Natural Language Processing with Transformers: Building Language Applications with Hugging Face (Paperback)
暫譯: 使用 Transformers 的自然語言處理：利用 Hugging Face 建立語言應用程式 (平裝本)

Tunstall, Lewis, Werra, Leandro Von, Wolf, Thomas

Natural Language Processing with Transformers: Building Language Applications with Hugging Face (Paperback)

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商品描述

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

商品描述(中文翻譯)

自2017年推出以來，變壓器（transformers）迅速成為在各種自然語言處理任務中實現最先進結果的主流架構。如果您是數據科學家或程式設計師，這本實用的書籍將向您展示如何使用 Hugging Face Transformers 這個基於 Python 的深度學習庫來訓練和擴展這些大型模型。

變壓器已被用來撰寫真實的新聞故事、改善 Google 搜尋查詢，甚至創建講冷笑話的聊天機器人。在這本指南中，作者 Lewis Tunstall、Leandro von Werra 和 Thomas Wolf，作為 Hugging Face Transformers 的創建者之一，採用實作的方法教您變壓器的運作原理以及如何將其整合到您的應用程式中。您將迅速學會它們可以幫助您解決的各種任務。

- 建立、除錯和優化變壓器模型以應對核心自然語言處理任務，例如文本分類、命名實體識別和問題回答
- 學習變壓器如何用於跨語言轉移學習
- 在標記數據稀缺的現實場景中應用變壓器
- 使用蒸餾、剪枝和量化等技術使變壓器模型在部署時更高效
- 從零開始訓練變壓器，並學習如何擴展到多個 GPU 和分散式環境

作者簡介

Lewis Tunstall is a data scientist in Switzerland, focused on building machine learning powered applications for startups and enterprises in the domains of natural language processing and time series. A former theoretical physicist, he has over 10 years experience translating complex subject matter to lay audiences and has taught machine learning to university students at both the graduate and undergraduate levels

Leandro von Werra is a data scientist at Swiss Mobiliar where he leads the company's natural language processing efforts to streamline and simplify processes for customers and employees. He has experience working across the whole machine learning stack and is the creator of a popular Python library that combines Transformers with reinforcement learning. He also teaches data science and visualization at the Bern University of Applied Sciences.

Thomas Wolf is Chief Science Officer and co-founder of HuggingFace. His team is on a mission to catalyze and democratize NLP research. Prior to HuggingFace, Thomas gained a Ph.D. in physics, and later a law degree. He worked as a physics researcher and a European Patent Attorney.

作者簡介(中文翻譯)

路易斯·坦斯塔爾（Lewis Tunstall）是一位位於瑞士的數據科學家，專注於為初創企業和大型企業構建基於機器學習的應用，特別是在自然語言處理和時間序列領域。作為一名前理論物理學家，他擁有超過10年的經驗，能夠將複雜的主題轉化為大眾易於理解的內容，並在研究生和本科生層級教授機器學習。

萊安德羅·馮·維拉（Leandro von Werra）是瑞士保險公司Swiss Mobiliar的數據科學家，他負責領導公司的自然語言處理工作，以簡化和優化客戶及員工的流程。他在整個機器學習技術棧方面擁有豐富的經驗，並且是結合了變壓器（Transformers）和強化學習的流行Python庫的創建者。他還在伯恩應用科學大學教授數據科學和可視化。

托馬斯·沃爾夫（Thomas Wolf）是HuggingFace的首席科學官和共同創始人。他的團隊致力於促進和民主化自然語言處理（NLP）研究。在加入HuggingFace之前，托馬斯獲得了物理學博士學位，並隨後取得法律學位。他曾擔任物理研究員和歐洲專利律師。

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