metadata
datasets:
- dpmendez/environmental-misinformation
language:
- en
base_model:
- distilbert/distilbert-base-uncased
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.