RoBERTa Fine-Tuned Model for Question Answering

This repository hosts a fine-tuned version of the RoBERTa 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: RoBERTa
Task: Question Answering
Dataset: SQuAD (Stanford Question Answering Dataset)
Quantization: FP16
Fine-tuning Framework: Hugging Face Transformers

🚀 Usage

Installation

pip install transformers torch

Loading the Model

from transformers import RobertaTokenizer, RobertaForQuestionAnswering
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/roberta-chatbot"
model = RobertaForQuestionAnswering.from_pretrained(model_name).to(device)
tokenizer = RobertaTokenizer.from_pretrained(model_name)

Chatbot Inference

from transformers import pipeline

# Load QA pipeline
qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer, device=0)

# Test sample question
# Updated context and question for a flight prediction example
# Updated context and question for flight prediction example
context = "Flight AI101 departs from New York at 10:00 AM and arrives in San Francisco at 1:30 PM. The flight duration is 5 hours and 30 minutes."
question = "What is the duration of Flight AI101?"


# Get answer
result = qa_pipeline(question=question, context=context)
print(result)

📊 Evaluation Results After fine-tuning the RoBERTa-base model for question answering, we evaluated the model's performance on the validation set from the SQuAD dataset. The following results were obtained: Metric Score Meaning F1 Score 89.5 Measures the balance between precision and recall for answer extraction. Exact Match 82.4 Percentage of questions where the predicted answer matches the ground truth exactly. Fine-Tuning Details Dataset The SQuAD dataset, containing over 100,000 question-answer pairs based on Wikipedia articles, was used for fine-tuning the model. Training Number of epochs: 3 Batch size: 8 Evaluation strategy: steps Quantization Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency. Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Quantized Model
├── README.md            # Model documentation

⚡ 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.