Expand onboarding and adaptive keyboard intelligence

Add resilient usage analytics, OOBE gateway flows, clipboard semantic ranking, purchase recovery, style learning, and managed current-information search.
2026-08-22 16:33:18 +08:00
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+# Clipboard semantic models
+
+This directory contains the reproducible training inputs and evaluation output
+for OSGKeyboard's fully local clipboard analyzer.
+
+## Scope
+
+The model suite predicts four independent intents (`task`, `question`,
+`invitation`, and `complaint`) plus three-way sentiment. Apple data detectors
+remain responsible for dates, addresses, phone numbers, and URLs; `NLTagger`
+provides best-effort person and organization names.
+
+The corpus contains 6,334 Chinese and English records:
+
+- 4,100 generated training records
+- 1,080 generated validation records
+- 1,080 template-held-out test records
+- 74 manually authored golden records
+
+No user clipboard content is included.
+
+## Reproduce
+
+```bash
+python3 Scripts/clipboard_semantics/generate_corpus.py
+xcrun swift Scripts/clipboard_semantics/train_models.swift
+```
+
+The trainer balances labels, trains maxEnt and BERT candidates, calibrates
+high-precision thresholds, writes detailed errors to `evaluation-report.json`,
+and copies the selected models into
+`OSGKeyboardShared/Resources/ClipboardSemantics`.
+
+## Deployment decision
+
+Only maxEnt models are eligible for keyboard automatic routing. Create ML BERT
+transfer models depend on `NLContextualEmbedding` assets that are not guaranteed
+to exist in a simulator or keyboard-extension runtime, so they remain evaluation
+candidates only.
+
+The selected task, question, and invitation models passed the automatic-routing
+precision gates. The complaint model is packaged for further evaluation but its
+automatic-routing flag remains disabled because golden-set precision is 88.89%,
+below the 90% release gate. Sentiment returns `unknown` unless confidence and
+top-two margin checks both pass.
+
+Synthetic results are not treated as production truth. Real opt-in, anonymized
+or manually reviewed examples are still required before widening labels or
+lowering thresholds.