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:

Metric	Score	Meaning
ROUGE-1	0.4125 (~41%)	Overlap of unigrams between reference and summary.
ROUGE-2	0.2167 (~22%)	Overlap of bigrams, indicating fluency.
ROUGE-L	0.3421 (~34%)	Longest common subsequence matching structure.
ROUGE-Lsum	0.3644 (~36%)	Sentence-level summarization effectiveness.

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.