Salīdzināt metodes

	Sintētiski pielāgotas teicienu iegultnes	BERT klasifikācija
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019	2019
Autors	Reimers, N. & Gurevych, I.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Tips	Supervised / contrastive fine-tuning of pre-trained sentence encoders	Pre-trained language model with fine-tuning
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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
Citi nosaukumi	SBERT fine-tuning, sentence transformer fine-tuning, domain-adapted sentence embeddings, fine-tuned sentence encoders	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
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.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.