# 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