dpmendez/environmental-misinformation-threshold

This model is a DistilBERT-based transformer fine-tuned for climate misinformation classification. It predicts the veracity of individual climate-related claims using contextualized language representations.

The model was trained on a dataset combining:

• Climate Fever
• Science Feedback fact-checked claims

Model Details

• Model type: DistilBERT (distilbert-base-uncased)
• Task: Sequence classification
• Input: Single climate-related claim (text)
• Output: Claim label probabilities
• Framework: Hugging Face Transformers
• Model weights: Stored in model.safetensors

Labels

Label	Description
LIKELY_TRUE	Claim is consistent with scientific consensus
LIKELY_FALSE	Claim contradicts scientific consensus

Label mappings are defined in config.json and label_map.json.

Training Procedure

• Fine-tuned from distilbert-base-uncased
• Cross-entropy loss
• Class imbalance handled via training strategy (no oversampling)
• Inference threshold tuned post-training to decrease cost function (less false positives is better)

The selected inference threshold is stored in threshold.json.