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.
53 lines
2.1 KiB
Markdown
53 lines
2.1 KiB
Markdown
# Clipboard semantic models
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This directory contains the reproducible training inputs and evaluation output
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for OSGKeyboard's fully local clipboard analyzer.
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## Scope
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The model suite predicts five independent intents (`task`, `question`,
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`invitation`, `complaint`, and `replyableMessage`) plus three-way sentiment.
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`replyableMessage` distinguishes messages that invite a response from terminal
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acknowledgments, personal notes, quoted questions, and factual notices. Apple
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data detectors remain responsible for dates, addresses, phone numbers, and
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URLs; `NLTagger` provides best-effort person and organization names.
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The corpus contains 7,272 Chinese and English records:
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- 4,660 generated training records
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- 1,260 generated validation records
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- 1,260 template-held-out test records
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- 92 manually authored golden records
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No user clipboard content is included.
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## Reproduce
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```bash
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python3 Scripts/clipboard_semantics/generate_corpus.py
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xcrun swift Scripts/clipboard_semantics/train_models.swift
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```
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The trainer balances labels, trains maxEnt and BERT candidates, calibrates
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high-precision thresholds, writes detailed errors to `evaluation-report.json`,
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and copies the selected models into
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`OSGKeyboardShared/Resources/ClipboardSemantics`.
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## Deployment decision
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Only maxEnt models are eligible for keyboard automatic routing. Create ML BERT
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transfer models depend on `NLContextualEmbedding` assets that are not guaranteed
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to exist in a simulator or keyboard-extension runtime, so they remain evaluation
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candidates only.
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The selected intent models passed the automatic-routing precision gates. The
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replyable-message model reached 100% test precision and 97.96% golden precision;
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its measured recall remains part of release monitoring. The complaint model is
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also high precision but remains conservative because golden recall is limited.
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Sentiment returns `unknown` unless confidence and top-two margin checks both
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pass.
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Synthetic results are not treated as production truth. Real opt-in, anonymized
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or manually reviewed examples are still required before widening labels or
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lowering thresholds.
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