Transformers for natural language processing : build innovative deep neural network architectures for NLP with python, pyTorch, tensorFlow, BERT, RoBERTa, and more / by Denis Rothman.

By:

Rothman, Denis [author]

Material type:

Computer fileLanguage: English Publication details: Birmingham : Packt Publishing, Limited, 2021Description: 1 online resource (xvi, 383, pages) : color illustrationsContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781800565791 (e-book)

Subject(s):

LOC classification:

Q336 R74 2021

Online resources:

Contents:

1. Getting started with the model architecture of the transformer -- 2. Fine-tuning BERT models -- 3. Pretraining a RoBERT a model from scratch -- 4. Downstream NLP tasks with transformers -- 5. Machine translation with the transformer -- 6. Text generation with OpenAI GPT-2 and GPT-3 models -- 7. Applying transformers to legal and financial documents for AI text summarization -- 8. Matching tokenizers and datasets -- 9. Semantic role labeling with BERT-Based transformers -- 10. Let your data do the talking : story, questions, and answers -- 11. Detecting customer emotions to make predictions -- 12. Analyzing fake news with transformers

Summary: The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.

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Holdings ( 2 )
Title notes ( 5 )

Holdings
Item type	Current library	Collection	Call number	Materials specified	Status	Notes	Date due	Barcode
Online E-Books	Ladislao N. Diwa Memorial Library Multimedia Section	Non-fiction	OEBP Q336 R74 2021 (Browse shelf(Opens below))		Available	PAV		OEBP000267
Compact Discs	Ladislao N. Diwa Memorial Library Multimedia Section	Non-fiction	EB Q336 R74 2021 (Browse shelf(Opens below))		Room use only	PAV		EB000267

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Includes bibliographical references and index

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.

The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.

The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.

By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.

Fund 164 CE-Logic Purchased April 14, 2022 OEBP000267 P. Roderno PHP 4,223.70
2022-04-230 0000