Transformers for Natural Language Processing - Second Edition
Cód:
491_9781803247335
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OpenAIs GPT-3 and Hugging Face transformers for language tasks in one book. Plus, get a taste of the future of transformers, including computer vision tasks and code writing and assistance with Codex and GitHub CopilotPurchase of the print or Kindle book includes a free eBook in PDF format.Key Features:Pretrain a BERT-based model from scratch using Hugging FaceFine-tune powerful transformer models, including OpenAIs GPT-3, to learn the logic of your dataPerform root cause analysis on hard NLP problemsBook Description:Transformers 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.Youll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model.If youre 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).Youll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using Codex.By the end of this book, youll know how transformers work and how to implement them and resolve issues like an AI detective!What You Will Learn:Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-EDiscover new techniques to investigate complex language problemsCompare and contrast the results of GPT-3 against
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