Stage 4 output of an applied-NLP NER project: a retrain of applied-nlp-ner-stage3-distilbert after diagnosing its errors against 20 out-of-distribution "wild" sentences. Same data as Stage 3 (Stage 2's train/test split — no relabeling was needed; an audit of the training data found no mislabeled PRODUCT/WORKOFART spans to fix) and the same architecture (distilbert-base-cased, 65.2M params).

PRODUCT (138 train spans) and WORKOFART (295 train spans) were 2-5x smaller than every other label (351-666 spans), and that data-scarcity, not a labeling error, explained most of the observed failure patterns.

Boundary truncation and spurious single-stopword spans were deliberately not patched with inference-time heuristics; see the project report for why.

Overall: precision 0.658 -> 0.681, recall 0.717 -> 0.757, F1 0.686 -> 0.717.

The same 20 out-of-distribution sentences from Stage 3 were re-run through this model (data/stage4/wild_predictions.json in the project repo). Full pattern-by-pattern comparison is in report/REPORT.md. Summary: 3 of the 10 Stage 3 patterns were resolved outright (Samsung/NASA organization mislabeling, football-score boundary truncation), several partially improved (multi-word span merging is now more reliable — "Treaty of Versailles" and "Inflation Reduction Act" are captured whole where they weren't consistently before), 2 remain unresolved (Elon Musk / Liverpool disambiguation), and one new regression appeared (Beethoven mistagged WORKOFART instead of PERSON) — reported honestly rather than cherry-picked.