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---
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license: mit
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tags:
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- truthfulness
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- bert
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- text-classification
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- dual-classifier
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pipeline_tag: text-classification
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---
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# Truthfulness Detection Model
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Fine-tuned BERT model for detecting truthfulness in text at both token and sentence levels.
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## Model Description
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This model uses a dual-classifier architecture on top of BERT to:
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- Classify truthfulness at the sentence level (returns probability 0-1)
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- Classify truthfulness for each token (returns probability 0-1 per token)
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Low scores indicate likely false statements, high scores indicate likely true statements.
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## Example Output
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For "The earth is flat.":
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- Sentence score: 0.0736 (7.36% - correctly identified as false)
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- Token scores: ~0.10 for each token
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## Training
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- Base model: bert-base-uncased
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- Training samples: 6,330
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- Epochs: 3
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- Batch size: 16
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- Training time: 49 seconds on H100
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## Custom Architecture Required
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⚠️ This model uses a custom `BERTForDualTruthfulness` class. You cannot load it with standard AutoModel.
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See the [implementation code](https://huggingface.co/prompterminal/classifier/blob/main/model_architecture.py) for the model class definition.---
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license: mit
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---
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