Transformers For Natural Language Processing 2nd Edition Denis Rothman by Denis Rothman 9781803247335, 1803247339, B09T34LVRM instant download after payment.

Under the hood working of transformers, fine-tuning GPT-3 models, DeBERTa, vision models, and the start of Metaverse, using a variety of NLP platforms: Hugging Face, OpenAI API, Trax, and AllenNLP Key Features: Implement models, such as BERT, Reformer, and T5, that outperform classical language models Compare NLP applications using GPT-3, GPT-2, and other transformers Analyze advanced use cases, including polysemy, cross-lingual learning, and computer vision Book Description: Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers can create code using just a brief description. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets. What You Will Learn: Discover new ways of performing NLP techniques with the latest pretrained transformers Grasp the workings of the original Transformer, GPT-3, BERT, T5, DeBERTa, and Reformer Find out how ViT and CLIP label images (including blurry ones!) and reconstruct images using DALL-E Carr