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Bert Base Uncased Hatexplain Rationale Two

Developed by Hate-speech-CNERG
A BERT-based text classification model for detecting hate speech and offensive content, with rationale prediction capability
Downloads 523
Release Time : 3/2/2022

Model Overview

This model classifies text as either 'hate speech' or 'normal content', trained on data from Gab and Twitter platforms, enhanced with human-annotated rationales for improved performance

Model Features

Rationale prediction
Predicts rationale segments for offensive statements, enhancing model interpretability
Multi-platform training data
Trained on data from both Gab and Twitter platforms, covering diverse online language expressions
Human annotation enhancement
Incorporates human-annotated rationales during training to improve classification accuracy

Model Capabilities

Text classification
Hate speech detection
Offensive content identification
Interpretability analysis

Use Cases

Content moderation
Social media content filtering
Automatically identifies and flags hate speech and offensive content on platforms
Reduces manual moderation workload
Online community management
Assists community administrators in quickly locating policy-violating content
Improves community management efficiency
Academic research
Hate speech analysis
Used for linguistic or sociological studies on hate speech patterns
Provides quantitative analysis tools
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