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Explainable RoBERTa-based Classification

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.

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Explainable RoBERTa-based Text Classification with Post-hoc Interpretation

Rekodi ya mbinu ya kiajenda · ml-model / deep-learning

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Taxonomic bucketBERT-based Classificationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketExplainable BERT-based Classificationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketExplainable Transformermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRoBERTa-based Classificationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSentence Embeddingsmachine-suggested · Relational suggestion, not evidence.