The Multi-Label Hate Speech Classifier is a machine learning model designed to detect and categorize multiple forms of hate speech within textual data. It leverages a OneVsRest Logistic Regression classifier combined with TF-IDF vectorization to analyze and classify text into multiple labels simultaneously.
Features
Multi-Label Detection: Assigns multiple hate speech categories to a single piece of text.
Supported Categories:
toxic
obscene
insult
threat
identity_hate
Custom Thresholds: Optimized thresholds are applied to each label to balance precision and recall.
Model Architecture
Text Vectorization: Utilizes TF-IDF (Term Frequency-Inverse Document Frequency) to convert raw text into a numerical format.
Classifier: Implements a OneVsRest Logistic Regression approach for multi-label classification.
Training Process: Trained on a balanced dataset with pre-processed text to achieve robust performance across all categories.
