| --- |
| 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) |
