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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# Title Duration
1 Introduction (Hugging Face Ecosystem and Text Classification) 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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