قارن الطرق

	ضبط نموذج بيرت الدقيق	الانتباه الذاتي متعدد الرؤوس
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2019	2017
صاحب الطريقة	Devlin, J. et al.	Vaswani, A. et al.
النوع	Transfer learning (fine-tuning a pre-trained transformer)	Attention mechanism (Transformer core)
المصدر التأسيسي	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
الأسماء البديلة	BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention
ذات صلة	5	5
الملخص	BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data.	Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5.