---
title: Smart MCQ Solver โ DeBERTa-v3-large
emoji: ๐ง
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
license: mit
---
# ๐ง Smart MCQ Solver ยท DeBERTa-v3 Multi-Model Engine
[](https://huggingface.co/spaces/Shitanshu06/smart-mcq-solver)
[](https://huggingface.co/Shitanshu06/mcq-deberta-v3-large)
[](https://huggingface.co/Shitanshu06/mcq-deberta-v3-large)
[](https://study.iitm.ac.in)
[](https://python.org)
[](https://gradio.app)
A state-of-the-art Multiple Choice Question (MCQ) Answering System fine-tuned on DeBERTa-v3-large (0.4B parameters) and DeBERTa-v3-base (0.2B parameters) using PyTorch. Built for high-accuracy inference with MAP@3 validation score of 1.0000.
---
## ๐ Executive Summary & Project Overview
This repository contains the complete inference pipeline, multi-model Gradio web application, and fine-tuned model integration for answering 5-option multiple-choice questions.
### ๐ Key Highlights:
- **Primary Model (`DeBERTa-v3-large`)**: 435M parameter transformer model fine-tuned on MCQ datasets using sequence classification scoring.
- **Fast Variant (`DeBERTa-v3-base`)**: 86M parameter lightweight model for fast real-time inference.
- **Dual Inference Engine**: Direct local PyTorch GPU/CPU inference with automatic fallback to **Hugging Face Serverless Router API**.
- **Interactive Full-Width Dashboard**: Gradio 5.x user interface with soft-max confidence bar charts, MAP@3 ranking order, test suite validation, and 100% responsive layout.
---
## ๐๏ธ Professional Project Directory Structure
```
Smart-MCQ-Solver-DeBERTa/
โ
โโโ app.py # ๐ Main Gradio multi-model web application & inference engine
โโโ requirements.txt # ๐ฆ Python dependencies (torch, transformers, gradio, etc.)
โโโ README.md # ๐ Full documentation with badges & benchmark table
โโโ LICENSE # โ๏ธ MIT Open Source License
โ
โโโ config/ # โ๏ธ Deployment & server configuration
โ โโโ render.yaml # Render cloud deployment configuration
โ
โโโ docs/ # ๐ Project documentation
โ โโโ README.md # Documentation index & key links
โ โโโ architecture.md # Model pipeline diagram & training config
โ
โโโ deberta_v3_large/ # ๐ค Fine-tuned model weights & tokenizer
โโโ config.json # Model architecture hyperparameters
โโโ tokenizer.json # DeBERTa-v3 Fast Tokenizer vocabulary
โโโ tokenizer_config.json # Tokenizer settings & special tokens
โโโ model.safetensors # PyTorch fine-tuned weights (~1.74 GB, gitignored)
```
---
## ๐ Model Evaluation & Benchmarks
| Model Architecture | Parameters | Evaluation Metric | Score | Inference Speed | Primary Use Case |
| :--- | :---: | :---: | :---: | :---: | :--- |
| **`Shitanshu06/mcq-deberta-v3-large`** | **0.4B (435M)** | **MAP@3** | **1.0000 โ
** | ~1.2s | **Main High-Accuracy Solver** |
| **`Shitanshu06/mcq-deberta-v3-best-v2`** | **0.2B (86M)** | **MAP@3** | **0.9420** | ~0.4s | **Fast Lightweight Variant** |
---
## ๐ Quickstart & Local Installation
### 1. Clone Repository
```bash
git clone https://github.com/24f2006167/Smart-MCQ-Solver-DeBERTa.git
cd Smart-MCQ-Solver-DeBERTa
```
### 2. Create Virtual Environment & Install Dependencies
```bash
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
### 3. Launch Web Application
```bash
python3 app.py
```
Open **`http://localhost:7860`** in your browser to access the application.
---
## ๐จโ๐ Author & Academic Context
- **Author**: Shitanshu Chaurasiya
- **Roll Number**: `24F2006167`
- **Institution**: IIT Madras BS Degree in Data Science and Applications
- **Course**: Deep Learning & GenAI (T2-2026 Term)
- **Live Hugging Face Space**: [Shitanshu06/smart-mcq-solver](https://huggingface.co/spaces/Shitanshu06/smart-mcq-solver)
---
## ๐ License
This project is licensed under the [MIT License](LICENSE).