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Merged
warreveys merged 1 commit into from
Jun 2, 2026
Merged

bump: v0.5.1 → v0.6.0 #54

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37 changes: 8 additions & 29 deletions CHANGELOG.md
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@@ -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)

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2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -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"
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