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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
```python
from transformers import AutoTokenizer, AutoModelForMultipleChoice
model_name = "rohitk123/deberta-mcq-solver"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMultipleChoice.from_pretrained(model_name) |