Salīdzināt metodes

	Sintētiski pielāgotas teicienu iegultnes	Ievietojumi teikumiem
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019	2015–2019
Autors	Reimers, N. & Gurevych, I.	Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)
Tips	Supervised / contrastive fine-tuning of pre-trained sentence encoders	Representation learning / embedding
Pirmavots	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), 3980–3990.
Citi nosaukumi	SBERT fine-tuning, sentence transformer fine-tuning, domain-adapted sentence embeddings, fine-tuned sentence encoders	sentence vectors, sentence representations, SBERT, semantic sentence encoding
Saistītās	5	4
Kopsavilkums	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.	Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines.