README.md

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title: Submission Template
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sdk: docker
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Random Baseline Model for Climate Disinformation Classification

Model Description

After getting the embeddings of the quotes by using an embedding model , a basic Neural Network has been trained for the classification part.

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 QuotaClimat/frugalaichallenge-text-train dataset:

• Size: ~6000 examples
• Split: 70% train, 30% 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: ~78.5%
• Environmental Impact:
  • Emissions tracked in gCO2eq
  • Energy consumption tracked in Wh

Model Architecture

The model implements a Neural Network between the 8 possible labels, serving as a first 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

• Serves only as a baseline reference

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