| """Score a run on browsecomp-plus-mini with normalized exact match (no LLM judge). |
| |
| predictions.jsonl: one {"query_id": ..., "answer": ...} per line (answer = the agent's final answer text; |
| if it contains an "Exact Answer:" line, that line is used). |
| gold.jsonl: the decrypted Tevatron/browsecomp-plus file you produced locally (query_id, query, answer). |
| |
| usage: python score.py predictions.jsonl gold.jsonl [data/tasks.jsonl] [data/metrics.json] |
| """ |
| import sys, json |
| from grader import grade, extract_answer |
|
|
| pred_path, gold_path = sys.argv[1], sys.argv[2] |
| tasks = sys.argv[3] if len(sys.argv) > 3 else "data/tasks.jsonl" |
| metrics = sys.argv[4] if len(sys.argv) > 4 else "data/metrics.json" |
| ids = [json.loads(l)["query_id"] for l in open(tasks)] |
| gold = {} |
| for l in open(gold_path): |
| r = json.loads(l) |
| if str(r["query_id"]) in ids: |
| gold[str(r["query_id"])] = r["answer"] |
| preds = {str(json.loads(l)["query_id"]): json.loads(l).get("answer") for l in open(pred_path)} |
| ok = 0 |
| for q in ids: |
| p = preds.get(q) |
| a = extract_answer(p) if p else None |
| c, _ = grade(a, gold[q]) |
| ok += int(c) |
| print(f"{q}\t{'correct' if c else 'wrong'}") |
| m = json.load(open(metrics))["linear_fit_full_from_mini"] |
| s = ok / len(ids) |
| print(f"\nmini score: {ok}/{len(ids)} = {100*s:.1f}%") |
| print(f"estimated full-830 score (normalized match, linear fit): {100*(m['slope']*s + m['intercept']):.1f}%") |
|
|
