benchmarking-clinical-ner
提供严格的实体级评估和每标签错误分析，提升模型可靠性。
评估需金标语料，使用真实临床数据时注意 DUA 合规。
对临床/生物医药 NER 模型做实体级精确度、召回与 F1 评分并按标签拆解错误。
给临床/生物医药 NER 模型做实体级的精确度、召回和 F1 打分，并按标签拆解错误类型，能输出每类实体的混淆矩阵和 FN/FP 示例。用在需要严格评估模型抓边界能力、调试为什么漏实体或边界错位、或者要做 seqeval 风格报告的时候。按 span（而不是 token）评分，支持严格和宽松匹配，能直观找出模型弱点。
▸ 展开 SKILL.md 英文原文
Score an OpenMed clinical or biomedical NER model against a user-supplied gold corpus with entity-level precision, recall, and F1, then break errors down per label. Use when the user wants a seqeval-style scorecard, strict vs partial (relaxed) span matching, a per-label confusion matrix, false-negative / false-positive examples, or to debug why a model misses entities. Trigger on "evaluate NER", "entity-level F1", "seqeval", "precision recall F1", "confusion matrix", "error analysis", "strict vs partial match", or "score against gold" in an OpenMed context. The gold corpus is user-supplied; OpenMed bundles no i2b2/n2c2/MIMIC data.
帮我安装这个 skill：https://raw.githubusercontent.com/maziyarpanahi/openmed/master/skills/benchmarking-clinical-ner/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/maziyarpanahi/openmed/master/skills/benchmarking-clinical-ner/SKILL.md"# Benchmarking Clinical NER
This skill produces an honest entity-level scorecard for an OpenMed NER model:
precision / recall / F1 plus a per-label error breakdown. It scores **spans**,
not tokens, because clinical entities are multi-token ("type 2 diabetes
mellitus") and token-level accuracy hides boundary errors. Reported numbers are
**entity-level** in the seqeval tradition (CoNLL-2000 / SemEval-2013 families).
## When to use this skill
- You have a gold-annotated clinical corpus and an OpenMed NER model to score.
- You want strict (exact-boundary) and partial (relaxed-overlap) span F1.
- You need per-label numbers, not one aggregate — DRUG recall ≠ DISEASE recall.
- You need to *expla