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--- |
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title: Submission Template |
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emoji: 🔥 |
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colorFrom: yellow |
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colorTo: green |
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sdk: docker |
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pinned: false |
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--- |
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# Random Baseline Model for Climate Disinformation Classification |
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## Model Description |
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This is a random baseline model for the Frugal AI Challenge 2024, specifically for the text classification task of identifying climate disinformation. The model serves as a performance floor, randomly assigning labels to text inputs without any learning. |
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### Intended Use |
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- **Primary intended uses**: Baseline comparison for climate disinformation classification models |
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- **Primary intended users**: Researchers and developers participating in the Frugal AI Challenge |
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- **Out-of-scope use cases**: Not intended for production use or real-world classification tasks |
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## Training Data |
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The model uses the QuotaClimat/frugalaichallenge-text-train dataset: |
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- Size: ~6000 examples |
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- Split: 80% train, 20% test |
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- 8 categories of climate disinformation claims |
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### Labels |
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0. No relevant claim detected |
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1. Global warming is not happening |
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2. Not caused by humans |
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3. Not bad or beneficial |
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4. Solutions harmful/unnecessary |
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5. Science is unreliable |
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6. Proponents are biased |
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7. Fossil fuels are needed |
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## Performance |
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### Metrics |
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- **Accuracy**: ~12.5% (random chance with 8 classes) |
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- **Environmental Impact**: |
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- Emissions tracked in gCO2eq |
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- Energy consumption tracked in Wh |
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### Model Architecture |
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The model implements a random choice between the 8 possible labels, serving as the simplest possible baseline. |
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## Environmental Impact |
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Environmental impact is tracked using CodeCarbon, measuring: |
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- Carbon emissions during inference |
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- Energy consumption during inference |
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This tracking helps establish a baseline for the environmental impact of model deployment and inference. |
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## Limitations |
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- Makes completely random predictions |
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- No learning or pattern recognition |
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- No consideration of input text |
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- Serves only as a baseline reference |
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- Not suitable for any real-world applications |
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## Ethical Considerations |
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- Dataset contains sensitive topics related to climate disinformation |
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- Model makes random predictions and should not be used for actual classification |
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- Environmental impact is tracked to promote awareness of AI's carbon footprint |
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``` |
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