विधियों की तुलना करें

	स्पष्टीकरणीय RoBERTa-आधारित वर्गीकरण	वाक्य एम्बेडिंग
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2019–2020	2015–2019
प्रवर्तक	Liu et al. (RoBERTa, 2019); Lundberg & Lee (SHAP, 2017); Ribeiro et al. (LIME, 2016)	Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)
प्रकार	Pre-trained transformer classifier with post-hoc XAI	Representation learning / embedding
मौलिक स्रोत	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.	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.
उपनाम	XAI-RoBERTa, Interpretable RoBERTa Classifier, RoBERTa with SHAP/LIME, Transparent RoBERTa NLP	sentence vectors, sentence representations, SBERT, semantic sentence encoding
संबंधित	5	4
सारांश	Explainable RoBERTa-based classification fine-tunes a RoBERTa transformer model on labeled text data and then applies post-hoc interpretability methods — such as SHAP, LIME, or attention analysis — to reveal which tokens or features drove each prediction. This bridges state-of-the-art NLP performance with human-understandable reasoning, satisfying both accuracy and transparency requirements.	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.