README.md

metadata

title: Frugal AI Challenge Submission
emoji: 🌍
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

Models for Climate Disinformation Classification

Evaluate locally

To evaluate the model locally, you can use the following command:

python main.py --config config_evaluation_{model_name}.json

where {model_name} is either distilBERT or embeddingML.

Models Description

DistilBERT Model

The model uses the distilbert-base-uncased model from the Hugging Face Transformers library, fine-tuned on the training dataset (see below).

Embedding + ML Model

The model uses a simple embedding layer followed by a classic ML model. Currently, the embedding layer is a simple TF-IDF vectorizer, and the ML model is a logistic regression.

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: ~12.5% (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