Mastering Transformers | 5. Fine-Tuning Language Models for Text Classification
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This is the “Code in Action” video for chapter 5 of Mastering Transformers by Savaş Yıldırım and Meysam Asgari-Chenaghlu, published by Packt. It includes the following topics:
00:13 Fine-tuning a BERT model for single-sentence binary classification
07:27 Training a classification model with native PyTorch
10:11 Fine-tuning BERT for multi-class classification with custom datasets
16:41 Fine-tuning the BERT model for sentence-pair regression
Explore the accurate and fast fine-tuning capabilities of transformer-based language models and understand how they outperform traditional machine learning-based approaches when solving challenging Natural Language Understanding (NLU) problems. Developers working with the transformers architecture will be able to put their knowledge to work with this practical guide.
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Video created by Savaş Yıldırım and Meysam Asgari-Chenaghlu