Machine learningDeep learning / NLP / CV
Explainable BERT-based Classification
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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- 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. DOI: 10.18653/v1/N19-1423 ↗
- Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774. link ↗
Related methods
Referenced by
Explainable Graph Neural NetworkExplainable LSTMExplainable Named Entity RecognitionExplainable NMF Topic ModelExplainable Question AnsweringExplainable Reinforcement LearningExplainable RoBERTa-based ClassificationExplainable Sentence EmbeddingsExplainable Sentiment AnalysisExplainable Text SummarizationExplainable Topic ModelingExplainable Transformer