A ModernBERT Bi-Encoder model is developed that achieves 98.4% accuracy on the PAN21 authorship verification task and identifies loss function, batch size, training duration, pre-trained model, input context length, and random text span data augmentation as important factors of model performance.
Abstract
Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify loss function, batch size, training duration, pre-trained model, input context length, and random text span data augmentation as important factors of model performance. Based on these considerations, we develop a ModernBERT Bi-Encoder model that achieves 98.4% accuracy on the PAN21 authorship verification task.
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Consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts are found.
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