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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- nlp |
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- text-classification |
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- political-analysis |
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- social-media-analysis |
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- transformers |
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- research |
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pipeline_tag: text-classification |
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--- |
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# the_poli |
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**the_poli** is a transformer-based NLP classification model developed as part of the **s0m3m0** research project. |
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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. |
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This repository contains **only the trained model artifacts** (weights, configuration, and tokenizer files). |
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The full data pipeline and application code are maintained separately. |
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--- |
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## Model Overview |
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- **Model type:** Transformer-based text classification |
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- **Framework:** Hugging Face Transformers |
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- **Primary language:** English |
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- **Domain:** Political and social media text |
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- **Use case:** Research, analysis, and experimentation |
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The model is intended to assist in identifying patterns and signals in text rather than making authoritative judgments. |
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--- |
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## Intended Use |
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The model is suitable for: |
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- Academic and research-based NLP experiments |
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- Political and social discourse analysis |
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- Text classification pipeline prototyping |
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- Educational demonstrations of NLP systems |
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### Not Intended For |
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- Political persuasion or targeting |
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- Surveillance or profiling of individuals |
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- Automated decision-making in real-world political contexts |
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- High-stakes or safety-critical applications |
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--- |
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## Example Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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model_id = "d42kw01f/the_poli" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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text = "Example political statement for analysis" |
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inputs = tokenizer(text, return_tensors="pt", truncation=True) |
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outputs = model(**inputs) |
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``` |
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## Training Data |
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- Trained on curated datasets derived from **publicly available sources** |
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- Data was preprocessed and filtered for research purposes |
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- No private, sensitive, or non-consensual data was intentionally included |
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> Dataset details are intentionally limited to reduce misuse risk. |
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--- |
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## Limitations & Bias |
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- Model performance depends on the quality and balance of the training data |
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- May reflect biases present in source datasets |
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- Not robust to domain shifts, sarcasm, or adversarial input |
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- Outputs should be treated as **probabilistic signals**, not factual conclusions |
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## Ethical Considerations |
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This model is released **strictly for research and educational use**. |
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Users are responsible for: |
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- Ensuring ethical deployment |
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- Respecting platform terms of service |
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- Avoiding harmful, misleading, or manipulative applications |
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--- |
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## Related Project |
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- **Code repository:** https://github.com/d42kw01f/s0m3m0 |
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- **Project name:** s0m3m0 |
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--- |
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## Author |
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**Dakshitha Navodya Perera** |
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AI • Cybersecurity • Data Engineering |
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Sri Lanka |
