Introduction to Transformer Models
Chapter 1: Sequence Modeling and Attention Fundamentals
Challenges in Sequence-to-Sequence Tasks
Recap: Recurrent Neural Networks (RNNs)
Limitations of Traditional RNN Approaches
Introducing the Attention Mechanism Concept
Attention Score Calculation: A High-Level View
Context Vectors from Attention Weights
Quiz for Chapter 1
Chapter 2: Self-Attention and Multi-Head Attention
The Idea Behind Self-Attention
Query, and Value Vectors in Self-Attention
Scaled Dot-Product Attention Mechanism
Visualizing Self-Attention Scores
Introduction to Multi-Head Attention
How Multi-Head Attention Works
Benefits of Multiple Attention Heads
Hands-on Practical: Implementing Scaled Dot-Product Attention
Quiz for Chapter 2
Chapter 3: The Transformer Encoder-Decoder Architecture
Overall Architecture Overview
Input Embedding Layer
The Need for Positional Information
Positional Encoding Explained
The Encoder Stack
Add & Norm Layers (Residual Connections)
Position-wise Feed-Forward Networks
The Decoder Stack
Masked Multi-Head Self-Attention
Encoder-Decoder Attention Mechanism
Final Linear Layer and Softmax
Hands-on Practical: Building an Encoder Layer
Quiz for Chapter 3
Chapter 4: Training and Implementing Transformers
Data Preparation: Tokenization
Creating Input Batches
Loss Functions for Sequence Tasks
Optimization Strategies
Regularization Techniques
Overview of a Basic Implementation
Using Pre-trained Model Libraries (Brief)
Practice: Assembling a Basic Transformer
Quiz for Chapter 4
Data Preparation: Tokenization
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- Neural Machine Translation of Rare Words with Subword Units, Rico Sennrich, Barry Haddow, Alexandra Birch, 2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1 DOI: 10.18653/v1/P16-1162 - This foundational paper introduced Byte Pair Encoding (BPE) to the field of natural language processing for handling rare and out-of-vocabulary words in neural machine translation, establishing it as a common subword tokenization method.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, 2019 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Vol. 1 DOI: 10.18653/v1/N19-1423 - This seminal paper introduced the BERT model, which prominently uses WordPiece tokenization. It demonstrates the practical application and importance of subword tokenization in large-scale transformer models for language understanding.
- SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing, Taku Kudo, 2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) DOI: 10.18653/v1/D18-2012 - This paper presents SentencePiece, a language-agnostic subword tokenizer that directly processes raw text, including whitespace, making it particularly suitable for diverse languages and consistent tokenization/detokenization.
- tokenizers: Fast State-of-the-Art Tokenizers, Hugging Face, 2023 (Hugging Face) - The official documentation for the Hugging Face
tokenizerslibrary, which provides optimized implementations of BPE, WordPiece, and other subword tokenization algorithms used in transformer models. - Natural Language Processing with Transformers, Lewis Tunstall, Leandro von Werra, Thomas Wolf, 2022 (O'Reilly Media) - A practical guide that includes detailed explanations and code examples for various tokenization methods, special tokens, and their application within the Hugging Face Transformers ecosystem.