---
language: en
license: apache-2.0
tags:
  - nlp
  - text-classification
  - political-analysis
  - social-media-analysis
  - transformers
  - research
pipeline_tag: text-classification
---

# the_poli

**the_poli** is a transformer-based NLP classification model developed as part of the **s0m3m0** research project.  
The model is designed to analyse political and socio-political text, primarily from online and social media sources, and generate structured predictions for analytical and experimental purposes.

This repository contains **only the trained model artifacts** (weights, configuration, and tokenizer files).  
The full data pipeline and application code are maintained separately.

---

## Model Overview

- **Model type:** Transformer-based text classification  
- **Framework:** Hugging Face Transformers  
- **Primary language:** English  
- **Domain:** Political and social media text  
- **Use case:** Research, analysis, and experimentation  

The model is intended to assist in identifying patterns and signals in text rather than making authoritative judgments.

---

## Intended Use

The model is suitable for:

- Academic and research-based NLP experiments  
- Political and social discourse analysis  
- Text classification pipeline prototyping  
- Educational demonstrations of NLP systems  

### Not Intended For

- Political persuasion or targeting  
- Surveillance or profiling of individuals  
- Automated decision-making in real-world political contexts  
- High-stakes or safety-critical applications  

---

## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "d42kw01f/the_poli"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "Example political statement for analysis"
inputs = tokenizer(text, return_tensors="pt", truncation=True)
outputs = model(**inputs)
```

## Training Data
- Trained on curated datasets derived from **publicly available sources**
- Data was preprocessed and filtered for research purposes
- No private, sensitive, or non-consensual data was intentionally included

> Dataset details are intentionally limited to reduce misuse risk.

---

## Limitations & Bias
- Model performance depends on the quality and balance of the training data
- May reflect biases present in source datasets
- Not robust to domain shifts, sarcasm, or adversarial input
- Outputs should be treated as **probabilistic signals**, not factual conclusions

---

## Ethical Considerations
This model is released **strictly for research and educational use**.
Users are responsible for:
- Ensuring ethical deployment
- Respecting platform terms of service
- Avoiding harmful, misleading, or manipulative applications

---

## Related Project
- **Code repository:** https://github.com/d42kw01f/s0m3m0
- **Project name:** s0m3m0

---

## Author
**Dakshitha Navodya Perera**  
AI • Cybersecurity • Data Engineering  
Sri Lanka