AventIQ-AI / Restaurant-Order-Summarizer

Text-to-Text Transfer Transformer Quantized Model for Food Order Summarization

This repository hosts a quantized version of the T5 model, fine-tuned specifically for text summarization of food order entries. The model extracts concise summaries from semi-structured or unstructured restaurant order texts, making it ideal for POS systems, kitchen displays, and chat-based food order logging.

Model Details

• Field: Description
• Model Architecture T5 (Text-to-Text Transfer Transformer)
• Task Text Summarization for Food Orders
• Input Format Free-form order text (includes Order ID, Customer, Items, etc.)
• Quantization 8-bit (int8) using bitsandbytes
• Framework Hugging Face Transformers
• Base Model t5-base
• Dataset Custom

Usage

Installation

pip install transformers accelerate bitsandbytes torch

Loading the Model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "your-username/food-order-summarizer-quantized"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, load_in_8bit=True, device_map="auto")

def test_summarization(model, tokenizer):
    user_text = input("\nEnter your food order text:\n")
    inputs = tokenizer("summarize: " + user_text, return_tensors="pt", truncation=True, max_length=512).to(model.device)

    output = model.generate(
        **inputs,
        max_new_tokens=100,
        num_beams=5,
        length_penalty=0.8,
        early_stopping=True
    )

    summary = tokenizer.decode(output[0], skip_special_tokens=True)
    return summary

print("\n📝 **Model Summary:**")
print(test_summarization(model, tokenizer))

ROUGE Evaluation Results

After fine-tuning the T5-Small model for text summarization, we obtained the following ROUGE scores:

Fine-Tuning Details

Dataset

Custom-labeled food order dataset containing fields like Order ID, Customer, and Order Details. The model was trained to extract clean, natural summaries from noisy or inconsistent order formats.

Training

• Number of epochs: 3

• Batch size: 4

• Evaluation strategy: epoch

• Learning rate: 3e-5

Quantization

Post-training 8-bit quantization using bitsandbytes library with Hugging Face integration. This reduced the model size and improved inference speed with negligible impact on summarization quality.

Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Quantized model weights
├── README.md            # Model documentation

Limitations

• The model may misinterpret or misformat input with excessive noise or missing key fields.

• Quantized versions may show slight accuracy loss compared to full-precision models.

• Best suited for English-language food order formats.

Contributing

Contributions are welcome! If you have suggestions, feature requests, or improvements, feel free to open an issue or submit a pull request.