Advanced Artificial Intelligence API

Text understanding/generation (NLP),

ready for production, at a fair price.
Fine-tune and deploy your own AI models.
No DevOps required.

Want to easily play with AI without using the API?
Have a look at our brand new NLP Cloud playground!

High Performance

Fast and accurate AI models suited for production. Highly-available inference API leveraging the most advanced NVIDIA GPUs.

Pre-trained AI Models

We selected the best open-source natural language processing (NLP) models from the community and deployed them for you

Custom Models

Fine-tune your own models - including GPT-J - or upload your in-house custom models, and deploy them easily to production

Data Privacy

No data is stored on our servers. Sensitive applications can be deployed on a specific cloud or region, or even on-premise.

No Complexity

Do not worry about DevOps or API programming and focus on text processing only. Deliver your AI project in no time.

Clean API

Simple and well-documented API with multiple client libraries that make integration a breeze

NLP Cloud is an NVIDIA partner

NLP Cloud is a member of NVIDIA Inception Program

curl https://api.nlpcloud.io/v1/en_core_web_lg/entities \ > -X POST -d '{"text":"John Doe is a Go Developer at Google"}' ^2000 `[ { "end": 8, "start": 0, "text": "John Doe", "type": "PERSON" }, { "end": 25, "start": 13, "text": "Go Developer", "type": "POSITION" }, { "end": 35, "start": 30, "text": "Google", "type": "ORG" }, ] [email protected]local:~$` ^3000

curl https://api.nlpcloud.io/v1/bart-large-mnli/classification \ > -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 }' ^2000 `{ "labels":["job", "space", "nature"], "scores":[0.9258800745010376, 0.1938474327325821, 0.010988450609147549] } [email protected]:~$` ^3000

curl https://api.nlpcloud.io/v1/roberta-base-squad2/question \ > -X POST -d '{ "context":"French president Emmanuel Macron said the country was at war with an invisible, elusive enemy, and the measures were unprecedented, but circumstances demanded them.", "question":"Who is the French president?" }' ^2000 `{ "answer":"Emmanuel Macron", "score":0.9595934152603149, "start":17, "end":32 } [email protected]:~$` ^3000

curl https://api.nlpcloud.io/v1/distilbert-finetuned-sst-2-english/sentiment \ > -X POST -d '{"context":"NLP Cloud proposes an amazing service!"}' ^2000 `{ "scored_labels":[ { "label":"POSITIVE", "score":0.9996881484985352 } ] } [email protected]:~$` ^3000

curl https://api.nlpcloud.io/v1/bart-large-cnn/summarization \ > -X POST -d '{"text":"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."}' ^2000 `{ "summary_text":"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world." } [email protected]:~$` ^3000

curl https://api.nlpcloud.io/v1/gpt-j/generation \ > -X POST -d '{ "text":"GPT-J is a powerful NLP model", "min_length":10, "max_length":30 }' ^2000 `{ "generated_text":"GPTJ 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." } [email protected]:~$` ^3000

curl https://api.nlpcloud.io/v1/opus-mt-en-fr/translation \ > -X POST -d '{"text":"John Doe has been working for Microsoft in Seattle since 1999."}' ^2000 `{ "translation_text": "John Doe travaille pour Microsoft à Seattle depuis 1999." } [email protected]:~$` ^3000

curl https://api.nlpcloud.io/v1/python-langdetect/langdetection \ > -X POST -d '{"text":"John Doe has been working for Microsoft in Seattle since 1999. Il parle aussi un peu français."}' ^2000 `{ "languages": [ { "en": 0.7142834369645996 }, { "fr": 0.28571521669868466 } ] } [email protected]:~$` ^3000

[email protected]:~$
Use Case Model Used
Blog Post Generation: give instructions to the AI and let it generate a whole blog post with a proper structure and consistent content in many languages. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Classification: send a piece of text with possible categories, and let the AI apply the right categories to your text, in many languages. We are using the Facebook's Bart Large MNLI and Joe Davison's XLM Roberta Large XNLI models for classification in 100 languages with PyTorch and Hugging Face transformers. You can also use your own model. For classification without input labels, use GPT-J and few-shot learning. See Docs
Test Now
Chatbot/Conversational AI: discuss fluently with an AI and get relevant answers. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Code generation: let the AI code for you, in any programming language. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Grammar and spelling correction: send a block of text and let the AI correct the mistakes for you. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Headline generation: send a text, and get a very short summary suited for headlines, in many languages We are using Michau's T5 Base EN Generate Headline with PyTorch and Hugging Face transformers. You can also use your own model. See Docs
Test Now
Intent Classification: detect the intent from a sentence. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Keywords and keyphrases extraction: extract the main keywords from a piece of text. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Language Detection: detect one or several languages from a text. We are simply using Python's LangDetect library See Docs
Test Now
Lemmatization: extract lemmas from a text, in many languages All the large spaCy models are available (15 languages), or upload your own custom spaCy model See Docs
Named Entity Recognition (NER): extract relevant entities from a text, like name, company, country, job title... in many languages. All the large spaCy models are available (15 languages), or upload your own custom spaCy model. You can also achieve great results with GPT-J and few-shot learning. See Docs
Test Now
Paraphrasing: generate a similar content with the same meaning. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Part-Of-Speech (POS) tagging: assign parts of speech to each word of your text, in many languages All the large spaCy models are available (15 languages), or upload your own custom spaCy model See Docs
Product description and ad generation: generate one sentence or several paragraphs containing specific keywords for your product descriptions or ads. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Question answering: send a piece of text as a context, and ask questions about anything related to this context, in many languages We are using the Deepset's Roberta Base Squad 2 model with PyTorch and Hugging Face transformers. You can also use your own model. See Docs
Test Now
Sentiment analysis: determine whether a text is rather positive or negative, in English, French, Spanish, German, and Japanese. We also have an AI for financial sentiment analysis. We are using DistilBERT Base Uncased Finetuned SST-2, Théophile Blard's TF Allociné, Sagorsarker's Codeswitch SpaEng Sentiment Analysis Lince, Daigo's Bert Base Japanese Sentiment, Oliver Guhr's German Sentiment Bert and Prosus AI's Finbert with PyTorch, Tensorflow, and Hugging Face transformers. You can also use your own model. See Docs
Test Now
Summarization: send a text, and get a smaller text keeping essential information only, in many languages We are using Facebook's Bart Large CNN and Google's Pegasus XSUM with PyTorch and Hugging Face transformers. You can also use your own model. See Docs
Test Now
Text generation: start a sentence and let the AI generate the rest for you, in many languages. You can achieve almost any text processing and text generation use case thanks to text generation with GPT-J and few-shot learning. You can also fine-tune GPT-J on NLP Cloud. We are using the GPT-J and GPT-Neo 2.7B models with PyTorch and Hugging Face transformers. They are powerful open-source equivalents of OpenAI GPT-3. You can also use your own model. See Docs
Test Now
Tokenization: extract tokens from a text, in many languages All the large spaCy models are available (15 languages), or upload your own custom spaCy model See Docs
Translation: translate text from one language to another. We are using the Helsinki NLP's Opus MT models for translation in 7 languages (more to come) with PyTorch and Hugging Face transformers. You can also use your own model. See Docs
Test Now

Looking for a specific use case or AI model that is not in the list above? Please let us know!
Implementation can be very quick on our side.

Clean API

NLP Cloud provides you with a simple and robust API.

Integration is easy, and we are taking care of the high-availability of AI models, no matter the load.

Client libraries are available in Python, Ruby, Go, Node.js, and PHP (more to come).

See the documentation for more details.

Fine-Tune and Deploy your Own Models

Upload or Train/Fine-Tune your own AI models - including GPT-J - from your dashboard, and use them straight away in production without worrying about deployment considerations like RAM usage, high-availability, scalability... You can upload and deploy as many models as you want to production.

Support

If you already have an account, send us a message from your dashboard.

Otherwise, send us an email here: [email protected].

Plans

All plans can be stopped anytime. You only pay for the time you used the service.

The invoiced amount is automatically prorated. In case of a downgrade, you will get a discount on your next invoice.

Pre-trained AI Models

Pre-trained AI Models on GPU (more than 10x faster)

Pay-As-You-Go

Custom AI Models

Fine-tuning

Sensitive Applications

Many more plans can be created for you (e.g. a custom number of requests per minute, a mix of pre-trained and custom models, a GPT-J specific plan with more requests, etc.), just let us know!

Plans can also be paid in other currencies. Please ask us for more information if needed.

Deliver Your AI Projects For Good

BBVA
Johnson & Johnson
Zapier
GSK
Ubisoft

"We spent a lot of energy fine-tuning our machine learning models, but we clearly underestimated the go-live process. NLP Cloud saved us a lot of time, and prices are really affordable."

Patrick, CTO at MatchMaker

"Simple and efficient. Typically the kind of out-of-the box service that will make NLP, and AI in general, even more popular."

Marc, Software Engineer

"We did the maths: developing the API by ourself, and then creating and maintaining the production platform for our entity extraction models, would have taken around 2 months of work. Finally we did the same thing in 1 hour for a very fair price by using NLP Cloud."

John, CTO

"We had developed a working API deployed with Docker for our model, but we quickly faced performance and scalability issues. After spending weeks on this we eventually went for this cloud solution and we haven't regretted it so far!"

Maria, CSO at CybelAI

"We eventually gave up fine-tuning GPT-J... We are now exclusively fine-tuning and deploying GPT-J on NLP Cloud and we are happy like this."

Whalid, Lead Dev at Direct IT

"The NLP Cloud API has been extremely reliable and the support team is very nice and reactive."

Bogdan, Data Scientist at Alternative.io