Transformers自然语言处理 修订版(影印版)

自2017年推出以来，transformer已迅速成为 在各种自然语言处理任务上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，现已改为全彩印刷，将向你展示如何使用基 于python的深度学习库Hugging Face Transformers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，甚至是创建会讲老套笑话 的聊天机器人。在这本指南中，作者Lewis Tunstall、Leandro von Werra、Thomas Wolf是 Hugging Face Transformers的创建者，他们通过 实践方法来教你如何使用Transformer以及如何将 其集成到你的应用中。你将快速学习可以由 transformer帮助解决的各种任务。

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