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Why It Matters
Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
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.
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.28471v1 · Indexed about 2 hours ago