Since their introduction in 2017, Transformers have become the de facto standard for tackling a wide range of NLP tasks in both academia and industry. In this workshop, we’ll teach you the core concepts behind Transformers and how to train these models in the Hugging Face ecosystem. Read more about the workshop here https://www.mlt.ai/hands-on-transformers-workshop
🚀 Find open source models, datasets and other resources on https://huggingface.co/
👉 GitHub repo: https://github.com/huggingface/workshop
Lewis Tunstall is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. A former theoretical physicist, he has over 10 years experience translating complex subject matter to lay audiences and has taught machine learning to university students at both the graduate and undergraduate levels.
Leandro von Werra is a machine learning engineer at Hugging Face. He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack, and is the creator of a popular Python library called TRL that combines Transformers with reinforcement learning.
This workshop is for data scientists and machine learning / software engineers who may have heard about the recent breakthroughs involving Transformers, but are lacking an in-depth guide to help them adapt these models to their own use-cases.
We assume that participants are comfortable programming in Python and common libraries for machine learning like pandas, numpy, scikit-learn, and matplotlib.
We also assume the participant has practical experience with deep learning, such as preparing data, training models on GPUs, and evaluating model performance.
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