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This paper compares transfer learning (TL) using pre-trained language models (PLMs) like BERT and DistilBERT with traditional machine learning approaches for text classification. The study finds that TL significantly outperforms traditional methods based on Term Frequency-Inverse Document Frequency (TF-IDF) in various datasets, promoting DistilBERT as a more efficient alternative due to its reduced training time and parameters. The research highlights the advantages of TL in NLP, particularly in scenarios w

Author

113shaa

Language

EN