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Add a verification note and regression test showing that training/data/prompt-enhancement/visual-scenes-500.jsonl already exists on main with 500 valid records.
42 lines
1.7 KiB
Markdown
42 lines
1.7 KiB
Markdown
# Issue #600 Verification
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Status: already implemented on `main`.
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Issue: Prompt Enhancement: Visual Scenes — 500 Terse→Rich Pairs
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What is already present on `main`
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- `training/data/prompt-enhancement/visual-scenes-500.jsonl`
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- 500 JSONL records
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- every record includes `terse`, `rich`, and `domain`
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- every `domain` value is `visual scenes`
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- 500/500 full records are unique
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Evidence gathered from a fresh clone
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- `python3` validation against `training/data/prompt-enhancement/visual-scenes-500.jsonl` returned:
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- `count = 500`
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- `unique_records = 500`
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- `unique_terse = 435`
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- `domains = ['visual scenes']`
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- `missing_keys = 0`
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- remote branch `fix/600` still exists from closed PR #731 (`feat: 500 visual scene prompt enhancement pairs (#600)`)
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- SHA-256 of `training/data/prompt-enhancement/visual-scenes-500.jsonl` on `origin/main` matches the same file on `fix/600`, which shows the requested dataset is already present on `main`
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Verification commands
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```bash
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python3 - <<'PY'
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import json
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from pathlib import Path
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path = Path('training/data/prompt-enhancement/visual-scenes-500.jsonl')
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records = [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
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print('count', len(records))
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print('unique_records', len({json.dumps(r, sort_keys=True) for r in records}))
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print('unique_terse', len({r['terse'] for r in records}))
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print('domains', sorted({r.get('domain') for r in records}))
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print('missing_keys', sum(any(k not in r or not str(r[k]).strip() for k in ('terse', 'rich', 'domain')) for r in records))
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PY
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```
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Recommendation
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- Close issue #600 as already implemented on `main`.
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- This branch only adds a durable verification note and regression test so the zombie issue can be closed cleanly without regenerating duplicate training data.
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