---
language:
- en
- multilingual
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
- persuasion
- moderation
- rhetoric
- explainable-ai
- trust
- labse
- timetotrust
pipeline_tag: text-classification
base_model: LaBSE
model-index:
- name: labse-persuasion-detection-agnostic
  results: []
---

# LaBSE Persuasion Detection (Domain-Agnostic)

**Model ID:** `jjprietotorres/labse-persuasion-detection-agnostic`  
**Part of:** [Time to Trust AI](https://your-link-here) initiative

---

## 🧠 Model Overview

This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for **text classification**.  
It implements a few-shot learning pipeline designed for *persuasion and rhetorical intent detection*.  

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer (LaBSE)](https://www.sbert.net) with **contrastive learning** to improve semantic representation.  
2. Training a **classification head** using features from the fine-tuned Sentence Transformer to moderate and classify persuasive content.

The model identifies five rhetorical or persuasion-related cohorts:
- **Supply Scarcity** — framing scarcity or urgency  
- **Authority Endorsement** — appealing to authority or credibility  
- **Misrepresentation** — containing misleading or manipulative language  
- **Logical Appeal** — reasoning-based persuasion  
- **Neutral** — factual or non-persuasive statements

---

## ⚙️ Intended Use

This model detects **persuasive or manipulative rhetoric** across content types and domains.  
It is **domain-agnostic**, suitable for use in:
- Chatbot or conversational AI safety layers  
- AI content moderation pipelines  
- Adversarial persuasion or prompt-injection detection  
- Rhetorical bias auditing in generated or user content  

The goal is to enhance **trust, transparency, and explainability** within AI systems.

---

## 🚀 Usage

To use this model for inference, first install the SetFit library:

```bash
python -m pip install setfit
```

You can then run inference as follows:

```python
from setfit import SetFitModel

# Download from the Hub and run inference
model = SetFitModel.from_pretrained("jjprietotorres/labse-persuasion-detection-agnostic")

labels = ["Supply Scarcity", "Authority Endorsement","Misrepresentation","Neutral","Logical Appeal"]

# Run inference
texts = [
    "Experts agree this is the only viable solution.",
    "Hurry, offer ends tonight!",
    "Data clearly supports our argument."
]
probas = model.predict_proba(texts)
pooled_probas = probas.max(axis=0).values.tolist()
idxs = probas.max(axis=0).indices.tolist()

result = zip(labels, pooled_probas, [texts[i] for i in idxs])
report = [{"text": text, "label": label, "score": score} for label, score, text in result if label != "Neutral"]

print(report)
```

**Expected output:**
```
[{'text': 'Hurry, offer ends tonight!', 'label': 'Supply Scarcity', 'score': 0.5646279805087063}, {'text': 'Data clearly supports our argument.', 'label': 'Authority Endorsement', 'score': 0.25684773715338116}, {'text': 'Experts agree this is the only viable solution.', 'label': 'Misrepresentation', 'score': 0.5366529547271727}, {'text': 'Data clearly supports our argument.', 'label': 'Logical Appeal', 'score': 0.23385324245461217}]
```

---

## 🧩 Training Setup

- **Base model:** LaBSE (Language-Agnostic BERT Sentence Embedding)
- **Architecture:** Sentence Transformer fine-tuned with contrastive learning  
- **Classifier:** SetFit head trained on limited labeled samples  
- **Frameworks:** [SetFit](https://github.com/huggingface/setfit), [Sentence Transformers](https://www.sbert.net), PyTorch  
- **Training data:** custom persuasion dataset covering rhetorical cohorts  
- **Few-shot strategy:** enables data-efficient fine-tuning

---

## 📊 Evaluation (example)

| Metric     | Score |
|-------------|--------|
| Accuracy    | 0.86 |
| F1 Score    | 0.84 |
| Precision   | 0.83 |
| Recall      | 0.85 |

*(replace with your actual metrics once validated)*

---

## ⚖️ Ethical Considerations

- This model identifies *persuasive tactics*, **not factual accuracy** or moral intent.  
- Use responsibly and pair with human oversight in moderation workflows.  
- Model outputs may reflect training data biases.  
- Designed to foster transparency and explainability in trust-critical AI use cases.

---

## 🔍 Integration within *Time to Trust AI*

This model is a **trust-building component** of the *Time to Trust AI* framework.  
By publishing it openly, the goal is to make trust operational — demonstrating transparency in model design, intent, and interpretability.

---

## 📚 Citation

If you use this model or methodology, please cite both the SetFit paper and this model:

```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
  doi = {10.48550/ARXIV.2209.11055},
  url = {https://arxiv.org/abs/2209.11055},
  author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
  title = {Efficient Few-Shot Learning Without Prompts},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

@misc{prietotorres2025labsepersuasion,
  title={LaBSE Persuasion Detection (Domain-Agnostic)},
  author={J.J. Prieto-Torres},
  year={2025},
  howpublished={\url{https://huggingface.co/jjprietotorres/labse-persuasion-detection-agnostic}},
}
```

---

## 🪪 License

Apache License 2.0 — you are free to use, modify, and distribute with attribution.

---

## 💬 Contact

**Author:** J.J. Prieto  
**Hugging Face:** [jjprietotorres](https://huggingface.co/jjprietotorres)