Transformers for natural language processing : (Record no. 1998)

MARC details
000 -LEADER
fixed length control field 03390cam a22003135i 4500
003 - CONTROL NUMBER IDENTIFIER
control field EG-CaNGU
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240131133103.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231214s2022 enka frb 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781803247335
040 ## - CATALOGING SOURCE
Original cataloging agency ORMDA
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency ORMDA
Modifying agency OCLCO
-- EG-CaNGU
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Item number ROT
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Rothman, Denis,
Relator term author.
9 (RLIN) 5986
245 10 - TITLE STATEMENT
Title Transformers for natural language processing :
Remainder of title build, train, and fine-tuning deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4 /
250 ## - EDITION STATEMENT
Edition statement Second edition.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Birmingham, England :
Name of producer, publisher, distributor, manufacturer Packt Publishing,
Date of production, publication, distribution, manufacture, or copyright notice 2022.
300 ## - PHYSICAL DESCRIPTION
Extent xxxiii, 565 pages :
Other physical details illustrations ;
Dimensions 24 cm.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term unmediated
Media type code n
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term volume
Carrier type code nc
Source rdacarrier
490 0# - SERIES STATEMENT
Series statement Expert insight
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
505 00 - FORMATTED CONTENTS NOTE
Formatted contents note What 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 ## - SUMMARY, ETC.
Summary, etc. 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.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Python (Computer program language).
Source of heading or term NGU-sh
9 (RLIN) 2383
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial intelligence
General subdivision computer programs.
Source of heading or term NGU-sh
9 (RLIN) 5361
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Cloud computing.
Source of heading or term NGU-sh
9 (RLIN) 5987
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Gulli, Antonio,
Relator term foreword writer.
9 (RLIN) 5988
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Date acquired Source of acquisition Cost, normal purchase price Arrivals Code Full call number Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification   Not For Loan Information Technology Library D Library D 12/14/2023 Dar Al Fajr 1773.00 ITS202312 006.3 ROT 1004606 08/14/2024 12/14/2023 Book Non-borrowing
    Dewey Decimal Classification     Information Technology Library D Library D 12/14/2023 Dar Al Fajr 1773.00 ITS202312 006.3 ROT 1004607 08/14/2024 12/14/2023 Book