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title: Submission Template
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# Random Baseline Model for Climate Disinformation Classification

## Model Description

This model is a fine-tuned version of answerdotai/ModernBERT-base on the Tonic/climate-guard-toxic-agent dataset. It achieves the following results on the evaluation set:

Loss: 4.9405
Accuracy: 0.4774
F1: 0.4600
Precision: 0.6228
Recall: 0.4774
F1 0 Not Relevant: 0.5064
F1 1 Not Happening: 0.6036
F1 2 Not Human: 0.3804
F1 3 Not Bad: 0.4901
F1 4 Solutions Harmful Unnecessary: 0.3382
F1 5 Science Is Unreliable: 0.4126
F1 6 Proponents Biased: 0.4433
F1 7 Fossil Fuels Needed: 0.4752

### 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 [Tonic/climate-guard-toxic-agent](https://huggingface.co/datasets/Tonic/Climate-Guard-Toxic-Agent) dataset:
- Size: ~84000 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**: ~95.5% (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
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