Update README.md

README.md

@@ -12,20 +12,8 @@ pinned: false
 
 ## Model Description
 
-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.
-
-### 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
+This space is related to the text task of the Frugal AI Challenge. The final model used is a ModernBert trained on a mix of around ~95 000 samples, both Real and Synthetic Data.
+The dataset was open-sourced at MatthiasPicard/Frugal-AI-Train-Data-88k. The finetuned model along the training logs was open-sourced at MatthiasPicard/ModernBERT_frugal_88k.
 
 ### Labels
 0. No relevant claim detected
@@ -37,35 +25,4 @@ The model uses the QuotaClimat/frugalaichallenge-text-train dataset:
 6. Proponents are biased
 7. Fossil fuels are needed
 
-## Performance
-
-### Metrics
-- **Accuracy**: ~12.5% (random chance with 8 classes)
-- **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 completely random predictions
-- No learning or pattern recognition
-- No consideration of input text
-- Serves only as a 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
-```