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|a Rothman, Denis.
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|a Transformers for Natural Language Processing
|h [electronic resource] :
|b Build Innovative Deep Neural Network Architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and More.
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|a Birmingham :
|b Packt Publishing, Limited,
|c 2021.
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|a 1 online resource (385 p.)
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|a text
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|a Description based upon print version of record.
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|a 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.
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|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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|a Artificial intelligence
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|a Python (Computer program language)
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|i Print version:
|a Rothman, Denis
|t Transformers for Natural Language Processing
|d Birmingham : Packt Publishing, Limited,c2021
|z 9781800565791
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|u https://learning.oreilly.com/library/view/~/9781800565791/?ar
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