Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Semi-supervised BERT-basert klassifisering× | Finputilpasset BERT-basert klassifisering× | |
|---|---|---|
| Fagfelt | Dyp læring | Dyp læring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2019–2020 | 2019 |
| Opphavsperson≠ | Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base) | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI) |
| Type≠ | Semi-supervised fine-tuning of pre-trained transformer | Pre-trained transformer fine-tuned for classification |
| Opprinnelig kilde≠ | Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792. link ↗ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186. DOI ↗ |
| Alias | Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning | BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification |
| Relaterte≠ | 6 | 5 |
| Sammendrag≠ | Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce. | Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets. |
| ScholarGateDatasett ↗ |
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