docs(semantics): record iterative retraining study

Co-authored-by: Rocky <hkgood@users.noreply.github.com>
This commit is contained in:
Cursor Agent
2026-08-27 10:23:44 +00:00
parent 00b90d9d28
commit de9cd332a5
13 changed files with 71684 additions and 72 deletions
@@ -0,0 +1,629 @@
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File diff suppressed because it is too large Load Diff
File diff suppressed because it is too large Load Diff
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"Explicit evidence gates raised golden precision to 0.9877 and research-only precision to 0.6097, but recall collapsed and the release gate still failed.",
"No surrogate candidate is eligible to replace or augment the deployed Apple models."
],
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"platformLimitation": "Linux cannot train, compile, run, or benchmark the deployable Apple Create ML models. These rounds are surrogate research and produce a macOS replay specification, not app-ready model assets."
}
File diff suppressed because it is too large Load Diff
@@ -122,6 +122,48 @@ Without human-reviewed gold labels, these results measure agreement with the
model committee, not production truth. A passing verifier may enter shadow
deployment, but cannot be described as having 95% real-user precision.
## Iterative weakly supervised research
The portable research harness runs a fixed 20-round matrix over bilingual
character/word n-grams, sparse logistic classifiers, class balancing,
deterministic label-preserving augmentation, hard-example weighting, and
three-round high-confidence self-training consensus:
```bash
python3 -m pip install -r \
Scripts/clipboard_semantics/requirements-research.txt
python3 Scripts/clipboard_semantics/generate_open_training_corpus.py \
--allow-unavailable-sources
python3 Scripts/clipboard_semantics/run_iterative_retraining.py
```
The harness fits thresholds only on the generated validation split. Synthetic
test, golden, random, targeted-release, and comprehensive online corpora never
enter training or threshold calibration. Exact normalized overlap with every
frozen holdout is a fatal error.
The 2026-08-27 study completed the requested 20 rounds and two additional
20-round fine-tuning phases after the first phase missed its release target:
- The shared-configuration phase selected round 1. Random-holdout macro F1 was
`0.5709`; research-only comprehensive macro F1 was `0.2383`.
- Per-intent selection improved those values to `0.5770` and `0.2645`.
- Explicit evidence gates raised golden macro precision to `0.9877` and
research-only precision to `0.6097`, but reduced recall too severely.
- The current deployed reference remains stronger: random-holdout macro F1
`0.7669` and research-only comprehensive macro F1 `0.2783`.
All three phases failed the release gate, so no model was promoted. Detailed
rounds, final evaluations, source breakdowns, and macOS replay instructions are
stored under `IterativeResearch/`.
This harness is deliberately a Linux surrogate. It cannot emit the
`NLModel`-compatible Create ML artifacts used by the keyboard extension.
Deployable training, Core ML compilation, simulator regression, latency, and
memory checks still require macOS with Xcode 26+. A surrogate result can
nominate a data/threshold policy for macOS replay, but cannot authorize
automatic deployment.
## Deployment decision
Only maxEnt models are trained and deployed because they are self-contained in
@@ -1,9 +1,9 @@
{
"baseCorpusRecords": 17692,
"combinedCorpusSHA256": "70d1547b41b08e4417cfa86ad93dd5637b557d174c6503575c44fbf152cc3ee5",
"combinedRecords": 23918,
"combinedCorpusSHA256": "f111bc7425912213082e01038b52dd3d25fe21418d156f532da8f01f27fa16e4",
"combinedRecords": 21537,
"excludedBaseOverlap": 6,
"excludedHoldoutOverlap": 151,
"excludedHoldoutOverlap": 72,
"excludedSources": [
{
"dataset": "MIDAS",
@@ -38,72 +38,11 @@
"reason": "No clean official train split independent from the frozen holdout."
}
],
"openTrainingRecords": 6226,
"openTrainingRecords": 3845,
"policy": "Licensed official training splits only. Per-record knownLabels prevent unannotated intents from becoming false negatives. Exact normalized text overlap with every local *holdout-corpus.jsonl file is excluded.",
"schemaVersion": 1,
"seed": 20260827,
"sources": [
{
"dataset": "AmazonScience/MASSIVE en-US",
"knownLabelCounts": {
"followUpReminder": 7,
"question": 223,
"sentiment": 4,
"task": 143
},
"languages": {
"en": 498
},
"license": "CC-BY-4.0",
"records": 498,
"revision": "1.1",
"url": "https://huggingface.co/datasets/AmazonScience/massive"
},
{
"dataset": "AmazonScience/MASSIVE zh-CN",
"knownLabelCounts": {
"followUpReminder": 3,
"question": 212,
"sentiment": 6,
"task": 165
},
"languages": {
"zh-Hans": 487
},
"license": "CC-BY-4.0",
"records": 487,
"revision": "1.1",
"url": "https://huggingface.co/datasets/AmazonScience/massive"
},
{
"dataset": "CFPB via hpe-ai/customer-complaints",
"knownLabelCounts": {
"complaint": 898,
"sentiment": 898
},
"languages": {
"en": 898
},
"license": "CC0-1.0 source / Apache-2.0 mirror",
"records": 898,
"revision": "e5fec64e1f0688e47699b9cf8c26fe4ed350123a",
"url": "https://www.consumerfinance.gov/data-research/consumer-complaints/search/detail/4696537"
},
{
"dataset": "CLINC150 UCI",
"knownLabelCounts": {
"followUpReminder": 5,
"question": 308,
"task": 143
},
"languages": {
"en": 498
},
"license": "CC-BY-3.0",
"records": 498,
"revision": "UCI-570-2020-05-07",
"url": "https://archive.ics.uci.edu/dataset/570/clinc150"
},
{
"dataset": "CrossWOZ",
"knownLabelCounts": {
@@ -121,13 +60,13 @@
{
"dataset": "Google Research GoEmotions",
"knownLabelCounts": {
"sentiment": 799
"sentiment": 798
},
"languages": {
"en": 1199
"en": 1198
},
"license": "Apache-2.0",
"records": 1199,
"records": 1198,
"revision": "5d8f4ac97c873bde3a792ba4628f00bb9103d3e6",
"url": "https://github.com/google-research/google-research/blob/5d8f4ac97c873bde3a792ba4628f00bb9103d3e6/goemotions/data/train.tsv"
},
@@ -164,17 +103,31 @@
{
"dataset": "MultiDoGO",
"knownLabelCounts": {
"confirmationDecision": 28,
"confirmationDecision": 27,
"question": 83,
"task": 222
},
"languages": {
"en": 464
"en": 465
},
"license": "CDLA-Permissive-1.0",
"records": 464,
"records": 465,
"revision": "baa30639c4b271f394b81443c842193407cdf26d",
"url": "https://github.com/awslabs/multi-domain-goal-oriented-dialogues-dataset/blob/baa30639c4b271f394b81443c842193407cdf26d/data/paper_splits/splits_annotated_at_turn_level/insurance/train.tsv"
"url": "https://github.com/awslabs/multi-domain-goal-oriented-dialogues-dataset/blob/baa30639c4b271f394b81443c842193407cdf26d/data/paper_splits/splits_annotated_at_turn_level/software/train.tsv"
}
],
"unavailableSources": [
{
"dataset": "MASSIVE",
"reason": "URLError: <urlopen error [SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1000)>"
},
{
"dataset": "CLINC150",
"reason": "URLError: <urlopen error [SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1000)>"
},
{
"dataset": "CFPB",
"reason": "URLError: <urlopen error [SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1000)>"
}
]
}