Update README.md

README.md

@@ -7,25 +7,14 @@ sdk: docker
 pinned: false
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-
-# Random Baseline Model for Climate Disinformation Classification
-
 ## 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
+This space is dedicated to the text task of the Frugal AI Challenge. The final model employed is a Qwen2.5-3B-Instruct with LoRA adapters trained on a diverse mix of approximately 95,000 samples, encompassing both real and synthetic data.
+The dataset was open-sourced at MatthiasPicard/Frugal-AI-Train-Data-88k. The fine-tuned model, along with training logs, was open-sourced at MatthiasPicard/ModernBERT_frugal_88k.
 
-## Training Data
+To optimize inference time, the model was quantized to 8 bits to reduce memory usage and increase performance speed.
 
-The model uses the QuotaClimat/frugalaichallenge-text-train dataset:
-- Size: ~6000 examples
-- Split: 80% train, 20% test
-- 8 categories of climate disinformation claims
+### Note: The inference script includes both model and tokenizer loading. As a result, the first evaluation of our model in the submission space will consume more energy than subsequent evaluations.
 
 ### Labels
 0. No relevant claim detected
@@ -36,36 +25,4 @@ The model uses the QuotaClimat/frugalaichallenge-text-train dataset:
 5. Science is unreliable
 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
 ```