مقایسهٔ روش‌ها

	ترنسفورمر (پردازش زبان طبیعی)	رگرسیون لجستیک
حوزه	یادگیری عمیق	آمار پژوهش
خانواده	Machine learning	Process / pipeline
سال پیدایش	2017	1958
پدیدآور	Vaswani, A. et al.	David Roxbee Cox
نوع	Attention-based deep neural network	Method
منبع بنیادین	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
نام‌های دیگر	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	logit model, binomial logistic regression, LR
مرتبط	4	3
خلاصه	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.