Create README.md

README.md

@@ -0,0 +1,80 @@
+# 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**:AventIQ-AI/t5-qa-chatbot
+- **Task**: Question Answering (QA-Chatbot)
+- **Dataset**: [SQuAD]
+- **Quantization**: FP16
+- **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 = "t5-small"
+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.