README.md

metadata

library_name: transformers
pipeline_tag: text-classification
tags:
  - deberta
  - multiple-choice
  - mcq
  - question-answering

DeBERTa MCQ Solver

A DeBERTa-based multiple-choice question solver trained using PyTorch and Hugging Face Transformers.

Model

This model is trained for multiple-choice question answering with five candidate options.

Architecture

• Base model: DeBERTa
• Framework: PyTorch
• Library: Hugging Face Transformers
• Number of choices: 5
• Maximum sequence length: 256

Files

• config.json — model configuration
• model.safetensors — trained model weights
• tokenizer.json — tokenizer
• tokenizer_config.json — tokenizer configuration
• app.py — Gradio application
• requirements.txt — Python dependencies

Usage

from transformers import AutoTokenizer, AutoModelForMultipleChoice

model_name = "rohitk123/deberta-mcq-solver"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForMultipleChoice.from_pretrained(model_name)