Overview
The goal of this project was to fine-tune two transformer models, namely DistilBERT and RoBERTa, and compare their effectiveness in fake news detection. Both models were trained on a labelled dataset of news articles and evaluated on two datasets, comparing their performance in terms of accuracy, precision, recall and F1-score. The results of the experiments showed that both models perform well, with RoBERTa achieving slightly better results overall. This project resulted in a paper that was published at the MIPRO Convention in Croatia.