Regardv3
| Property | Value |
|---|---|
| Author | sasha |
| Model Type | BERT Classifier |
| Training Data | 1.7K biased language samples |
| Research Paper | arXiv:1909.01326 |
What is regardv3?
Regardv3 is a specialized BERT-based classification model designed to analyze and measure demographic bias in language. Unlike traditional sentiment analysis that focuses on general language polarity, this model specifically evaluates social perceptions and biases towards different demographic groups. It emerged from research focused on controlling biases in language generation systems.
Implementation Details
The model implements a BERT architecture trained on a carefully curated dataset of 1,700 samples containing biased language. It's designed to detect and classify "regard" - a metric that quantifies how language expresses social perceptions towards specific demographics.
- Single BERT classifier implementation (non-ensemble)
- Specialized training focusing on demographic bias detection
- Built for analyzing social perception patterns in text
Core Capabilities
- Measurement of language polarity towards specific demographics
- Analysis of social perception patterns in text
- Distinction between general sentiment and demographic-specific regard
- Bias detection in natural language processing
Frequently Asked Questions
Q: What makes this model unique?
Unlike general sentiment analysis models, regardv3 specifically focuses on measuring how language expresses social perceptions and biases towards demographic groups, making it valuable for studying and addressing bias in natural language processing systems.
Q: What are the recommended use cases?
The model is particularly useful for: analyzing bias in language generation systems, studying demographic representation in text, evaluating fairness in NLP applications, and research in social computing and computational linguistics.
