| # 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. | |