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  1. Урок 1. 00:06:53
    Introduction (Hugging Face Ecosystem and Text Classification)
  2. Урок 2. 00:04:41
    More Text Classification Examples
  3. Урок 3. 00:07:22
    What We're Going To Build!
  4. Урок 4. 00:05:53
    Getting Setup: Adding Hugging Face Tokens to Google Colab
  5. Урок 5. 00:09:36
    Getting Setup: Importing Necessary Libraries to Google Colab
  6. Урок 6. 00:16:01
    Downloading a Text Classification Dataset from Hugging Face Datasets
  7. Урок 7. 00:12:49
    Preparing Text Data for Use with a Model - Part 1: Turning Our Labels into Numbers
  8. Урок 8. 00:06:19
    Preparing Text Data for Use with a Model - Part 2: Creating Train and Test Sets
  9. Урок 9. 00:12:54
    Preparing Text Data for Use with a Model - Part 3: Getting a Tokenizer
  10. Урок 10. 00:10:27
    Preparing Text Data for Use with a Model - Part 4: Exploring Our Tokenizer
  11. Урок 11. 00:17:58
    Preparing Text Data for Use with a Model - Part 5: Creating a Function to Tokenize Our Data
  12. Урок 12. 00:08:54
    Setting Up an Evaluation Metric (to measure how well our model performs)
  13. Урок 13. 00:07:11
    Introduction to Transfer Learning (a powerful technique to get good results quickly)
  14. Урок 14. 00:12:20
    Model Training - Part 1: Setting Up a Pretrained Model from the Hugging Face Hub
  15. Урок 15. 00:12:27
    Model Training - Part 2: Counting the Parameters in Our Model
  16. Урок 16. 00:03:54
    Model Training - Part 3: Creating a Folder to Save Our Model
  17. Урок 17. 00:15:00
    Model Training - Part 4: Setting Up Our Training Arguments with TrainingArguments
  18. Урок 18. 00:05:06
    Model Training - Part 5: Setting Up an Instance of Trainer with Hugging Face Transformers
  19. Урок 19. 00:13:35
    Model Training - Part 6: Training Our Model and Fixing Errors Along the Way
  20. Урок 20. 00:14:40
    Model Training - Part 7: Inspecting Our Models Loss Curves
  21. Урок 21. 00:08:02
    Model Training - Part 8: Uploading Our Model to the Hugging Face Hub
  22. Урок 22. 00:05:59
    Making Predictions on the Test Data with Our Trained Model
  23. Урок 23. 00:12:49
    Turning Our Predictions into Prediction Probabilities with PyTorch
  24. Урок 24. 00:05:11
    Sorting Our Model's Predictions by Their Probability
  25. Урок 25. 00:09:41
    Performing Inference - Part 1: Discussing Our Options
  26. Урок 26. 00:10:02
    Performing Inference - Part 2: Using a Transformers Pipeline (one sample at a time)
  27. Урок 27. 00:06:39
    Performing Inference - Part 3: Using a Transformers Pipeline on Multiple Samples at a Time (Batching)
  28. Урок 28. 00:10:34
    Performing Inference - Part 4: Running Speed Tests to Compare One at a Time vs. Batched Predictions
  29. Урок 29. 00:12:07
    Performing Inference - Part 5: Performing Inference with PyTorch
  30. Урок 30. 00:34:29
    OPTIONAL - Putting It All Together: from Data Loading, to Model Training, to making Predictions on Custom Data
  31. Урок 31. 00:03:48
    Turning Our Model into a Demo - Part 1: Gradio Overview
  32. Урок 32. 00:07:08
    Turning Our Model into a Demo - Part 2: Building a Function to Map Inputs to Outputs
  33. Урок 33. 00:06:47
    Turning Our Model into a Demo - Part 3: Getting Our Gradio Demo Running Locally
  34. Урок 34. 00:08:02
    Making Our Demo Publicly Accessible - Part 1: Introduction to Hugging Face Spaces and Creating a Demos Directory
  35. Урок 35. 00:12:15
    Making Our Demo Publicly Accessible - Part 2: Creating an App File
  36. Урок 36. 00:07:08
    Making Our Demo Publicly Accessible - Part 3: Creating a README File
  37. Урок 37. 00:03:34
    Making Our Demo Publicly Accessible - Part 4: Making a Requirements File
  38. Урок 38. 00:18:44
    Making Our Demo Publicly Accessible - Part 5: Uploading Our Demo to Hugging Face Spaces and Making it Publicly Available
  39. Урок 39. 00:05:56
    Summary Exercises and Extensions