Name: Learn Hugging Face by Building a Custom AI Model
Price: -1 USD

Overview

Learn the Hugging Face ecosystem from scratch including Transformers, Datasets, Hub/Spaces, and more by building and customizing your own AI text classification model and launch it for use in the real-world!

How to prepare and process datasets using Hugging Face Datasets
Techniques for training and fine-tuning text classification models with Hugging Face Transformers
Methods for evaluating model performance using Hugging Face Evaluate
Steps to deploy your trained model to the Hugging Face Hub
How to create interactive demos for machine learning models using Gradio
Practical experience in the full lifecycle of a machine learning project, from data preparation to deployment

Syllabus

Introduction

Introduction (Hugging Face Ecosystem and Text Classification)
More Text Classification Examples
What You Are Going To Build!
Exercise: Meet Your Classmates and Instructor
Course Resources

Let's Get Started!

Getting Setup: Adding Hugging Face Tokens to Google Colab
Getting Setup: Importing Necessary Libraries to Google Colab
Downloading a Text Classification Dataset from Hugging Face Datasets

Preparing Text Data & Evaluation Metric

Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers
Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets
Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer
Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer
Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data
Setting Up an Evaluation Metric (to measure how well our model performs)

Model Training

Introduction to Transfer Learning (a powerful technique to get good results quickly)
Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub
Model Training - Part 2: Counting the Parameters in Our Model
Model Training - Part 3: Creating a Folder to Save Our Model
Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments
Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers
Model Training - Part 6: Training Our Model and Fixing Errors Along the Way
Model Training - Part 7: Inspecting Our Models Loss Curves
Model Training - Part 8: Uploading Our Model to the Hugging Face Hub

Making Predictions

Making Predictions on the Test Data with Our Trained Model
Turning Our Predictions into Prediction Probabilities with PyTorch
Sorting Our Model's Predictions by Their Probability

Performing Inference

Performing Inference - Part 1: Discussing Our Options
Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time)
Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching)
Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions
Performing Inference - Part 5: Performing Inference with PyTorch
OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data

Launching Our Model!

Turning Our Model into a Demo - Part 1: Gradio Overview
Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs
Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally
Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory
Making Our Demo Publicly Accessible - Part 2: Creating an App File
Making Our Demo Publicly Accessible - Part 3: Creating a README File
Making Our Demo Publicly Accessible - Part 4: Making a Requirements File
Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available
Summary Exercises and Extensions