Hugging Face is more than just an adorable emoji — it’s a company that’s demystifying AI by transforming the latest developments in deep learning into usable code for businesses and researchers.
Research engineer Sam Shleifer spoke with AI Podcast host Noah Kravitz about Hugging Face NLP technology, which is in use at over 1,000 companies, including Apple, Bing and Grammarly, across fields ranging from finance to medical technology.
Hugging Face’s models serve a variety of purposes for their customers, including autocompletion, customer service automation and translation. Their popular web application, Write with Transformer, can even take half-formed thoughts and suggest options for completion.
Schleifer is currently at work developing models that are accessible to everyone, whether they are proficient coders or not.
In the next few years, Schleifer envisions the continued growth of smaller NLP models that power a wave of chat apps with state-of-the-art translation capabilities.
Key Points From This Episode:
- Hugging Face first launched an original chatbot app, before moving into natural language processing models. The move was well-received, and last year the company announced a $15 million funding round.
- The company is a member of NVIDIA Inception, a virtual accelerator that Schleifer credits with significantly accelerating their experiments.
- Hugging Face has released over 1,000 models trained with unsupervised learning and the Open Parallel Corpus project, pioneered by the University of Helsinki. These models are capable of machine translation in a huge variety of languages, even for low-resource languages with minimal training data.
“We’re trying to make state-of-the-art NLP accessible to everyone who wants to use it, whether they can code or not code.” — Sam Shleifer [1:44]
“Our research is targeted at this NLP accessibility mission — and NLP isn’t really accessible when models can’t fit on a single GPU.” — Sam Shleifer [10:38]
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The post There’s a Code for That: Hugging Face’s Sam Shleifer Talks Natural Language Processing appeared first on The Official NVIDIA Blog.