TY  - BOOK
AU  - Rothman,Denis
AU  - Gulli,Antonio
ED  - Ohio Library and Information Network
TI  - Transformers for natural language processing: build, train, and fine-tuning deep neural network architectures for NLP with Python, Hugging face, and openAI's GPT-3, ChatGPT, and GPT4 
T2  - Expert insight
AV  - Q336 
U1  - 006.3 ROT 2022 23
PY  - 2022///]
CY  - [Birmingham, United Kingdom]
PB  - Packt Publishing
KW  - Artificial intelligence
KW  - Data processing
KW  - Computer programs
KW  - Python (Computer program language)
KW  - Cloud computing
KW  - Electronic books
N1  - Includes index; Available to OhioLINK libraries
N2  - 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
ER  -