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Transformers for natural language processing: Build train and finetune deep neural network architectures for NLP with python hugging face and open AIs GPT-3 ChatGP and GPT-4.

Rothman Denis.

Transformers for natural language processing: Build train and finetune deep neural network architectures for NLP with python hugging face and open AIs GPT-3 ChatGP and GPT-4. Dennis Rothmsn. - 2rd ed. - Birmingham: Packt; c2022. - xxxiii, 565p.: ill.; 24cm.

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.

9781803247335

QA76.9 .N38 / .R68 2022