Foreword Preface 1. Hello Transformers The Encoder-Decoder Framework Attention Mechanisms Transfer Learning in NLP Hugging Face Transformers: Bridging the Gap A Tour of Transformer Applications Text Classification Named Entity Recognition Question Answering Summarization Translation Text Generation The Hugging Face Ecosystem The Hugging Face Hub Hugging Face Tokenizers Hugging Face Datasets Hugging Face Accelerate Main Challenges with Transformers Conclusion 2. Text Classification The Dataset A First Look at Hugging Face Datasets From Datasets to DataFrames Looking at the Class Distribution How Long Are Our Tweets? From Text to Tokens Character Tokenization Word Tokenization Subword Tokenization Tokenizing the Whole Dataset Training a Text Classifier Transformers as Feature Extractors Fine-Tuning Transformers Conclusion 3. Transformer Anatomy The Transformer Architecture The Encoder Self-Attention The Feed-Forward Layer Adding Layer Normalization Positional Embeddings Adding a Classification Head The Decoder Meet the Transformers The Transformer Tree of Life The Encoder Branch The Decoder Branch The Encoder-Decoder Branch Conclusion 4. Multilingual Named Entity Recognition The Dataset Multilingual Transformers A Closer Look at Tokenization The Tokenizer Pipeline The SentencePiece Tokenizer Transformers for Named Entity Recognition The Anatomy of the Transformers Model Class Bodies and Heads Creating a Custom Model for Token Classification Loading a Custom Model Tokenizing Texts for NER Performance Measures Fine-Tuning XLM-RoBERTa Error Analysis Cross-Lingual Transfer When Does Zero-Shot Transfer Make Sense? Fine-Tuning on Multiple Languages at Once Interacting with Model Widgets Conclusion 5. Text Generation The Challenge with Generating Coherent Text Greedy Search Decoding Beam Search Decoding Sampling Methods Top-k and Nucleus Sampling Which Decoding Method Is Best? Conclusion 6. Summarization The CNN/DailyMail Dataset Text Summarization Pipelines Summarization Baseline GPT-2 T5 BART PEGASUS Comparing Different Summaries Measuring the Quality of Generated Text BLEU ROUGE Evaluating PEGASUS on the CNN/DailyMail Dataset Training a Summarization Model Evaluating PEGASUS on SAMSum Fine-Tuning PEGASUS Generating Dialogue Summaries Conclusion 7. Question Answering Building a Review-Based QA System The Dataset Extracting Answers from Text Using Haystack to Build a QA Pipeline Improving Our QA Pipeline Evaluating the Retriever Evaluating the Reader Domain Adaptation Evaluating the Whole QA Pipeline Going Beyond Extractive QA Conclusion 8. Making Transformers Efficient in Production Intent Detection as a Case Study Creating a Performance Benchmark Making Models Smaller via Knowledge Distillation Knowledge Distillation for Fine-Tuning Knowledge Distillation for Pretraining Creating a Knowledge Distillation Trainer Choosing a Good Student Initialization Finding Good Hyperparameters with Optuna Benchmarking Our Distilled Model Making Models Faster with Quantization Benchmarking Our Quantized Model Optimizing Inference with ONNX and the ONNX Runtime Making Models Sparser with Weight Pruning Sparsity in Deep Neural Networks Weight Pruning Methods Conclusion 9. Dealing with Few to No Labels Building a GitHub Issues Tagger Getting the Data Preparing the Data Creating Training Sets Creating Training Slices Implementing a Naive Bayesline Working with No Labeled Data Working with a Few Labels Data Augmentation Using Embeddings as