Text understanding/generation (NLP),
ready for production, at a fair price.
Fine-tune and deploy your own AI models.
No DevOps required.
Need help on your AI project? NLP Cloud now has a consulting offer to help make the most of language AI. Please contact us!
Fast and accurate AI models suited for production. Highly-available inference API leveraging the most advanced NVIDIA GPUs.
We selected the best open-source natural language processing (NLP) models from the community and deployed them for you
Fine-tune your own models - including GPT-J - or upload your in-house custom models, and deploy them easily to production
We are HIPAA / GDPR / CCPA compliant. No data is stored on our servers. We offer specific plans for sensitive applications.
Do not worry about DevOps or API programming and focus on text processing only. Deliver your AI project in no time.
Do everything in your own language and stopping sticking to English. Use all our AI models in many languages.
"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 Natural Language Processing, 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 on 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
See this customer reference: LAO using our classification API for automatic support tickets triaging
NLP Cloud is an NVIDIA partner
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]:~$` ^3000
curl https://api.nlpcloud.io/v1/bart-large-mnli-yahoo-answers/classification \ > -X POST -d '{ "text":"John Doe is a Go Developer at Google. He has been working there for 10 years and has been awarded employee of the year.", "labels":["job", "nature", "space"], "multi_class": true }' ^2000 `{ "labels":["job", "space", "nature"], "scores":[0.9258800745010376, 0.1938474327325821, 0.010988450609147549] } [email protected]:~$` ^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 GPT-J and GPT-NeoX 20B 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, and let the AI apply the right categories to your text, in many languages. As an option, you can suggest the potential categories you want to assess. | We are using Joe Davison's Bart Large MNLI Yahoo Answers, Joe Davison's XLM Roberta Large XNLI, and GPT for classification in 100 languages with PyTorch, Jax, and Hugging Face transformers. You can also use your own model. For classification without potential categories, use GPT-J/GPT-NeoX. | See Docs Test Now |
Chatbot/Conversational AI: discuss fluently with an AI and get relevant answers, in many languages. | We are using GPT-J and GPT-NeoX 20B 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 GPT-J and GPT-NeoX 20B 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 |
Dialogue Summarization: summarize a conversation, in many languages | We are using Bart Large CNN SamSum with PyTorch and Hugging Face transformers. You can also use your own model. | See Docs Test Now |
Embeddings: calculate embeddings from several pieces of text, in more than 50 languages. | We are using Paraphrase Multilingual Mpnet Base V2 with PyTorch and Sentence Transformers, and GPT-J with PyTorch and Transformers. You can also use your own model. | See Docs |
Grammar and spelling correction: send a block of text and let the AI correct the mistakes for you, in many languages. | We are using GPT-J and GPT-NeoX 20B 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, in many languages. | We are using GPT-J and GPT-NeoX 20B 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, in many languages. | We are using GPT-J and GPT-NeoX 20B 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 Ginza for Japanese, or upload your own custom spaCy model | See Docs |
Named Entity Recognition (NER): extract structured information from an unstructured text, like name, company, country, job title... in many languages. | You can perform NER with all the large spaCy models (15 languages), or Ginza for Japanese, or GPT-J/GPT-NeoX, or use your own custom model. | See Docs Test Now |
Noun Chunks: extract noun chunks from a text, in many languages | All the large spaCy models are available (15 languages), or Ginza for Japanese, or upload your own custom spaCy model | See Docs |
Paraphrasing and rewriting: generate a similar content with the same meaning, in many languages. | We are using GPT-J and GPT-NeoX 20B 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 Ginza for Japanese, 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, in many languages. | We are using GPT-J and GPT-NeoX 20B 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: ask questions about anything, in many languages. As an option you can give a context so the AI uses this context to answer your question. | We are using the Deepset's Roberta Base Squad 2 with PyTorch and Hugging Face transformers, and GPT-J/GPT-NeoX. If you don't want to use a context, you should use GPT-J. You can also use your own model. | See
Docs Test Now |
Semantic Similarity: detect whether 2 pieces of text have the same meaning or not, in more than 50 languages. | We are using Paraphrase Multilingual Mpnet Base V2 with PyTorch and Sentence Transformers. You can also use your own model. | See
Docs Test Now |
Sentiment and emotion analysis: determine sentiments and emotions from a text (positive, negative, fear, joy...), in many languages. We also have an AI for financial sentiment analysis. | 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. 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, Google's Pegasus XSUM, and GPT-J/GPT-NeoX, with PyTorch, Jax, 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 GPT-J and GPT-NeoX 20B 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 Ginza for Japanese, or upload your own custom spaCy model | See Docs |
Translation: translate text from one language to another. | We use Facebook's M2M100 1.2B for translation in 100 languages and Helsinki NLP's Opus MT for translation in 7 languages 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.
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.
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.
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.
If you already have an account, send us a message from your dashboard.
Otherwise, send us an email here: [email protected].