modelcard.md

# Random Baseline Model Card

## Model Description

**Model Type:** Random Baseline Classifier  
**Task:** Climate Change Disinformation Classification  
**Version:** 1.0.0  
**Last Updated:** 2024

### Overview
This is a random baseline model for climate change disinformation classification. It randomly assigns labels to text inputs, serving as a baseline for comparing more sophisticated models.

### Intended Use
- **Primary Use:** Baseline comparison for climate disinformation classification models
- **Intended Users:** Researchers and developers working on climate disinformation detection
- **Out-of-Scope Uses:** Not intended for production or real-world classification tasks

## Training Data

**Dataset:** QuotaClimat/frugalaichallenge-text-train
- Size: ~6000 examples
- Split: 80% train, 20% test
- Labels: 8 categories of climate disinformation claims

### Labels
0. No relevant claim detected
1. Global warming is not happening
2. Not caused by humans
3. Not bad or beneficial
4. Solutions harmful/unnecessary
5. Science is unreliable
6. Proponents are biased
7. Fossil fuels are needed

## Performance

### Metrics
- **Accuracy:** ~12.5% (random chance)
- **Environmental Impact:**
  - Emissions (kgCO2eq)
  - Energy Consumed (kWh)

### Limitations
- Random predictions with no learning
- No consideration of input text
- Serves only as a baseline reference

## Ethical Considerations
- Model makes random predictions and should not be used for actual classification
- Dataset contains sensitive topics related to climate disinformation
- Environmental impact is tracked to promote awareness of AI's carbon footprint

## Environmental Impact
This model tracks its environmental impact using CodeCarbon, measuring:
- Carbon emissions
- Energy consumption

## Caveats and Recommendations
- Use only as a baseline comparison
- Not suitable for production use
- Consider environmental impact when running experiments