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Transformers for Natural Language Processing

Transformers for Natural Language Processing - Second Edition

By : Denis Rothman
3.8 (28)
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Transformers for Natural Language Processing

Transformers for Natural Language Processing

3.8 (28)
By: Denis Rothman

Overview of this book

Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.
Table of Contents (25 chapters)
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18
Other Books You May Enjoy
19
Index
1
Appendix I — Terminology of Transformer Models

Implementing GPUs in code

PyTorch, among other languages and frameworks, manages GPUs. PyTorch contains tensors just as TensorFlow does. A tensor may look like NumPy np.arrays(). However, NumPy is not fit for parallel processing. Tensors use the parallel processing features of GPUs.

Tensors open the doors to distributed data over GPUs in PyTorch, among other frameworks: https://pytorch.org/tutorials/intermediate/ddp_tutorial.html

In the Chapter03 notebook, BERT_Fine_Tuning_Sentence_Classification_GPU.ipynb, we used CUDA (Compute Unified Device Architecture) to communicate with NVIDIA GPUs. CUDA is an NVIDIA platform for general computing on GPUs. Specific instructions can be added to our source code. For more, see https://developer.nvidia.com/cuda-zone.

In the Chapter03 notebook, we used CUDA instructions to transfer our model and data to NVIDIA GPUs. PyTorch has an instruction to specify the device we wish to use: torch.device.

For more, see https://pytorch.org/docs...

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Transformers for Natural Language Processing
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