Transformers for Natural Language Processing

Table of Contents - What are Transformers?
- Getting Started with the Architecture of the Transformer Model
- Fine-Tuning BERT Models
- Pretraining a RoBERTa Model from Scratch
- Downstream NLP Tasks with Transformers
- Machine Translation with the Transformer
- The Rise of Suprahuman Transformers with GPT-3 Engines
- Applying Transformers to Legal and Financial Documents for AI Text Summarization
- Matching Tokenizers and Datasets
- Semantic Role Labeling with BERT-Based Transformers
- Let Your Data Do the Talking: Story, Questions, and Answers
- Detecting Customer Emotions to Make Predictions
- Analyzing Fake News with Transformers
- Interpreting Black Box Transformer Models
- From NLP to Task-Agnostic Transformer Models
- The Emergence of Transformer-Driven Copilots
- The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4
- Appendix I - Terminology of Transformer Models
- Appendix II - Hardware Constraints for Transformer Models
- Appendix III - Generic Text Completion with GPT-2
- Appendix IV - Custom Text Completion with GPT-2
- Appendix V - Answers to the Questions