版本：Transformers for Natural Language Processing_Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 2nd Edition

官方代码： https://github.com/Denis2054/Transformers-for-NLP-2nd-Edition

目录：

• Chapter 1: What are Transformers?
• Chapter 2: Getting Started with the Architecture of the Transformer Model
• Chapter 3: Fine-Tuning BERT Models
• Chapter 4: Pretraining a RoBERTa Model from Scratch
• Chapter 5: Downstream NLP Tasks with Transformers
• Chapter 6: Machine Translation with the Transformer
• Chapter 7: The Rise of Suprahuman Transformers with GPT-3 Engines
• Chapter 8: Applying Transformers to Legal and Financial Documents for AI Text Summarization
• Chapter 9: Matching Tokenizers and Datasets
• Chapter 10: Semantic Role Labeling with BERT-Based Transformers
• Chapter 11: Let Your Data Do the Talking: Story, Questions, and Answers
• Chapter 12: Detecting Customer Emotions to Make Predictions
• Chapter 13: Analyzing Fake News with Transformers
• Chapter 14: Interpreting Black Box Transformer Models
• Chapter 15: From NLP to Task-Agnostic Transformer Models
• Chapter 16: The Emergence of Transformer-Driven Copilots