README.md: This file!- Notebook examples of using Pre-trained models on various tasks (TTS, VQA, OC, etc.)
- Notebook examples of using Datasets
The "ML with Hugging Face" workshop will introduce Hugging Face to hackers, enabling them to utilize pre-trained models, access datasets, and deploy models for their hackathon projects. We will cover the core features of Hugging Face Hub, particularly its pre-trained models, demonstrating to hackers how to navigate the model list and get started. Additionally, we will highlight other useful features of Hugging Face Hub, such as Datasets and Spaces, which are beneficial for those aiming to train or deploy their models swiftly.
- Basic knowledge of programming (Classes, Functions, etc.)
- A Gmail account or Jupyter notebook locally installed
- Get familiar with the Hugging Face Hub
- Understand how to use Hugging Face Hub to access pre-trained models
- Learn how to use Hugging Face Hub to access datasets
- Learn how to use Hugging Face Hub to deploy models
| Time | Section | Details |
|---|---|---|
| 5 minutes | Introduction | Introduce the club and summarize contents |
| 5 minutes | Overview of ML | Show structure of ML problems to give background to what services are available |
| 5 minutes | Setup Hugging Face | Creating a hugging face account |
| 5 minutes | Setup the environment | Signing into google colab and installing dependencies and logging into hugging face in the notebook |
| 20 minutes | Using a Model | Show models list and how to use those model with an example |
| 10 minutes | Using a Dataset | Show dataset list and how to use a dataset with an example |
| 5 minutes | Hugging Face Spaces | Overview of what hugging face spaces is and how they could use it |
| 5 minutes | Q&A |
My Example of Deploying Using Spaces
Computer Vision Pre-trained Models Docs