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Fine-Tuned Transformer

Fine-tuning a Transformer adapts a large pre-trained model — such as BERT, GPT, or ViT — to a specific downstream task by continuing gradient-based training on a labelled target dataset. This two-stage paradigm (pre-train then fine-tune) consistently achieves state-of-the-art results across NLP and computer vision tasks with far less task-specific data than training from scratch.

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Fine-Tuned Transformer (Task-Specific Adaptation of Pre-Trained Transformer Models)

Rekod kaedah taksonomik · ml-model / deep-learning

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