and 1. Defaults to 0.7.
temperature float Optional. The value used to module the next token probabilities. Should be between 0 and 1. Defaults to 1.
repetition_penalty float Optional. The parameter for repetition penalty. 1.0 means no penalty. Defaults to 1.0.
length_penalty float Optional. Exponential penalty to the length. 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.
bad_words list of strings Optional. List of tokens that are not allowed to be generated. Defaults to null.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
generated_text string The generated text

Translation

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/translation" \
  -H "Authorization: Token <token>" \
  -X POST -d '{"text":"John Doe has been working for Microsoft in Seattle since 1999."}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns a json object.
client.translation("John Doe has been working for Microsoft in Seattle since 1999.")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.translation("John Doe has been working for Microsoft in Seattle since 1999.")
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("<model_name>", "<token>", false)
// Return a Translation struct.
client.Translation("John Doe has been working for Microsoft in Seattle since 1999.")
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.translation(`John Doe has been working for Microsoft in Seattle since 1999.`)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->translation("John Doe has been working for Microsoft in Seattle since 1999.")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using the opus-mt-en-fr (English to French) model for the example):

{
  "translation_text": "John Doe travaille pour Microsoft à Seattle depuis 1999."
}

This endpoint uses Helsinki NLP's Opus MT models to translate text in several languages thanks to deep learning. Pass your block of text, and the model will return a translation. It can also use your own transformers-based custom model (replace the model name with the ID of your model in the URL).

Do not hesitate to use translation if you need to use other models, like classification of sentiment analysis, in non-English languages. Just translate your text first before sending it to another model.

Here are all the Helsinki NLP's Opus MT pre-trained models you can use:

We are planning to add many more models for translation in the future depending on customer requests. So if your use case is not listed above, please let us know and we will add it promptly (it should take about 1 day).

Here is an example of English to French traduction with the opus-mt-en-fr model, using Postman:

Translation example with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/translation

POST Values

These values must be encoded as JSON.

Key Type Description
text string The sentence that you want to translate. 1000 characters maximum.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
translation_text string The translation of your text

Language Detection

Input:

curl "https://api.nlpcloud.io/v1/python-langdetect/langdetection" \
  -H "Authorization: Token <token>" \
  -X POST -d '{"text":"John Doe has been working for Microsoft in Seattle since 1999. Et il parle aussi un peu français."}'
import nlpcloud

client = nlpcloud.Client("python-langdetect", "<token>")
# Returns a json object.
client.langdetection("John Doe has been working for Microsoft in Seattle since 1999. Et il parle aussi un peu français.")
require 'nlpcloud'

client = NLPCloud::Client.new('python-langdetect','<token>')
# Returns a json object.
client.langdetection("John Doe has been working for Microsoft in Seattle since 1999. Et il parle aussi un peu français.")
// Not implemented yet.
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('python-langdetect','<token>')
// Returns an Axios promise with the results.
// 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`.
client.langdetection(`John Doe has been working for Microsoft in Seattle since 1999. Et il parle aussi un peu français.`)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('python-langdetect','<token>');
# Returns a json object.
$client->langdetection("John Doe has been working for Microsoft in Seattle since 1999. Et il parle aussi un peu français.")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "languages": [
    {
      "en": 0.7142834369645996
    },
    {
      "fr": 0.28571521669868466
    }
  ]
}

This endpoint uses Python's LangDetect library to detect languages from a text. It returns an array with all the languages detected in the text and their likelihood. The results are sorted by likelihood, so the first language in the array is the most likely. The languages follow the 2 characters ISO codes.

This endpoint is not using deep learning under the hood so the response time is extremely fast.

Here is an example of language detection using Postman:

Language detection example with Postman

Put your JSON data in Body > raw. Note that if your text contains double quotes (") you will need to escape them (using \") in order for your JSON to be properly decoded. This is not needed when using a client library.

HTTP Request

POST https://api.nlpcloud.io/v1/python-langdetect/langdetection

POST Values

These values must be encoded as JSON.

Key Type Description
text string The block of text containing one or more languages your want to detect. 100,000 characters maximum.

Output

This endpoint returns a JSON object called languages. Each object contains a detected language and its likelihood. The languages are sorted with the most likely first:

Key Type Description
languages array of objects. Each object has a string as key and float as value The list of detected languages (in 2 characters ISO format) with their likelihood

Dependencies

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/dependencies" \
  -H "Authorization: Token <token>" \
  -X POST \
  -d '{"text":"John Doe is a Go Developer at Google"}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns a json object.
client.dependencies("John Doe is a Go Developer at Google")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.dependencies("John Doe is a Go Developer at Google")
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("<model_name>", "<token>", false)
// Return a Dependencies struct.
client.Dependencies("John Doe is a Go Developer at Google")
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.dependencies('John Doe is a Go Developer at Google')
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->dependencies("John Doe is a Go Developer at Google")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using en_core_web_lg for the example):

{
  "words": [
    {
      "text": "John",
      "tag": "NNP"
    },
    {
      "text": "Doe",
      "tag": "NNP"
    },
    {
      "text": "is",
      "tag": "VBZ"
    },
    {
      "text": "a",
      "tag": "DT"
    },
    {
      "text": "Go",
      "tag": "NNP"
    },
    {
      "text": "Developer",
      "tag": "NN"
    },
    {
      "text": "at",
      "tag": "IN"
    },
    {
      "text": "Google",
      "tag": "NNP"
    }
  ],
  "arcs": [
    {
      "start": 0,
      "end": 1,
      "label": "compound",
      "text": "John",
      "dir": "left"
    },
    {
      "start": 1,
      "end": 2,
      "label": "nsubj",
      "text": "Doe",
      "dir": "left"
    },
    {
      "start": 3,
      "end": 5,
      "label": "det",
      "text": "a",
      "dir": "left"
    },
    {
      "start": 4,
      "end": 5,
      "label": "compound",
      "text": "Go",
      "dir": "left"
    },
    {
      "start": 2,
      "end": 5,
      "label": "attr",
      "text": "Developer",
      "dir": "right"
    },
    {
      "start": 5,
      "end": 6,
      "label": "prep",
      "text": "at",
      "dir": "right"
    },
    {
      "start": 6,
      "end": 7,
      "label": "pobj",
      "text": "Google",
      "dir": "right"
    }
  ]
}

This endpoint uses any spaCy model (it can be either a spaCy pre-trained model or your own spaCy custom model) to perform Part-of-Speech (POS) tagging in many languages and returns dependencies (arcs) extracted from the passed in text.

See the spaCy dependency parsing documentation for more details.

Here are all the spaCy models you can use in multiple languages (see the models section for more details) :

Each spaCy pre-trained model has a list of supported built-in part-of-speech tags and dependency labels. For example, the list of tags and dependency labels for the en_core_web_lg model can be found here:

For more details about what these abbreviations mean, see spaCy's glossary.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/dependencies

POST Values

These values must be encoded as JSON.

Key Type Description
text string The sentence of text you want to analyze. 1000 characters maximum.

Output

This endpoint returns 2 objects: words and arcs.

words contains an array of the following elements:

Key Type Description
text string The content of the word
tag string The part of speech tag for the word (https://spacy.io/api/annotation#pos-tagging)

arcs contains an array of the following elements:

Key Type Description
text string The content of the word
label string The syntactic dependency connecting child to head (https://spacy.io/api/annotation#pos-tagging)
start integer Position of the word if direction of the arc is left. Position of the head if direction of the arc is right.
end integer Position of the head if direction of the arc is left. Position of the word if direction of the arc is right.
dir string Direction of the dependency arc (left or right)

Sentence Dependencies

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/sentence-dependencies" \
  -H "Authorization: Token <token>" \
  -X POST \
  -d '{"text":"John Doe is a Go Developer at Google. Before that, he worked at Microsoft."}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns json object.
client.sentence_dependencies("John Doe is a Go Developer at Google. Before that, he worked at Microsoft.")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns json object.
client.sentence_dependencies("John Doe is a Go Developer at Google. Before that, he worked at Microsoft.")
import "github.com/nlpcloud/nlpcloud-go"

client := nlpcloud.NewClient("<model_name>", "<token>", false)
// Return a SentenceDependencies struct.
client.SentenceDependencies("John Doe is a Go Developer at Google. Before that, he worked at Microsoft.")
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.sentenceDependencies('John Doe is a Go Developer at Google. Before that, he worked at Microsoft.')
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->sentenceDependencies("John Doe is a Go Developer at Google. Before that, he worked at Microsoft.")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using en_core_web_lg for the example):

{
  "sentence_dependencies": [
    {
      "sentence": "John Doe is a Go Developer at Google.",
      "dependencies": {
        "words": [
          {
            "text": "John",
            "tag": "NNP"
          },
          {
            "text": "Doe",
            "tag": "NNP"
          },
          {
            "text": "is",
            "tag": "VBZ"
          },
          {
            "text": "a",
            "tag": "DT"
          },
          {
            "text": "Go",
            "tag": "NNP"
          },
          {
            "text": "Developer",
            "tag": "NN"
          },
          {
            "text": "at",
            "tag": "IN"
          },
          {
            "text": "Google",
            "tag": "NNP"
          },
          {
            "text": ".",
            "tag": "."
          }
        ],
        "arcs": [
          {
            "start": 0,
            "end": 1,
            "label": "compound",
            "text": "John",
            "dir": "left"
          },
          {
            "start": 1,
            "end": 2,
            "label": "nsubj",
            "text": "Doe",
            "dir": "left"
          },
          {
            "start": 3,
            "end": 5,
            "label": "det",
            "text": "a",
            "dir": "left"
          },
          {
            "start": 4,
            "end": 5,
            "label": "compound",
            "text": "Go",
            "dir": "left"
          },
          {
            "start": 2,
            "end": 5,
            "label": "attr",
            "text": "Developer",
            "dir": "right"
          },
          {
            "start": 5,
            "end": 6,
            "label": "prep",
            "text": "at",
            "dir": "right"
          },
          {
            "start": 6,
            "end": 7,
            "label": "pobj",
            "text": "Google",
            "dir": "right"
          },
          {
            "start": 2,
            "end": 8,
            "label": "punct",
            "text": ".",
            "dir": "right"
          }
        ]
      }
    },
    {
      "sentence": "Before that, he worked at Microsoft.",
      "dependencies": {
        "words": [
          {
            "text": "Before",
            "tag": "IN"
          },
          {
            "text": "that",
            "tag": "DT"
          },
          {
            "text": ",",
            "tag": ","
          },
          {
            "text": "he",
            "tag": "PRP"
          },
          {
            "text": "worked",
            "tag": "VBD"
          },
          {
            "text": "at",
            "tag": "IN"
          },
          {
            "text": "Microsoft",
            "tag": "NNP"
          },
          {
            "text": ".",
            "tag": "."
          }
        ],
        "arcs": [
          {
            "start": 9,
            "end": 13,
            "label": "prep",
            "text": "Before",
            "dir": "left"
          },
          {
            "start": 9,
            "end": 10,
            "label": "pobj",
            "text": "that",
            "dir": "right"
          },
          {
            "start": 11,
            "end": 13,
            "label": "punct",
            "text": ",",
            "dir": "left"
          },
          {
            "start": 12,
            "end": 13,
            "label": "nsubj",
            "text": "he",
            "dir": "left"
          },
          {
            "start": 13,
            "end": 14,
            "label": "prep",
            "text": "at",
            "dir": "right"
          },
          {
            "start": 14,
            "end": 15,
            "label": "pobj",
            "text": "Microsoft",
            "dir": "right"
          },
          {
            "start": 13,
            "end": 16,
            "label": "punct",
            "text": ".",
            "dir": "right"
          }
        ]
      }
    }
  ]
}

This endpoint uses a spaCy model (it can be either a spaCy pre-trained model or your own spaCy custom model) to perform Part-of-Speech (POS) tagging , in many languages and returns dependencies (arcs) extracted from the passed in text, for several sentences.

See the spaCy dependency parsing documentation for more details.

Here are all the spaCy models you can use in multiple languages (see the models section for more details) :

Each spaCy pre-trained model has a list of supported built-in part-of-speech tags and dependency labels. For example, the list of tags and dependency labels for the en_core_web_lg model can be found here:

For more details about what these abbreviations mean, see spaCy's glossary.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/sentence-dependencies

POST Values

These values must be encoded as JSON.

Parameter Type Description
text string The sentences containing parts of speech to extract. 1000 characters maximum.

Output

This endpoint returns a sentence_dependencies object containing an array of sentence dependencies objects. Each sentence dependency object contains the following:

Key Type Description
sentence string The sentence being analyzed
dependencies object An object containing the words and arcs

words contains an array of the following elements:

Key Type Description
text string The content of the word
tag string The part of speech tag for the word (https://spacy.io/api/annotation#pos-tagging)

arcs contains an array of the following elements:

Key Type Description
text string The content of the word
label string The syntactic dependency connecting child to head (https://spacy.io/api/annotation#pos-tagging)
start integer Position of the word if direction of the arc is left. Position of the head if direction of the arc is right.
end integer Position of the head if direction of the arc is left. Position of the word if direction of the arc is right.
dir string Direction of the dependency arc (left or right)

Tokens

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/tokens" \
  -H "Authorization: Token <token>" \
  -X POST \
  -d '{"text":"John is a Go Developer at Google."}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns json object.
client.tokens("John is a Go Developer at Google.")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns json object.
client.tokens("John is a Go Developer at Google.")
//  Not available yet.
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>')
// Returns an Axios promise with the results.
// 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`.
client.tokens('John is a Go Developer at Google.')
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->tokens("John is a Go Developer at Google.")
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output (using en_core_web_lg for the example):

{
  "tokens": [
    {
      "start": 0,
      "end": 4,
      "index": 1,
      "text": "John",
      "lemma": "John",
      "ws_after": true
    },
    {
      "start": 5,
      "end": 7,
      "index": 2,
      "text": "is",
      "lemma": "be",
      "ws_after": true
    },
    {
      "start": 8,
      "end": 9,
      "index": 3,
      "text": "a",
      "lemma": "a",
      "ws_after": true
    },
    {
      "start": 10,
      "end": 12,
      "index": 4,
      "text": "Go",
      "lemma": "Go",
      "ws_after": true
    },
    {
      "start": 13,
      "end": 22,
      "index": 5,
      "text": "Developer",
      "lemma": "developer",
      "ws_after": true
    },
    {
      "start": 23,
      "end": 25,
      "index": 6,
      "text": "at",
      "lemma": "at",
      "ws_after": true
    },
    {
      "start": 26,
      "end": 32,
      "index": 7,
      "text": "Google",
      "lemma": "Google",
      "ws_after": false
    },
    {
      "start": 32,
      "end": 33,
      "index": 8,
      "text": ".",
      "lemma": ".",
      "ws_after": false
    }
  ]
}

This endpoint uses a spaCy model (it can be either a spaCy pre-trained model or your own spaCy custom model) to tokenize and lemmatize a passed in text, in many languages.

See the spaCy tokenization and lemmatization documentations for more details.

Here are all the spaCy models you can use in many languages (see the models section for more details):

It returns a list of tokens and their corresponding lemmas. Each token is an object made up of several elements. See below for the details.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/tokens

POST Values

These values must be encoded as JSON.

Parameter Type Description
text string The sentence containing the tokens to extract. 1000 characters maximum.

Output

This endpoint returns a tokens object containing an array of token objects. Each token object contains the following:

Key Type Description
text string The content of the extracted token.
lemma string The corresponding lemma of the extracted token.
start int The position of the 1st character of the token (starting at 0)
end int The position of the 1st character after the token
index int The position of the token in the sentence (starting at 1)
ws_after boolean Says whether there is a whitespace after the token, or not

Library Versions

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/versions"
# Returns a json object.
client.lib_versions()
# Returns a json object.
client.lib_versions()
// Returns a LibVersion struct.
client.LibVersions()
// Returns an Axios promise with the results.
// 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`.
client.libVersions()
# Returns a json object.
$client->libVersions()
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

This endpoint returns the versions of the libraries used behind the hood with the model.

Output:

// Example (using bart-large-mnli for the example):
{
  "pytorch": "1.7.1",
  "transformers": "4.3.2"
}

HTTP Request

GET https://api.nlpcloud.io/v1/<model_name>/versions

Batch Processing

If you have a large batch of requests to process, it is possible to process them all at once thanks to batch processing.

Batch processing is available in your dashboard, in the "Batch Processing" section.

See more details below, and if you have any questions please don't hesitate to ask us for help, it will be a pleasure to assist.

Input

In order to launch a batch processing job, you should create a comma separated CSV file containing all the requests you want to process.

The column names should be the names of the fields expected by the API endpoint you are using. For example if you are sending a batch to the en_core_web_lg/entities endpoint, you should have 1 column called text.

Each new row is a new request to process.

In addition to the file, you should also specify the API endpoint you want to use.

Examples

CSV file for batch processing on the en_core_web_lg/entities endpoint:

text
John Doe is working for Microsoft
Max paid his shoes ten dollars
The meeting will take place in NYC at 10am

CSV file for batch processing on the bart-large-mnli/classification endpoint:

text, labels, multi_class
John Doe is working for Microsoft, "['job', 'space', 'nature]", true
Max paid his shoes ten dollars, "['job', 'space', 'nature]", true
The meeting will take place in NYC at 10am, "['job', 'space', 'nature]", true

See CSV files examples on the right.

Output

The output file will be your initial file, but with an additional column called result containing the JSON result from the API.

Lifecycle

Once your file is uploaded, a first formatting analysis is performed. If the CSV file has formatting issues, you will receive an email to inform you.

Once the batch process starts, if the processing takes more than one day, you receive one email per day informing you about the progress made on your file.

If an error occurs, the batch processing stops and you receive an email with the results generated until the error occurs, along with the line number that caused the error in your CSV file, and the error itself.

Once the batch processing is finished, you receive an email containing a link for you to download the result file.

Rate Limiting

Rate limiting depends on the plan you subscribed to.

For example for the free plan, you can create up to 3 requests per minute. If you reach the limit, the API will return a 429 HTTP error.

Errors

The NLP Cloud API uses the following error HTTP codes:

Code Meaning
400 Bad Request -- Your request is invalid.
401 Unauthorized -- Your API token is wrong.
402 Payment Required -- You are trying to access a resource that is only accessible after payment.
403 Forbidden -- You do not have the sufficient rights to access the resource. Please make sure you subscribed to the proper plan that grants you access to this resource.
404 Not Found -- The specified resource could not be found.
405 Method Not Allowed -- You tried to access a resource with an invalid method.
406 Not Acceptable -- You requested a format that isn't json.
413 Request Entity Too Large -- The piece of text that you are sending it too large. Please see the maximum sizes in the documentation.
429 Too Many Requests -- You made too many requests in a short while, please slow down.
500 Internal Server Error -- Sorry, we had a problem with our server. Please try again later.
503 Service Unavailable -- Sorry, we are temporarily offline for maintenance. Please try again later.

If any problem, do not hesitate to contact us: [email protected].