README.md

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title: Fine-Tuned BERT Model
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sdk: docker
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# Fine-Tuned BERT Model for Climate Disinformation Classification

## Model Description

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**: 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

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**: 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
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 and training  
- Energy consumption during inference and training  

This tracking aligns with the Frugal AI Challenge's commitment to promoting sustainable AI practices.  

## Limitations
- 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 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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