AventIQ-AI/t5-stockmarket-qa-chatbot

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

pip install transformers torch

Loading the Model

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

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.