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Applied Hugging Face 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!

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.

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

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.

Ultimate Natural Language Processing With Spacy And Hugging Face Master The End To End Journey Of Nlp By Using Foundational Methods Building Projects With Spacy And Fine Tuning Transformer Models With Hugging Face

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Author : Abhinaba Banerjee
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-10-15

Ultimate Natural Language Processing With Spacy And Hugging Face Master The End To End Journey Of Nlp By Using Foundational Methods Building Projects With Spacy And Fine Tuning Transformer Models With Hugging Face written by Abhinaba Banerjee and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-15 with Computers categories.

Your One-stop Destination to Learn NLP Theory and Build Real-life Use Cases and Projects! Key Features● Learn NLP from scratch with exposure to Deep Learning concepts.● Build NLP-based projects using the latest frameworks and libraries.● Define AI-based use cases from scratch, and build NLP applications. Book DescriptionNatural Language Processing (NLP) is at the core of modern AI, powering everything from chatbots to recommendation systems. “Ultimate Natural Language Processing with spaCy and Hugging Face” is a practical guide that takes you from essential NLP foundations to advanced transformer models and large language applications, equipping you to build real-world AI projects with confidence. You begin with the fundamentals—tokenization, lemmatization, Bag-of-Words, TF-IDF, embeddings, POS tagging, and Named Entity Recognition—and apply them to practical use cases such as sentiment analysis, topic classification, and text classification. The book then moves into Deep Learning for NLP with hands-on coding of CNNs, RNNs, and LSTMs, progressing from theory to applied projects. spaCy is explored in depth, with guidance on building and customizing pipelines for NER, POS tagging, and sentiment analysis. Real-world projects, including extracting dates and events from news articles, ensure that every concept connects to practical applications. The journey concludes with Hugging Face and transformers, where you train and fine-tune models for summarization, classification, and recommendation. Large Language Models (LLMs) such as GPT, Llama, and Claude are introduced alongside efficient training techniques like LoRA and Retrieval-Augmented Generation. By the end, you will gain the confidence to design and deploy responsible AI-powered solutions. What you will learn● Understand NLP fundamentals, including embeddings, POS tagging and NER.● Implement CNN, RNN and LSTM models for text applications.● Create and customize spaCy pipelines for real-world NLP tasks.● Train and fine-tune transformer models using Hugging Face tools.● Apply large language models to build AI-powered applications.● Discover responsible AI, RAG and upcoming NLP practices.

Natural Language Processing With Transformers Revised Edition

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Author : Lewis Tunstall
language : en
Publisher: O'Reilly Media
Release Date : 2022-07-12

Natural Language Processing With Transformers Revised Edition written by Lewis Tunstall and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-12 with categories.

Hugging Face In Action

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Author : Wei-Meng Lee
language : en
Publisher: Simon and Schuster
Release Date : 2025-11-11

Hugging Face In Action written by Wei-Meng Lee and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-11 with Computers categories.

Everything you need to know about using the tools, libraries, and models at Hugging Face--from transformers, to RAG, LangChain, and Gradio.Hugging Face is the ultimate resource for machine learning engineers and AI developers.

Ultimate Natural Language Processing With Spacy And Hugging Face

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Author : Abhinaba Banerjee
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-10-15

Ultimate Natural Language Processing With Spacy And Hugging Face written by Abhinaba Banerjee and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-15 with Computers categories.

TAGLINE Your One-stop Destination to Learn NLP Theory and Build Real-life Use Cases and Projects! KEY FEATURES ● Learn NLP from scratch with exposure to Deep Learning concepts. ● Build NLP-based projects using the latest frameworks and libraries. ● Define AI-based use cases from scratch, and build NLP applications. DESCRIPTION Natural Language Processing (NLP) is at the core of modern AI, powering everything from chatbots to recommendation systems. “Ultimate Natural Language Processing with spaCy and Hugging Face” is a practical guide that takes you from essential NLP foundations to advanced transformer models and large language applications, equipping you to build real-world AI projects with confidence. You begin with the fundamentals—tokenization, lemmatization, Bag-of-Words, TF-IDF, embeddings, POS tagging, and Named Entity Recognition—and apply them to practical use cases such as sentiment analysis, topic classification, and text classification. The book then moves into Deep Learning for NLP with hands-on coding of CNNs, RNNs, and LSTMs, progressing from theory to applied projects. spaCy is explored in depth, with guidance on building and customizing pipelines for NER, POS tagging, and sentiment analysis. Real-world projects, including extracting dates and events from news articles, ensure that every concept connects to practical applications. The journey concludes with Hugging Face and transformers, where you train and fine-tune models for summarization, classification, and recommendation. Large Language Models (LLMs) such as GPT, Llama, and Claude are introduced alongside efficient training techniques like LoRA and Retrieval-Augmented Generation. By the end, you will gain the confidence to design and deploy responsible AI-powered solutions. WHAT WILL YOU LEARN ● Understand NLP fundamentals, including embeddings, POS tagging and NER. ● Implement CNN, RNN and LSTM models for text applications. ● Create and customize spaCy pipelines for real-world NLP tasks. ● Train and fine-tune transformer models using Hugging Face tools. ● Apply large language models to build AI-powered applications. ● Discover responsible AI, RAG and upcoming NLP practices. WHO IS THIS BOOK FOR? This book is tailored for students, developers, data scientists, AI engineers, machine learning practitioners, researchers, and technology professionals who want practical exposure to Natural Language Processing from scratch. It is ideal for beginners to intermediates aiming for career growth and project building. TABLE OF CONTENTS 1. Introduction to NLP and the Essential Libraries 2. Building Blocks and Techniques for NLP Algorithms 3. Sentiment Analysis Using NLP 4. Deep Learning in NLP 5. Working with CNN 6. Building NLP Pipelines Using spaCy 7. Building a spaCy Pipeline for Extracting Information 8. Building a Transformer Using Hugging Face 9. Training Language Models 10. Importance of Large Language Models and Their Applications 11. Fine-Tuning LLMs and Building Text-Powered Tools 12. Best Practices and Future Trends of NLP Index

The Practical Guide To Large Language Models

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Author : Ivan Gridin
language : en
Publisher: Springer Nature
Release Date : 2025-12-12

The Practical Guide To Large Language Models written by Ivan Gridin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-12 with Computers categories.

This book is a practical guide to harnessing Hugging Face's powerful transformers library, unlocking access to the largest open-source LLMs. By simplifying complex NLP concepts and emphasizing practical application, it empowers data scientists, machine learning engineers, and NLP practitioners to build robust solutions without delving into theoretical complexities. The book is structured into three parts to facilitate a step-by-step learning journey. Part One covers building production-ready LLM solutions introduces the Hugging Face library and equips readers to solve most of the common NLP challenges without requiring deep knowledge of transformer internals. Part Two focuses on empowering LLMs with RAG and intelligent agents exploring Retrieval-Augmented Generation (RAG) models, demonstrating how to enhance answer quality and develop intelligent agents. Part Three covers LLM advances focusing on expert topics such as model training, principles of transformer architecture and other cutting-edge techniques related to the practical application of language models. Each chapter includes practical examples, code snippets, and hands-on projects to ensure applicability to real-world scenarios. This book bridges the gap between theory and practice, providing professionals with the tools and insights to develop practical and efficient LLM solutions. What you will learn: What are the different types of tasks modern LLMs can solve How to select the most suitable pre-trained LLM for specific tasks How to enrich LLM with a custom knowledge base and build intelligent systems What are the core principles of Language Models, and how to tune them How to build robust LLM-based AI Applications Who this book is for: Data scientists, machine learning engineers, and NLP specialists with basic Python skills, introductory PyTorch knowledge, and a primary understanding of deep learning concepts, ready to start applying Large Language Models in practice.

Transformers For Natural Language Processing

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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-01-29

Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild 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 modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook 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 learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho 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 experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.

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Applied Hugging Face Transformers For Natural Language Processing

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