Transformers自然语言处理（NaturalLanguageProcessingw pdf下载

自2017年推出以来，transformers已迅速成为在各种自然语言处理任务中实现最佳结果的主导架构。如果你是一名数据科学家或程序员，这本实践用书将向你展示如何使用Hugging Face Transformers(基于Python的深度学习库)训练和扩展这些大型模型。
Transformers已经被用来撰写真实的新闻故事、改进Google搜索查询，甚至创建会讲老套笑话的聊天机器人。在这本指南中，作者Lewis Tunstall、Leandro von Werra、Thomas Wolf(Hugging Face Transformers的创建者)通过实践方法来教你如何使用transformers以及如何将它集成到你的应用中。你将快速学习可以由transformers帮助解决的各种任务。
为核心NLP任务构建、调试和优化transformers模型，例如文本分类、命名实体识别和问答；
学习如何使用transformers进行跨语言迁移学习；
在缺乏标记数据的实际场景中应用transformers；
使用提取、修剪和量化等技术高效部署transformers模型；
从头开始训练transformers并学习如何扩展到多个GPU和分布式环境。

刘易斯·汤斯顿，Lewis Tunstall是Hugging Face的机器学习工程师。他目前的工作重点是为NLP社区开发工具并教人们如何有效地使用这些工具。

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