Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing techniques (Paperback)

Yıldırım, Savaş, Asgari-Chenaghlu, Meysam

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商品描述

Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP

 

Key Features:

  • Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems
  • Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI
  • Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard

 

Book Description:

Transformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library.

 

The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment.

 

By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.

 

What You Will Learn:

  • Explore state-of-the-art NLP solutions with the Transformers library
  • Train a language model in any language with any transformer architecture
  • Fine-tune a pre-trained language model to perform several downstream tasks
  • Select the right framework for the training, evaluation, and production of an end-to-end solution
  • Get hands-on experience in using TensorBoard and Weights & Biases
  • Visualize the internal representation of transformer models for interpretability

 

Who this book is for:

This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.

商品描述(中文翻譯)

以問題解決的方式學習變壓器,並通過實施能夠建立自然語言處理(NLP)未來的方法輕鬆上手。

主要特點:
- 使用最新的Python庫進行快速原型設計,以解決工業問題
- 解決高級NLP問題,如命名實體識別、信息提取、語言生成和對話AI
- 使用BertViz、exBERT和TensorBoard監控模型性能

書籍描述:
基於變壓器的語言模型主導了自然語言處理(NLP)研究,並成為一種新的範式。通過本書,您將學習如何使用Python Transformers庫構建各種基於變壓器的NLP應用。

本書首先向您展示如何編寫第一個hello-world程序,介紹變壓器。然後,您將學習分詞器的工作原理以及如何訓練自己的分詞器。隨著進一步的學習,您將探索自編碼模型(如BERT)和自回歸模型(如GPT)的架構。您將了解如何訓練和微調模型,解決各種自然語言理解(NLU)和自然語言生成(NLG)問題,包括文本分類、標記分類和文本表示。本書還幫助您學習如何為具有有限計算能力的長文本NLP任務設計高效模型。您還將處理多語言和跨語言問題,通過監控模型性能優化模型,並了解如何解構這些模型以實現可解釋性。最後,您將能夠在生產環境中部署變壓器模型。

通過閱讀本書,您將學習如何使用變壓器解決高級NLP問題,並使用先進模型。

學到的內容:
- 使用Transformers庫探索最先進的NLP解決方案
- 使用任何變壓器架構在任何語言中訓練語言模型
- 微調預訓練的語言模型以執行多個下游任務
- 選擇適合的框架進行訓練、評估和生產端到端解決方案
- 使用TensorBoard和Weights & Biases進行實踐操作
- 可視化變壓器模型的內部表示以實現可解釋性

本書適合深度學習研究人員、實踐型NLP從業者,以及機器學習/自然語言處理教育工作者和學生,他們希望從變壓器開始自己的學習之旅。初級機器學習知識和良好的Python掌握能力將有助於您充分利用本書。