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---
license: apache-2.0
datasets:
- msamogh/indirect-requests
language:
- en
metrics:
- accuracy
base_model:
- google-t5/t5-base
pipeline_tag: text2text-generation
library_name: transformers
tags:
- prompt_restructuring
- 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.
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