Comparer des méthodes

	Classification basée sur BERT	Long Short-Term Memory (LSTM)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2019	1997
Auteur d'origine	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)	Hochreiter, S. & Schmidhuber, J.
Type	Pre-trained language model with fine-tuning	Recurrent neural network with gated memory cells
Source fondatrice	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Alias	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
Apparentées	4	4
Résumé	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.	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.