Transformers for Natural Language Processing: Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More.
Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2021.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and AllenNLP. |
| Beschreibung: | Description based upon print version of record. |
| Beschreibung: | 1 online resource (385 p.) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 1800568630 9781800568631 |
Internformat
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| 505 | 0 | |a Table of Contents Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models 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 Appendix: Answers to the Questions. | |
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| contents | Table of Contents Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models 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 Appendix: Answers to the Questions. |
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| genre | Software fast |
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| id | ZDB-4-EBA-on1236261667 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:12Z |
| institution | BVB |
| isbn | 1800568630 9781800568631 |
| language | English |
| oclc_num | 1236261667 |
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| physical | 1 online resource (385 p.) |
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| publisher | Packt Publishing, Limited, |
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| spelling | Rothman, Denis. Transformers for Natural Language Processing [electronic resource] : Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. Birmingham : Packt Publishing, Limited, 2021. 1 online resource (385 p.) text rdacontent computer rdamedia online resource rdacarrier Description based upon print version of record. Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and AllenNLP. Table of Contents Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models 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 Appendix: Answers to the Questions. Includes bibliographical references and index. Artificial intelligence Data processing. http://id.loc.gov/authorities/subjects/sh85008182 Artificial intelligence Software. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Intelligence artificielle Informatique. Python (Langage de programmation) Infonuagique. Artificial intelligence. bicssc Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Artificial intelligence Data processing fast Cloud computing fast Python (Computer program language) fast Software fast has work: Transformers for natural language processing (Text) https://id.oclc.org/worldcat/entity/E39PCGCmQfdMGRpQv7MFpdmfdP https://id.oclc.org/worldcat/ontology/hasWork Print version: Rothman, Denis Transformers for Natural Language Processing Birmingham : Packt Publishing, Limited,c2021 9781800565791 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2739556 Volltext |
| spellingShingle | Rothman, Denis Transformers for Natural Language Processing Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. Table of Contents Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models 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 Appendix: Answers to the Questions. Artificial intelligence Data processing. http://id.loc.gov/authorities/subjects/sh85008182 Artificial intelligence Software. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Intelligence artificielle Informatique. Python (Langage de programmation) Infonuagique. Artificial intelligence. bicssc Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Artificial intelligence Data processing fast Cloud computing fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008182 http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh2008004883 |
| title | Transformers for Natural Language Processing Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. |
| title_auth | Transformers for Natural Language Processing Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. |
| title_exact_search | Transformers for Natural Language Processing Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. |
| title_full | Transformers for Natural Language Processing [electronic resource] : Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. |
| title_fullStr | Transformers for Natural Language Processing [electronic resource] : Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. |
| title_full_unstemmed | Transformers for Natural Language Processing [electronic resource] : Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. |
| title_short | Transformers for Natural Language Processing |
| title_sort | transformers for natural language processing build innovative deep neural network architectures for nlp with python pytorch tensorflow bert roberta and more |
| title_sub | Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More. |
| topic | Artificial intelligence Data processing. http://id.loc.gov/authorities/subjects/sh85008182 Artificial intelligence Software. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Intelligence artificielle Informatique. Python (Langage de programmation) Infonuagique. Artificial intelligence. bicssc Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Artificial intelligence Data processing fast Cloud computing fast Python (Computer program language) fast |
| topic_facet | Artificial intelligence Data processing. Artificial intelligence Software. Python (Computer program language) Cloud computing. Intelligence artificielle Informatique. Python (Langage de programmation) Infonuagique. Artificial intelligence. Natural language & machine translation. Neural networks & fuzzy systems. Computers Intelligence (AI) & Semantics. Computers Natural Language Processing. Computers Neural Networks. Artificial intelligence Artificial intelligence Data processing Cloud computing Software |
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| work_keys_str_mv | AT rothmandenis transformersfornaturallanguageprocessingbuildinnovativedeepneuralnetworkarchitecturesfornlpwithpythonpytorchtensorflowbertrobertaandmore |