NLP Cloud helps app developers add language processing
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NLP tools and services are taking off, but developers often struggle with the hurdle of getting NLP models into production. NLP Cloud is a new AI startup focused on lowering the barriers for developers trying to create apps for sorting support tickets, extracting leads, analyzing social networks, and developing tools for economic intelligence.
NLP has been around for decades, but interest has seen a dramatic uptick with the recent introduction of transformers, a new type of neural network. Google researchers demonstrated in 2017 how transformers dramatically improved the speed, performance, and precision of NLP tools. Transformers made possible the much larger models Google’s BERT and OpenAI’s GPT-3. The capabilities are available through innovative open source libraries Hugging Face and spaCy.
Developing accurate models and pushing models into production are two different processes. NLP Cloud intends to close this gap by reducing the barriers to production — providing NLP capabilities via an API, rather than a raw AI model that must be pushed into production. Developers only need to worry about integrating the API into their application.
“Today, the main challenge remaining in NLP projects is clearly the production side,” NLP Cloud CTO and founder Julien Salinas told VentureBeat in an email. New NLP models make it easier for more types of developers to experiment with weaving language capabilities into their projects.
Above: Julien Salina, CTO and founder of NLP Cloud
Possible use cases include scanning web pages and other unstructured text and extracting name entities as part of lead generation before conducting sentiment analysis on support tickets and sorting them based on urgency. Content marketers can use the platform to summarize text and generate headlines.
Properly deploying and running AI models in production requires strong DevOps, programming, and AI capabilities. Few developers have mastered all three disciplines, especially within smaller companies. The team may have data science knowledge but not the DevOps capabilities, or software engineers who need to deploy NLP without hiring a data science team.
The company is focusing on making the best available open source models easier to deploy rather than developing its own models. This allows it to focus on improving the developer experience rather than tweaking the underlying models. Salinas said the company selected Hugging Face and spaCy for their respective strengths.
Hugging Face’s transformers are more advanced and accurate than spaCy, Salinas said. Hugging Face is also building a huge open source repository for NLP models, which makes selecting the best model for a given use case more convenient.
SpaCy is faster and less resource-intensive than other NLP libraries. The library has been around longer and recently added the capability to natively support transformer-based models.
In the future, Salinas plans to add conversational models for chatbots, new summarization models that can handle bigger pieces of text, and text generation models. He also hopes to eventually support more languages but believes non-English models still need more work.
Since its launch three months ago, NLP Cloud has been growing rapidly. It currently has around 500 users, 30 of them paid users. While most of the users are startups the company has begun to see some larger customers.
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