Document dataset quality harness checks
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PROGRESS.md
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Updated: 2026-09-25
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## Completed
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- Expanded the hospitality ontology from 4 to 20 intents, with department/action relations and TR/EN/DE/RU lexical mappings.
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- Generated a separate 80-record hard-negative challenge set (10 contrast pairs × 2 examples × 4 languages); labels were checked by EVREN. It is marked `hard_negative_eval`, unreviewed, and training-disabled.
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- Ran a development-only word/character TF-IDF + LinearSVC baseline: test accuracy 0.988, macro-F1 0.987; hard-negative foil accuracy 0.613. These synthetic-pilot metrics are not publishable benchmark claims.
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- Basic email and phone-pattern screening found no matches. This does not replace full privacy review.
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- Uploaded the final full draft and reports to the public Hugging Face dataset repository `badblli/HOSIA-Intent` (commit `f9391a7e188367ec63c9eb858d6d0ac24f6b5f43`). The superseded pilot and partial JSONL files were removed from the main branch to avoid duplicate ingestion; they remain in commit history.
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## Platform limitation
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- `scripts/build_multilingual_pilot.py` — resumable generation, extraction, validation, and group splitting.
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- `scripts/generate_hard_negatives.py` — contrast-set generation and validation.
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- `scripts/evaluate_tfidf_baseline.py` — development-only baseline.
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- `huggingface/dataset-card.md` — dataset card source.
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## Remaining before a training-ready release
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Updated: 2026-09-25
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## Quality harness update
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- Added `requirements-dev.txt` with pytest and wired GitHub Actions to run unit tests plus ontology validation.
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- Added `requirements-ml.txt` for the optional scikit-learn baseline dependency so CI stays lightweight.
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- Added `tests/test_dataset_validator.py` and `scripts/validate/validate_dataset.py` to validate generated main and hard-negative JSONL drafts locally. Dataset artifacts remain excluded from Git, so CI uses small synthetic fixtures; the full local datasets were also checked directly.
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- Latest checks: 8 pytest tests passed; ontology validation passed (40 concepts, 40 relations, 80 lexical mappings); main draft passed (1,000 rows, balanced 10 intents × 4 languages, 840/80/80 group-safe splits); hard-negative draft passed (80 rows).
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## Completed
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- Expanded the hospitality ontology from 4 to 20 intents, with department/action relations and TR/EN/DE/RU lexical mappings.
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- Generated a separate 80-record hard-negative challenge set (10 contrast pairs × 2 examples × 4 languages); labels were checked by EVREN. It is marked `hard_negative_eval`, unreviewed, and training-disabled.
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- Ran a development-only word/character TF-IDF + LinearSVC baseline: test accuracy 0.988, macro-F1 0.987; hard-negative foil accuracy 0.613. These synthetic-pilot metrics are not publishable benchmark claims.
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- Basic email and phone-pattern screening found no matches. This does not replace full privacy review.
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- Configured an EVREN API key locally for selected generation and validation models; the key itself is not stored in this repository. Accepted EVREN LLM Gateway terms v1 after user approval.
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- Uploaded the final full draft and reports to the public Hugging Face dataset repository `badblli/HOSIA-Intent` (commit `f9391a7e188367ec63c9eb858d6d0ac24f6b5f43`). The superseded pilot and partial JSONL files were removed from the main branch to avoid duplicate ingestion; they remain in commit history.
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## Platform limitation
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- `scripts/build_multilingual_pilot.py` — resumable generation, extraction, validation, and group splitting.
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- `scripts/generate_hard_negatives.py` — contrast-set generation and validation.
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- `scripts/evaluate_tfidf_baseline.py` — development-only baseline.
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- `scripts/validate/validate_dataset.py`, `tests/test_dataset_validator.py`, `requirements-dev.txt` — pytest and JSONL validation harness.
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- `huggingface/dataset-card.md` — dataset card source.
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## Remaining before a training-ready release
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