Update README.md

README.md

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-title: Submission Template
-emoji: 🔥
-colorFrom: yellow
-colorTo: green
+title: Fine-Tuned BERT Model
+emoji: 🌍
+colorFrom: blue
+colorTo: purple
 sdk: docker
-pinned: false
+pinned: true
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-
-# Random Baseline Model for Climate Disinformation Classification
+# Fine-Tuned BERT 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.
+This is a fine-tuned BERT model trained for the Frugal AI Challenge 2024. The model has been fine-tuned on the climate disinformation dataset to classify text inputs into 8 distinct categories related to climate disinformation. It leverages BERT's pretrained language understanding capabilities and has been optimized for accuracy in this domain.
 
 ### 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
+- **Primary intended uses**: Classifying text inputs to detect specific claims of climate disinformation
+- **Primary intended users**: Researchers, developers, and participants in the Frugal AI Challenge
+- **Out-of-scope use cases**: Not recommended for tasks outside climate disinformation classification or production-level applications without further evaluation
 
 ## Training Data
 
@@ -28,44 +27,47 @@ The model uses the QuotaClimat/frugalaichallenge-text-train dataset:
 - 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
+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**: ~12.5% (random chance with 8 classes)
-- **Environmental Impact**:
-  - Emissions tracked in gCO2eq
-  - Energy consumption tracked in Wh
+- **Accuracy**: Achieved XX.X% on the test set (replace `XX.X%` with the actual accuracy from your evaluation)  
+- **Environmental Impact**:  
+  - Carbon 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.
+This model fine-tunes the BERT base architecture (`bert-base-uncased`) for the climate disinformation task. The classifier head includes:  
+- Dense layers  
+- Dropout for regularization  
+- Softmax activation for multi-class classification  
 
 ## Environmental Impact
 
-Environmental impact is tracked using CodeCarbon, measuring:
-- Carbon emissions during inference
-- Energy consumption during inference
+Environmental impact is tracked using CodeCarbon, measuring:  
+- Carbon emissions during inference and training  
+- Energy consumption during inference and training  
 
-This tracking helps establish a baseline for the environmental impact of model deployment and inference.
+This tracking aligns with the Frugal AI Challenge's commitment to promoting sustainable AI practices.  
 
 ## 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
+- Fine-tuned specifically for climate disinformation; performance on other text classification tasks may degrade  
+- Requires computational resources (e.g., GPU) for efficient inference  
+- Predictions rely on the training dataset's representativeness; may struggle with unseen or out-of-distribution data  
 
 ## 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
-```
+- Dataset contains sensitive topics related to climate disinformation  
+- Model performance depends on the quality of the dataset and annotation biases  
+- Environmental impact during training and inference is disclosed to encourage awareness of AI's carbon footprint  
+- Users must validate outputs before using in sensitive or high-stakes applications  
+
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