# Model Card: DeBERTa v3 Small Explicit Content Classifier v2.0

## Model Summary

A fine-tuned DeBERTa-v3-small model for classifying literary content explicitness across 7 categories with significant improvements over v1.0.

## Intended Use

**Primary Use Cases:**
- Literary content analysis and research
- Digital humanities applications  
- Content curation for libraries and educational institutions
- Publishing workflow assistance

**Out of Scope:**
- Real-time content moderation without human oversight
- Legal content filtering decisions
- Content outside of literary/educational domains

## Performance Summary

| Metric | Value |
|--------|-------|
| Overall Accuracy | 81.8% |
| Macro F1 | 0.754 |
| Best Performing Class | EXPLICIT-DISCLAIMER (F1: 0.977) |
| Most Challenging Class | SUGGESTIVE (F1: 0.476) |

## Training Data

- **Size**: 119,023 samples (deduplicated)
- **Sources**: Literary texts, reviews, academic content
- **Quality**: Cross-split contamination eliminated
- **Balance**: Class weights applied during training

## Ethical Considerations

- Designed for academic and educational use
- Requires human oversight for sensitive applications
- May reflect biases present in training data
- Not suitable for automated content blocking

## Technical Specifications

- **Architecture**: DeBERTa-v3-small (141.9M parameters)
- **Training**: Focal loss, 4.79 epochs, cosine LR schedule
- **Input**: Text sequences up to 512 tokens
- **Output**: 7-class probability distribution