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| BERT-pohjainen luokittelu× | Rekurrentti neuroverkko× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2019 | 1986–1990 |
| Kehittäjä≠ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language) | Rumelhart, D. E.; Elman, J. L. |
| Tyyppi≠ | Pre-trained language model with fine-tuning | Sequential neural network |
| Alkuperäislähde≠ | 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 ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| Rinnakkaisnimet | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS | RNN, Elman network, Jordan network, simple recurrent network |
| Liittyvät≠ | 4 | 3 |
| Tiivistelmä≠ | 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. | A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. |
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