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fixes, naming

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wassname
2026-05-08 15:23:54 +08:00
parent c96d02a675
commit b12770cb78
15 changed files with 1001 additions and 977 deletions
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scripts/07_multilabel.py
+5 -5
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@@ -22,7 +22,7 @@ across all 7 foundations. We use these to:
Calibration is fitted on the classic set ONLY then applied to all sets.
Non-classic sets (scifi, clifford_ai) have no human ground truth, so their
calibrated values are extrapolated — treat with appropriate caution.
ai values are extrapolated -- treat with appropriate caution.
Outputs:
data/multilabel[_<name>].jsonl — one row per vignette with all ratings
@@ -450,7 +450,7 @@ async def amain(args) -> None:
continue
if cfg_name != "classic" and cal_results:
logger.warning(f"[{cfg_name}] Calibration was fitted on classic set only — "
f"calibrated values for '{cfg_name}' are extrapolated")
f"ai values for '{cfg_name}' are extrapolated")
for rec in records:
for f in FOUNDATIONS:
@@ -458,19 +458,19 @@ async def amain(args) -> None:
cal = cal_results.get(f)
if llm_v is not None and cal and not np.isnan(cal.get("slope", float("nan"))):
cal_v = cal["slope"] * llm_v + cal["intercept"]
rec[f"calibrated_{f}"] = round(max(0.0, min(100.0, float(cal_v))), 1)
rec[f"ai_{f}"] = round(max(0.0, min(100.0, float(cal_v))), 1)
w_v = rec.get("llm_wrongness")
w_cal = cal_results.get("wrongness")
if w_v is not None and w_cal and not np.isnan(w_cal.get("slope", float("nan"))):
cal_w = w_cal["slope"] * w_v + w_cal["intercept"]
rec["calibrated_wrongness"] = round(max(1.0, min(5.0, float(cal_w))), 2)
rec["ai_wrongness"] = round(max(1.0, min(5.0, float(cal_w))), 2)
out = out_path(cfg_name if cfg_name != "classic" else "")
with out.open("w") as fh:
for rec in records:
fh.write(json.dumps(rec) + "\n")
logger.info(f"[{cfg_name}] wrote {len(records)} records (with calibrated labels) to {out}")
logger.info(f"[{cfg_name}] wrote {len(records)} records (with ai labels) to {out}")
print("\n" + "=" * 60)
print("SUMMARY")