Name: Learn Hugging Face by Building a Custom AI Model
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Learn Hugging Face by Building a Custom AI Model

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!

Introduction to Hugging Face

Dive deep into the Hugging Face ecosystem and understand its core components. This section will cover an overview of Transformers, Datasets, Hub/Spaces, and their relevance in AI model development.

Understanding Transformers

Learn about Transformers and their applications in various natural language processing tasks. We'll explore how these models revolutionize the field of AI.

Datasets and Their Importance

Discover the role of Datasets in training machine learning models. Get hands-on experience with the Hugging Face Datasets library to streamline your model preparation process.

Exploring Hub/Spaces

Understand the functionality of Hub/Spaces on Hugging Face. Learn how you can host your models and share them with the community for easier collaboration and deployment.

Building Your Custom AI Model

Step through the process of creating a custom AI model with Hugging Face. From data preprocessing to model training, this section will provide you with the knowledge to develop models tailored to specific tasks.

Configuring the Model

Learn how to configure your model to maximize its performance. Discover techniques to adjust hyperparameters and fine-tune the model for optimal results.

Implementing Text Classification

Apply your skills to implement a text classification model using Hugging Face frameworks. This practical application will solidify your understanding of the entire model-building process.

Deploying Your AI Model

Finally, put your model into production. Learn the steps and tools necessary for deploying your AI model for real-world use cases, ensuring it is scalable and accessible.

Real-World Deployment Techniques

Explore different deployment strategies and technologies that can help you get your model up and running effectively. Understand how to manage any operational challenges in deployment.

Optimizing Deployment for Scalability

Focus on making your deployment scalable to handle varying loads and ensure reliability. Learn about best practices in resource management and service scaling.

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Watch Online 39 lessons

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#1: Introduction (Hugging Face Ecosystem and Text Classification)

All Course Lessons (39)

#	Lesson Title	Duration
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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