Course Launch Community Event

• Day 1 (November 15th): A high-level view of Transformers and how to train them
• Day 2 (November 16th): The tools you will use

We are excited to share that after a lot of work from the Hugging Face team, part 2 of the Hugging Face Course will be released on November 15th! Part 1 focused on teaching you how to use a pretrained model, fine-tune it on a text classification task then upload the result to the Model Hub. Part 2 will focus on all the other common NLP tasks: token classification, language modeling (causal and masked), translation, summarization and question answering. It will also take a deeper dive in the whole Hugging Face ecosystem, in particular 🤗 Datasets and 🤗 Tokenizers.

To go with this release, we are organizing a large community event to which you are invited! The program includes two days of talks, then team projects focused on fine-tuning a model on any NLP task ending with live demos like this one. Those demos will go nicely in your portfolio if you are looking for a new job in Machine Learning. We will also deliver a certificate of completion to all the participants that achieve building one of them.

AWS is sponsoring this event by offering free compute to participants via Amazon SageMaker.

To register, please fill out this form. You will find below more details on the two days of talks.

Day 1 (November 15th): A high-level view of Transformers and how to train them

The first day of talks will focus on a high-level presentation of Transformers models and the tools we can use to train or fine-tune them.

Thomas Wolf: Transfer Learning and the birth of the Transformers library

Margaret Mitchell: On Values in ML Development

Jakob Uszkoreit: It Ain't Broke So Don't Fix Let's Break It

Jay Alammar: A gentle visual intro to Transformers models

Matthew Watson: NLP workflows with Keras

Chen Qian: NLP workflows with Keras

Mark Saroufim: How to Train a Model with Pytorch

Day 2 (November 16th): The tools you will use

Merve Noyan: Showcase your model demos with 🤗 Spaces