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