Transformers For Natural Language Processing
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Transformers For Natural Language Processing
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-25
Transformers For Natural Language Processing written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-25 with Computers categories.
OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Improve your productivity with OpenAI’s ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models Pretrain a BERT-based model from scratch using Hugging Face Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data Book DescriptionTransformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.What you will learn Discover new techniques to investigate complex language problems Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3 Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4 Who this book is for If you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community to help guide you on your transformers journey!
Natural Language Processing With Transformers Revised Edition
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Author : Lewis Tunstall
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-05-26
Natural Language Processing With Transformers Revised Edition written by Lewis Tunstall and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-26 with Computers categories.
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 -now revised in full color- 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
Natural Language Processing With Transformers
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Author : Cuantum Technologies
language : en
Publisher: Staten House
Release Date : 2025-01-07
Natural Language Processing With Transformers written by Cuantum Technologies and has been published by Staten House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-07 with Computers categories.
This Book grants Free Access to our e-learning Platform, which includes: ✅ Free Repository Code with all code blocks used in this book ✅ Access to Free Chapters of all our library of programming published books ✅ Free premium customer support ✅ Much more... Unlock the Full Potential of Transformers for Natural Language Processing and Beyond Transformers are reshaping the world of AI, powering innovations in natural language processing (NLP) and enabling groundbreaking multimodal applications. Whether you're an aspiring machine learning practitioner or an experienced developer, "Natural Language Processing with Transformers: Advanced Techniques and Multimodal Applications" is your definitive guide to mastering these cutting-edge models. What You'll Learn Dive into advanced NLP techniques: Explore machine translation, text summarization, sentiment analysis, named entity recognition, and more using state-of-the-art transformer architectures. Harness the Hugging Face ecosystem: Gain hands-on experience with tools and libraries that streamline model training, fine-tuning, and deployment. Build real-world solutions: Develop practical applications, including a sentiment analysis API and a custom NER pipeline, with detailed step-by-step instructions and code examples. Expand into multimodal AI: Discover how transformers integrate text, images, and video to power innovative use cases like medical image analysis and video summarization. Why This Book Stands Out Authored with clarity and precision, this book combines theoretical insights with practical guidance. Through hands-on projects, you'll learn to fine-tune models for domain-specific tasks, optimize them for real-world deployment, and explore multimodal AI's potential to revolutionize industries such as healthcare, education, and content creation. Who This Book Is For This book is perfect for: Machine learning enthusiasts looking to deepen their understanding of transformers. Data scientists and engineers seeking practical knowledge to build and deploy real-world applications. Academics and researchers exploring advanced NLP and multimodal techniques. Practical Projects to Solidify Your Learning Put theory into practice with projects that include: Creating a Named Entity Recognition pipeline fine-tuned for custom datasets. Building a scalable sentiment analysis API with FastAPI and Hugging Face models. Developing multimodal applications such as medical image-text integration and video summarization. Your Journey Into the Future of AI Starts Here Transform your skills and become a leader in NLP and multimodal AI. With "Natural Language Processing with Transformers: Advanced Techniques and Multimodal Applications", you'll gain the expertise needed to build impactful AI solutions that leverage the full power of transformer models.
Transformers For Natural Language Processing
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Author : Denis Rothman
language : en
Publisher:
Release Date : 2021-01-28
Transformers For Natural Language Processing written by Denis Rothman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-28 with categories.
Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models Key Features Build and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine Learn training tips and alternative language understanding methods to illustrate important key concepts Book Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What You Will Learn Use the latest pretrained transformer models Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models Create language understanding Python programs using concepts that outperform classical deep learning models Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more Measure productivity of key transformers to define their scope, potential, and limits, in production Who this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include deep learning & NLP practitioners, data analysts and data scientists who want an introduction to AI language understanding to process the increasing amounts of language-driven functions.
Transformers For Natural Language Processing
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Author : Denis Rothman
language : en
Publisher:
Release Date : 2022
Transformers For Natural Language Processing written by Denis Rothman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Artificial intelligence categories.
Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers can create code using just a brief description. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets.
Transformers For Natural Language Processing And Computer Vision
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-02-29
Transformers For Natural Language Processing And Computer Vision written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Computers categories.
The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal AI, risk mitigation, and practical implementations with ChatGPT, Hugging Face, and Vertex AI Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Compare and contrast 20+ models (including GPT, BERT, and Llama) and multiple platforms and libraries to find the right solution for your project Apply RAG with LLMs using customized texts and embeddings Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases Book DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, practical applications, and popular platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through a range of transformer architectures from foundation models and generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to question-answering systems leveraging embedding-based search. You'll also implement Retrieval Augmented Generation (RAG) to enhance accuracy and gain greater control over your LLM outputs. Additionally, you’ll understand common LLM risks, such as hallucinations, memorization, and privacy issues, and implement mitigation strategies using moderation models alongside rule-based systems and knowledge integration. Dive into generative vision transformers and multimodal architectures, and build practical applications, such as image and video classification. Go further and combine different models and platforms to build AI solutions and explore AI agent capabilities. This book provides you with an understanding of transformer architectures, including strategies for pretraining, fine-tuning, and LLM best practices.What you will learn Breakdown and understand the architectures of the Transformer, BERT, GPT, T5, PaLM, ViT, CLIP, and DALL-E Fine-tune BERT, GPT, and PaLM models Learn about different tokenizers and the best practices for preprocessing language data Pretrain a RoBERTa model from scratch Implement retrieval augmented generation and rules bases to mitigate hallucinations Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP Go in-depth into vision transformers with CLIP, DALL-E, and GPT Who this book is for This book is ideal for NLP and CV engineers, data scientists, machine learning practitioners, software developers, and technical leaders looking to advance their expertise in LLMs and generative AI or explore latest industry trends. Familiarity with Python and basic machine learning concepts will help you fully understand the use cases and code examples. However, hands-on examples involving LLM user interfaces, prompt engineering, and no-code model building ensure this book remains accessible to anyone curious about the AI revolution.
Introduction To Transformers For Nlp
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Author : Shashank Mohan Jain
language : en
Publisher: Apress
Release Date : 2022-10-21
Introduction To Transformers For Nlp written by Shashank Mohan Jain and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-21 with Computers categories.
Get a hands-on introduction to Transformer architecture using the Hugging Face library. This book explains how Transformers are changing the AI domain, particularly in the area of natural language processing. This book covers Transformer architecture and its relevance in natural language processing (NLP). It starts with an introduction to NLP and a progression of language models from n-grams to a Transformer-based architecture. Next, it offers some basic Transformers examples using the Google colab engine. Then, it introduces the Hugging Face ecosystem and the different libraries and models provided by it. Moving forward, it explains language models such as Google BERT with some examples before providing a deep dive into Hugging Face API using different language models to address tasks such as sentence classification, sentiment analysis, summarization, and text generation. After completing Introduction to Transformers for NLP, you will understand Transformer concepts and be able to solve problems using the Hugging Face library. What You Will Learn Understand language models and their importance in NLP and NLU (Natural Language Understanding) Master Transformer architecture through practical examples Use the Hugging Face library in Transformer-based language models Create a simple code generator in Python based on Transformer architecture Who This Book Is ForData Scientists and software developers interested in developing their skills in NLP and NLU (Natural Language Understanding)
Mastering Transformers
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Author : Savaş Yıldırım
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-09-15
Mastering Transformers written by Savaş Yıldırım and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-15 with Computers categories.
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 DescriptionTransformer-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.
Applied Hugging Face Transformers For Natural Language Processing
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-09-26
Applied Hugging Face Transformers For Natural Language Processing written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-26 with Computers categories.
"Applied Hugging Face Transformers for Natural Language Processing" "Applied Hugging Face Transformers for Natural Language Processing" is a comprehensive and practical guide to harnessing the power of transformer models for advanced natural language processing applications. This book takes readers on a curated journey, beginning with the architectural foundations of transformer models—including attention mechanisms, multi-head attention, and the latest innovations for long-context and sparse computation. Through clear explanations and in-depth explorations, it demystifies both the encoder-only and encoder-decoder paradigms, providing a solid conceptual basis for understanding the modern NLP landscape. The subsequent chapters form a hands-on blueprint for effectively utilizing the Hugging Face ecosystem, covering not only the popular Transformers library but also an integrated suite of tools for tokenization, dataset management, distributed training, and efficient inference. Readers are guided through best practices in data preprocessing, dynamic batching, feature augmentation, and robust handling of multilingual or noisy corpora. From fine-tuning models on specialized tasks to deploying them at scale, the book delivers actionable insights, detailed workflows, and advanced techniques such as transfer learning, prompt-based fine-tuning, and hardware-aware optimization. Positioned at the intersection of research and real-world deployment, this book goes beyond engineering to address the responsibilities and challenges of modern NLP. It provides rigorous approaches to model evaluation, interpretability, fairness, and adversarial robustness, alongside frameworks for ethical deployment, privacy, and compliance. The final chapters survey frontiers such as massive model scaling, continual learning, federated NLP, and AutoML, equipping practitioners, researchers, and leaders with both a practical toolkit and a forward-looking perspective on transformer-driven AI.
The Transformer Architecture
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Author : TOMMY. HOGAN
language : en
Publisher: Independently Published
Release Date : 2023-09-27
The Transformer Architecture written by TOMMY. HOGAN and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-27 with Computers categories.
Experience the Future of Language Processing with "The Transformer Architecture: A Practical Guide to Natural Language Processing" Learn how Transformers are revolutionizing the way machines understand, interpret, and generate human language Master the fundamentals of Transformers and build your own Transformer models for a range of real-world NLP applications Gain hands-on experience with Transformers through practical use cases, from chatbots and sentiment analysis to machine translation and question answering Understand the latest advances in Transformers and stay ahead of the curve Who is this "The Transformer Architecture" for? AI enthusiasts of all levels Developers who want to learn how to implement and deploy Transformer models Data scientists who want to leverage the power of Transformers for NLP tasks Tech innovators who want to stay updated with the latest advancements in NLP technology What's inside? An introduction to the history and key components of Transformers A hands-on guide to implementing Transformers from scratch Tips on optimizing model performance Real-world use cases of Transformers Discussions of data handling, ethics, evaluation, and deployment "The Transformer Architecture: A Practical Guide to Natural Language Processing" is a comprehensive guide that covers everything you need to know to get started with Transformers, from the basics to advanced topics. The book is packed with practical examples that will help you learn by doing. Order your copy today and start your building advanced Transformer models!