পদ্ধতির তুলনা করুন

	মাল্টি-হেড সেলফ-অ্যাটেনশন	BERT ফাইন-টিউনিং
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017	2019
প্রবর্তক	Vaswani, A. et al.	Devlin, J. et al.
ধরন	Attention mechanism (Transformer core)	Transfer learning (fine-tuning a pre-trained transformer)
মৌলিক উৎস	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.
অপর নাম	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention	BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT
সম্পর্কিত	5	5
সারসংক্ষেপ	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.	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.