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Arshavk/
dl-genai-project

Name: dl-genai-project
Availability: InStock

safetensors

roberta

apache-2.0

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Deep Learning & Generative AI Project – Multiple Choice Question Answering

Overview

This repository contains a fine-tuned RoBERTa model for Multiple Choice Question Answering (MCQA). The model was developed as part of the IIT Madras BS Degree - Deep Learning and Generative AI Project.

The complete project combines three models:

RoBERTa (Transformer)
BiLSTM
Support Vector Machine (SVM)

The final predictions are generated using a weighted ensemble of these models, while this repository contains the fine-tuned RoBERTa model used in the ensemble.

Dataset

Multiple Choice Question Answering dataset
Five answer options (A, B, C, D, E)
Train/Validation split for model evaluation
Duplicate text samples removed before training

Model

Base Model: roberta-base
Framework: Hugging Face Transformers
Task: Text Classification
Number of Classes: 5 (A-E)

Training Details

Optimizer: AdamW
Early Stopping enabled
Weight Decay: 0.01
Evaluation Strategy: Every Epoch
Best model selected using Validation Macro F1

Validation Performance

Metric	Score
Validation Accuracy	1.0000
Validation Macro F1	1.0000

Ensemble

The complete project also evaluates:

Support Vector Machine (SVM)
BiLSTM
Fine-tuned RoBERTa
Weighted Ensemble

RoBERTa is the strongest individual deep learning model used in the ensemble.

Files

model.safetensors
config.json
tokenizer.json
tokenizer_config.json

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Arshavk/dl-genai-project")
model = AutoModelForSequenceClassification.from_pretrained("Arshavk/dl-genai-project")

Author

Arsha Vinesh

IIT Madras BS Degree Programme

Deep Learning and Generative AI Project