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| title: Submission Template | |
| emoji: 🔥 | |
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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 | |
| ``` | |