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Introduction (Hugging Face Ecosystem and Text Classification)
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More Text Classification Examples
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What We're Going To Build!
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Getting Setup: Adding Hugging Face Tokens to Google Colab
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Getting Setup: Importing Necessary Libraries to Google Colab
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Downloading a Text Classification Dataset from Hugging Face Datasets
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Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers
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Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets
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Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer
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Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer
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Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data
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Setting Up an Evaluation Metric (to measure how well our model performs)
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Introduction to Transfer Learning (a powerful technique to get good results quickly)
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Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub
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Model Training - Part 2: Counting the Parameters in Our Model
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Model Training - Part 3: Creating a Folder to Save Our Model
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Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments
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Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers
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Model Training - Part 6: Training Our Model and Fixing Errors Along the Way
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Model Training - Part 7: Inspecting Our Models Loss Curves
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Model Training - Part 8: Uploading Our Model to the Hugging Face Hub
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Making Predictions on the Test Data with Our Trained Model
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Turning Our Predictions into Prediction Probabilities with PyTorch
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Sorting Our Model's Predictions by Their Probability
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Performing Inference - Part 1: Discussing Our Options
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Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time)
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Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching)
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Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions
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Performing Inference - Part 5: Performing Inference with PyTorch
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OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data
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Turning Our Model into a Demo - Part 1: Gradio Overview
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Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs
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Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally
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Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory
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Making Our Demo Publicly Accessible - Part 2: Creating an App File
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Making Our Demo Publicly Accessible - Part 3: Creating a README File
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Making Our Demo Publicly Accessible - Part 4: Making a Requirements File
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Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available
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Summary Exercises and Extensions
