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AventIQ-AI/
t5-qa-chatbot

Name: t5-qa-chatbot
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T5-Base Fine-Tuned Model for Question Answering

This repository hosts a fine-tuned version of the T5-Base model optimized for question-answering tasks using the [SQuAD] dataset. The model is designed to efficiently perform question answering while maintaining high accuracy.

Model Details

Model Architecture:t5-qa-chatbot
Task: Question Answering (QA-Chatbot)
Dataset: [SQuAD]
Quantization: FP16
Fine-tuning Framework: Hugging Face Transformers

🚀 Usage

Installation

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-qa-chatbot"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)

Chatbot Inference

python

def answer_question(question, context):
    input_text = f"question: {question} context: {context}"
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
    
    # Move input tensors to the same device as the model
    inputs = {key: value.to(device) for key, value in inputs.items()}  
    
    # Generate answer
    with torch.no_grad():
        output = model.generate(**inputs, max_length=150)

    # Decode and return answer
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Test Case
question = "What is overfitting in machine learning?"
context = "Overfitting occurs when a model learns the training data too well, capturing noise instead of actual patterns.
predicted_answer = answer_question(question, context)
print(f"Predicted Answer: {predicted_answer}")

⚡ 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.