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README.md

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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.