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deberta-mcq-solver – AI Model by rohitk123 | AlphaNeural AI

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rohitk123/
deberta-mcq-solver

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multiple-choice

question-answering

text-classification

text-embeddings-inference

endpoints_compatible

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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

python

from transformers import AutoTokenizer, AutoModelForMultipleChoice

model_name = "rohitk123/deberta-mcq-solver"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForMultipleChoice.from_pretrained(model_name)