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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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+ # Submission Portal for the Frugal AI Challenge