bias-detection-model
Property | Value |
---|---|
Author | d4data |
Base Architecture | DistilBERT |
Training Accuracy | 76.97% |
Validation Accuracy | 62.00% |
CO2 Emissions | 0.319355 kg |
What is bias-detection-model?
The bias-detection-model is a specialized sequence classification model designed to identify bias and fairness issues in English text, particularly news articles. Built on the DistilBERT architecture, this model was developed as part of research on "Bias and Fairness in AI" and has been trained on the MBAD Dataset.
Implementation Details
The model was trained for 30 epochs using carefully selected hyperparameters: batch size of 16, learning rate of 5e-5, and maximum sequence length of 512. It achieves a training accuracy of 76.97% and validation accuracy of 62%, with respective loss metrics of 0.45 and 0.96.
- Built on distilbert-base-uncased architecture
- Optimized for English language text analysis
- Implements TensorFlow framework
- Environmental impact tracked (0.319355 kg CO2)
Core Capabilities
- Detection of bias in news articles and general text
- Binary classification of biased vs. non-biased content
- Support for long text sequences (up to 512 tokens)
- Easy integration via Hugging Face Transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in detecting subtle biases in text content, particularly news articles, using a fine-tuned DistilBERT architecture. Its training on the MBAD Dataset makes it particularly effective for real-world bias detection applications.
Q: What are the recommended use cases?
The model is ideal for content moderation, news analysis, and research applications where identifying potential bias in text is crucial. It can be used to analyze news articles, social media content, and other text-based media for potential bias.