Linganisha mbinu

	Domain-adaptive sentence embeddings	Uainishaji unaotegemea RoBERTa
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2019–2020	2019
Mwanzilishi	Reimers, N. & Gurevych, I. (Sentence-BERT); Gururangan et al. (domain-adaptive pretraining)	Liu, Y. et al. (Facebook AI Research / University of Washington)
Aina	Domain-adaptive representation learning	Pre-trained transformer fine-tuned for sequence classification
Chanzo asilia	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of EMNLP-IJCNLP 2019, pp. 3982–3992.	Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.
Majina mbadala	domain-adapted sentence transformers, domain-specific sentence embeddings, target-domain sentence representations, DAPT sentence embeddings	RoBERTa classifier, RoBERTa text classification, Robustly Optimized BERT Classification, RoBERTa fine-tuning for classification
Zinazohusiana	6	5
Muhtasari	Domain-adaptive sentence embeddings extend general-purpose sentence encoders — such as Sentence-BERT — by continuing their training on domain-specific text. The result is a fixed-length vector representation that captures both universal language understanding and the vocabulary, style, and semantic nuances of the target domain, improving downstream NLP tasks such as semantic search, clustering, and classification.	RoBERTa-based Classification applies the RoBERTa pre-trained transformer — trained more robustly than BERT with dynamic masking and larger batches — to text categorisation tasks by adding a lightweight classification head on top of the [CLS] token representation and fine-tuning the entire model on labelled examples. It consistently matches or outperforms BERT on standard NLP benchmarks.