Understanding Large Language Models A Guide To Transformer Architectures And Nlp Applications
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Understanding Large Language Models: A Guide to Transformer Architectures and NLP Applications
In the ever-evolving world of language processing, "Understanding Large Language Models" offers a comprehensive guidebook. It delves into the inner workings of both Large Language Models (LLMs) and the revolutionary Transformer architectures that power them. The book begins by establishing the foundation. Part 1 introduces Natural Language Processing (NLP) and the challenges it tackles. It then unveils LLMs, exploring their capabilities and the impact they have on various industries. Ethical considerations and limitations of these powerful tools are also addressed. Part 2 equips you with the necessary background. It dives into the essentials of Deep Learning for NLP, explaining Recurrent Neural Networks (RNNs) and their shortcomings. Traditional NLP techniques like word embeddings and language modeling are also explored, providing context for the advancements brought by transformers. Part 3 marks the turning point. Here, the book unveils the Transformer architecture, the engine driving LLMs. You'll grasp its core principles, including the encoder-decoder structure and the critical concept of attention, which allows the model to understand relationships within text. The chapter delves into the benefits transformers offer, such as speed, accuracy, and their ability to capture long-range dependencies in language. Part 4 bridges the gap between theory and practice. It explores the data preparation process for training LLMs and the challenges associated with handling massive datasets. Optimization techniques for efficient learning are explained, along with the concept of fine-tuning pre-trained LLMs for specific applications. Finally, Part 5 showcases the power of LLMs in action. It explores a range of applications, from creative text generation and machine translation to text summarization and question answering. The book concludes by looking towards the future, discussing potential societal impacts, addressing ethical considerations, and exploring advancements in transformer architectures that will continue to shape the landscape of NLP. This book is your key to unlocking the world of LLMs and Transformers. Whether you're a student, developer, or simply curious about the future of language technology, this guide provides a clear and engaging roadmap to understanding these groundbreaking advancements.
Transformers for Natural Language Processing
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.
Natural Language Processing with Transformers, Revised Edition
Author: Lewis Tunstall
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2022-05-26
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