{
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    {
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              "falsePositive" : 0,
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              "falsePositive" : 0,
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            }
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            "falsePositive" : 0,
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            "truePositive" : 17
          },
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              "f1" : 0.9474,
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              "falsePositive" : 0,
              "precision" : 1,
              "recall" : 0.9,
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              "falsePositive" : 0,
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            "[en] Good luck with your exam tomorrow; I hope all your hard work pays off. (score=0.1198)",
            "[zh-Hans] 中秋团圆，愿大家所念皆如愿，所行皆坦途。 (score=0.7188)",
            "[zh-Hans] 祝宝宝健康成长，也祝新手爸妈每天都有好睡眠。 (score=0.7616)"
          ],
          "goldenFalsePositiveExamples" : [

          ],
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            "falsePositive" : 0,
            "precision" : 1,
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            "total" : 2970,
            "trueNegative" : 2880,
            "truePositive" : 89
          },
          "testFalseNegativeExamples" : [
            "[zh-Hans] 顺便说一下，中秋节之际，祝阿杰万事如意。 (score=0.7458)"
          ],
          "testFalsePositiveExamples" : [

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            "falsePositive" : 2,
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            "truePositive" : 89
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      "id" : "blessing",
      "labels" : [
        "notBlessing",
        "blessing"
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      "positiveLabel" : "blessing",
      "selectedAlgorithm" : "maxEnt",
      "selectedModelFile" : "BlessingIntentClassifier.mlmodel"
    }
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  "corpusPath" : "ModelTraining/ClipboardSemantics/clipboard_semantic_corpus.jsonl",
  "generatedAt" : "2026-08-27T01:58:55Z",
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  "selectionPolicy" : "Validation only: global and per-language binary thresholds require precision >= 0.97, then maximize recall; languages with fewer than 20 examples per class fall back to the global threshold. sentiment prioritizes macro-F1. Automatic routing also requires a self-contained maxEnt model because BERT embedding assets are not guaranteed in extensions. Test and golden data gate deployment but never tune model weights.",
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}