Rocky
2cc81c4628
Add local clipboard intent recommendations, webpage and phone actions, and safer host handoffs. Refine managed gateway, catalog refresh, onboarding, and adaptive polish behavior.
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 five independent intents (task, question,
invitation, complaint, and replyableMessage) plus three-way sentiment.
replyableMessage distinguishes messages that invite a response from terminal
acknowledgments, personal notes, quoted questions, and factual notices. Apple
data detectors remain responsible for dates, addresses, phone numbers, and
URLs; NLTagger provides best-effort person and organization names.
The corpus contains 7,272 Chinese and English records:
- 4,660 generated training records
- 1,260 generated validation records
- 1,260 template-held-out test records
- 92 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 intent models passed the automatic-routing precision gates. The
replyable-message model reached 100% test precision and 97.96% golden precision;
its measured recall remains part of release monitoring. The complaint model is
also high precision but remains conservative because golden recall is limited.
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.