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Cursor: Apply local changes for cloud agent

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Rocky
2026-08-27 18:01:46 +08:00
parent 39002336c0
commit 42e6252f01
148 changed files with 120105 additions and 8122 deletions
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ModelTraining/ClipboardSemantics/targeted-v3-training-report.json
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{
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{
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{
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"[en] Alex owns the meeting notes and should post them in the project channel today. (score=0.8878)",
"[en] Could these documents be ready by the end of the month? (score=0.9042)",
"[en] Do not forget to check whether the refund arrives on Friday. (score=0.9049)",
"[en] After the meeting, summarize the three decisions in the project channel. (score=0.7419)",
"[en] When approval arrives, contact the applicant and explain the reason. (score=0.621)",
"[en] Follow up with the vendor about the delivery date next Monday. (score=0.8885)",
"[en] Next action: create the release tag after the tests pass. (score=0.4708)",
"[zh-Hans] 小王负责整理会议纪要，今天发到项目群。 (score=0.4243)",
"[zh-Hans] 方便帮我约一下周三下午的会议室吗？ (score=0.1551)",
"[zh-Hans] 这些材料可以在月底前准备好吗？ (score=0.6415)",
"[zh-Hans] 别忘了周五检查退款有没有到账。 (score=0.6693)",
"[zh-Hans] 会后需要整理三个决定并发到项目群。 (score=0.1071)"
],
"goldenFalsePositiveExamples" : [
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"modelBytes" : 27115,
"runtimeAssetIndependent" : true,
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"falsePositive" : 1,
"precision" : 0.998,
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"truePositive" : 498
},
"testFalseNegativeExamples" : [
"[en] Also, Reminder: call the client back before the next meeting. (score=0.7075)",
"[en] One more thing: Reminder: book the follow-up visit by Friday. (score=0.7023)",
"[en] Reminder: check the invoice status by the end of the month. (score=0.8633)",
"[en] Reminder: call the client back by the end of the month. (score=0.8156)",
"[en] A quick note: Reminder: call the client back before the end of today. (score=0.7158)",
"[en] One more thing: Reminder: create the release tag before 3 PM. (score=0.6997)",
"[en] Reminder: contact the applicant by the end of the month. (score=0.8711)",
"[en] Also, Make review the approval result the next action and do it by the end of the month. (score=0.861)",
"[en] A quick note: Make check the vendor delivery date the next action and do it by the end of the month. (score=0.7974)",
"[en] Reminder: contact the applicant after you receive this message. (score=0.8494)",
"[en] Make summarize the meeting decisions the next action and do it after you receive this message. (score=0.8998)",
"[en] A quick note: Reminder: verify the refund status before the next meeting. (score=0.7043)"
],
"testFalsePositiveExamples" : [
"[en] Todo visible only to me: summarize the test results. (score=0.9116)"
],
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"precision" : 0.9709,
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"truePositive" : 533
}
}
],
"id" : "task",
"labels" : [
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"task"
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"selectedAlgorithm" : "maxEnt",
"selectedModelFile" : "TaskIntentClassifier.mlmodel"
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{
"candidates" : [
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"binaryByLanguage" : {
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"falsePositive" : 0,
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"recall" : 0.8722,
"total" : 1395,
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"zh-Hans" : {
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"truePositive" : 165
}
},
"confidenceThresholdsByLanguage" : {
"en" : 0.89,
"zh-Hans" : 0.75
},
"goldenBinary" : {
"accuracy" : 0.9575,
"f1" : 0.4706,
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"precision" : 0.8,
"recall" : 0.3333,
"total" : 212,
"trueNegative" : 199,
"truePositive" : 4
},
"goldenBinaryByLanguage" : {
"en" : {
"accuracy" : 0.9528,
"f1" : 0.2857,
"falseNegative" : 5,
"falsePositive" : 0,
"precision" : 1,
"recall" : 0.1667,
"total" : 106,
"trueNegative" : 100,
"truePositive" : 1
},
"zh-Hans" : {
"accuracy" : 0.9623,
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"falsePositive" : 1,
"precision" : 0.75,
"recall" : 0.5,
"total" : 106,
"trueNegative" : 99,
"truePositive" : 3
}
},
"goldenFalseNegativeExamples" : [
"[en] The app has crashed constantly since the update, and I lost important data. (score=0.6932)",
"[en] The package arrived damaged and the cup inside was broken. (score=0.8516)",
"[en] My account was locked for no reason. Can you restore it today? (score=0.4141)",
"[en] Messages keep failing to send. Please provide a solution as soon as possible. (score=0.8782)",
"[en] Support has not replied for three days. When will this be handled? (score=0.7423)",
"[zh-Hans] 收到的商品外包装破损，里面的杯子也碎了。 (score=0.6318)",
"[zh-Hans] 消息一直发送失败，请尽快给我一个解决方案。 (score=0.2063)",
"[zh-Hans] 客服已经三天没有回复了，请问什么时候能处理？ (score=0.7074)"
],
"goldenFalsePositiveExamples" : [
"[zh-Hans] 事情终于解决了，我现在轻松多了。 (score=0.8132)"
],
"modelBytes" : 24298,
"runtimeAssetIndependent" : true,
"testBinary" : {
"accuracy" : 0.986,
"f1" : 0.9429,
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"falsePositive" : 1,
"precision" : 0.9969,
"recall" : 0.8944,
"total" : 2790,
"trueNegative" : 2429,
"truePositive" : 322
},
"testFalseNegativeExamples" : [
"[en] Just to add, I need a clear answer: the page loads extremely slowly. How are you going to resolve it? (score=0.639)",
"[en] I need a clear answer: messages never send. How are you going to resolve it? (score=0.5932)",
"[en] I need a clear answer: the invoice information is wrong. How are you going to resolve it? (score=0.4813)",
"[en] I need a clear answer: the order was charged twice. How are you going to resolve it? (score=0.6328)",
"[en] I need a clear answer: my reservation disappeared. How are you going to resolve it? (score=0.6104)",
"[en] Just to add, I need a clear answer: the app keeps crashing. How are you going to resolve it? (score=0.5869)",
"[en] Also, I need a clear answer: the app keeps crashing. How are you going to resolve it? (score=0.4881)",
"[en] One more thing: I need a clear answer: the order was charged twice. How are you going to resolve it? (score=0.4748)",
"[en] A quick note: I need a clear answer: messages never send. How are you going to resolve it? (score=0.4082)",
"[en] A quick note: I need a clear answer: the item arrived damaged. How are you going to resolve it? (score=0.3109)",
"[en] One more thing: I need a clear answer: the app keeps crashing. How are you going to resolve it? (score=0.4723)",
"[en] Also, I need a clear answer: the page loads extremely slowly. How are you going to resolve it? (score=0.543)"
],
"testFalsePositiveExamples" : [
"[zh-Hans] 对于订单状态，这次有没有明确答案？ (score=0.7592)"
],
"threshold" : 0.7,
"validationBinary" : {
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"f1" : 0.9836,
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"falsePositive" : 11,
"precision" : 0.9703,
"recall" : 0.9972,
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"trueNegative" : 2419,
"truePositive" : 359
}
}
],
"id" : "complaint",
"labels" : [
"notComplaint",
"complaint"
],
"positiveLabel" : "complaint",
"selectedAlgorithm" : "maxEnt",
"selectedModelFile" : "ComplaintIntentClassifier.mlmodel"
}
],
"corpusCount" : 16572,
"corpusPath" : "ModelTraining/ClipboardSemantics/clipboard_semantic_corpus.jsonl",
"generatedAt" : "2026-08-27T01:08:34Z",
"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
}