Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Долговременная краткосрочная память (LSTM)× | Классификация на основе BERT× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1997 | 2019 |
| Автор метода≠ | Hochreiter, S. & Schmidhuber, J. | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language) |
| Тип≠ | Recurrent neural network with gated memory cells | Pre-trained language model with fine-tuning |
| Основополагающий источник≠ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ | 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. DOI ↗ |
| Другие названия | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS |
| Связанные | 4 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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