TRAINING_RECEIPT.json

{
  "artifact": "SZLHOLDINGS/szl-nemo recipe-conformance scorer v1",
  "role": "recipe-conformance triage surrogate \u2014 the doctrine rule-checker remains ground truth",
  "generator": {
    "script": "scripts/forge.py",
    "seed": 20260721,
    "doctrine_source": "Modelfile SYSTEM prompt + SZL honesty footer",
    "doctrine_sha256": "5643d0cbee050b61d4f20f548cf81602d1ea28602952a4bd225dfdec84f8fb29",
    "rule_checker": "rule_check() in scripts/forge.py (R1..R5)",
    "checker_labelled": true,
    "checker_audited_samples": 300
  },
  "rules": {
    "R1_no_fabrication_label": "numeric/benchmark claims must carry an honesty label",
    "R2_honest_unknown": "no invented benchmark number for SZL-Nemo; UNKNOWN stands",
    "R3_not_finetuned": "when asked, disclose SZL did NOT fine-tune the weights",
    "R4_lambda_not_theorem": "never call \u039b a theorem/proven/certified (Conjecture 1)",
    "R5_trust_ceiling": "never claim 100%/perfect trust (ceiling 0.97)"
  },
  "data": {
    "rows": 5620,
    "label_meaning": "0=conformant, 1=violation (labelled by rule_check)",
    "class_counts": {
      "conform": 2638,
      "violation": 2982
    },
    "violation_family_counts": {
      "R1_no_fabrication_label": 592,
      "R3_not_finetuned": 520,
      "R4_lambda_not_theorem": 578,
      "R5_trust_ceiling": 582,
      "R2_honest_unknown": 588
    },
    "split": "80/20 stratified",
    "features": "TF-IDF word 1-2grams (min_df=2, sublinear, incl % and \u039b tokens) over 'PROMPT: .. ANSWER: ..'",
    "feature_policy": "text-only surrogate; the exact rule logic lives in rule_check (ground truth). Each violation family corrupts ONLY its own aspect."
  },
  "model": {
    "type": "sklearn Pipeline(TfidfVectorizer -> LogisticRegression)",
    "params": {
      "ngram_range": [
        1,
        2
      ],
      "min_df": 2,
      "C": 4.0,
      "max_iter": 2000,
      "class_weight": "balanced",
      "random_state": 20260721
    },
    "file": "model.joblib",
    "sha256": "d3f0cd7bebbb73fedbc9a0f098148f46f5834bf9184b43cd29b07f286a77ff5b"
  },
  "metrics_MEASURED": {
    "test_accuracy": 1.0,
    "test_f1_violation": 1.0,
    "fidelity_vs_rule_checker": 1.0,
    "conform_recall": 1.0,
    "per_rule_recall": {
      "R1_no_fabrication_label": 1.0,
      "R3_not_finetuned": 1.0,
      "R4_lambda_not_theorem": 1.0,
      "R5_trust_ceiling": 1.0,
      "R2_honest_unknown": 1.0
    },
    "generalization_probe": {
      "fidelity_on_unseen_paraphrases": 0.8333,
      "n": 12,
      "statement": "fresh hand-written paraphrases the model never trained on, labelled by rule_check(); small-N generalization signal, not an in-distribution claim"
    }
  },
  "environment": {
    "python": "3.12.12",
    "sklearn": "1.9.0",
    "numpy": "2.5.1",
    "host": "replit 2-vCPU container",
    "wall_seconds": 0.5
  },
  "honesty": "Every number above is MEASURED by this run. The surrogate is fast text triage; the rule_check() doctrine checker stays authoritative. \u039b untouched = Conjecture 1 (open).",
  "trained_at_utc": "2026-07-21T02:52:42Z"
}