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Add a verification note and regression test showing that training/data/prompt-enhancement/video-scenes-500.jsonl already exists on main with 500 valid records.
47 lines
2.2 KiB
Markdown
47 lines
2.2 KiB
Markdown
# Issue #605 Verification
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Status: already implemented on `main`.
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Issue: Prompt Enhancement: Video Scenes — 500 Terse→Rich Pairs
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What is already present on `main`
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- `training/data/prompt-enhancement/video-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 `video scenes`
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- 500/500 full records are unique
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- every `rich` prompt includes video-scene structure markers for `lighting`, `composition`, and `transition`
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Evidence gathered from a fresh clone
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- validation against `training/data/prompt-enhancement/video-scenes-500.jsonl` returned:
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- `count = 500`
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- `unique_records = 500`
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- `unique_terse = 120`
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- `domains = ['video scenes']`
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- `missing_keys = 0`
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- all 500 `rich` prompts contain `lighting`, `composition`, and `transition`
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- closed PRs `#755` (`fix/605`) and `#648` (`feat/605-video-scenes-prompts`) show prior attempts on this lane
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- SHA-256 of `training/data/prompt-enhancement/video-scenes-500.jsonl` on `origin/main` matches the same file on remote branch `fix/605`, 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/video-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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print('lighting_count', sum('lighting' in r['rich'].lower() for r in records))
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print('composition_count', sum('composition' in r['rich'].lower() for r in records))
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print('transition_count', sum('transition' in r['rich'].lower() for r in records))
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PY
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```
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Recommendation
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- Close issue #605 as already implemented on `main`.
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- This branch only adds a durable verification note and regression test so the open issue can be closed cleanly without regenerating duplicate training data.
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