Abstract: 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,
- Use hardware acceleration with the Hugging Face Optimum library to optimize training and prediction times,
- 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.
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.).
Bio: Julien is currently Chief Evangelist at Hugging Face. He's recently spent 6 years at 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.