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Roberta Hate Speech Dynabench R4 Target
This model improves online hate detection through dynamic dataset generation, focusing on learning from worst-case scenarios to enhance detection effectiveness.
Downloads 2.0M
Release Time : 6/10/2022
Model Overview
The LFTW R4 Target Model is designed for online hate detection, utilizing dynamically generated datasets to identify and classify hate speech, with special attention to hard-to-detect cases.
Model Features
Dynamic Dataset Generation
Improves the model's hate speech detection capabilities by dynamically generating datasets, particularly targeting hard-to-identify cases.
Learning from Worst-Case Scenarios
The model focuses on learning from the worst cases of hate speech to enhance detection accuracy and robustness.
Model Capabilities
Hate Speech Detection
Text Classification
Dynamic Dataset Generation
Use Cases
Social Media
Hate Speech Detection
Automatically detects and classifies hate speech on social media platforms, aiding in content moderation.
Improves the accuracy and coverage of hate speech detection.
Online Safety
Content Moderation
Used in content moderation systems for online forums and communities to automatically identify and filter hate speech.
Reduces manual moderation workload and increases efficiency.
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