| # Offline evaluation demo |
|
|
| Python 3 standard library only. No model calls, credentials or network access. |
|
|
| `evaluate_predictions.py` compares predictions with the released QA answers. |
| It supports JSONL or gzipped JSONL QA files and an optional explicit aliases |
| mapping. Its simple string normalization is not the paper's complete alias judge. |
| See the root README for invocation and limitations. |
|
|
| `score_frozen.py` aggregates an explicitly selected, existing boolean correctness |
| field. It does not generate predictions or infer aliases. Supply one model per file. |
|
|
| ```sh |
| python code/score_frozen.py --answers predictions.jsonl --correct-field correct_alias --error-policy exclude --out scores.json |
| ``` |
|
|
| Each input record must contain `qa_id`, `condition` (or `setting`) and the selected |
| boolean correctness field. Optional fields are `subtype`, `error` and `api_error`. |
| Duplicate QA/condition pairs are rejected. Missing judgments are unscored, not |
| automatically incorrect. The output reports scored denominators explicitly. |
|
|
| Error policy must be chosen explicitly: |
| - `exclude`: exclude explicitly flagged API failures. |
| - `count_as_incorrect`: count explicitly flagged API failures as incorrect. |
| - `stored`: use the stored judgment without overriding it. |
|
|
| This small demo is not a replacement for the benchmark's full answer-parsing and |
| alias-judging pipeline and makes no claim of reproducing missing model runs. |
|
|
