Upload README.md

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+---
+title: Submission Template
+emoji: 🔥
+colorFrom: yellow
+colorTo: green
+sdk: docker
+pinned: false
+---
+
+
+# Logistic regression Model for Climate Disinformation Classification
+
+## Model Description
+
+This is a Logistic regression 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.
+
+### Intended Use
+
+- **Primary intended uses**: Baseline comparison for climate disinformation classification models
+- **Primary intended users**: Researchers and developers participating in the Frugal AI Challenge
+- **Out-of-scope use cases**: Not intended for production use or real-world classification tasks
+
+## Training Data
+
+The model uses the QuotaClimat/frugalaichallenge-text-train dataset:
+- Size: ~6000 examples
+- Split: 80% train, 20% test
+- 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**: ~63.5% 
+- **Environmental Impact**:
+  - Emissions tracked in gCO2eq
+  - Energy consumption tracked in Wh
+
+### Model Architecture
+The model implements a random choice between the 8 possible labels, serving as the simplest possible baseline.
+
+## Environmental Impact
+
+Environmental impact is tracked using CodeCarbon, measuring:
+- Carbon emissions during inference
+- Energy consumption during inference
+
+This tracking helps establish a baseline for the environmental impact of model deployment and inference.
+
+## Limitations
+- Makes Logistic regression predictions
+- No learning or pattern recognition
+- Input text vectorized
+- Serves only as a LR baseline reference
+- Not suitable for any real-world applications
+
+## Ethical Considerations
+
+- Dataset contains sensitive topics related to climate disinformation
+- Model makes random predictions and should not be used for actual classification
+- Environmental impact is tracked to promote awareness of AI's carbon footprint
+```