000			03390cam a22003135i 4500
003			EG-CaNGU
005			20240131133103.0
008			231214s2022 enka frb 001 0 eng d
020			_a9781803247335
040			_aORMDA _beng _erda _epn _cORMDA _dOCLCO _dEG-CaNGU
082	0	4	_a006.3 _bROT _223
100	1		_aRothman, Denis, _eauthor. _95986
245	1	0	_aTransformers for natural language processing : _bbuild, train, and fine-tuning deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4 /
250			_aSecond edition.
264		1	_aBirmingham, England : _bPackt Publishing, _c2022.
300			_axxxiii, 565 pages : _billustrations ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aExpert insight
504			_aIncludes bibliographical references and index.
505	0	0	_aWhat are Transformers? -- Getting Started with the Architecture of the Transformer Model -- Fine-Tuning BERT Models -- Pretraining a RoBERTa Model from Scratch -- Downstream NLP Tasks with Transformers -- Machine Translation with the Transformer -- The Rise of Suprahuman Transformers with GPT-3 Engines -- Applying Transformers to Legal and Financial Documents for AI Text Summarization -- Matching Tokenizers and Datasets -- Semantic Role Labeling with BERT-Based Transformers -- Let Your Data Do the Talking: Story, Questions, and Answers -- Detecting Customer Emotions to Make Predictions -- Analyzing Fake News with Transformers -- Interpreting Black Box Transformer Models -- From NLP to Task-Agnostic Transformer Models -- The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4 -- Other Books You May Enjoy -- Index.
520			_aTransformers 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.
650		7	_aPython (Computer program language). _2NGU-sh _92383
650		7	_aArtificial intelligence _xcomputer programs. _2NGU-sh _95361
650		7	_aCloud computing. _2NGU-sh _95987
700	1		_aGulli, Antonio, _e foreword writer. _95988
999			_c1998 _d1998