Vertaile menetelmiä

	Siirto-oppiminen lauseupotuksilla	Hienosäädetyt lauseupotukset
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2017–2019	2019
Kehittäjä	Reimers, N. & Gurevych, I. (SBERT); Conneau, A. et al. (InferSent)	Reimers, N. & Gurevych, I.
Tyyppi	Transfer learning / sentence representation	Supervised / contrastive fine-tuning of pre-trained sentence encoders
Alkuperäislähde	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.	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.
Rinnakkaisnimet	sentence embedding transfer learning, pre-trained sentence encoder fine-tuning, SBERT transfer learning, sentence representation transfer	SBERT fine-tuning, sentence transformer fine-tuning, domain-adapted sentence embeddings, fine-tuned sentence encoders
Liittyvät	5	5
Tiivistelmä	Transfer Learning with Sentence Embeddings takes a large pre-trained encoder — such as Sentence-BERT or the Universal Sentence Encoder — that already encodes general language knowledge into fixed-length vectors, and adapts it to a new task or domain with little additional labelled data. The pre-trained representations give a head start that often outperforms task-specific models trained from scratch on modest corpora.	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.