Book Content
chapters • 12h48m total length
1. Getting Started with the Model Architecture of the Transformer
2. Fine-Tuning BERT Models
3. Pretraining a RoBERTa Model from Scratch
4. Downstream NLP Tasks with Transformers
5. Machine Translation with the Transformer
6. Text Generation with OpenAI GPT-2 and GPT-3 Models
7. Applying Transformers to Legal and Financial Documents for AI Text Summarization
8. Matching Tokenizers and Datasets
9. Semantic Role Labeling with BERT-Based Transformers
10. Let Your Data Do the Talking: Story, Questions, and Answers
11. Detecting Customer Emotions to Make Predictions
12. Analyzing Fake News with Transformers
13. Appendix: Answers to the Questions
