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We propose a novel method of fine-tuning the model for a particular downstream task, which proves to be more efficient and generalizable. We show that in an example of a fake news detection task, utilizing three distinct datasets and outperforming the baseline model in both the same dataset and cross-dataset zero-shot test.

Jupyter Notebook 63.28% Python 0.10% HTML 36.62%
bert covid19-data embedding-vectors fake-news fake-news-detection generalized-linear-models kaggle-dataset liar-dataset natural-language-processing nlp politifact transformers zero-shot-classification

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