Create README.md

README.md

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+# Text-to-Text Transfer Transformer Quantized Model for News Summarization
+
+This repository hosts a quantized version of the T5 model, fine-tuned specifically for text summarization of news. The model extracts concise summaries from semi-structured or unstructured news 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 News
+  - **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
+
+```sh
+pip install transformers accelerate bitsandbytes torch
+```
+### Loading the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "AventIQ-AI/T5-News-Summarization"
+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 News 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.