látogató számláló

An introduction to NLP with Hugging Face Transformers tutorial


In this workshop, you’ll walk through a complete end-to-end example of using Hugging Face Transformers, involving both our open-source libraries and some of our commercial products. Starting from a dataset containing real-life product reviews from Amazon.com, you’ll train and deploy a text classification model predicting the star rating for similar reviews. Along the way, you’ll learn how to:

  • Explore models and datasets on the Hugging Face Hub,
  • Load, prepare and save datasets with the Hugging Face datasets library,
  • Load, train and save models with the Hugging Face transformers library,
  • Build ML applications with Hugging Spaces to showcase your models,
  • and maybe a few more things, if we have time!

Of course, all code will be shared with you, and you’ll be able to use it easily in your own projects.


  • This is a hands-on, code-level workshop all the way. Coding proficiency is required
  • Participants don’t need to be ML experts, but they must be familiar with basic ML concepts and workflows, as well as Python and Python-based tools for ML (Jupyter, numpy, pandas, etc.).
  • Participants must bring their own laptop.
  • Prior to the workshop, participants must set up a cloud-based Jupyter environment with GPU support (preferably on Amazon SageMaker) and Git LFS support (https://git-lfs.github.com).
  • No time will be allocated for setup during the workshop.



Julien Simon

Chief Evangelist
Hugging Face
LinkedIn · Twitter · YouTube

Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years as Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups where he led large Software and Ops teams in charge of thousands of servers worldwide.


Date: June 5th, 2023. Monday
Time: Half-day session
Format: In-person
Language: English
Location: Danubius Hotel Helia**** (Budapest, Kárpát Street 62-64.)