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Transformers for Natural Language Processing.pdf
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图书简介 该书将带您学习使用Python的NLP,并研究了由Google,Facebook,Microsoft,OpenAI和Hugging Face等先驱者创建的变压器体系结构中的各种杰出模型和数据集。 这本书分三个阶段训练您。在向RoBERTa,BERT和DistilBERT模型过渡之前,第一阶段向您介绍从原始变压器开始的变压器体系结构。您会发现一些小型变压器的培训方法在某些情况下可以胜过GPT-3。在第二阶段,您将应用自然语言理解(NLU)和自然语言生成(NLG)的转换器。最后,第三阶段将帮助您掌握高级语言理解技术,例如优化社交网络数据集和假新闻识别。 在这本NLP书籍的最后,您将从认知科学的角度理解变压器,并精通将技术巨头预先训练好的变压器模型应用于各种数据集。 您将学到什么 使用最新的预训练变压器模型 掌握原始Transformer,GPT-2,BERT,T5和其他变压器模型的工作原理 使用优于经典深度学习模型的概念创建理解语言的Python程序 使用各种NLP平台,包括Hugging Face,Trax和AllenNLP 将Python,TensorFlow和Keras程序应用于情感分析,文本摘要,语音识别,机器翻译等 测量关键变压器的生产率,以定义其范围,潜力和生产限制
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Transformers for Natural
Language Processing
Build innovative deep neural network architectures
for NLP with Python, PyTorch, TensorFlow, BERT,
RoBERTa, and more
Denis Rothman
BIRMINGHAM - MUMBAI
Transformers for Natural Language Processing
Copyright © 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval
system, or transmitted in any form or by any means, without the prior written
permission of the publisher, except in the case of brief quotations embedded in
critical articles or reviews.
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of the information presented. However, the information contained in this book is
sold without warranty, either express or implied. Neither the author, nor Packt
Publishing or its dealers and distributors, will be held liable for any damages
caused or alleged to have been caused directly or indirectly by this book.
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companies and products mentioned in this book by the appropriate use of capitals.
However, Packt Publishing cannot guarantee the accuracy of this information.
Producer: Tushar Gupta
Acquisition Editor – Peer Reviews: Saby D'Silva, Divya Mudaliar
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First published: January 2021
Production reference: 3150221
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ISBN 978-1-80056-579-1
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Contributors
About the author
Denis Rothman graduated from Sorbonne University and Paris Diderot
University, designing one of the very fi rst word2matrix patented embedding
and vectorizing systems. He began his career authoring one of the fi rst AI
cognitive Natural Language Processing (NLP) chatbots applied as an automated
language teacher for Moët et Chandon and other companies. He has authored an
AI resource optimizer for IBM and apparel producers and an advanced planning
and scheduling (APS) solution used worldwide.
I want to thank the corporations who trusted me from the start to deliver
artifi cial intelligence solutions and share the risks of continuous innovation.
I also thank my family, who believed I would make it big at all times.
About the reviewers
George Mihaila is currently a PhD candidate in computer science at University of
North Texas. The main research areas he works on are Deep Learning and Natural
Language Processing (NLP) with a focus on dialogue generation. His research thesis
is on casual dialogue generation with persona.
George is very passionate when it comes to AI and NLP. He always keeps up with
the latest language models. Every time a new groundbreaking model comes along,
he likes to study the code to better understand its inner workings.
Besides his research, George is also involved in writing tutorials on how to use
transformer models in various machine learning tasks. He loves the idea of open
source and likes sharing his knowledge and helping others in NLP through his
GitHub projects and personal website.
In his free time, George likes to cook and travel with his signifi cant other.
Thanks to everyone on the publishing team and to Denis Rothman for
allowing me this opportunity and for making the review process so much
fun and easy.
Malte Pietsch is co-founder and CTO at deepset, where he builds Haystack – an
end-to-end framework for building enterprise search engines fueled by open source
and NLP. He holds an M.Sc. with honors from TU Munich and conducted research
at Carnegie Mellon University. Before founding deepset, he worked as a data scientist
for multiple startups. He is an open-source lover, likes reading papers before
breakfast, and is obsessed with automating the boring parts of his work.
Carlos Toxtli is a human-computer interaction researcher who studies the impact
of artifi cial intelligence in the future of work. He studied a Ph.D. in computer science
at West Virginia University and a master's degree in technological innovation and
entrepreneurship at the Monterrey Institute of Technology and Higher Education.
He has worked for international organizations such as Google, Microsoft, Amazon,
and the United Nations. He was also the technical reviewer on the Artifi cial
Intelligence By Example, Second Edition and Hands-On Explainable AI (XAI) with Python
books. He has also built companies that use artifi cial intelligence in the fi nancial,
educational, customer service, and parking industries. Carlos has published
numerous research papers, manuscripts, and book chapters for different conferences
and journals in his fi eld.
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