There’s a Code for That: Hugging Face’s Sam Shleifer Talks Natural Language Processing


Hugging Face is additional than just an adorable emoji — it is a organization that’s demystifying AI by transforming the latest developments in deep learning into usable code for organizations and researchers.

Investigate engineer Sam Shleifer spoke with AI Podcast host Noah Kravitz about Hugging Facial area NLP engineering, which is in use at above 1,000 companies, which include Apple, Bing and Grammarly, throughout fields ranging from finance to medical engineering.

Hugging Face’s versions serve a assortment of functions for their buyers, together with autocompletion, consumer provider automation and translation. Their common net application, Publish with Transformer, can even consider 50 %-shaped feelings and advise solutions for completion.

Shleifer is presently at get the job done producing designs that are obtainable to anyone, no matter whether they are proficient coders or not.

In the up coming number of a long time, Shleifer envisions the ongoing advancement of smaller NLP models that energy a wave of chat apps with condition-of-the-art translation capabilities.

Essential Points From This Episode:

  • Hugging Encounter 1st introduced an initial chatbot application, before moving into natural language processing designs. The shift was properly-received, and last calendar year the business announced a $15 million funding round.
  • The enterprise is a member of NVIDIA Inception, a digital accelerator that Shleifer credits with drastically accelerating their experiments.
  • Hugging Face has released in excess of one,000 types experienced with unsupervised studying and the Open Parallel Corpus job, pioneered by the College of Helsinki. These styles are capable of machine translation in a enormous variety of languages, even for small-source languages with minimum teaching details.


“We’re seeking to make condition-of-the-artwork NLP obtainable to every person who wishes to use it, no matter if they can code or not code.” — Sam Shleifer [1:44]

“Our analysis is targeted at this NLP accessibility mission — and NLP isn’t truly accessible when designs just can’t healthy on a single GPU.” — Sam Shleifer [10:38]

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