Rocky
e5a83843db
Add resilient usage analytics, OOBE gateway flows, clipboard semantic ranking, purchase recovery, style learning, and managed current-information search.
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
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.