Learn Hugging Face by Building a Custom AI Model
6h 32m 55s
English
Paid
Course description
Explore the Hugging Face ecosystem from scratch, including Transformers, Datasets, Hub/Spaces, and much more, by creating and configuring your own AI model for text classification. In this course, you will learn how to deploy your model for real-world use!
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/ #1: Introduction (Hugging Face Ecosystem and Text Classification)
All Course Lessons (39)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction (Hugging Face Ecosystem and Text Classification) Demo | 06:53 | |
| 2 | More Text Classification Examples | 04:41 | |
| 3 | What We're Going To Build! | 07:22 | |
| 4 | Getting Setup: Adding Hugging Face Tokens to Google Colab | 05:53 | |
| 5 | Getting Setup: Importing Necessary Libraries to Google Colab | 09:36 | |
| 6 | Downloading a Text Classification Dataset from Hugging Face Datasets | 16:01 | |
| 7 | Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers | 12:49 | |
| 8 | Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets | 06:19 | |
| 9 | Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer | 12:54 | |
| 10 | Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer | 10:27 | |
| 11 | Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data | 17:58 | |
| 12 | Setting Up an Evaluation Metric (to measure how well our model performs) | 08:54 | |
| 13 | Introduction to Transfer Learning (a powerful technique to get good results quickly) | 07:11 | |
| 14 | Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub | 12:20 | |
| 15 | Model Training - Part 2: Counting the Parameters in Our Model | 12:27 | |
| 16 | Model Training - Part 3: Creating a Folder to Save Our Model | 03:54 | |
| 17 | Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments | 15:00 | |
| 18 | Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers | 05:06 | |
| 19 | Model Training - Part 6: Training Our Model and Fixing Errors Along the Way | 13:35 | |
| 20 | Model Training - Part 7: Inspecting Our Models Loss Curves | 14:40 | |
| 21 | Model Training - Part 8: Uploading Our Model to the Hugging Face Hub | 08:02 | |
| 22 | Making Predictions on the Test Data with Our Trained Model | 05:59 | |
| 23 | Turning Our Predictions into Prediction Probabilities with PyTorch | 12:49 | |
| 24 | Sorting Our Model's Predictions by Their Probability | 05:11 | |
| 25 | Performing Inference - Part 1: Discussing Our Options | 09:41 | |
| 26 | Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time) | 10:02 | |
| 27 | Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching) | 06:39 | |
| 28 | Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions | 10:34 | |
| 29 | Performing Inference - Part 5: Performing Inference with PyTorch | 12:07 | |
| 30 | OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data | 34:29 | |
| 31 | Turning Our Model into a Demo - Part 1: Gradio Overview | 03:48 | |
| 32 | Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs | 07:08 | |
| 33 | Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally | 06:47 | |
| 34 | Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory | 08:02 | |
| 35 | Making Our Demo Publicly Accessible - Part 2: Creating an App File | 12:15 | |
| 36 | Making Our Demo Publicly Accessible - Part 3: Creating a README File | 07:08 | |
| 37 | Making Our Demo Publicly Accessible - Part 4: Making a Requirements File | 03:34 | |
| 38 | Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available | 18:44 | |
| 39 | Summary Exercises and Extensions | 05:56 |
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