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 TitleDurationAccess
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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