Master Nlp With Hugging Face A Fine Tuning Toolkit
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Master NLP with Hugging Face: A Fine-tuning Toolkit
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.
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.
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