---
title: Smart MCQ Solver
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
app_file: app.py
pinned: false
---

# Smart MCQ Solver (BiLSTM)
This is the final deep learning project for the DL & GenAI course.

# Deep Learning & Generative AI Project

## Student Information

**Name:** Varnit Chourasiya
**Roll Number:** *23f3000843*
**Course:** Deep Learning & Generative AI Project (BS in Data Science and Applications)

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## Project Overview

This project focuses on developing and evaluating multiple Deep Learning and Generative AI approaches for a Natural Language Processing (NLP) task. The workflow includes data preprocessing, semantic similarity analysis, transformer-based models, retrieval-augmented generation (RAG), model fine-tuning, and ensemble techniques.

The project is part of the Deep Learning & Generative AI curriculum and will be evaluated through Kaggle performance, GitHub repository quality, experiment tracking, report submission, and viva examinations.

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## Objectives

* Perform text preprocessing and feature engineering.
* Build baseline NLP models using traditional embedding techniques.
* Explore transformer-based architectures such as BERT and RoBERTa.
* Implement Retrieval-Augmented Generation (RAG) pipelines.
* Fine-tune pretrained language models.
* Compare multiple models using standard evaluation metrics.
* Track experiments using Weights & Biases (W&B).
* Improve prediction performance through ensemble methods.

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## Technologies Used

* Python
* Pandas
* NumPy
* Scikit-learn
* PyTorch
* Hugging Face Transformers
* Weights & Biases (W&B)
* Kaggle
* Git & GitHub

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## Repository Structure

```text
.
├── data/
│   ├── raw/
│   └── processed/
│
├── notebooks/
│
├── src/
│
├── models/
│
├── reports/
│
├── screenshots/
│
├── requirements.txt
│
└── README.md
```

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## Project Milestones

* Milestone 1: NLP Foundations & Semantic Similarity
* Milestone 2: Transformer-Based Models
* Milestone 3: Retrieval-Augmented Generation (RAG)
* Milestone 4: Model Fine-Tuning
* Milestone 5: Ensemble Learning
* Final Submission & Evaluation

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## Experiment Tracking

All experiments, training runs, and model comparisons will be tracked using Weights & Biases (W&B).

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## Kaggle Competition

The final model performance will be evaluated through the official Kaggle competition associated with this project.