🧠Smart MCQ Solver
A Deep Learning based Multiple Choice Question Solver built using RoBERTa-Base and Hugging Face Transformers.
Project Overview
This project predicts the correct answer among five options for a multiple-choice question.
The model is fine-tuned using the Hugging Face AutoModelForMultipleChoice architecture.
Features
- Fine-tuned RoBERTa-base
- Multiple Choice Question Answering
- Gradio Web Interface
- Hugging Face Transformers
- PyTorch Implementation
Model
RoBERTa-base
Task:
Multiple Choice Question Answering
Performance
| Metric | Score |
|---|---|
| MAP@3 | 0.9893 |
| F1 Score | 0.99 |
Cross Validation
| Fold | MAP@3 |
|---|---|
| 1 | 0.9871 |
| 2 | 0.9854 |
| 3 | 0.9963 |
| 4 | 0.9933 |
| 5 | 0.9846 |
Mean MAP@3
0.9893
Installation
git clone https://github.com/YOUR_USERNAME/smart-mcq-solver.git
cd smart-mcq-solver
pip install -r requirements.txt
Run
python app.py
Screenshots
Home Page
Prediction
Tech Stack
- Python
- PyTorch
- Transformers
- Hugging Face
- Gradio
Author
Rohit Kumar
IIT Madras BS Degree
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