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metadata
title: Submission Template
emoji: 🔥
colorFrom: yellow
colorTo: green
sdk: docker
pinned: false

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 dataset:

  • Size: ~84000 examples
  • Split: 80% train, 20% test
  • 8 categories of climate disinformation claims

Labels

  1. No relevant claim detected
  2. Global warming is not happening
  3. Not caused by humans
  4. Not bad or beneficial
  5. Solutions harmful/unnecessary
  6. Science is unreliable
  7. Proponents are biased
  8. 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