---
license: mit
tags:
- truthfulness
- bert
- text-classification
- dual-classifier
pipeline_tag: text-classification
---

# Truthfulness Detection Model

Fine-tuned BERT model for detecting truthfulness in text at both token and sentence levels.

## Model Description

This model uses a dual-classifier architecture on top of BERT to:
- Classify truthfulness at the sentence level (returns probability 0-1)
- Classify truthfulness for each token (returns probability 0-1 per token)

Low scores indicate likely false statements, high scores indicate likely true statements.

## Example Output

For "The earth is flat.":
- Sentence score: 0.0736 (7.36% - correctly identified as false)
- Token scores: ~0.10 for each token

## Training

- Base model: bert-base-uncased  
- Training samples: 6,330
- Epochs: 3
- Batch size: 16
- Training time: 49 seconds on H100

## Custom Architecture Required

⚠️ This model uses a custom `BERTForDualTruthfulness` class. You cannot load it with standard AutoModel.
See the [implementation code](https://huggingface.co/prompterminal/classifier/blob/main/model_architecture.py) for the model class definition.---
license: mit
---