Update README.md

README.md

@@ -15,4 +15,53 @@ tags:
 - prompt_refining
 - indirect_requests
 - pragmatics
----
+---
+# PragmaticLM - T5 for Prompt Restructuring
+
+## 📌 Overview
+**PragmaticLM** is a fine-tuned T5 model designed to **restructure and reframe user prompts** for better understanding by downstream LLMs. The model enhances prompt clarity by leveraging **contextual restructuring** techniques.
+
+## 🚀 Model Details
+- **Base Model**: [T5-Base](https://huggingface.co/t5-base)
+- **Training Data**: [Indirect Requests] (https://huggingface.co/datasets/msamogh/indirect-requests)
+- **Task Type**: Text-to-text transformation
+- **Library**: [Hugging Face Transformers](https://github.com/huggingface/transformers)
+
+## 📊 Training Configuration
+- **Epochs**: 10
+- **Batch Size**: 8
+- **Learning Rate**: Encoder: `1e-5`, Decoder: `3e-5`
+- **Optimizer**: AdamW
+- **Loss Function**: Cross-entropy loss
+- **Hardware**: GPU (T4)
+
+## ⚡ Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("aliMohammad16/pragmaticLM")
+model = AutoModelForSeq2SeqLM.from_pretrained("aliMohammad16/pragmaticLM")
+
+def restructure_prompt(input_prompt):
+    input_text = f"Restructure Prompt: {input_prompt}"
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True)
+    
+    output = model.generate(
+        inputs.input_ids,
+        max_length=64,
+        num_beams=4,
+        early_stopping=True
+    )
+    
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Example Usage
+test_prompt = "I am not feeeling well. I need to consult a doctor nearby."
+print(restructure_prompt(test_prompt))
+```
+
+## ⏳ Improvements
+- **Work in progress**: This is a work in progress. I am actively working on this model.
+- **Update**: Next I am implementing a multimodular pipeline, integrating TinyLlama 1.1B and Llama Index RAG with `prompt-restructuring` model, to improve output generation.
+