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# 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.
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