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Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you´re a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.

Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You´ll quickly learn a variety of tasks they can help you solve.

Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering
Learn how transformers can be used for cross-lingual transfer learning
Apply transformers in real-world scenarios where labeled data is scarce
Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization
Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments




Foreword
Preface
Who Is This Book For?
What You Will Learn
Software and Hardware Requirements
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
Lewis
Leandro
Thomas
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 a Lookup Table
Fine-Tuning a Vanilla Transformer
In-Context and Few-Shot Learning with Prompts
Leveraging Unlabeled Data
Fine-Tuning a Language Model
Fine-Tuning a Classifier
Advanced Methods
Conclusion
10. Training Transformers from Scratch
Large Datasets and Where to Find Them
Challenges of Build