README.md

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title: Submission Template
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sdk: docker
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Climate Disinformation Classification using XGBOOST over TF-IDF vectorized input optimized using RandomizedSearchCV

Model Description

This is a model based on XGBOOST classifier for TF-IDF vectorized texts for the Frugal AI Challenge 2024, specifically for the text classification task of identifying climate disinformation. The model serves as a performance floor.

Intended Use

• Primary intended uses: 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: 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: 0.9815384615384616
• Environmental Impact:
  • Emissions tracked in gCO2eq: 0.19426531051455168
  • Energy consumption tracked in Wh: 0.5262726046395284

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

• Text Classification using XGBOOST
• Input text vectorized with TF-IDF
• XGBOOST parameter search with RandomizedSearchCV
• Serves as 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