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Transformers for Natural Language Processing

You're reading from   Transformers for Natural Language Processing Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

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Product type Paperback
Published in Jan 2021
Publisher Packt
ISBN-13 9781800565791
Length 384 pages
Edition 1st Edition
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with the Model Architecture of the Transformer 2. Fine-Tuning BERT Models FREE CHAPTER 3. Pretraining a RoBERTa Model from Scratch 4. Downstream NLP Tasks with Transformers 5. Machine Translation with the Transformer 6. Text Generation with OpenAI GPT-2 and GPT-3 Models 7. Applying Transformers to Legal and Financial Documents for AI Text Summarization 8. Matching Tokenizers and Datasets 9. Semantic Role Labeling with BERT-Based Transformers 10. Let Your Data Do the Talking: Story, Questions, and Answers 11. Detecting Customer Emotions to Make Predictions 12. Analyzing Fake News with Transformers 13. Other Books You May Enjoy
14. Index
Appendix: Answers to the Questions

Preprocessing a WMT dataset

Vaswani et al. (2017) present the Transformer's achievements on the WMT 2014 English-to-German translation task and the WMT 2014 English-to-French translation task. The Transformer achieves a state-of-the-art BLEU score. BLEU will be described in the Evaluating machine translation with BLEU section of this chapter.

The 2014 Workshop on Machine Translation (WMT) contained several European language datasets. One of the datasets contained data taken from version 7 of the Europarl corpus. We will be using the French-English dataset from the European Parliament Proceedings Parallel Corpus 1996-2011. The link is https://www.statmt.org/europarl/v7/fr-en.tgz.

Once you have downloaded the files and have extracted them, we will preprocess the two parallel files:

  • europarl-v7.fr-en.en
  • europarl-v7.fr-en.fr

We will load, clear, and reduce the size of the corpus.

Let's start the preprocessing.

Preprocessing the raw data...

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