---
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.