Master NLP with Hugging Face: A Fine-tuning Toolkit PDF Download
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Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 44
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Book Description
In the ever-evolving world of Natural Language Processing (NLP), "Master NLP with Hugging Face: A Fine-tuning Toolkit" equips you to unlock the power of pre-trained models from Hugging Face. This comprehensive guide empowers you to transform these powerful models into workhorses for your specific NLP tasks. Gone are the days of training complex NLP models from scratch. This book dives into the art of fine-tuning, a technique that leverages the vast knowledge pre-trained models have already acquired and tailors it to your specific needs. You'll delve into the fundamentals of fine-tuning, understanding how to take a pre-trained model and adjust its final layers to excel on your chosen NLP task, whether it's text classification, sentiment analysis, question answering, or summarization. The book doesn't just provide theory - it's a hands-on toolkit. You'll establish your NLP development environment, ensuring you have the necessary tools to get started. By following step-by-step guides, you'll navigate the treasure trove of pre-trained models on the Hugging Face Model Hub, selecting the perfect model for your project. Data is the fuel for fine-tuning, and this book equips you to prepare your data effectively. Learn essential data cleaning and pre-processing techniques to ensure your model receives high-quality input. Master the art of data splitting, creating distinct training, validation, and test sets to optimize your model's performance and generalization capabilities. As you venture into fine-tuning, the book equips you to tackle challenges like overfitting and data requirements. Explore techniques to mitigate these issues and ensure your fine-tuned model performs exceptionally well on unseen data. Moving beyond the basics, "Master NLP with Hugging Face" introduces you to advanced concepts like building custom pipelines for text processing and customizing training configurations for optimal performance. You'll also gain insights into evaluation metrics, allowing you to precisely measure the effectiveness of your fine-tuned model for your specific NLP task. This book is your gateway to the exciting world of fine-tuning Hugging Face Transformers. With its comprehensive guidance and practical approach, you'll be well on your way to building robust and efficient NLP applications that can handle real-world challenges.
Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 44
Get Book Here
Book Description
In the ever-evolving world of Natural Language Processing (NLP), "Master NLP with Hugging Face: A Fine-tuning Toolkit" equips you to unlock the power of pre-trained models from Hugging Face. This comprehensive guide empowers you to transform these powerful models into workhorses for your specific NLP tasks. Gone are the days of training complex NLP models from scratch. This book dives into the art of fine-tuning, a technique that leverages the vast knowledge pre-trained models have already acquired and tailors it to your specific needs. You'll delve into the fundamentals of fine-tuning, understanding how to take a pre-trained model and adjust its final layers to excel on your chosen NLP task, whether it's text classification, sentiment analysis, question answering, or summarization. The book doesn't just provide theory - it's a hands-on toolkit. You'll establish your NLP development environment, ensuring you have the necessary tools to get started. By following step-by-step guides, you'll navigate the treasure trove of pre-trained models on the Hugging Face Model Hub, selecting the perfect model for your project. Data is the fuel for fine-tuning, and this book equips you to prepare your data effectively. Learn essential data cleaning and pre-processing techniques to ensure your model receives high-quality input. Master the art of data splitting, creating distinct training, validation, and test sets to optimize your model's performance and generalization capabilities. As you venture into fine-tuning, the book equips you to tackle challenges like overfitting and data requirements. Explore techniques to mitigate these issues and ensure your fine-tuned model performs exceptionally well on unseen data. Moving beyond the basics, "Master NLP with Hugging Face" introduces you to advanced concepts like building custom pipelines for text processing and customizing training configurations for optimal performance. You'll also gain insights into evaluation metrics, allowing you to precisely measure the effectiveness of your fine-tuned model for your specific NLP task. This book is your gateway to the exciting world of fine-tuning Hugging Face Transformers. With its comprehensive guidance and practical approach, you'll be well on your way to building robust and efficient NLP applications that can handle real-world challenges.
Author: Rick Spair
Publisher: Rick Spair
ISBN:
Category : Computers
Languages : en
Pages : 888
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Book Description
In the rapidly evolving landscape of artificial intelligence, Generative AI stands out as a transformative force with the potential to revolutionize industries and reshape our understanding of creativity and automation. From its inception, Generative AI has captured the imagination of researchers, developers, and entrepreneurs, offering unprecedented capabilities in generating new data, simulating complex systems, and solving intricate problems that were once considered beyond the reach of machines. This book, "200 Tips for Mastering Generative AI," is a comprehensive guide designed to empower you with the knowledge and practical insights needed to harness the full potential of Generative AI. Whether you are a seasoned AI practitioner, a curious researcher, a forward-thinking entrepreneur, or a passionate enthusiast, this book provides valuable tips and strategies to navigate the vast and intricate world of Generative AI. We invite you to explore, experiment, and innovate with the knowledge you gain from this book. Together, we can unlock the full potential of Generative AI and shape a future where intelligent machines and human creativity coexist and collaborate in unprecedented ways. Welcome to "200 Tips for Mastering Generative AI." Your journey into the fascinating world of Generative AI begins here.
Author: Denis Rothman
Publisher: Packt Publishing Ltd
ISBN: 1800568630
Category : Computers
Languages : en
Pages : 385
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Book Description
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.
Author: Ankur A. Patel
Publisher: "O'Reilly Media, Inc."
ISBN: 1492062545
Category : Computers
Languages : en
Pages : 336
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Book Description
NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production
Author: Denis Rothman
Publisher: Packt Publishing Ltd
ISBN: 1803243481
Category : Computers
Languages : en
Pages : 603
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Book Description
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!
Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
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Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author: Darwish, Dina
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 403
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Book Description
In the field of information retrieval, the challenge lies in the speed and accuracy with which users can access relevant data. With the increasing complexity of digital interactions, the need for a solution that transcends traditional methods becomes evident. Human involvement and manual investigation are not only time-consuming but also prone to errors, hindering the seamless exchange of information in various sectors. Design and Development of Emerging Chatbot Technology emerges as a comprehensive solution to the predicament posed by traditional information retrieval methods. Focusing on the transformative power of chatbots, it delves into the intricacies of their operation, applications, and development. Designed for academic scholars across diverse disciplines, the book serves as a beacon for those seeking a deeper understanding of chatbots and their potential to revolutionize information retrieval in customer service, education, healthcare, e-commerce, and more.
Author: Paul Azunre
Publisher: Simon and Schuster
ISBN: 163835099X
Category : Computers
Languages : en
Pages : 262
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Book Description
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions
Author: Abhishek Thakur
Publisher: Abhishek Thakur
ISBN: 8269211508
Category : Computers
Languages : en
Pages : 300
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Book Description
This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub
Author: Nicole Forsgren, PhD
Publisher: IT Revolution
ISBN: 1942788355
Category : Business & Economics
Languages : en
Pages : 251
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Book Description
Winner of the Shingo Publication Award Accelerate your organization to win in the marketplace. How can we apply technology to drive business value? For years, we've been told that the performance of software delivery teams doesn't matter―that it can't provide a competitive advantage to our companies. Through four years of groundbreaking research to include data collected from the State of DevOps reports conducted with Puppet, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance―and what drives it―using rigorous statistical methods. This book presents both the findings and the science behind that research, making the information accessible for readers to apply in their own organizations. Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance. This book is ideal for management at every level.
