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…val output Surfaces overall precision/recall/f1/accuracy (not just per-entity) and the PIIRemover config (entity types, threshold, NLP engine, model, on_fail) used for the run, so metrics.json is self-describing for external readers.
Clarifies that this is PII's overall detection-level score alongside entity_metrics, distinct from the shared compute_binary_metrics() helper used as the sole metric in other eval scripts.
…s output Mirrors the pii_remover eval script, which already records the config used (entity types, threshold, etc.) alongside the metrics for reproducibility. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
…valuations Introduces build_validator_config() in common/helper.py, which reads on_fail from validator.on_fail_descriptor (set by every guardrails Validator base class) and normalizes any extra constructor params passed in (enums -> their .value). PII and gender_assumption_bias now use it instead of hand-rolled dicts, and ban_list, lexical_slur, topic_relevance, and toxicity gain a config block in their metrics.json for the first time.
…s to topic relevance eval Lets topic_relevance/run.py target a single validator backend without installing llm_critic, and adds a shared helper to aggregate per-domain binary metrics into one combined report. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Summary
--backendflag to the topic relevance eval so a single backend (topic_relevanceortopic_relevance_llm) can be run instead of bothtopic_relevance(theLLMCritic-based validator) is now imported lazily, so--backend topic_relevance_llmworks without thellm_critichub validator installedcombine_binary_metrics()to the shared helper and writes acombined-metrics.jsonaggregating the education + healthcare domain results by summing confusion-matrix countsTest plan
python3 -m app.evaluation.topic_relevance.run --backend topic_relevance_llmcombined-metrics.jsontotals match the sum of the two per-domain metrics files🤖 Generated with Claude Code