Production-ready NLP API

for entity extraction,

for text classification,

for sentiment analysis,

for text summarization,

for question answering,

for text generation,

for translation,

for language detection,

for part-of-speech tagging,

for tokenization,

High performance models,

ready for production, at a fair price.
Deploy your own models.
No DevOps required.

Want to know why we created NLP Cloud? Read this TechCrunch article!

High Performance

Fast and accurate models suited for production. Highly-available inference API.

Pre-trained Models

We selected the best deep-learning NLP pre-trained models from spaCy and Hugging Face.

Custom models

Use your own Hugging Face transformers-based models, and spaCy models

Reasonable Pricing

Prices are fair and easy to understand: no surprise at the end of the month!

No Complexity

Do not worry about DevOps or API programming and focus on NLP only

Clean API

Simple and well-documented API with client libs 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]:~$` ^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
Named Entity Recognition (NER): extract relevant entities from a text, like name, company, country, job title... All the large spaCy models are available (15 languages), or upload your own custom spaCy model See Docs
Classification: send a text with possible categories, and let the model apply the right categories to your text 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. See Docs
Summarization: send a text, and get a smaller text keeping essential information only We are using the Facebook's Bart Large CNN model with PyTorch and Hugging Face transformers. You can also use your own model. See Docs
Question answering: send a text as a context, and ask questions about anything related to this context 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
Sentiment analysis: determine whether a text is rather positive or negative We are using the DistilBERT's Base Uncased Finetuned SST-2 model with PyTorch and Hugging Face transformers. You can also use your own model. See Docs
Text generation: start a sentence and let the model generate the rest 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
Translation: translate text from one language to another. We are using the Helsinki NLP's Opus MT models for translation in 6 languages (more to come) with PyTorch and Hugging Face transformers. You can also use your own model. See Docs
Language Detection: detect one or several languages from a text. We are simply using Python's LangDetect library See Docs
Part-Of-Speech (POS) tagging: assign parts of speech to each word of your text All the large spaCy models are available (15 languages), or upload your own custom spaCy model See Docs
Tokenization: extract tokens from a text All the large spaCy models are available (15 languages), or upload your own custom spaCy model See Docs

Looking for a specific use case or 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 the NLP 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.

Deploy your Own Models

Upload your own custom Hugging Face transformers-based models, or spaCy models, 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

Pre-trained Models

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

Custom Models (Transformers or spaCy)

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!

Prices can also be paid in euros. Please ask us for the price list in euros if needed.

Deliver Your NLP Projects For Good

BBVA
Johnson & Johnson
Zapier
GSK
Chattermill

"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 spaCy, 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 our own models... We are now exclusively using pre-trained models 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