Mastering Transformers. The Journey from BERT to Large Language Models and Stable Diffusion - Second Edition Katowice

Transformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated NLP studies and become a new paradigm. Thanks to their accurate and fast fine-tuning capabilities, transformer-based language models have been able to outperform traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems.Aside from NLP, a fast-growing area in multimodal learning and generative AI has recently been established, showing promising results. Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book. You’ll get started by understanding various transformer models before learning how to train different autoregressive language models such as GPT and XLNet. The book will also get you up to speed with boosting model performance, as well as tracking model training using the TensorBoard toolkit. In the later chapters, you’ll focus on using vision transformers to solve computer vision problems. Finally, you’ll discover how to harness the power of transformers to model time series data and for predicting.By the end of this transformers book, you’ll have an understanding of transformer models and how to use them to solve challenges in NLP and CV. Spis treści: 1. From Bag-of-Words to the Transformer2. A Hands-On Introduction to the Subject3. Autoencoding Language Models4. Autoregressive Language Models5. Fine-Tuning Language Model for Text Classification6. Fine-Tuning Language Models for Token Classification7. Text Representation8. Boosting Your Model Performance9. Parameter Efficient Fine-Tuning10. Zero-Shot and Few-Shot Learning in NLP11. Explainable AI (XAI) for NLP12. Working with Efficient Transformers13. Cross-Lingual Language Modeling14. Serving Transformer Models15. Model Tracking and Monitoring16. Vision Transformers17. Tabular Transformers18. Multi-Model Transformers