---
license: apache-2.0
datasets:
- synapti/nci-propaganda-production
base_model: answerdotai/ModernBERT-base
tags:
- transformers
- modernbert
- text-classification
- propaganda-detection
- binary-classification
- nci-protocol
library_name: transformers
pipeline_tag: text-classification
---

# NCI Binary Detector

Fast binary classifier that detects whether text contains propaganda techniques.

## Model Description

This model is **Stage 1** of the NCI (Narrative Credibility Index) two-stage propaganda detection pipeline:

- **Stage 1 (this model)**: Fast binary detection - "Does this text contain propaganda?"
- **Stage 2**: Multi-label technique classification - "Which specific techniques are used?"

The binary detector serves as a fast filter with high recall, passing flagged content to the more detailed technique classifier.

## Labels

| Label | Description |
|-------|-------------|
| `no_propaganda` | Text does not contain propaganda techniques |
| `has_propaganda` | Text contains one or more propaganda techniques |

## Performance

**Test Set Results:**

| Metric | Score |
|--------|-------|
| Accuracy | 99.5% |
| F1 Score | 99.6% |
| Precision | 99.2% |
| Recall | 100.0% |
| ROC AUC | 99.9% |

## Usage

### Basic Usage

```python
from transformers import pipeline

detector = pipeline(
    "text-classification",
    model="synapti/nci-binary-detector"
)

text = "The radical left is DESTROYING our country!"
result = detector(text)[0]

print(f"Label: {result['label']}")  # 'has_propaganda' or 'no_propaganda'
print(f"Confidence: {result['score']:.2%}")
```

### Two-Stage Pipeline

For best results, use with the technique classifier:

```python
from transformers import pipeline

# Stage 1: Binary detection
detector = pipeline("text-classification", model="synapti/nci-binary-detector")

# Stage 2: Technique classification (only if propaganda detected)
classifier = pipeline("text-classification", model="synapti/nci-technique-classifier", top_k=None)

text = "Your text to analyze..."

# Quick check first
detection = detector(text)[0]
if detection["label"] == "has_propaganda" and detection["score"] > 0.5:
    # Detailed technique analysis
    techniques = classifier(text)[0]
    detected = [t for t in techniques if t["score"] > 0.3]
    for t in detected:
        print(f"{t['label']}: {t['score']:.2%}")
else:
    print("No propaganda detected")
```

## Training Data

Trained on [synapti/nci-propaganda-production](https://huggingface.co/datasets/synapti/nci-propaganda-production):

- **23,000+ examples** from multiple sources
- **Positive examples**: Text with 1+ propaganda techniques (from SemEval-2020, augmented data)
- **Hard negatives**: Factual content from LIAR2, QBias datasets
- **Class-weighted Focal Loss** to handle imbalance (gamma=2.0)

## Model Architecture

- **Base Model**: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
- **Parameters**: 149.6M
- **Max Sequence Length**: 512 tokens
- **Output**: 2 labels (binary classification)

## Training Details

- **Loss Function**: Focal Loss (gamma=2.0, alpha=0.25)
- **Optimizer**: AdamW
- **Learning Rate**: 2e-5
- **Batch Size**: 16 (effective 32 with gradient accumulation)
- **Epochs**: 5 with early stopping (patience=3)
- **Hardware**: NVIDIA A10G GPU

## Limitations

- Trained primarily on English text
- Works best on content similar to training distribution (news articles, social media posts)
- May not detect subtle or novel propaganda techniques not in training data
- Should be used alongside human review for high-stakes applications

## Related Models

- [synapti/nci-technique-classifier](https://huggingface.co/synapti/nci-technique-classifier) - Stage 2 multi-label technique classifier

## Citation

```bibtex
@inproceedings{da-san-martino-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 11: Detection of Propaganda Techniques in News Articles",
    author = "Da San Martino, Giovanni and others",
    booktitle = "Proceedings of SemEval-2020",
    year = "2020",
}

@misc{nci-binary-detector,
  author = {NCI Protocol Team},
  title = {NCI Binary Detector},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/synapti/nci-binary-detector}
}
```

## License

Apache 2.0