Spaces:
Sleeping
Sleeping
File size: 2,456 Bytes
7eeb968 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | ---
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)
---
## 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.
---
## 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.
---
## Technologies Used
* Python
* Pandas
* NumPy
* Scikit-learn
* PyTorch
* Hugging Face Transformers
* Weights & Biases (W&B)
* Kaggle
* Git & GitHub
---
## Repository Structure
```text
.
├── data/
│ ├── raw/
│ └── processed/
│
├── notebooks/
│
├── src/
│
├── models/
│
├── reports/
│
├── screenshots/
│
├── requirements.txt
│
└── README.md
```
---
## 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
---
## Experiment Tracking
All experiments, training runs, and model comparisons will be tracked using Weights & Biases (W&B).
---
## Kaggle Competition
The final model performance will be evaluated through the official Kaggle competition associated with this project.
|