readme

README.md

@@ -8,46 +8,39 @@ pinned: false
 ---
 
 
-# Random Baseline Model for Climate Disinformation Classification
+# Conformer model
 
 ## 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.
-
+This is a CNN followed by Conformer encoder
 ### 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
+- baseline for audio predictions
 
 ## 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
+The model uses the rfcx audio dataset:
+- Size: ~35000 examples
+- Split: 80% train, 20% validation 
+- Binary classification
 
 ### 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
+0. Chain Saw in audio
+1. no Chain Saw in audio
 
 ## Performance
+90% accuracy on validation
 
 ### Metrics
-- **Accuracy**: ~12.5% (random chance with 8 classes)
+- **Accuracy**: 90% on validation
 - **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.
-
+CNN and Conformer. Conformer is a mixture between
+transformer (MHSA with RoPE
+positional encoding), and CNN blocks.
 ## Environmental Impact
 
 Environmental impact is tracked using CodeCarbon, measuring:
@@ -57,15 +50,9 @@ Environmental impact is tracked using CodeCarbon, measuring:
 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
+- simple
 
 ## 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
 ```