{
  "classifiers" : [
    {
      "candidates" : [
        {
          "acceptedForAutomaticRouting" : true,
          "algorithm" : "maxEnt",
          "balancedTrainingCount" : 5760,
          "balancedValidationCount" : 1440,
          "binaryByLanguage" : {
            "en" : {
              "accuracy" : 0.8903,
              "f1" : 0.7311,
              "falseNegative" : 152,
              "falsePositive" : 1,
              "precision" : 0.9952,
              "recall" : 0.5778,
              "total" : 1395,
              "trueNegative" : 1034,
              "truePositive" : 208
            },
            "zh-Hans" : {
              "accuracy" : 0.9527,
              "f1" : 0.8991,
              "falseNegative" : 66,
              "falsePositive" : 0,
              "precision" : 1,
              "recall" : 0.8167,
              "total" : 1395,
              "trueNegative" : 1035,
              "truePositive" : 294
            }
          },
          "confidenceThresholdsByLanguage" : {
            "en" : 0.91,
            "zh-Hans" : 0.9
          },
          "goldenBinary" : {
            "accuracy" : 0.9434,
            "f1" : 0.7273,
            "falseNegative" : 12,
            "falsePositive" : 0,
            "precision" : 1,
            "recall" : 0.5714,
            "total" : 212,
            "trueNegative" : 184,
            "truePositive" : 16
          },
          "goldenBinaryByLanguage" : {
            "en" : {
              "accuracy" : 0.9528,
              "f1" : 0.7826,
              "falseNegative" : 5,
              "falsePositive" : 0,
              "precision" : 1,
              "recall" : 0.6429,
              "total" : 106,
              "trueNegative" : 92,
              "truePositive" : 9
            },
            "zh-Hans" : {
              "accuracy" : 0.934,
              "f1" : 0.6667,
              "falseNegative" : 7,
              "falsePositive" : 0,
              "precision" : 1,
              "recall" : 0.5,
              "total" : 106,
              "trueNegative" : 92,
              "truePositive" : 7
            }
          },
          "goldenFalseNegativeExamples" : [
            "[en] Do not forget to check whether the refund arrives on Friday. (score=0.899)",
            "[en] After the meeting, summarize the three decisions in the project channel. (score=0.7019)",
            "[en] When approval arrives, contact the applicant and explain the reason. (score=0.4227)",
            "[en] Follow up with the vendor about the delivery date next Monday. (score=0.9017)",
            "[en] Next action: create the release tag after the tests pass. (score=0.5645)",
            "[zh-Hans] 小王负责整理会议纪要，今天发到项目群。 (score=0.5646)",
            "[zh-Hans] 方便帮我约一下周三下午的会议室吗？ (score=0.0988)",
            "[zh-Hans] 这些材料可以在月底前准备好吗？ (score=0.575)",
            "[zh-Hans] 别忘了周五检查退款有没有到账。 (score=0.6978)",
            "[zh-Hans] 会后需要整理三个决定并发到项目群。 (score=0.129)",
            "[zh-Hans] 等审批结果出来，请联系申请人说明原因。 (score=0.7829)",
            "[zh-Hans] 下周一跟进供应商的交货日期。 (score=0.0068)"
          ],
          "goldenFalsePositiveExamples" : [

          ],
          "modelBytes" : 27275,
          "runtimeAssetIndependent" : true,
          "testBinary" : {
            "accuracy" : 0.9215,
            "f1" : 0.8209,
            "falseNegative" : 218,
            "falsePositive" : 1,
            "precision" : 0.998,
            "recall" : 0.6972,
            "total" : 2790,
            "trueNegative" : 2069,
            "truePositive" : 502
          },
          "testFalseNegativeExamples" : [
            "[en] Also, Reminder: call the client back before the next meeting. (score=0.7217)",
            "[en] One more thing: Reminder: book the follow-up visit by Friday. (score=0.6472)",
            "[en] Reminder: check the invoice status by the end of the month. (score=0.8201)",
            "[en] Reminder: call the client back by the end of the month. (score=0.8283)",
            "[en] A quick note: Reminder: call the client back before the end of today. (score=0.7477)",
            "[en] One more thing: Reminder: create the release tag before 3 PM. (score=0.6731)",
            "[en] Reminder: contact the applicant by the end of the month. (score=0.7488)",
            "[en] Also, Make review the approval result the next action and do it by the end of the month. (score=0.8723)",
            "[en] A quick note: Make check the vendor delivery date the next action and do it by the end of the month. (score=0.8494)",
            "[en] Reminder: contact the applicant after you receive this message. (score=0.754)",
            "[en] A quick note: Reminder: verify the refund status before the next meeting. (score=0.7248)",
            "[en] A quick note: Reminder: record my temperature tomorrow morning. (score=0.5867)"
          ],
          "testFalsePositiveExamples" : [
            "[en] Todo visible only to me: prepare the presentation deck. (score=0.9134)"
          ],
          "threshold" : 0.9,
          "validationBinary" : {
            "accuracy" : 0.9258,
            "f1" : 0.8358,
            "falseNegative" : 193,
            "falsePositive" : 14,
            "precision" : 0.9741,
            "recall" : 0.7319,
            "total" : 2790,
            "trueNegative" : 2056,
            "truePositive" : 527
          }
        }
      ],
      "id" : "task",
      "labels" : [
        "notTask",
        "task"
      ],
      "positiveLabel" : "task",
      "selectedAlgorithm" : "maxEnt",
      "selectedModelFile" : "TaskIntentClassifier.mlmodel"
    }
  ],
  "corpusCount" : 16572,
  "corpusPath" : "ModelTraining/ClipboardSemantics/clipboard_semantic_corpus.jsonl",
  "generatedAt" : "2026-08-27T01:11:10Z",
  "goldenCount" : 212,
  "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.",
  "testCount" : 2790,
  "trainingCount" : 10780,
  "validationCount" : 2790
}