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
1question ="How can I start investing in stocks?"2input_text ="question: "+ question
3input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)45with torch.no_grad():6 outputs = model.generate(input_ids, max_length=50)7 answer = tokenizer.decode(outputs[0], skip_special_tokens=True)89print(f"Question: {question}")10print(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.
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.
