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---
title: Submission Template
emoji: 🔥
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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](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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