Natural Language Processes, Amazon Sagemaker and Hugging Face

Language Processing? Hugging Face? Now that’s an insight title that’s bound to cause some head scratches! 🤔

So before we look at some of the exciting advancements in natural language processing (often referred to as NLP) and the AI community Hugging Face, let’s break down what each is and how it operates.

Hugging Face is a global AI community helping to build the future in state of the art models powered by the reference open source in natural language processing. They provide highly intelligent and scalable AI models, datasets for metrics, languages and organisations as well as integrations into some of the most used AI/ML cloud services.

Over the years they’ve been used by companies such as Facebook AI, Google AI, Grammarly and of course, Amazon Web Services.

It’s those integrations with AWS that we’ll be talking about today as the use of Hugging Face with Amazon SageMaker is a fairly recent development with some exciting future applications.

So what’s natural language processing? We’re glad you asked! It’s best to think of it as the interaction between the human language and computers. It’s formed of a blend between artificial intelligence, machine learned patterns, computer science and linguistic studies.

When we try and ask computers to process the human language, tools like Hugging Face can help us program and map out large amounts of natural language data, enabling computers to ‘understand’ it. This ‘understanding’ can automate the categorisation and filing of huge amounts of data and provide insights that humans simply couldn’t structure (or at least structure into a usable/functional form quickly).

The announcement of Hugging Face integrations with Amazon SageMaker came earlier this year in March with the aim to help data scientists train, develop and fine-tune NLPs faster, more efficiently and more accurately.

The field of natural language processing, which drives popular use cases like chat bots, sentiment analysis, question answering and live search, has experienced a true ‘cloud renaissance’ over the past few years. In particular, the Transformer deep learning architecture has been responsible for some of the largest state-of-the-art models to date such as T5 and GPT-3.

The future of language interpretation into functional data is here (and it’s here to stay)!

As an AWS Advanced Consultancy that has a thing for data, we’re keeping an eye on the enhancements and availability of models by organisations like Hugging Face. And we’re certainly excited to see how we can tailor such processes in combination with AWS services like Amazon SageMaker.