Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Learn Hugging Face by Building a Custom AI Model
Introduction
Introduction (Hugging Face Ecosystem and Text Classification) (6:52)
More Text Classification Examples (4:40)
What We're Going To Build! (7:21)
Exercise: Meet Your Classmates and Instructor
Course Resources
Let's Get Started!
Getting Setup: Adding Hugging Face Tokens to Google Colab (5:52)
Getting Setup: Importing Necessary Libraries to Google Colab (9:35)
Downloading a Text Classification Dataset from Hugging Face Datasets (16:00)
Preparing Text Data & Evaluation Metric
Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers (12:48)
Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets (6:18)
Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer (12:53)
Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer (10:26)
Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data (17:57)
Setting Up an Evaluation Metric (to measure how well our model performs) (8:53)
Model Training
Introduction to Transfer Learning (a powerful technique to get good results quickly) (7:10)
Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub (12:19)
Model Training - Part 2: Counting the Parameters in Our Model (12:27)
Model Training - Part 3: Creating a Folder to Save Our Model (3:53)
Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments (14:59)
Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers (5:05)
Model Training - Part 6: Training Our Model and Fixing Errors Along the Way (13:34)
Model Training - Part 7: Inspecting Our Models Loss Curves (14:39)
Model Training - Part 8: Uploading Our Model to the Hugging Face Hub (8:01)
Making Predictions
Making Predictions on the Test Data with Our Trained Model (5:58)
Turning Our Predictions into Prediction Probabilities with PyTorch (12:48)
Sorting Our Model's Predictions by Their Probability (5:10)
Performing Inference
Performing Inference - Part 1: Discussing Our Options (9:40)
Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time) (10:01)
Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching) (6:38)
Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions (10:33)
Performing Inference - Part 5: Performing Inference with PyTorch (12:06)
OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data (34:28)
Launching Our Model!
Turning Our Model into a Demo - Part 1: Gradio Overview (3:47)
Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs (7:07)
Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally (6:46)
Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory (8:01)
Making Our Demo Publicly Accessible - Part 2: Creating an App File (12:14)
Making Our Demo Publicly Accessible - Part 3: Creating a README File (7:07)
Making Our Demo Publicly Accessible - Part 4: Making a Requirements File (3:33)
Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available (18:43)
Summary Exercises and Extensions (5:55)
Where To Go From Here?
Review This Project!
Introduction to Transfer Learning (a powerful technique to get good results quickly)
This lecture is available exclusively for ZTM Academy members.
If you're already a member,
you'll need to login
.
Join ZTM To Unlock All Lectures