Transformers for Natural Language Processing and Computer Vision: Take Generative AI and LLMs to the next level with Hugging Face, Google Vertex AI, ChatGPT, GPT-4V, and DALL-E 3 3rd Edition

by Denis Rothman

This repo is continually updated and upgraded.
Last update: April 29, 2024
Look for 🐬 to explore new bonus notebooks!
Look for 🎏 to find the latest upgrades such as OpenAI's latest gpt-4-turbo model or code optimization
Look for 🛠 to detect resolved issues such as new package version dependency conflicts or hardware upgrades(GPU, TPU) that impact code.

🚩If you see anything that doesn't run as expected, raise an issue and we'll work on it!

Transformers-for-NLP-and-Computer-Vision-3rd-Edition

This is the code repository for Transformers for Natural Language Processing and Computer Vision, published by Packt.

Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3

About the book

Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV).

Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication.

What you will learn

• Learn how to pretrain and fine-tune LLMs
• Learn how to work with multiple platforms, such as Hugging Face, OpenAI, and Google Vertex AI
• Learn about different tokenizers and the best practices for preprocessing language data
• Implement Retrieval Augmented Generation and rules bases to mitigate hallucinations
• Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP
• Create and implement cross-platform chained models, such as HuggingGPT
• Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V

Table of Contents

Chapters

1. What Are Transformers?
2. Getting Started with the Architecture of the Transformer Model
3. Emergent vs Downstream Tasks: The Unseen Depths of Transformers
4. Advancements in Translations with Google Trax, Google Translate, and Gemini
5. Diving into Fine-Tuning through BERT
6. Pretraining a Transformer from Scratch through RoBERTa
7. The Generative AI Revolution with ChatGPT
8. Fine-Tuning OpenAI GPT Models
9. Shattering the Black Box with Interpretable Tools
10. Investigating the Role of Tokenizers in Shaping Transformer Models
11. Leveraging LLM Embeddings as an Alternative to Fine-Tuning
12. Toward Syntax-Free Semantic Role Labeling with ChatGPT and GPT-4
13. Summarization with T5 and ChatGPT
14. Exploring Cutting-Edge LLMs with Vertex AI and PaLM 2
15. Guarding the Giants: Mitigating Risks in Large Language Models
16. Beyond Text: Vision Transformers in the Dawn of Revolutionary AI
17. Transcending the Image-Text Boundary with Stable Diffusion
18. Hugging Face AutoTrain: Training Vision Models without Coding
19. On the Road to Functional AGI with HuggingGPT and its Peers
20. Beyond Human-Designed Prompts with Generative Ideation

Appendix

Appendix: Answers to the Questions

Platforms

You can run the notebooks directly from below table:

Chapter	Colab	Kaggle	Gradient	StudioLab
Part I The Foundations of Transformer Models
Chapter 1: What are Transformers?
🛠O_1_and_Accelerators.ipynb ChatGPT_Plus_writes_and_explains_AI.ipynb
Chapter 2: Getting Started with the Architecture of the Transformer Model
Multi_Head_Attention_Sub_Layer.ipynb positional_encoding.ipynb
Chapter 3: Emergent vs Downstream Tasks: the Unseen Depths of Transformers
From_training_to_emergence.ipynb Transformer_tasks_with_Hugging_Face.ipynb
Chapter 4: Advancements in Translations with Google Trax, Google Translate, and Google Bard
WMT_translations.ipynb Trax_Google_Translate.ipynb
Chapter 5: Diving into Fine-Tuning through BERT
BERT_Fine_Tuning_Sentence_Classification_GPU.ipynb
Chapter 6: Pretraining a Transformer from Scratch through RoBERTa
KantaiBERT.ipynb 🛠 Customer_Support_for_X.ipynb
Part II: The Rise of Suprahuman NLP
Chapter 7: The Generative AI Revolution with ChatGPT
OpenAI_Models.ipynb OpenAI_GPT_4_Assistant.ipynb 🎏Getting_Started_GPT_4_API.ipynb 🎏GPT_4_RAG.ipynb
Chapter 8: Fine-tuning OpenAI Models
🎏Fine_tuning_OpenAI_Models.ipynb
Chapter 9: Shattering the Black Box with Interpretable tools
BertViz_Interactive.ipynb Hugging_Face_SHAP.ipynb
Chapter 10: Investigating the Role of Tokenizers in Shaping Transformer Models
Tokenizers.ipynb Sub_word_tokenizers.ipynb Exploring_tokenizers.ipynb
Chapter 11: Leveraging LLM Embeddings as an Alternative to Fine-Tuning
Embedding_with_NLKT_Gensim.ipynb 🎏Question_answering_with_embeddings.ipynb Transfer_Learning_with_Ada_Embeddings.ipynb
Chapter 12: Towards Syntax-Free Semantic Role Labeling with BERT and OpenAI's ChatGPT
Semantic_Role_Labeling_GPT-4.ipynb
Chapter 13: Summarization with T5 and ChatGPT
🛠Summerizing_Text_T5.ipynb Summarizing_ChatGPT.ipynb
Chapter 14: Exploring Cutting-Edge NLP with Google Vertex AI and PaLM 2
Google_Vertex_AI.ipynb
Chapter 15: Guarding the Giants: Mitigating Risks in Large Language Models
🎏Auto_Big_bench.ipynb WandB_Prompts_Quickstart.ipynb Encoder_decoder_transformer.ipynb Mitigating_Generative_AI.ipynb
Part III: Generative Computer Vision: A New Way to See the World
Chapter 16: Vision Transformers in the Dawn of Revolutionary AI
ViT_CLIP.ipynb Getting_Started_DALL_E_API.ipynb GPT-4V.ipynb
Chapter 17: Transcending the Image-Text Boundary with Stable Diffusion
Stable_Diffusion_Keras.ipynb Stable__Vision_Stability_AI.ipynb Stable__Vision_Stability_AI_Animation.ipynb Text_to_video_synthesis.ipynb TimeSformer.ipynb
Chapter 18: Automated Vision Transformer Training
Hugging_Face_AutoTrain.ipynb
Chapter 19: On the Road to Functional AGI with HuggingGPT and its Peers
Computer_Vision_Analysis.ipynb
Chapter 20: Generative AI Ideation Vertex AI, Langchain, and Stable Diffusion
Automated_Design.ipynb Midjourney_bot.ipynb 🎏Automated_Ideation.ipynb 🐬 MyMidjourney_API.ipynb

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Get to Know the Author

Denis Rothman graduated from Sorbonne University and Paris Diderot University, designing one of the first patented encoding and embedding systems. He authored one of the first patented AI cognitive robots and bots. He began his career delivering Natural Language Processing (NLP) chatbots for Moët et Chandon and as an AI tactical defense optimizer for Airbus (formerly Aerospatiale). Denis then authored an AI resource optimizer for IBM and luxury brands, leading to an Advanced Planning and Scheduling (APS) solution used worldwide. LinkedIn