From 7ceea679fc9fe87d86081923ffbb02551a80dece Mon Sep 17 00:00:00 2001
From: "warre.veys" <warre.veys@techwolf.ai>
Date: Tue, 2 Jun 2026 16:31:29 +0200
Subject: [PATCH] =?UTF-8?q?bump:=20v0.5.1=20=E2=86=92=20v0.6.0?=
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---
 CHANGELOG.md   | 37 ++++++++-----------------------------
 pyproject.toml |  2 +-
 2 files changed, 9 insertions(+), 30 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 12abffc..e2eb7c1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,35 +1,14 @@
-## Unreleased
+## v0.6.0 (2026-06-02)
 
 ### Feat
 
-- ``HouseGradedSkillExtractRanking``: graded-relevance skill-extraction ranking
-  task on the HOUSE subset of CAREER, re-annotated against the full ESCO v1.1.0
-  taxonomy with a 0-4 score scale (HF: ``TechWolf/Skill-extraction-House-graded``,
-  BEIR layout, validation split only).
-- ``TechGradedSkillExtractRanking``: graded-relevance skill-extraction ranking
-  task on the TECH subset of CAREER, re-annotated against the full ESCO v1.1.0
-  taxonomy with a 0-4 score scale (HF: ``TechWolf/Skill-extraction-Tech-graded``,
-  BEIR layout, validation split only).
-- ``SkillSkapeGradedSkillExtractRanking``: graded-relevance skill-extraction
-  ranking task on SkillSkape, re-annotated against the full ESCO v1.1.0 taxonomy
-  with a 0-4 score scale (HF: ``TechWolf/Skill-extraction-SkillSkape-graded``,
-  BEIR layout, validation split only).
-- ``ESCOGradedSkillNormRanking``: graded-relevance skill-normalization ranking
-  task that maps surface skill terms (ESCO alt-labels) to canonical ESCO skills,
-  annotated against the full ESCO v1.1.0 taxonomy with a 0-4 score scale
-  (HF: ``TechWolf/Skill-normalisation-ESCO-graded``, BEIR layout, validation
-  split only).
-- graded relevance support for ranking metrics: ``RankingDataset`` accepts an
-  optional ``target_relevance`` field aligned 1-to-1 with ``target_indices``.
-  ``ndcg@k`` uses a (2^rel - 1) gain when graded labels are provided; binary
-  metrics (``map``, ``mrr``, ``recall@k``, ``hit@k``, ``rp@k``) ignore the
-  field. Binary nDCG behavior is preserved when ``target_relevance`` is
-  ``None``. See ``examples/custom_task_graded_relevance_example.py``.
-- ``RankingTask.binary_relevance_threshold`` (default ``1e-9``) lets a graded
-  task choose which grades count as positives for binary metrics. Items with
-  relevance below the threshold are dropped from the binary positive set but
-  still contribute to graded metrics like ``ndcg@k``. Has no effect when
-  ``target_relevance`` is ``None``.
+- enabled graded (non-binary) relevancy for ranking tasks
+- added graded skill extraction and normalisation tasks
+- store rankings for quick recomputation of new metrics
+
+### Fix
+
+- avoid KeyError when evaluating with a metric set that omits the default key metric
 
 ## v0.5.1 (2026-03-13)
 
diff --git a/pyproject.toml b/pyproject.toml
index 38f0428..cac26b2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ packages = ["src/workrb"]
 
 [project]
 name = "workrb"
-version = "0.5.1"
+version = "0.6.0"
 description = "WorkRB: Work Research Benchmark. Easy benchmarking of AI progress in the work domain."
 readme = "README.md"
 requires-python = ">=3.10,<4.0"