Pegi1727/DIINA-

Official implementation of the DIINA model for Afrikaans negation # DIINA- ```markdown # DIINA: Inhibitory Neural Architecture for Afrikaans ‌ Official implementation of the DIINA model, an inhibitory neural architecture designed for double negation resolution and lexical ambiguity reduction in context-rich languages like Afrikaans. ‌ ## Abstract Afrikaans presents a compelling test case for computational models of negation: it is morphologically streamlined yet relies heavily on context and syntactic licensing. DIINA operationalizes inhibitory control as a learned mechanism that suppresses competing interpretations when negation is structurally licensed. ‌ ## Key Performance Metrics - Double Negation Accuracy: 94% - Lexical Ambiguity Error Rate: 6% - Inhibition Score (Secondary Negation): 0.92 ‌ ## Core Architecture The model is built on a gated inhibitory framework where: Contextual Encoding: Features are extracted via dense blocks. Inhibitory Gating: A learned suppression function (InhibitoryGate) reduces activation of incompatible hypotheses. Progressive Refinement: Inhibition intensifies in later layers to consolidate semantic interpretation. ‌ ## Repository Structure - `model.py`: The core PyTorch implementation of the DIINA architecture and Inhibitory Gate. - `visualization.py`: Tools for generating inhibitory heatmaps and interpretability plots. ‌ ## Citation If you use this model or the findings in your research, please cite our article: "Linguistic Efficiency and Inhibitory Control: Evaluating the DIINA Model on Afrikaans as a Morphologically Simplified but Context-Rich Language" ```