مقایسهٔ روش‌ها

	طبقه‌بندی مبتنی بر RoBERTa	حافظه طولانی کوتاه‌مدت (LSTM)
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2019	1997
پدیدآور	Liu, Y. et al. (Facebook AI Research / University of Washington)	Hochreiter, S. & Schmidhuber, J.
نوع	Pre-trained transformer fine-tuned for sequence classification	Recurrent neural network with gated memory cells
منبع بنیادین	Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
نام‌های دیگر	RoBERTa classifier, RoBERTa text classification, Robustly Optimized BERT Classification, RoBERTa fine-tuning for classification	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
مرتبط	5	4
خلاصه	RoBERTa-based Classification applies the RoBERTa pre-trained transformer — trained more robustly than BERT with dynamic masking and larger batches — to text categorisation tasks by adding a lightweight classification head on top of the [CLS] token representation and fine-tuning the entire model on labelled examples. It consistently matches or outperforms BERT on standard NLP benchmarks.	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.