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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