# Stock Market QA Chatbot with Text-to-Text Transfer Transformer(T5)

## 📌 Overview

This repository hosts the quantized version of the T5 model fine-tuned for question-answer tasks related to stock market. The model has been trained on the stock_trading_QA dataset from Hugging Face. The model is quantized to Float16 (FP16) to optimize inference speed and efficiency while maintaining high performance.

## 🏗 Model Details

- **Model Architecture:** t5-base
- **Task:** QA Chatbot for Stock Market  
- **Dataset:** Hugging Face's `stock_trading_QA`  
- **Quantization:** Float16 (FP16) for optimized inference  
- **Fine-tuning Framework:** Hugging Face Transformers  

## 🚀 Usage

### Installation

```bash
pip install transformers torch
```

### Loading the Model

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

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

model_name = "AventIQ-AI/t5-stockmarket-qa-chatbot"
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
tokenizer = T5Tokenizer.from_pretrained(model_name)
```

### Question Answer Example

```python
question = "How can I start investing in stocks?"
input_text = "question: " + question
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(input_ids, max_length=50)
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(f"Question: {question}")
print(f"Predicted Answer: {answer}")
```

## 📊 Evaluation Metric: BLEU Score

For question answer tasks, a high BLEU score indicates that the model’s corrected sentences closely match human-annotated corrections.  

## **Interpreting Our BLEU Score**  
Our model achieved a **BLEU score of 0.7888**, which indicates:  
✅ **Good answer generating ability**  
✅ **Moderate sentence fluency**    

BLEU is computed by comparing the **1-gram, 2-gram, 3-gram, and 4-gram overlaps** between the model’s output and the reference sentence while applying a **brevity penalty** if the model generates shorter sentences.  

### **BLEU Score Ranges for Chatbot**

| BLEU Score | Interpretation |
| --- | --- |
| **0.8 - 1.0** | Near-perfect corrections, closely matching human annotations. |
| **0.7 - 0.8** | High-quality corrections, minor variations in phrasing. |
| **0.6 - 0.7** | Good corrections, but with some grammatical errors or missing words. |
| **0.5 - 0.6** | Decent corrections, noticeable mistakes, lacks fluency. |
| **Below 0.5** | Needs improvement, frequent incorrect corrections. |


## ⚡ Quantization Details

Post-training quantization was applied using PyTorch's built-in quantization framework. The model was quantized to Float16 (FP16) to reduce model size and improve inference efficiency while balancing accuracy.

## 📂 Repository Structure

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

## ⚠️ Limitations

- The model may struggle with highly ambiguous sentences.
- Quantization may lead to slight degradation in accuracy compared to full-precision models.
- Performance may vary across different writing styles and sentence structures.

## 🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.