Cursor: Apply local changes for cloud agent
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Rocky
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{
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"classifiers" : [
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{
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"candidates" : [
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{
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"acceptedForAutomaticRouting" : true,
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"algorithm" : "maxEnt",
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"balancedTrainingCount" : 5760,
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"balancedValidationCount" : 1440,
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"binaryByLanguage" : {
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"en" : {
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"accuracy" : 0.8903,
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"f1" : 0.7311,
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"falseNegative" : 152,
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"falsePositive" : 1,
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"precision" : 0.9952,
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"recall" : 0.5778,
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"total" : 1395,
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"trueNegative" : 1034,
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"truePositive" : 208
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},
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"zh-Hans" : {
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"accuracy" : 0.9527,
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"f1" : 0.8991,
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"falseNegative" : 66,
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"falsePositive" : 0,
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"precision" : 1,
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"recall" : 0.8167,
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"total" : 1395,
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"trueNegative" : 1035,
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"truePositive" : 294
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}
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},
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"confidenceThresholdsByLanguage" : {
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"en" : 0.91,
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"zh-Hans" : 0.9
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},
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"goldenBinary" : {
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"accuracy" : 0.9434,
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"f1" : 0.7273,
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"falseNegative" : 12,
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"falsePositive" : 0,
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"precision" : 1,
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"recall" : 0.5714,
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"total" : 212,
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"trueNegative" : 184,
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"truePositive" : 16
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},
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"goldenBinaryByLanguage" : {
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"en" : {
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"accuracy" : 0.9528,
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"f1" : 0.7826,
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"falseNegative" : 5,
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"falsePositive" : 0,
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"precision" : 1,
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"recall" : 0.6429,
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"total" : 106,
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"trueNegative" : 92,
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"truePositive" : 9
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},
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"zh-Hans" : {
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"accuracy" : 0.934,
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"f1" : 0.6667,
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"falseNegative" : 7,
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"falsePositive" : 0,
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"precision" : 1,
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"recall" : 0.5,
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"total" : 106,
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"trueNegative" : 92,
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"truePositive" : 7
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}
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},
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"goldenFalseNegativeExamples" : [
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"[en] Do not forget to check whether the refund arrives on Friday. (score=0.899)",
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"[en] After the meeting, summarize the three decisions in the project channel. (score=0.7019)",
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"[en] When approval arrives, contact the applicant and explain the reason. (score=0.4227)",
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"[en] Follow up with the vendor about the delivery date next Monday. (score=0.9017)",
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"[en] Next action: create the release tag after the tests pass. (score=0.5645)",
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"[zh-Hans] 小王负责整理会议纪要，今天发到项目群。 (score=0.5646)",
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"[zh-Hans] 方便帮我约一下周三下午的会议室吗？ (score=0.0988)",
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"[zh-Hans] 这些材料可以在月底前准备好吗？ (score=0.575)",
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"[zh-Hans] 别忘了周五检查退款有没有到账。 (score=0.6978)",
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"[zh-Hans] 会后需要整理三个决定并发到项目群。 (score=0.129)",
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"[zh-Hans] 等审批结果出来，请联系申请人说明原因。 (score=0.7829)",
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"[zh-Hans] 下周一跟进供应商的交货日期。 (score=0.0068)"
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],
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"goldenFalsePositiveExamples" : [
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],
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"modelBytes" : 27275,
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"runtimeAssetIndependent" : true,
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"testBinary" : {
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"accuracy" : 0.9215,
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"f1" : 0.8209,
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"falseNegative" : 218,
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"falsePositive" : 1,
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"precision" : 0.998,
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"recall" : 0.6972,
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"total" : 2790,
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"trueNegative" : 2069,
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"truePositive" : 502
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},
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"testFalseNegativeExamples" : [
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"[en] Also, Reminder: call the client back before the next meeting. (score=0.7217)",
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"[en] One more thing: Reminder: book the follow-up visit by Friday. (score=0.6472)",
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"[en] Reminder: check the invoice status by the end of the month. (score=0.8201)",
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"[en] Reminder: call the client back by the end of the month. (score=0.8283)",
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"[en] A quick note: Reminder: call the client back before the end of today. (score=0.7477)",
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"[en] One more thing: Reminder: create the release tag before 3 PM. (score=0.6731)",
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"[en] Reminder: contact the applicant by the end of the month. (score=0.7488)",
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"[en] Also, Make review the approval result the next action and do it by the end of the month. (score=0.8723)",
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"[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)",
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"[en] Reminder: contact the applicant after you receive this message. (score=0.754)",
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"[en] A quick note: Reminder: verify the refund status before the next meeting. (score=0.7248)",
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"[en] A quick note: Reminder: record my temperature tomorrow morning. (score=0.5867)"
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],
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"testFalsePositiveExamples" : [
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"[en] Todo visible only to me: prepare the presentation deck. (score=0.9134)"
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],
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"threshold" : 0.9,
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"validationBinary" : {
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"accuracy" : 0.9258,
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"f1" : 0.8358,
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"falseNegative" : 193,
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"falsePositive" : 14,
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"precision" : 0.9741,
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"recall" : 0.7319,
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"total" : 2790,
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"trueNegative" : 2056,
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"truePositive" : 527
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}
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}
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],
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"id" : "task",
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"labels" : [
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"notTask",
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"task"
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],
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"positiveLabel" : "task",
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"selectedAlgorithm" : "maxEnt",
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"selectedModelFile" : "TaskIntentClassifier.mlmodel"
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}
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],
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"corpusCount" : 16572,
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"corpusPath" : "ModelTraining/ClipboardSemantics/clipboard_semantic_corpus.jsonl",
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"generatedAt" : "2026-08-27T01:11:10Z",
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"goldenCount" : 212,
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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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"testCount" : 2790,
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"trainingCount" : 10780,
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"validationCount" : 2790
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}
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