Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Ajustement fin de BERT× | XGBoost× | |
|---|---|---|
| Domaine≠ | Apprentissage profond | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2019 | 2016 |
| Auteur d'origine≠ | Devlin, J. et al. | Chen, T. & Guestrin, C. |
| Type≠ | Transfer learning (fine-tuning a pre-trained transformer) | Ensemble (gradient-boosted decision trees) |
| Source fondatrice≠ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT | XGBoost, extreme gradient boosting, scalable tree boosting |
| Apparentées | 5 | 5 |
| Résumé≠ | BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
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