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| title: Smart MCQ Solver | |
| emoji: 🧠 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.9.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: DeBERTa-v3-large ranking five candidate MCQ answers | |
| # Smart MCQ Solver | |
| Five-option multiple-choice question answering over science and philosophy, | |
| scored by MAP@3. Fine-tuned `microsoft/deberta-v3-large` used as an | |
| `AutoModelForMultipleChoice` cross-encoder. | |
| | Metric | Value | | |
| |---|---| | |
| | 3-fold grouped CV MAP@3 | **0.7567** | | |
| | Random MAP@3 baseline | 0.3667 | | |
| | Leakage-free project estimate | 0.6817 | | |
| Set the `MODEL_ID` Space variable to your model repo, e.g. | |
| `your-username/smart-mcq-deberta-v3-large`. | |
| ## How it works | |
| Each of the five options is paired with the question to form five | |
| `(question, option)` sequences of shape `(5, L)`. The encoder scores each pair | |
| independently; a linear head reduces each to one logit; the five logits are | |
| reshaped to `(1, 5)` and softmaxed, so the options compete in a single | |
| distribution. The top three, in order, are the MAP@3 submission. | |
| ## Limitations | |
| Trained on 2,000 rows covering 252 unique questions. Closed-book — no retrieval, | |
| no citations, and no abstention mechanism, so it ranks five options confidently | |
| regardless of whether it knows the topic. | |