Transformers for natural language processing build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3
Transformers for natural language processing: build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3

Intro -- Copyright -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: What are Transformers? -- The ecosystem of transformers -- Industry 4.0 -- Foundation models -- Is programming becoming a sub-domain of NLP? -- The future of artificial intelligence specialists -- Optimizing...

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Bibliographische Detailangaben
1. Verfasser: Rothman, Denis (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Birmingham ; Mumbai Packt [March 2022]
Ausgabe:Second edition
Schriftenreihe:Expert insight
Schlagworte:
Computers / Artificial Intelligence / Natural Language Processing
Computers / Data Science / Neural Networks
Deep Learning
Natürliche Sprache
Electronic books
Online-Zugang:DE-Aug4
DE-M347
DE-898
DE-706
DE-29
DE-573
Zusammenfassung:Intro -- Copyright -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: What are Transformers? -- The ecosystem of transformers -- Industry 4.0 -- Foundation models -- Is programming becoming a sub-domain of NLP? -- The future of artificial intelligence specialists -- Optimizing NLP models with transformers -- The background of transformers -- What resources should we use? -- The rise of Transformer 4.0 seamless APIs -- Choosing ready-to-use API-driven libraries -- Choosing a Transformer Model -- The role of Industry 4.0 artificial intelligence specialists -- Summary -- Questions -- References -- Chapter 2: Getting Started with the Architecture of the Transformer Model -- The rise of the Transformer: Attention is All You Need -- The encoder stack -- Input embedding -- Positional encoding -- Sublayer 1: Multi-head attention -- Sublayer 2: Feedforward network -- The decoder stack -- Output embedding and position encoding -- The attention layers -- The FFN sublayer, the post-LN, and the linear layer -- Training and performance -- Tranformer models in Hugging Face -- Summary -- Questions -- References -- Chapter 3: Fine-Tuning BERT Models -- The architecture of BERT -- The encoder stack -- Preparing the pretraining input environment -- Pretraining and fine-tuning a BERT model -- Fine-tuning BERT -- Hardware constraints -- Installing the Hugging Face PyTorch interface for BERT -- Importing the modules -- Specifying CUDA as the device for torch -- Loading the dataset -- Creating sentences, label lists, and adding BERT tokens -- Activating the BERT tokenizer -- Processing the data -- Creating attention masks -- Splitting the data into training and validation sets -- Converting all the data into torch tensors -- Selecting a batch size and creating an iterator -- BERT model configuration
Beschreibung:Description based on publisher supplied metadata and other sources
Beschreibung:1 Online-Ressource (xxxiii, 565 Seiten) Illustrationen (teilweise farbig)
ISBN:9781803243481
1803243481

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