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Uplaod the model

README.md

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+
+
+---
+language: en
+tags:
+- nlp
+- text-classification
+- social-media-analysis
+- transformers
+- research
+license: apache-2.0
+---
+
+# 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 social-media-related text and produce structured predictions for analytical and experimental purposes.
+
+This repository contains **only the trained model artifacts** (weights and configuration).  
+The source code and data pipeline are maintained separately.
+
+---
+
+## Model Description
+
+- **Model type:** Transformer-based text classification model  
+- **Framework:** Hugging Face Transformers  
+- **Language:** English (primary)  
+- **Domain:** Political and social media text analysis  
+
+The model focuses on extracting patterns and signals from text rather than making authoritative or real-world decisions.
+
+---
+
+## Intended Use
+
+The model is intended for:
+
+- Academic and research experimentation  
+- NLP pipeline development  
+- Social media text analysis  
+- Educational demonstrations  
+
+### Not Intended For
+
+- High-stakes decision making  
+- Political persuasion or targeting  
+- Surveillance, profiling, or enforcement  
+- Production systems without extensive validation  
+
+---
+
+## Usage Example
+
+```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 text for analysis"
+inputs = tokenizer(text, return_tensors="pt", truncation=True)
+outputs = model(**inputs)
+```
+
+---
+
+## Limitations & Biases
+
+- Performance depends heavily on the training dataset
+- May reflect biases present in source data
+- Not robust to domain shifts or adversarial inputs
+- Predictions should be interpreted as probabilistic signals, not facts
+
+---
+
+## Ethical Considerations
+
+This model is released **strictly for research and educational use**.
+Users are responsible for:
+- Complying with platform terms of service
+- Respecting data privacy and ethical boundaries
+- Avoiding harmful, misleading, or unethical applications
+
+---
+
+## Related Project
+
+- **GitHub (codebase):** [https://github.com/d42kw01f/s0m3m0](https://github.com/d42kw01f/s0m3m0)
+- **Project name:** s0m3m0
+
+---
+
+## Author
+
+**Dakshitha Navodya Perera**  
+AI • Cybersecurity • Data Engineering  
+Sri Lanka
+

config.json

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+{
+  "attention_probs_droput_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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