| --- |
| license: cc-by-nc-4.0 |
| language: en |
| tags: |
| - education |
| - mathematics |
| - misconceptions |
| - deberta-v3 |
| --- |
| |
| # Why did I get this wrong? |
|
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| Reads a student's own explanation of a multiple-choice maths answer and names the misconception behind it. |
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| Trained on MAP (Charting Student Math Misunderstandings, Kaggle 2025, by Eedi and The Learning Agency), licensed |
| CC BY-NC 4.0. These weights carry the same licence: non-commercial use only, with attribution. No student rows are |
| included here; `val_preds.csv` files hold row ids and labels only. |
|
|
| ## Runs |
|
|
| - `runs/pair-v1-fold0`: deberta-v3-base cross-encoder, (question + chosen answer + explanation, misconception |
| description) scored listwise. Fold 0 of 5: MAP@3 0.9135, top-1 0.84, misconception recall 0.91. This is the served model. |
| - `runs/pair-v1-newq`: same recipe with two questions held out entirely. MAP@3 0.646, misconception recall 0.06. |
| It does not transfer: 32 of the 35 misconceptions in MAP occur on exactly one question, so there is nothing to |
| transfer from. |
| - `runs/pair-smoke`: a 1,500-row smoke test, ignore. |
|
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| Each run has `model.safetensors`, `tokenizer/`, `config.json` (classes, descriptions, questions, log) and `log.jsonl`. |
| The model takes a text pair: the prompt (question, choices, chosen answer, whether it is right, explanation) and one |
| misconception description from `config.json["descs"]`; the highest logit over the question's classes wins. |
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