Toxic Comment Classification AI Training & Annotation
Developed a multi-label classification system to identify toxic comments in a large dataset of online text. Leveraged advanced transformer models (BERT, DeBERTa-v3, LLaMA-2) to annotate and classify 160K+ comments by toxicity type and severity. Applied Multi-Label SMOTE to address extreme class imbalance and enhance rare class detection. • Built label definition and annotation schema for toxicity detection, including rare categories. • Implemented manual review and evaluation of automatic toxic comment predictions. • Tuned models based on labeling outcomes to increase minority class F1-scores. • Facilitated QA and correction of misclassified or ambiguous toxic comments.