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	Wyjaśnialny Model Tematyczny NMF	Osadzanie zdań
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2001 (NMF); XAI integration ~2017–present	2015–2019
Twórca	Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016	Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)
Typ	Interpretable unsupervised topic model	Representation learning / embedding
Źródło pierwotne	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	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.
Inne nazwy	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	sentence vectors, sentence representations, SBERT, semantic sentence encoding
Pokrewne	6	4
Podsumowanie	An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such as coherence metrics, word contribution scores, and SHAP-style attribution to make discovered topics transparent and auditable by human readers.	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.