Rothman Transformers for Natural Language Processing

Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4
2. Auflage 2022
ISBN: 978-1-80324-348-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 602 Seiten

No detailed description available for "Transformers for Natural Language Processing".

Autoren/Hrsg.

Rothman, Denis

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Textverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Table of Contents - 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 Emergence of Transformer-Driven Copilots
- The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4
- Appendix I — Terminology of Transformer Models
- Appendix II — Hardware Constraints for Transformer Models
- Appendix III — Generic Text Completion with GPT-2
- Appendix IV — Custom Text Completion with GPT-2
- Appendix V — Answers to the Questions

Über Autor(innen)

Rothman Denis:

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.