Sentence-Level-Stereotype-Detector
| Property | Value |
|---|---|
| Model Type | Text Classification |
| Base Architecture | DistilBERT |
| Author | wu981526092 |
| Hugging Face URL | Link |
What is Sentence-Level-Stereotype-Detector?
The Sentence-Level-Stereotype-Detector is a sophisticated transformer-based model designed to identify and classify stereotypes in text at the sentence level. Built on DistilBERT architecture and fine-tuned on the MGS Dataset, this model can detect both stereotypical and anti-stereotypical content across four major categories: gender, race, profession, and religion.
Implementation Details
The model leverages the DistilBERT architecture and implements a nine-class classification system. It's easily integrated into workflows using Hugging Face's pipeline API, making it accessible for various applications focused on bias detection and inclusive content creation.
- Built on pre-trained DistilBERT architecture
- Fine-tuned on MGS Dataset
- Implements 9 distinct classification categories
- Seamless integration via Hugging Face pipeline
Core Capabilities
- Detection of gender-based stereotypes and anti-stereotypes
- Identification of racial stereotypes and anti-stereotypes
- Analysis of profession-related stereotypes and anti-stereotypes
- Recognition of religious stereotypes and anti-stereotypes
- Classification of unrelated content
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive approach to stereotype detection, covering multiple domains and distinguishing between stereotypes and anti-stereotypes, making it particularly valuable for content moderation and bias analysis.
Q: What are the recommended use cases?
The model is ideal for content moderation, bias detection in writing, educational tools for inclusive language, and research applications focused on studying stereotypes in text. It can be integrated into content management systems, writing assistance tools, and automated review processes.
