Võrdle meetodeid

	Peenhäälestatud lausemanused	BERT-põhine peenhäälestatud klassifikaator
Valdkond	Süvaõpe	Süvaõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	2019	2019
Looja	Reimers, N. & Gurevych, I.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)
Tüüp	Supervised / contrastive fine-tuning of pre-trained sentence encoders	Pre-trained transformer fine-tuned for classification
Algallikas	Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992.	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.
Rööpnimetused	SBERT fine-tuning, sentence transformer fine-tuning, domain-adapted sentence embeddings, fine-tuned sentence encoders	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification
Seotud	5	5
Kokkuvõte	Fine-Tuned Sentence Embeddings adapt a general-purpose pre-trained sentence encoder — such as Sentence-BERT — to a specific domain or task by continuing training on labeled or paired text data from that domain. The resulting embeddings capture domain-specific semantic structure far better than off-the-shelf vectors, improving downstream tasks such as semantic similarity, clustering, classification, and retrieval.	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.