Fine-Tune DistilBERT, MobileBERT, and TinyBERT for Fake News Detection | Model Accuracy Benchmarking

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In this video, we dive into fine-tuning DistilBERT, MobileBERT, and TinyBERT for fake news detection using a text classification architecture. We'll start with dataset loading and analysis, followed by data tokenization and model building. You'll learn how to fine-tune these models and evaluate their performance, achieving an impressive 96% accuracy on fake news detection.

We'll also conduct benchmarking to compare the performance of DistilBERT, MobileBERT, and TinyBERT, analyzing how they perform in terms of latency and accuracy. By the end, you'll have a clear understanding of which model is best suited for different types of data based on real-world results. Perfect for those looking to enhance their skills in model selection and knowledge distillation.

Join us to master BERT model fine-tuning and benchmarking techniques!

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