Linganisha mbinu

	Embeddings za sentensi zinazoelezeka	Ufafanuzi wa Uainishaji wa BERT
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2016–2018	2019–2020
Mwanzilishi	Conneau et al.; Ribeiro et al. (probing + LIME frameworks)	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Aina	Post-hoc interpretability applied to sentence encoders	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Chanzo asilia	Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018). What you can cram into a single $\vec{v}$ector: Probing sentence embeddings for linguistic properties. In Proceedings of ACL 2018, pp. 2126–2136.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.
Majina mbadala	interpretable sentence representations, XAI sentence embeddings, probing sentence embeddings, explainable sentence vectors	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Zinazohusiana	6	6
Muhtasari	Explainable sentence embeddings combine dense sentence representation learning with post-hoc or intrinsic interpretability tools — such as probing classifiers, LIME, SHAP, or attention attribution — to reveal what linguistic and semantic information is encoded in a sentence vector and why a downstream model makes a given prediction. The goal is to retain the representational power of modern encoders while making their behavior auditable.	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.