Multi-Label Hate Speech Classifier
Overview
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.
Setup & Installation
Requirements
- Python 3.x
- Dependencies:
numpypandasscikit-learnjoblib
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