Rocky
e5a83843db
Add resilient usage analytics, OOBE gateway flows, clipboard semantic ranking, purchase recovery, style learning, and managed current-information search.
50 lines
1.8 KiB
Markdown
50 lines
1.8 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 four independent intents (`task`, `question`,
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`invitation`, and `complaint`) plus three-way sentiment. Apple data detectors
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remain responsible for dates, addresses, phone numbers, and URLs; `NLTagger`
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provides best-effort person and organization names.
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The corpus contains 6,334 Chinese and English records:
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- 4,100 generated training records
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- 1,080 generated validation records
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- 1,080 template-held-out test records
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- 74 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 task, question, and invitation models passed the automatic-routing
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precision gates. The complaint model is packaged for further evaluation but its
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automatic-routing flag remains disabled because golden-set precision is 88.89%,
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below the 90% release gate. Sentiment returns `unknown` unless confidence and
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top-two margin checks both 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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