NAV
cURL Python Ruby Go Node.js PHP C#

Introduction

Summarize a text using the Bart Large CNN pre-trained model:

curl "https://api.nlpcloud.io/v1/bart-large-cnn/summarization" \
  -H "Authorization: Token 4eC39HqLyjWDarjtT1zdp7dc" \
  -H "Content-Type: application/json" \
  -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("bart-large-cnn", "4eC39HqLyjWDarjtT1zdp7dc")
# 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('bart-large-cnn','4eC39HqLyjWDarjtT1zdp7dc')
# 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.")
package main

import (
    "net/http"

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

func main() {
    client := nlpcloud.NewClient(&http.Client{}, "bart-large-cnn", "4eC39HqLyjWDarjtT1zdp7dc", false, "")
    summary, err := client.Summarization(nlpcloud.SummarizationParams{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."})
    ...
}
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('bart-large-cnn','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.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.`)
  .then(function (response) {
    console.log(response.data);
  })
  .catch(function (err) {
    console.error(err.response.status);
    console.error(err.response.data.detail);
  });
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('bart-large-cnn','4eC39HqLyjWDarjtT1zdp7dc');
# 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

Summarize a text using the Bart Large CNN pre-trained model on GPU:

curl "https://api.nlpcloud.io/v1/gpu/bart-large-cnn/summarization" \
  -H "Authorization: Token 4eC39HqLyjWDarjtT1zdp7dc" \
  -H "Content-Type: application/json" \
  -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("bart-large-cnn", "4eC39HqLyjWDarjtT1zdp7dc", gpu=True)
# 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('bart-large-cnn','4eC39HqLyjWDarjtT1zdp7dc', true)
# 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.")
package main

import (
    "net/http"

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

func main() {
    client := nlpcloud.NewClient(&http.Client{}, "bart-large-cnn", "4eC39HqLyjWDarjtT1zdp7dc", true, "")
    summary, err := client.Summarization(nlpcloud.SummarizationParams{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."})
    ...
}
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('bart-large-cnn','4eC39HqLyjWDarjtT1zdp7dc', 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.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.`)
  .then(function (response) {
    console.log(response.data);
  })
  .catch(function (err) {
    console.error(err.response.status);
    console.error(err.response.data.detail);
  });
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('bart-large-cnn','4eC39HqLyjWDarjtT1zdp7dc', True);
# 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:

{
  "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."
}

Welcome to the NLP Cloud API documentation.

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

Use Case Model Used
Chatbot/Conversational AI: create an advanced chatbot (see endpoint) We are using GPT with PyTorch and Jax. This endpoint can leverage our multilingual add-on.
Classification: send a text, and let the model categorize the text for you in many languages (as an option you can give the candidate categories you want to assess) (see endpoint) We are using the Joe Davison's Bart Large MNLI Yahoo Answers, Joe Davison's XLM Roberta Large XNLI and GPT, with PyTorch, Jax, and Hugging Face transformers. This endpoint can leverage our multilingual add-on.
Dialogue Summarization: get a summary of a conversation (see endpoint) We are using Bart Large CNN SamSum. This endpoint can leverage our multilingual add-on.
Embeddings: calculate embeddings from a list of texts, in many languages (see endpoint) We are using the Paraphrase Multilingual Mpnet Base V2 model and the GPT-J model, with PyTorch, Transformers, and Sentence Transformers.
Headline Generation: send a text, and get a one sentence headline summarizing everything, in many languages (see endpoint) We are using Michau's T5 Base EN Generate Headline, with PyTorch, and Hugging Face transformers. This endpoint can leverage our multilingual add-on.
Grammar And Spelling Correction: remove the grammar and spelling errors from your text (see endpoint) We are using GPT with PyTorch and Jax. This endpoint can leverage our multilingual add-on.
Intent Classification: detect the intent hidden behind a text (see endpoint) We are using GPT with PyTorch and Jax. This endpoint can leverage our multilingual add-on.
Keywords and Keyphrases Extraction: extract the main ideas in a text (see endpoint) We are using GPT with PyTorch and Jax. This endpoint can leverage our multilingual add-on.
Language Detection: detect one or several languages from a text (see endpoint) We are simply using Python's Langdetect library.
Lemmatization: extract lemmas from a text, in many languages (see endpoint) All the large spaCy models are available (15 languages) and Megagon Lab's Ginza for Japanese
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), and Megagon Lab's Ginza for Japanese, and GPT with PyTorch and Jax. .
Noun Chunks Extraction: extract noun chunks from a text, in many languages (see endpoint) All the large spaCy models are available (15 languages) and Megagon Lab's Ginza for Japanese
Paraphrasing and rewriting: send a text, and get a rephrased version that has the same meaning but with new words (see endpoint) We are using GPT with PyTorch and Jax. This endpoint can leverage our multilingual add-on.
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) and Megagon Lab's Ginza for Japanese
Product Description and Ad Generation: generate a product description or an ad out of keywords (see endpoint) We are using GPT with PyTorch and Jax. This endpoint can leverage our multilingual add-on.
Question answering: ask questions about anything (as an option you can give a context and ask specific questions about this context) in many languages (see endpoint) We are using the Deepset's Roberta Base Squad 2 model and GPT with PyTorch, Jax, and Hugging Face transformers. This endpoint can leverage our multilingual add-on.
Semantic Similarity: detect whether 2 pieces of text have the same meaning or not, in many languages (see endpoint) We are using the Paraphrase Multilingual Mpnet Base V2 model with PyTorch and Sentence Transformers.
Sentiment analysis: determine whether a text is rather positive or negative or detect emotions, in many languages (see endpoint) We are using DistilBERT Base Uncased Finetuned SST-2, DistilBERT Base Uncased Emotion, and Prosus AI's Finbert, with PyTorch, Tensorflow, and Hugging Face transformers. This endpoint can leverage our multilingual add-on.
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 GPT, with PyTorch, Jax, and Hugging Face transformers. This endpoint can leverage our multilingual add-on.
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 NeoX 20B models with PyTorch and Hugging Face transformers, as well as an optimized version of GPT-J called Fast GPT-J, and a fine-tuned version of GPT-NeoX 20B called Finetuned GPT NeoX 20B. They are powerful open-source equivalents of "OpenAI GPT-3". This endpoint can leverage our multilingual add-on.
Tokenization: extract tokens from a text, in many languages (see endpoint) All the large spaCy models are available (15 languages) and Megagon Lab's Ginza for Japanese
Translation: translate text from one language to another (see endpoint) We use Facebook's M2M100 1.2B for translation in 100 languages, and Helsinki NLP's Opus MT models in 7 languages, with PyTorch and Hugging Face transformers

All these use cases can be tested for free with a maximum of 3 requests per minute (except text generation that requires a paid plan because of the huge computation costs involved). For more requests and features, please see the paid plans.

If not done yet, please retrieve a free API token from your dashboard and don't hesitate to easily test models on the playground. Also do not hesitate to contact us: [email protected].

We do recommend to subscribe to a GPU plan for better performance, especially for computation-intensive use cases like summarization, paraphrasing, text generation... We do our best to provide affordable GPU prices.

If you need to process non-English languages, we do encourage you to use our multilingual add-on.

See on the right a full example about summarizing block of text, using Facebook's Bart Large CNN pre-trained model, both on CPU and GPU. And the same example below using Postman:

Authentication example with Postman

Summarization example with Postman

You can train/fine-tune your own models. You can also upload your own custom models in your dashboard.

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 install.

go install 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 (
    "net/http"

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

func main() {
    client := nlpcloud.NewClient(&http.Client, "<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>" \
  -H "Content-Type: application/json" \
  -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.

Don't forget to set the content-type accordingly: "Content-Type: application/json".

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(&http.Client, "<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(&http.Client, "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 private 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(&http.Client, "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 various sources like spaCy, Hugging Face, and more, in order to perform Named Entity Recognition (NER), sentiment analysis, classification, summarization, text generation, question answering, machine translation, language detection, tokenization, lemmatization, POS tagging, and dependency parsing.

You can also also use your own private models, in 2 different ways:

In case of a private model, your private API endpoint appears in your dashboard once the fine-tuning is finished, or the model upload is finished.

Here is an example on the right performing Named Entity Recognition (NER) with spaCy's pre-trained en_core_web_lg model, and another example doing the same thing with your own private spaCy model with ID 7894.

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_ginza_electra: Megagon Lab's Ginza See on Github 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-yahoo-answers: Joe Davison's Bart Large MNLI Yahoo Answers 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
bart-large-samsum: Bart Large CNN SamSum 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
distilbert-base-uncased-emotion: Distilbert Emotion 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-neox-20b: GPT-NeoX 20B See on Hugging Face PyTorch
fast-gpt-j: Fast GPT-J An optimized version of GPT-J Jax
finetuned-gpt-neox-20b: Finetuned GPT-NeoX 20B A fine-tuned version of GPT-NeoX 20B PyTorch
m2m100-1-2b: Facebook's M2M100 1.2B 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
paraphrase-multilingual-mpnet-base-v2: Paraphrase Multilingual Mpnet Base V2 See on Hugging Face PyTorch / Sentence Transformers
python-langdetect: Python LangDetect library See on Pypi LangDetect

Train/Fine-Tune Your Own Model

See the dedicated fine-tuning section for more details.

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 your model comes with a custom script, you can send this script to [email protected], together with any relevant instruction necessary to make your model run. If your model must support custom input or output formats, no problem, just let us know so we can adapt the API signature. If we have questions we will let you know.

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 your model comes with a custom script, you can send this script to [email protected], together with any relevant instruction necessary to make your model run. If your model must support custom input or output formats, no problem, just let us know so we can adapt the API signature. If we have questions we will let you know.

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

Upload Other Models

You can also deploy NLP models not based on spaCy or Transformers.

Please contact us!

GPU

Text classification with Bart Large MNLI Yahoo Answers on GPU

curl "https://api.nlpcloud.io/v1/gpu/bart-large-mnli-yahoo-answers/classification" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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 (
    "net/http"

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

func newTrue() *bool {
    b := true
    return &b
}

func main() {
    client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", true, "")
    // Returns a Classification struct.
    classes, err := client.Classification(nlpcloud.ClassificationParams{
      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: []string{"job", "nature", "space"},
      MultiClass: newTrue(),
    })
    ...
}
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', 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 Yahoo Answers classification model on a GPU, you should use the following endpoint:

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

See a full example on the right.

Multilingual Add-On

Example: performing French summarization with Bart Large CNN:

curl "https://api.nlpcloud.io/v1/fr/bart-large-cnn/summarization" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{
    "text":"Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille."
}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>", lang="fr")
# Returns a json object.
client.summarization("""Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.""")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>', lang: 'fr')
# Returns a json object.
client.summarization("Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.")
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "fr")
  // Returns a Summarization struct.
  summary, err := client.Summarization(nlpcloud.SummarizationParams{
    Text: `Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.`,
  })
  ...
}

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', false, 'fr')
// 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(`Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.`)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>', False, 'fr');
# Returns a json object.
$client->summarization("Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.");
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{"summary_text": "Selon l'organisation mondiale de la santé, une centaine
de maisons ont été endommagées, dont 50 détruites sur l'île principale
de Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
a également fait état de deux morts, dont une femme britannique de 50 ans,
Angela Glover. Glover a été emportée par le tsunami après avoir tenté
de sauver des chiens de son refuge."}

Example: performing French summarization with Bart Large CNN on GPU:

curl "https://api.nlpcloud.io/v1/gpu/fr/bart-large-cnn/summarization" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{
    "text":"Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille."
}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>", gpu=True, lang="fr")
# Returns a json object.
client.summarization("""Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.""")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>', gpu: true, lang: 'fr')
# Returns a json object.
client.summarization("Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.")
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", true, "fr")
  // Returns a Summarization struct.
  summary, err := client.Summarization(nlpcloud.SummarizationParams{
    Text: `Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.`,
  })
  ...
}

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', true, 'fr')
// 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(`Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.`)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>', True, 'fr');
# Returns a json object.
$client->summarization("Sur des images aériennes, prises la veille par un vol de surveillance 
    de la Nouvelle-Zélande, la côte d’une île est bordée d’arbres passés du vert 
    au gris sous l’effet des retombées volcaniques. On y voit aussi des immeubles
    endommagés côtoyer des bâtiments intacts. « D’après le peu d’informations
    dont nous disposons, l’échelle de la dévastation pourrait être immense, 
    spécialement pour les îles les plus isolées », avait déclaré plus tôt 
    Katie Greenwood, de la Fédération internationale des sociétés de la Croix-Rouge.
    Selon l’Organisation mondiale de la santé (OMS), une centaine de maisons ont
    été endommagées, dont cinquante ont été détruites sur l’île principale de
    Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
    a également fait état de deux morts, dont une Britannique âgée de 50 ans,
    Angela Glover, emportée par le tsunami après avoir essayé de sauver les chiens
    de son refuge, selon sa famille.");
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{"summary_text": "Selon l'organisation mondiale de la santé, une centaine
de maisons ont été endommagées, dont 50 détruites sur l'île principale
de Tonga, Tongatapu. La police locale, citée par les autorités néo-zélandaises,
a également fait état de deux morts, dont une femme britannique de 50 ans,
Angela Glover. Glover a été emportée par le tsunami après avoir tenté
de sauver des chiens de son refuge."}

NLP has a critical weakness: it doesn't work well with non-English languages.

We do our best to add non-English models when it's possible. See for example XLM Roberta Large XNLI, TF Allociné, German Sentiment Bert... Unfortunately few models are available so it's not possible to cover all the NLP use cases with that method.

In order to solve this challenge, we developed a multilingual AI that automatically translates your input into English, performs the actual NLP operation, and then translates the result back to your original language. It makes your requests a bit slower but returns impressive results.

Simply add your language code in the endpoint URL, after the API version and before the name of the model: https://api.nlpcloud.io/v1/{language code}/{model}

If you are using a GPU, add your language code after the GPU, and before the name of the model: https://api.nlpcloud.io/v1/gpu/{language code}/{model}

For example, here is the endpoint you should use for summarization of French text with Bart Large CNN: https://api.nlpcloud.io/v1/fr/bart-large-cnn/summarization. And here is the endpoint you should use for summarization of French text with Bart Large CNN on GPU: https://api.nlpcloud.io/v1/fr/bart-large-cnn/summarization.

Here is the full list of supported language codes:

The multilingual add-on can be used with the following endpoints:

Endpoints

Chatbot and Conversational AI

Input:

curl "https://api.nlpcloud.io/v1/gpu/<model>/chatbot" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{"input":"I just broke up with my girlfriend...","history":[{"input":"Hello friend", "response":"Hi there, how is it going today?" }, {"input":"Well, not that good...", "response":"Oh? What happened?"}]}'
import nlpcloud

client = nlpcloud.Client("<model>", "<token>", True)
# Returns a json object.
client.chatbot("I just broke up with my girlfriend...", [{"input":"Hello friend", "response":"Hi there, how is it going today?"}, {"input":"Well, not that good...", "response":"Oh? What happened?"}])
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>', gpu: true)
# Returns a json object.
client.chatbot("I just broke up with my girlfriend...", [{"input":"Hello friend", "response":"Hi there, how is it going today?"}, {"input":"Well, not that good...", "response":"Oh? What happened?"}])
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model>", "<token>", true, "")
  exchange1 := nlpcloud.Exchange{Input:"Hello friend", Response:"Hi there, how is it going today?"}
  exchange2 := nlpcloud.Exchange{Input:"Well, not that good...", Response:"Oh? What happened?"}
  // Returns a Chatbot struct.
  chatbot, err := client.Chatbot(nlpcloud.ChatbotParams{
    Input: "I just broke up with my girlfriend...",
    History: *[]nlpcloud.Exchange{exchange1, exchange2}
  })
  ...
}

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', 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.chatbot("I just broke up with my girlfriend...", [{"input":"Hello friend", "response":"Hi there, how is it going today?"}, {"input":"Well, not that good...", "response":"Oh? What happened?"}])
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>', True);
# Returns a json object.
$client.chatbot("I just broke up with my girlfriend...", [["input"=>"input 1","response"=>"response 1"], ["input"=>"input 2","response"=>"response 2"], ...])
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "response": "I'm really sorry to hear that...",
  "history": [{"input":"Hello friend", "response":"Hi there, how is it going today?"}, {"input":"Well, not that good...", "response":"Oh? What happened?"}, {"input":"I just broke up with my girlfriend...", "response":"I'm really sorry to hear that..."}]
}

Test it on the playground.

This endpoint uses Fast GPT-J or Finetuned GPT-NeoX 20B, for conversational AI, which is perfect for chatbots. The model takes your input together with a conversation history, and returns an answer based on that. You can also use your own custom model.

Fast GPT-J does not have any memory, so you need to always send the conversation history together with your request so the model remembers what you talked about earlier. For more details, see our blog article about chatbots.

For conversational AI in non-English languages, please use our multilingual add-on.

If you want more control over your Fast GPT-J chatbot, you should use the text generation endpoint together with few shot learning. And for the best results, you should fine-tune your own model.

You can use the following models:

HTTP Request

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

POST Values

These values must be encoded as JSON.

Key Type Description
input string The thing you want to say to the chatbot. 2048 tokens maximum.
history array of objects The history of your previous exchanges with the chatbot. The order of the array is important: the last elements in the array should be the more recent discussions with the model. Each exchange is made of a an input (string) and a response (string). Optional. 2048 tokens maximum.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
response string The response from the chatbot.
history array of objects The history of all your exchanges with the chatbot, including the current response. Each exchange is made of a an input (string) and a response (string). Optional. 2048 tokens maximum.

Classification

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/classification" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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 (
    "net/http"

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

func newTrue() *bool {
    b := true
    return &b
}

func main() {
    client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
    // Returns a Classification struct.
    classes, err := client.Classification(nlpcloud.ClassificationParams{
      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: []string{"job", "nature", "space"},
      MultiClass: newTrue(),
    })
    ...
}
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.",
  ["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-yahoo-answers for the example):

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

Test it on the playground.

This endpoint uses Joe Davison's Bart Large MNLI Yahoo Answers, Joe Davison's XLM Roberta Large XNLI, Fast GPT-J, or Finetuned GPT-NeoX 20B, to perform classification on a piece of text, in many languages. You can also use your own custom model (replace <model_name> with the ID of your model in the URL).

Bart Large MNLI Yahoo Answers and XLM Roberta Large XNLI force you to propose some candidate labels, and then the model picks the label that is the most likely to apply to your piece of text. Fast GPT-J works differently: you can either propose a list of labels, or don't send any label at all. If you don't send any label to Fast GPT-J, the model will try to categorize your piece of text from scratch. Fast GPT-J gives the best results but it is slower.

For classification in non-English languages, if XLM Roberta Large XNLI is not satisfactory, please use our multilingual add-on.

Here are the 3 models you can use:

Pass your text along with a list of labels. The model will return a score for each label. The higher the score, the more likely the text is related to this label. If you're using a GPT model, no meaningful score will be returned.

You also need to say if you want more than one label to apply to your text, by passing the multi_class boolean. If you're using Fast GPT-J, this parameter will be ignored.

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. 2,500 tokens maximum for Bart Large MNLI Yahoo Answers and XLM Roberta Large XNLI. 1024 tokens maximum for Fast GPT-J.
labels array A list of labels you want to use to classify your text. 25 labels maximum (if you have more labels, you should make separate requests). Optional if you're using Fast GPT-J.
multi_class boolean Whether multiple labels should be applied to your text, meaning that the model will calculate an independent score for each label. Defaults to true. Ignored if you're using Fast GPT-J.

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. Not meaningful if you're using Fast GPT-J.

Dependencies

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/dependencies" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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 (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a Dependencies struct.
  dependencies, err := client.Dependencies(nlpcloud.DependenciesParams{
    Text: "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), or Megagon Lab's Ginza model for Japanese, 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.

If you are using Megagon Lab's Ginza model for Japanese, see the documentation here.

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

Each spaCy or Ginza 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. 350 tokens 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)

Embeddings

Input:

curl "https://api.nlpcloud.io/v1/<model name>/embeddings" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST \
  -d '{"sentences":["John Does works for Google.","Janette Doe works for Microsoft.","Janie Does works for NLP Cloud."]}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns json object.
client.embeddings(["John Does works for Google.","Janette Doe works for Microsoft.","Janie Does works for NLP Cloud."])
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.embeddings(["John Does works for Google.","Janette Doe works for Microsoft.","Janie Does works for NLP Cloud."])
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns an Embeddings struct.
  embeddings, err := client.Embeddings(nlpcloud.EmbeddingsParams{
    Sentences: []string{"John Does works for Google.","Janette Doe works for Microsoft.","Janie Does works for NLP Cloud."},
  })
  ...
}
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.embeddings(["<Text 1>", "<Text 2>", "<Tex 3>", ...])
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->embeddings(["<Text 1>", "<Text 2>", "<Text 3>", ...]);
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "embeddings": [
    [0.0927242711186409,-0.19866740703582764,...,-0.013638739474117756,],
    [0.03159608319401741,0.021390020847320557,...,0.1698218137025833],
    ...
    [0.027558118104934692,0.06297887861728668,...,0.09421529620885849]
  ]
}

This endpoint calculates word embeddings from several pieces of texts in many languages.

The endpoint returns a list of vectors. Each vector is a list of floats. See below for the details.

HTTP Request

POST https://api.nlpcloud.io/v1/paraphrase-multilingual-mpnet-base-v2/embeddings

POST Values

These values must be encoded as JSON.

Parameter Type Description
sentences array of strings The pieces of text you want to analyze. The array can contain 50 elements maximum. Each element should contain 128 tokens maximum.

Output

This endpoint returns an embeddings object containing an array of vectors. Each vector is an array of floats:

Key Type Description
embeddings array of array of floats The list of calculated embeddings.

Entities

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/entities" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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 (
    "net/http"

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

func main() {
    client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
    // Returns an Entities struct.
    entities, err := client.Entities(nlpcloud.EntitiesParams{
      Text: "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"
    }
  ]
}

Test it on the playground.

This endpoint uses any spaCy model to perform Named Entity Recognition (NER), in many languages, or Megagon Lab's Ginza model for Japanese, or the Fast GPT-J model and the Finetuned GPT-NeoX 20B model for advanced entity extraction. It can be a pre-trained model or your own custom model.

If you are using spaCy, give a block of text to the model and it will try to extract entities from it like persons, organizations, countries... See the spaCy named entity recognition documentation for more details.

If you are using Fast GPT-J, give a block of text + the entity your are looking for in this text (persons, positions, restaurants, ...) and let the model return the corresponding values if they exist.

If you are using Megagon Lab's Ginza model for Japanese, see the documentation here.

Here are all the models you can use:

Each spaCy or Ginza pre-trained model has a fixed 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. 350 tokens maximum.
searched_entity string Only applies to Fast GPT-J, so it will be ignored if you're using spaCy. This is the entity you are looking for. You can use anything, like positions, countries, programming languages, frameworks, restaurant names... If you use a singular you will be more likely to get one single result, while if you use a plural the model will try to extract several entities from the text.

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, position, company, 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

Generation

Input:

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

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

client = NLPCloud::Client.new('<model_name>','<token>', gpu: true)
# Returns a json object.
client.generation('GPT is a powerful NLP model', max_length: 50)
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", true, "")
  // Returns a Generation struct.
  generatedText, err := client.Generation(nlpcloud.GenerationParams{
    Text: "GPT is a powerful NLP model",
    MaxLength: 50,
  })
  ...
}
const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', 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.generation('GPT is a powerful NLP model', null, 50)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>', True);
# Returns a json object.
$client->generation("GPT-J is a powerful NLP model", NULL, 50);
// 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 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.",
  "nb_generated_tokens": 33,
  "nb_input_tokens": 7
}

Test it on the playground.

This endpoint uses either EleutherAI GPT-J, EleutherAI GPT-NeoX 20B, Fast GPT-J (an optimized version of GPT-J that is faster and can address bigger texts), or Finetuned GPT-NeoX 20B (a more powerful version of GPT-NeoX 20B), to generate a block of text (these models are GPT-3 equivalents). Start a sentence and let the model generate the rest for you. It can also use your own custom model (replace <model> with the ID of your model in the URL).

Text generation is for advanced users. Many parameters are available and it works better with few-shot learning. Many other API endpoints (paraphrasing, intent classification, etc.) also use text generation under the hood but are simpler to use as optimal parameters are already pre-set and you don't need to use few-shot learning.

For text generation in non-English languages, please use our multilingual add-on.

These following models are available:

You can achieve almost any NLP use case with a great accuracy thanks to the so called "few-shot learning" technique:

For advanced text generation tuning, you can play with many parameters like top p, temperature, num_beams, 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, you can also read this very good article from Hugging Face (it's a bit technical though).

You can also train/fine-tune your own GPT-J model if few-shot learning is not meeting your expectations.

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. 256 tokens maximum for GPT-J on CPU, 1024 tokens maximum for GPT-J and GPT-NeoX 20B on GPU, and 2048 tokens maximum for Fast GPT-J and Finetuned GPT-NeoX 20B on GPU.
min_length int Optional. The minimum number of tokens that the generated text should contain. 256 tokens maximum for GPT-J on CPU, 1024 tokens maximum for GPT-J and GPT-NeoX 20B on GPU, and 2048 tokens maximum for Fast GPT-J and Finetuned GPT-NeoX 20B on GPU.. 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. 256 tokens maximum for GPT-J on CPU, 1024 tokens maximum for GPT-J and GPT-NeoX 20B on GPU, and 2048 tokens maximum for Fast GPT-J and Finetuned GPT-NeoX 20B on GPU. 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_end_sequence bool Optional. Whether you want to remove the end_sequence string from the result. Defaults to false.
remove_input bool Optional. Whether you want to remove the input text from the result. Defaults to false.
bad_words list of strings Optional. List of tokens that are not allowed to be generated. Defaults to null.
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. The higher this value, the less deterministic the result will be. It's recommended to play with top_p if you want to produce original content for applications that require accurate results, while you should use temperature if you want to generate more funny results. You should not use both at the same time. Should be between 0 and 1. Defaults to 1.0.
temperature float Optional. The value used to module the next token probabilities. The higher this value, the less deterministic the result will be. For example if temperature=0 the output will always be the same, while if temperature=1 each new request will produce very different results. It's recommended to play with top_p if you want to produce original content for applications that require accurate results, while you should use temperature if you want to generate more funny results. You should not use both at the same time. Should be between 0 and 1. Defaults to 0.8.
repetition_penalty float Optional. Prevents the same word from being repeated too many times. 1.0 means no penalty. Defaults to 1.0. Not supported yet by Fast GPT-J and GPT-NeoX.
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. Not supported yet by Fast GPT-J and GPT-NeoX.
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. If num_beams > 1, the size of the input text should not exceed 40 tokens on GPU (please contact us if you need a bigger input length with num_beams > 1). Defaults to 1. Not supported yet by Fast GPT-J and GPT-NeoX.
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. Not supported yet by Fast GPT-J and GPT-NeoX.
no_repeat_ngram_size int Optional. If set to int > 0, all ngrams of that size can only occur once. Defaults to 0. Not supported yet by Fast GPT-J and GPT-NeoX.
num_return_sequences int Optional. The number of independently computed returned sequences. Defaults to 1.
top_k int Optional. The number of highest probability vocabulary tokens to keep for top-k-filtering. Maximum 1000 tokens. The lower this value, the less likely GPT-J is going to generate off-topic text. Defaults to 50.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
generated_text string The generated text
nb_generated_tokens int The number of tokens generated by the model
nb_input_tokens int The number of tokens sent to the model

Grammar and Spelling Correction

Input:

curl "https://api.nlpcloud.io/v1/gpu/<model>/gs-correction" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{"text":"Two month after the United States begun what has become a troubled rollout of a national COVID vaccination campaign, the effort is finaly gathering real steam."}'
import nlpcloud

client = nlpcloud.Client("<model>", "<token>", True)
# Returns a json object.
client.gs_correction("Two month after the United States begun what has become a troubled rollout of a national COVID vaccination campaign, the effort is finaly gathering real steam.")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>', true)
# Returns a json object.
client.gsCorrection('Two month after the United States begun what has become a troubled rollout of a national COVID vaccination campaign, the effort is finaly gathering real steam.')
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model>", "<token>", true, "")
  // Returns a GSCorrection struct.
  gSCorrection, err := client.GSCorrection(nlpcloud.GSCorrectionParams{
    Text: "Two month after the United States begun what has become a troubled rollout of a national COVID vaccination campaign, the effort is finaly gathering real steam.",
  })
  ...
}

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', 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.gsCorrection('Two month after the United States begun what has become a troubled rollout of a national COVID vaccination campaign, the effort is finaly gathering real steam.')
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>', True);
# Returns a json object.
$client->gsCorrection("Two month after the United States begun what has become a troubled rollout of a national COVID vaccination campaign, the effort is finaly gathering real steam.");
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "correction": "Two months after the United States began what has become a troubled rollout of a national COVID vaccination campaign, the effort is finally gathering real steam."
}

Test it on the playground.

This endpoint uses GPT-J, Fast GPT-J or Finetuned GPT-NeoX 20B, for grammar and spelling correction. The model takes your input and returns the same thing, but without any mistake. You can also use your own custom model.

For keywords and keyphrases extraction in non-English languages, please use our multilingual add-on.

If you want more control over the keywords and keyphrases extraction, you should use the text generation endpoint together with few shot learning. And for the best results, you should fine-tune your own model.

You can use the following models:

HTTP Request

POST https://api.nlpcloud.io/v1/gpu/<model_name>/gs-correction

POST Values

These values must be encoded as JSON.

Key Type Description
text string The text you want to correct. 250 tokens maximum.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
correction string The corrected text.

Intent Classification

Input:

curl "https://api.nlpcloud.io/v1/gpu/fast-gpt-j/intent-classification" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{"text":"Hello\nI spent some time on your documentation but I could not figure how to add a new credit card.\nIt is a problem because my current card is going to expire soon and I am affraid that it will cause a service disruption.\nHow can I update my credit card?\nThanks in advance,\nLooking forward to hearing from you,\nJohn Doe"}'
import nlpcloud

client = nlpcloud.Client("fast-gpt-j", "<token>", True)
# Returns a json object.
client.intent_classification("Hello\nI spent some time on your documentation but I could not figure how to add a new credit card.\nIt is a problem because my current card is going to expire soon and I am affraid that it will cause a service disruption.\nHow can I update my credit card?\nThanks in advance,\nLooking forward to hearing from you,\nJohn Doe")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>', true)
# Returns a json object.
client.intentClassification('Hello\nI spent some time on your documentation but I could not figure how to add a new credit card.\nIt is a problem because my current card is going to expire soon and I am affraid that it will cause a service disruption.\nHow can I update my credit card?\nThanks in advance,\nLooking forward to hearing from you,\nJohn Doe')
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "fast-gpt-j", "<token>", true, "")
  // Returns an IntentClassification struct.
  intentClassification, err := client.IntentClassification(nlpcloud.IntentClassificationParams{
    Text: "Hello\nI spent some time on your documentation but I could not figure how to add a new credit card.\nIt is a problem because my current card is going to expire soon and I am affraid that it will cause a service disruption.\nHow can I update my credit card?\nThanks in advance,\nLooking forward to hearing from you,\nJohn Doe",
  })
  ...
}

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', 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.intentClassification('Hello\nI spent some time on your documentation but I could not figure how to add a new credit card.\nIt is a problem because my current card is going to expire soon and I am affraid that it will cause a service disruption.\nHow can I update my credit card?\nThanks in advance,\nLooking forward to hearing from you,\nJohn Doe')
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>', True);
# Returns a json object.
$client->intentClassification("Hello\nI spent some time on your documentation but I could not figure how to add a new credit card.\nIt is a problem because my current card is going to expire soon and I am affraid that it will cause a service disruption.\nHow can I update my credit card?\nThanks in advance,\nLooking forward to hearing from you,\nJohn Doe");
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "intent": "update credit card"
}

Test it on the playground.

This endpoint uses Fast GPT-J for intent classification. The model tries to detect which is the intent hidden in the text. You can also use your own custom model.

For product intent classification in non-English languages, please use our multilingual add-on.

If you want more control over the intent classification, you should use the text generation endpoint together with few shot learning. And for the best results, you should fine-tune your own model.

HTTP Request

POST https://api.nlpcloud.io/v1/gpu/fast-gpt-j/intent-classification

POST Values

These values must be encoded as JSON.

Key Type Description
text string The text you want to detect intent from. 1024 tokens maximum.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
intent string The main intent hidden in your text.

Keywords and Keyphrases Extraction

Input:

curl "https://api.nlpcloud.io/v1/gpu/<model>/kw-kp-extraction" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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."}'
import nlpcloud

client = nlpcloud.Client("<model>", "<token>", True)
# Returns a json object.
client.kw_kp_extraction("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.")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>', true)
# Returns a json object.
client.kwKpExtraction('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.')
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model>", "<token>", true, "")
  // Returns an KwKpExtraction struct.
  kwKpExtraction, err := client.KwKpExtraction(nlpcloud.KwKpExtractionParams{
    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.",
  })
  ...
}

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', 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.kwKpExtraction('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.')
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

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

Output:

{
  "keywords_and_keyphrases": ["COVID","vaccination","United States"]
}

Test it on the playground.

This endpoint uses Fast GPT-J or Finetuned GPT-NeoX 20B, for keywords and keyphrases extraction. The model extracts the main ideas from your text. These ideas can be keywords or a couple of keywords (also known as keyphrase). You can also use your own custom model.

For keywords and keyphrases extraction in non-English languages, please use our multilingual add-on.

If you want more control over the keywords and keyphrases extraction, you should use the text generation endpoint together with few shot learning. And for the best results, you should fine-tune your own model.

You can use the following models:

HTTP Request

POST https://api.nlpcloud.io/v1/gpu/<model_name>/kw-kp-extraction

POST Values

These values must be encoded as JSON.

Key Type Description
text string The text you want to extract keywords and keyphrases from. 1024 tokens maximum.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
keywords_and_keyphrases string The main keywords and keyphrases in your text.

Language Detection

Input:

curl "https://api.nlpcloud.io/v1/python-langdetect/langdetection" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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.")
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a LangDetection object.
  languages, err := client.LangDetection(nlpcloud.LangDetectionParams{
    Text: `John Doe has been working for Microsoft in Seattle since 1999. 
      Et il parle aussi un peu français.`,
  })
  ...
}
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
    }
  ]
}

Test it on the playground.

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. 25,000 tokens 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

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()
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a LibVersions struct.
  versions, err := 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-yahoo-answers for the example):
{
  "pytorch": "1.11.1",
  "transformers": "4.19.2"
}

HTTP Request

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

Noun Chunks

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/nouns-chunks" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST \
  -d '{"text":"John Doe has been working for the Microsoft company in Seattle since 1999."}'
# Not implemented yet.
# Not implemented yet.
// Not implemented yet.
// Not implemented yet.
// Not implemented yet.
// 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):

{
  "noun_chunks":[
    {
      "text":"John Doe",
      "root_text":"Doe",
      "root_dep":"nsubj",
      "root_head_text":"working"
    },
    {
      "text":"the Microsoft company",
      "root_text":"company",
      "root_dep":"pobj",
      "root_head_text":"for"
    },
    {
      "text":"Seattle",
      "root_text":"Seattle",
      "root_dep":"pobj",
      "root_head_text":"in"
      }
  ]
}

This endpoint uses a spaCy model (it can be either a spaCy pre-trained model or your own spaCy custom model), or Megagon Lab's Ginza model for Japanese, to extract noun chunks from a piece of text, in many languages.

See the spaCy noun chunks documentation for more details.

If you are using Megagon Lab's Ginza model for Japanese, see the documentation here.

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

It returns a list of noun chunks. Each noun chunk is an object made up of several elements. See below for the details.

HTTP Request

POST https://api.nlpcloud.io/v1/<model_name>/noun-chunks

POST Values

These values must be encoded as JSON.

Parameter Type Description
text string The sentence containing the noun chunks to extract. 350 tokens maximum.

Output

This endpoint returns a noun_chunks object containing an array of noun chunk objects. Each noun chunk object contains the following:

Key Type Description
text string The content of the extracted noun chunk
root_text string The original text of the word connecting the noun chunk to the rest of the parse
root_dep string Dependency relation connecting the root to its head
root_head_text string The text of the root token’s head

Paraphrasing And Rewriting

Input:

curl "https://api.nlpcloud.io/v1/gpu/<model>/paraphrasing" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{"text":"Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language context."}'
import nlpcloud

client = nlpcloud.Client("<model>", "<token>", True)
# Returns a json object.
client.paraphrasing("""Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language context.""")
require 'nlpcloud'

client = NLPCloud::Client.new('<model>','<token>', true)
# Returns a json object.
client.paraphrasing("Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language context.")
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model>", "<token>", true, "")
  // Returns a Paraphrasing struct.
  summary, err := client.Paraphrasing(nlpcloud.ParaphrasingParams{
    Text: `Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language context.`,
  })
  ...
}

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model>','<token>', 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.paraphrasing(`Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language context.`)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model>','<token>', True);
# Returns a json object.
$client->paraphrasing("Language has historically been difficult for computers to ‘understand’. Sure, computers can collect, store, and read text inputs but they lack basic language context.");
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "paraphrased_text": "Language is difficult for computers to understand. Computers can read texts but they can’t interpret context."
}

Test it on the playground.

This endpoint uses GPT-J, Fast GPT-J or Finetuned GPT-NeoX 20B, for text paraphrasing and rewriting. The model rephrases your original text so the words are different but the meaning remains the same. You can also use your own custom model.

For paraphrasing in non-English languages, please use our multilingual add-on.

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

If you want more control over the paraphrasing, you should use the text generation endpoint together with few shot learning. And for the best results, you should fine-tune your own model.

You can use the following models:

HTTP Request

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

POST Values

These values must be encoded as JSON.

Key Type Description
text string The sentences you want to paraphrase. 250 tokens maximum.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
paraphrased_text string The paraphrase of your text

Product Description and Ad Generation

Input:

curl "https://api.nlpcloud.io/v1/gpu/<model>/ad-generation" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{"keywords":["gpu","gaming","$1299"]}'
import nlpcloud

client = nlpcloud.Client("<model>", "<token>", True)
# Returns a json object.
client.ad_generation(["gpu","gaming","$1299"])
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>', gpu: true)
# Returns a json object.
client.ad_generation(["gpu","gaming","$1299"])
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model>", "<token>", true, "")
  // Returns an AdGeneration struct.
  adGeneration, err := client.AdGeneration(nlpcloud.AdGenerationParams{
    Keywords: []string{"gpu","gaming","$1299"},
  })
  ...
}

const NLPCloudClient = require('nlpcloud');

const client = new NLPCloudClient('<model_name>','<token>', 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.adGeneration(["gpu","gaming","$1299"])
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>', True);
# Returns a json object.
$client.adGeneration(["gpu","gaming","$1299"])
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "generated_text": "Powerful gaming GPU for $1299 only."
}

Test it on the playground.

This endpoint uses GPT-J, Fast GPT-J or Finetuned GPT-NeoX 20B, for product description generation and ad generation. The model takes a list of keywords and creates a nice description out of them. You can also use your own custom model.

For product description generation and ad generation in non-English languages, please use our multilingual add-on.

If you want more control over the product description generation and ad generation, you should use the text generation endpoint together with few shot learning. And for the best results, you should fine-tune your own model.

You can use the following models:

HTTP Request

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

POST Values

These values must be encoded as JSON.

Key Type Description
keywords array of strings The keywords you want to see included in your in your final text. 10 items maximum.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
generated_text string The generated text containing your keywords

Question Answering

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/question" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{
    "question":"When can plans be stopped?",
    "context":"All NLP Cloud plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice."
  }'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns a json object.
client.question("When can plans be stopped?",
"""All NLP Cloud plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.""")
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.question("When can plans be stopped?",
"All NLP Cloud plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.")
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a Question struct.
  answer, err := client.Question(nlpcloud.QuestionParams{
    Question: "When can plans be stopped?",
    Context: `All NLP Cloud plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.`,
  })
  ...
}
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.question(`When can plans be stopped?`,
`All NLP Cloud plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.`)
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->question("When can plans be stopped?",
"All NLP Cloud plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.");
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

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

Test it on the playground.

This endpoint uses Deepset's Roberta Base Squad 2, Fast GPT-J, or Finetuned GPT-NeoX 20B, to answer questions about anything. As an option, you can give a context and ask a specific question about it. You can also use your own custom model (replace <model_name> with the ID of your model in the URL).

For question answering in non-English languages, please use our multilingual add-on.

Here are the 2 models you can use:

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/<model_name>/question

POST Values

These values must be encoded as JSON.

Key Type Description
question string The question you want to ask
context string The block of text that the model will use in order to find an answer to your question. 25,000 tokens maximum for Roberta Base Squad 2. 1024 tokens maximum for Fast GPT-J. Optional if you're using Fast GPT-J.

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. Not meaningful if you're using Fast GPT-J.
start integer Position of the starting character of the response in your context. Not meaningful if you're using Fast GPT-J.
end integer Position of the ending character of the response in your context. Not meaningful if you're using Fast GPT-J.

Sentence Dependencies

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/sentence-dependencies" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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 (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a SentenceDependencies struct.
  sentenceDependencies, err := client.SentenceDependencies(nlpcloud.SentenceDependenciesParams{
    Text: "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), or Megagon Lab's Ginza model for Japanese, 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.

If you are using Megagon Lab's Ginza model for Japanese, see the documentation here.

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

Each spaCy and Ginza 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. 350 tokens 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)

Sentiment Analysis

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/sentiment" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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 (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a Sentiment struct.
  sentiment, err := client.Sentiment(nlpcloud.SentimentParams{
    Text: "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
    }
  ]
}

Test it on the playground.

This endpoint uses either Distilbert Base Uncased Finetuned SST 2 for sentiment analysis, or Distilbert Base Uncased Emotion for emotion analysis.

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

For sentiment and emotions analysis in non-English languages, please use our multilingual add-on.

It can also use your own 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 sentiment and emotion labels, 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 The sentiment or emotion detected (POSITIVE, NEGATIVE, sadness, joy, love, anger, fear, surprise)
score float The score applied to the label. It goes from 0 to 1. The higher the score, the more important the sentiment or emotion is.

Semantic Similarity

Input:

curl "https://api.nlpcloud.io/v1/paraphrase-multilingual-mpnet-base-v2/semantic-similarity" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST \
  -d '{"sentences":["John Does works for Google and he hates it.","John Does works for NLP Cloud and he love it."]}'
import nlpcloud

client = nlpcloud.Client("<model_name>", "<token>")
# Returns json object.
client.semantic_similarity(["John Does works for Google and he hates it.","John Does works for NLP Cloud and he love it."])
require 'nlpcloud'

client = NLPCloud::Client.new('<model_name>','<token>')
# Returns a json object.
client.semantic_similarity(["John Does works for Google and he hates it.","John Does works for NLP Cloud and he love it."])
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns SemanticSimilarity struct.
  semanticSimilarity, err := client.SemanticSimilarity(nlpcloud.SemanticSimilarityParams{
    Sentences: [2]string{"John Does works for Google and he hates it.","John Does works for NLP Cloud and he love it."},
  })
  ...
}
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.semanticSimilarity(["<Block of text 1>", "<Block of text 2>"])
require 'vendor/autoload.php';

use NLPCloud\NLPCloud;

$client = new \NLPCloud\NLPCloud('<model_name>','<token>');
# Returns a json object.
$client->semanticSimilarity(["<Block of text 1>", "<Block of text 2>"]);
// This is a client built by the community:
// https://github.com/DaveCS1/NLPCloud.io-Simple-CSharpSamples

Output:

{
  "score": 0.31693190336227417
}

This endpoint uses the Paraphrase Multilingual Mpnet Base V2 model, based on Sentence Transformers, to calculate the semantic similarity between 2 pieces of text, in more than 50 languages (see the full list here).

It returns a score. The higher the score, the more likely the 2 pieces of text have the same meaning.

HTTP Request

POST https://api.nlpcloud.io/v1/paraphrase-multilingual-mpnet-base-v2/semantic-similarity

POST Values

These values must be encoded as JSON.

Parameter Type Description
sentences array of strings The pieces of text you want to analyze. The array should contain exactly 2 elements. Each element should contain 128 tokens maximum.

Output

This endpoint returns a score that indicates whether the input pieces of text have the same meaning or not:

Key Type Description
score float The score that indicates whether the input texts have the same meaning or not. It goes from 0 to 1. The higher the score, the more likely the 2 pieces of text have the same meaning.

Summarization

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/summarization" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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 (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a Summarization struct.
  summary, err := client.Summarization(nlpcloud.SummarizationParams{
    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.`,
  })
  ...
}

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."
}

Test it on the playground.

This endpoint uses either Facebook's Bart Large CNN model, Google's Pegasus XSUM, Fast GPT-J, or Finetuned GPT-NeoX 20B, for text summarization in English, Michau's T5 Base EN Generate Headline for headline generation in English, or Bart Large CNN SamSum for dialogues summarization 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 custom model (replace <model_name> with the ID of your model in the URL).

For summarization in non-English languages, please use our multilingual add-on.

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

Here are the 4 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 for bart-large-cnn or bart-large-samsum or fast-gpt-j, 512 tokens maximum for pegasus-xsum, and 8192 tokens maximum for t5-base-en-generate-headline.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
summary_text string The summary of your text

Tokens

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/tokens" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -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.")
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a Tokens struct.
  tokens, err := client.Tokens(nlpcloud.TokensParams{
    Text: "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.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), or Megagon Lab's Ginza model for Japanese, to tokenize and lemmatize a passed in text, in many languages.

See the spaCy tokenization and lemmatization documentations for more details.

If you are using Megagon Lab's Ginza model for Japanese, see the documentation here.

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. 350 tokens 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

Translation

Input:

curl "https://api.nlpcloud.io/v1/<model_name>/translation" \
  -H "Authorization: Token <token>" \
  -H "Content-Type: application/json" \
  -X POST -d '{
    "text":"John Doe has been working for Microsoft in Seattle since 1999.",
    "source":"en",
    "target":"fr"
  }'
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.",'en','fr')
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.","en","fr")
import (
  "net/http"

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

func main() {
  client := nlpcloud.NewClient(&http.Client{}, "<model_name>", "<token>", false, "")
  // Returns a Translation struct.
  translatedText, err := client.Translation(nlpcloud.TranslationParams{
    Text: "John Doe has been working for Microsoft in Seattle since 1999.",
    Source: "en",
    Target: "fr",
  })
  ...
}
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.`,'en','fr')
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.","en","fr");
// 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."
}

Test it on the playground.

This endpoint uses Facebook's M2M100 1.2B and Helsinki NLP's Opus MT models to translate text in many languages thanks to deep learning. Pass your block of text, and the model will return a translation. M2M100 lets you select a source language and a target language so you can translate between 100 languages without using English at all! This endpoint can also use your own 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 (GPT-J, GPT-NeoX, Bart Large, etc.) in non-English languages. Just translate your text first before sending it to another model.

Here are all the pre-trained models you can use:

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. 250 tokens maximum.
source string The language of the input text. This parameter is for M2M100 only. Optional. If ignored, the model will try to automatically detect the input language, but if you know the language it is recommended to explicitly mention it.
target string The language of the translated text. This parameter is for M2M100 only.

Output

This endpoint returns a JSON object containing the following elements:

Key Type Description
translation_text string The translation of your text

Fine-tuning

This is possible to train/fine-tune your own models on NLP Cloud and have them available in production right away. This is the best way to get the most advanced results from NLP!

Subscribe to a fine-tuning plan and then go to the Fine-Tuning section in your dashboard:

Fine-tuning interface

First select the task you want to achieve, 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. Validation datasets should be made up of examples that are not in your training dataset, and the size of your validation dataset you be roughly 10% of your training dataset. As far as GPT-J is concerned, validation is a bit tricky, see our parapgraph below about GPT-J validation.

If you select "GPT-J for any task", we will fine-tune the GPT-J model for you. Otherwise, we will automatically select the best non-GPT-J model that has the best performance and accuracy results (BERT, DistilBERT, BART, RoBERTa...).

If you want to fine-tune a specific model, please contact us before starting the fine-tuning, and ideally please share a link to the model.

Datasets should be in text format for GPT-J and CSV format for other tasks (see below for more details about how to build your dataset). If your dataset is very heavy, you can compress it as a ZIP archive and upload your ZIP file. There is no limit regarding the size of the dataset.

You don't necessarily have to create the dataset by yourself since many great open-source datasets already exist. Maybe one of them is perfect for your use case? An advanced list of open-source datasets can be found on the Hugging Face website.

If you are unsure about which data you should use for your fine-tuning, please contact us so we can advise!

You can upload a new dataset, and start a new fine-tuning based on it, as many times as you want. When the new fine-tuning is finished, we replace your existing model with the new one. It causes a short downtime (around 10 minutes). If you don't want your previous model to be deleted, or if you want to avoid the short downtime, you should launch several fine-tunings in parallel.

Once the fine-tuning is finished and your model is deployed, you will be informed by email, and you will get a dedicated API URL for your new fine-tuned model.

GPT-J for any task

You can fine-tune GPT-J for text generation and any NLP task based on text generation (paraphrase, summarization, classification, sentiment analysis, chatbots, code generation, etc.).

Your dataset should be a text file (.txt) or a zip archive (.zip) containing a text file. It doesn't need to follow any specific formatting, except that you should add <|endoftext|> at the end of each example.

If you are coming from OpenAI, you can also use your GPT-3 dataset, as a CSV (.csv) or a JSONL (.jsonl) file (you can also zip the file if needed). This file should contain the 2 following columns or keys:

Each example should not exceed 2048 tokens.

The size of your dataset depends on your use case but good news is that fine-tuning GPT-J requires relatively few examples (compared to traditional NLP fine-tuning). Here are a couple of guidelines, depending on your use case (this is a minimum, if you can provide more examples, it's even better!):

If you are unsure about the format or the size of your dataset, please contact us so we can help!

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. And then combining fine-tuning and few-shot learning is the best way to get a GPT-J model perfectly tailored to your needs.

Here are examples of how you could format your dataset for various use cases (that's only suggestions of course). Basically you can apply the same technique that you would use during few-shot learning. Note that the trailing ### token is not compulsory. We recommend to add it at the end of all your examples so the model will add it to every response. Then you can conveniently use end_sequence="###" in your requests in production to make sure that the model does not generate more text than wanted. Most of the time, after a fine-tuning, GPT-J does not generate more text than necessary, but it still occasionnally happens, even when properly adding <|endoftext|> at the end of your examples, so thanks to this parameter you will be able to force GPT-J to stop the text generation once your answer is generated.

GPT-J Dataset for Short Story Generation

Let's say you want to teach GPT-J how to generate short stories about specific topics. You could build a dataset like the following (many more examples would be needed of course):

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

GPT-J Dataset for Sentiment Analysis

A fine-tuning dataset for sentiment analysis with GPT-J could look like this:

[Message]: Support has been terrible for 2 weeks...
[Sentiment]: Negative
###
<|endoftext|>
[Message]: I love your API, it is simple and so fast!
[Sentiment]: Positive
###
<|endoftext|>
[Message]: GPT-J has been released 2 months ago.
[Sentiment]: Neutral
###
<|endoftext|>

GPT-J Dataset for NER (Entity Extraction)

[Sentence]: My name is Julien and I work for NLP Cloud as a Chief Technical Officer.
[Position]: Chief Technical Officer
[Company]: NLP Cloud
###
<|endoftext|>
[Sentence]: Hi, I am a marketing assistant at Microsoft.
[Position]: marketing assistant
[Company]: Microsoft
###
<|endoftext|>
[Sentence]: John was the CEO of AquaFun until 2020.
[Position]: CEO
[Company]: AquaFun
###
<|endoftext|>

GPT-J Dataset for Text Classification

[Sentence]: I love skiing, rugby, and boxing. These are great for the body and the mind.
[Category]: Sport
###
<|endoftext|>
[Sentence]: In order to cook a pizza you need flour, tomatoes, ham, and cheese.
[Category]: Food
###
<|endoftext|>
[Sentence]: The Go programming language is a statically typed language, perfect for concurrent programming.
[Category]: Programming
###
<|endoftext|>

GPT-J Dataset for Question Answering

[Context]: NLP Cloud was founded in 2021 when the team realized there was no easy way to reliably leverage NLP in production.
[Question]: When was NLP Cloud founded?
[Answer]: 2021
###
<|endoftext|>
[Context]: NLP Cloud developed their API by mid-2020 and they added many pre-trained open-source models since then
[Question]: What did NLP Cloud develop?]
[Answer]: API
###
<|endoftext|>
[Context]: The main challenge with GPT-J is memory consumption. Using a GPU plan is recommended.
[Question]: Which plan is recommended for GPT-J?
[Answer]: a GPU plan
###
<|endoftext|>

GPT-J Dataset for Code Generation

[Question]: Fetch the companies that have less than five people in it.
[Answer]: SELECT COMPANY, COUNT(EMPLOYEE_ID) FROM Employee GROUP BY COMPANY HAVING COUNT(EMPLOYEE_ID) < 5;
###
<|endoftext|>
[Question]: Show all companies along with the number of employees in each department
[Answer]: SELECT COMPANY, COUNT(COMPANY) FROM Employee GROUP BY COMPANY;
###
<|endoftext|>
[Question]: Show the last record of the Employee table
[Answer]: SELECT * FROM Employee ORDER BY LAST_NAME DESC LIMIT 1;
###
<|endoftext|>

GPT-J Dataset for Paraphrasing

[Original]: Algeria recalled its ambassador to Paris on Saturday and closed its airspace to French military planes a day later after the French president made comments about the northern Africa country.
[Paraphrase]: Last Saturday, the Algerian government recalled its ambassador and stopped accepting French military airplanes in its airspace. It happened one day after the French president made comments about Algeria.
###
<|endoftext|>
[Original]: President Macron was quoted as saying the former French colony was ruled by a "political-military system" with an official history that was based not on truth, but on hatred of France.
[Paraphrase]: Emmanuel Macron said that the former colony was lying and angry at France. He also said that the country was ruled by a "political-military system".
###
<|endoftext|>
[Original]: The diplomatic spat came days after France cut the number of visas it issues for citizens of Algeria and other North African countries.
[Paraphrase]: Diplomatic issues started appearing when France decided to stop granting visas to Algerian people and other North African people.
###
<|endoftext|>

GPT-J Dataset for Chatbot / Conversational AI

The trick here is that a discussion should be split into several examples (one per AI response):

This is a discussion between a [human] and a [robot]. The [robot] is very nice and empathetic.

[human]: Hello nice to meet you.
[robot]: Nice to meet you too.
###
<|endoftext|>
This is a discussion between a [human] and a [robot]. The [robot] is very nice and empathetic.

[human]: Hello nice to meet you.
[robot]: Nice to meet you too.
###
[human]: How is it going today?
[robot]: Not so bad, thank you! How about you?
###
<|endoftext|>
This is a discussion between a [human] and a [robot]. The [robot] is very nice and empathetic.

[human]: Hello nice to meet you.
[robot]: Nice to meet you too.
###
[human]: How is it going today?
[robot]: Not so bad, thank you! How about you?
###
[human]: I am ok, but I am a bit sad...
[robot]: Oh? Why that?
###
<|endoftext|>

It can also be interesting to pass more knowledge to the AI you're creating by adding examples made up of "pure knowledge", like this:

[robot] is 34 years old and he was born in Grenoble, France.

GPT-J Dataset for Product and Ad Descriptions

[Keywords]: shoes, women, $59
[Sentence]: Beautiful shoes for women at the price of $59.
###
<|endoftext|>
[Keywords]: trousers, men, $69
[Sentence]: Modern trousers for men, for $69 only.
###
<|endoftext|>
[Keywords]: gloves, winter, $19
[Sentence]: Amazingly hot gloves for cold winters, at $19.
###
<|endoftext|>

GPT-J Dataset for Knowledge Feeding

You might want to simply pass new knowledge to the model, without necessarily fine-tuning for a specific task. For example you can feed the model with internal contracts, recent news, technical knowledge specific to your industry... It's very simple: simply give it pure text. For example here we want GPT-J to become a Go programming expert, so we feed it with Go-related knowledge.

Channels are the pipes that connect concurrent goroutines. You can send values into channels from one goroutine and receive those values into another goroutine.
<|endoftext|>
Send a value into a channel using the channel <- syntax. Here we send "ping" to the messages channel we made above, from a new goroutine.
<|endoftext|>
The <-channel syntax receives a value from the channel. Here we’ll receive the "ping" message we sent above and print it out.
<|endoftext|>

GPT-J Validation

Assessing the accuracy of text generation models like GPT-J is hard because these models are non-deterministic, meaning that for the same input you can get different outputs.

If you are training GPT-J for a use-case that produces undetermined results (blog post generation, summarization, paraphrasing, chatbots, product description and ad generation...), you should not upload a validation dataset as the results would not mean anything. For such use cases, the best solution would be for you to manually run a batch of examples on your fine-tuned models, once they are deployed. If you want to make your models comparison easier, you could set a low top p or a low temperature during your tests, as they make results much more deterministic.

If you are training GPT-J for a use-case that does produce deterministic results (text classification, entity extraction, keywords/keyphrases extraction, question answering, intent classification, code generation...), you can upload a validation dataset that will help you automatically assess the quality of your new fine-tuned model.

Text Classification

You can fine-tune a model for text classification. We will automatically select the best model for you (BERT, DistilBERT, BART, RoBERTa...). If you want to fine-tune a specific model, please contact us before starting the fine-tuning to let us know.

Your comma-separated CSV dataset should contain 2 columns:

Each row is a new example you want to teach the model. For example, you could build a dataset like the following (many more examples would be needed of course):

text class
I love skiing, rugby, and boxing. These are great for the body and the mind. sport
In order to cook a pizza you need flour, tomatoes, ham, and cheese. food
The Go programming language is a statically typed language, perfect for concurrent programming. programming

Size of your dataset: we recommend at least 150 examples per class. For example, if you want to create 6 categories, you will need at least 900 examples.

Summarization

You can fine-tune a model for text summarization. We will automatically select the best model for you (BERT, DistilBERT, BART, RoBERTa...). If you want to fine-tune a specific model, please contact us before starting the fine-tuning to let us know.

Your comma-separated CSV dataset should contain 2 columns:

Each row is a new example you want to teach the model. For example, you could build a dataset like the following (many more examples would be needed of course):

text summary
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. 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.
The community is large enough that, instead of assuming everyone knows what is expected of them, our Code of Conduct serves as an agreement, setting explicit expectations for our behavior in both online and offline interactions. If we don’t live up to the agreement, people can point that out and we can correct our behavior. In this post we want to provide two updates: first, an update about how we approach enforcement of the Code of Conduct, and second, an update to the Gopher Values themselves. We want everyone to feel welcome here. What happens when members of our community make others feel unwelcome? Those behaviors can be reported to the Project Steward, who works with a committee from Google’s Open Source Programs Office to determine what to do about each report. Since the May 2018 revision to the Code of Conduct, community members have submitted more than 300 conduct reports, an average between one and two a week. A typical outcome is to meet with the person whose conduct was reported and help them understand how to take responsibility for and correct their actions moving forward. The Gopher community is large enough that people can point out bad behavior. Since the May 2018 revision to the Code of Conduct, community members have submitted more than 300 conduct reports. A typical outcome is to meet with the person whose conduct was reported and help them understand how to take responsibility for and correct their actions.

Size of your dataset: we recommend at least 800 examples.

Question Answering

You can fine-tune a model for question answering. We will automatically select the best model for you (BERT, DistilBERT, BART, RoBERTa...). If you want to fine-tune a specific model, please contact us before starting the fine-tuning to let us know.

Your comma-separated CSV dataset should contain 4 columns:

Each row is a new example you want to teach the model. For example, you could build a dataset like the following (many more examples would be needed of course):

context question answer answer_start_index
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? Emmanuel Macron 17
John would really like to work for Google but he is not sure which position would suit him best... Where would John like to work? Google 35

Size of your dataset: we recommend at least 800 examples.

Sentiment Analysis

You can fine-tune a model for sentiment analysis. We will automatically select the best model for you (BERT, DistilBERT, BART, RoBERTa...). If you want to fine-tune a specific model, please contact us before starting the fine-tuning to let us know.

Your comma-separated CSV dataset should contain 2 columns:

Each row is a new example you want to teach the model. For example, you could build a dataset like the following (many more examples would be needed of course):

text sentiment
I just love this movie! positive
I hate this guy... negative
NLP sometimes looks like magic! positive

Size of your dataset: we recommend at least 500 examples.

Sensitive Applications

No data sent to our API is stored on our servers, but sometimes this is not enough.

Here are 3 advanced solutions we propose for sensitive applications.

Specific Region

For legal reasons you might want to make sure that the data you send is processed in a specific region of the world. It can be a specific continent (e.g. North America, Europe, Asia,...), or a specific country (e.g. US, France, Germany, ...).

If that is the case, please contact us at [email protected].

Specific Cloud Provider

You might want to avoid a specific cloud provider, or proactively choose a cloud provider (eg. AWS, GCP, OVH, Scaleway...).

If that is the case, please contact us at [email protected].

On-Premise

If you cannot afford to send any data to NLP Cloud for confidentiality reasons (e.g. medical applications, financial applications...) you can deploy our models on your own in-house infrastructure.

If that is the case, please contact us at [email protected].

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.

The default concurrency on all our pre-trained AI models is 2, which means that you can make 2 requests at the same time. If you need to increase concurrency, please contact support!

If you reach these limits, the API will return a 429 HTTP error.

Teams

In your dashboard, you can create an organization and invite team members to join the organization.

Each team member is given a role: Admin, Manager, or Reader. Depending on his role, a user has access to different features:

Role API Start a Fine-tuning Install a Custom Model Plans
Admin Yes Yes Yes Yes
Manager Yes Yes Yes No
Reader Yes No No No

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.
422 Unprocessable Entity -- Your request is not properly formatted. Happens for example if your JSON payload is not correctly formatted, or if you omit the "Content-Type: application/json" header.
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.
502 Bad Gateway -- Sorry, our reverse proxy was not able to contact the model you're requesting. Please try again later.
503 Service Unavailable -- Sorry, the model you are requesting had a temporary issue. Please try again later. The error is returned together with a Retry-After header, mentioning the number of seconds you should wait before trying again.
504 Gateway Timeout -- Sorry, the model you are requesting is temporarily overloaded. Please try again later.

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