Rothman Transformers for Natural Language Processing

Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
1. Auflage 2021
ISBN: 978-1-80056-863-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 384 Seiten

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

Autoren/Hrsg.

Rothman, Denis

Fachgebiete

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

Inhaltsverzeichnis

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

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.