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TechWolf/Skill-normalisation-ESCO-graded

skill-normalisation-esco-graded Graded-relevance annotations for surface skill terms (ESCO alt-labels) from ESCO v1.1.0 skill-normalisation pairs against the ESCO v1.1.0 skill taxonomy. Layout follows the BEIR convention so it is drop-in for MTEB-style retrieval evaluators. This dataset was created for the RecSys-HR 2026 WorkRB challenge. Configs config split rows columns queries validation 50 _id (query id), text (ESCO alt-label / surface term to… See the full description on the dataset page: https://huggingface.co/datasets/TechWolf/Skill-normalisation-ESCO-graded.

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Dataset Card

skill-normalisation-esco-graded

Graded-relevance annotations for surface skill terms (ESCO alt-labels) from `ESCO v1.1.0 skill-normalisation pairs` against the ESCO v1.1.0 skill taxonomy. Layout follows the BEIR convention so it is drop-in for MTEB-style retrieval evaluators.

This dataset was created for the RecSys-HR 2026 WorkRB challenge.

Configs

config	split	rows	columns
`queries`	`validation`	50	`_id` (query id), `text` (ESCO alt-label / surface term to normalise)
`queries`	`test`	450	`_id` (query id), `text` (ESCO alt-label / surface term to normalise)
`corpus`	`corpus`	13,891	`_id` (ESCO skill URI), `title` (English preferred label), `text` (English description), `esco_version`
`qrels`	`validation`	694,550	`query-id`, `corpus-id`, `score` (0-4)
`qrels`	`test`	6,250,950	`query-id`, `corpus-id`, `score` (0-4)

Higher is more relevant. The corpus is the COMPLETE ESCO v1.1.0 skill list (13,891 skills) The qrels are the full query x corpus grid: every query is scored against all 13,891 skills, with score 0 for any skill that was not surfaced as relevant.

Score scale, `validation` split

score	volume	meaning
0	665,668	The skill is totally unrelated to the query.
1	28,438	The skill's domain is correct. It's a plausible skill in a broader context, but not mentioned in this query.
2	312	The skill could be recommended, but its granularity makes it not core to the query.
3	58	The skill is strongly relevant for this query, although it is more implied than explicitly demonstrated.
4	74	The skill is explicitly demonstrated or requested by the query, and is therefore a clearly correct recommendation.

Score scale, `test` split

score	volume	meaning
0	5,989,638	The skill is totally unrelated to the query.
1	257,118	The skill's domain is correct. It's a plausible skill in a broader context, but not mentioned in this query.
2	2,723	The skill could be recommended, but its granularity makes it not core to the query.
3	966	The skill is strongly relevant for this query, although it is more implied than explicitly demonstrated.
4	505	The skill is explicitly demonstrated or requested by the query, and is therefore a clearly correct recommendation.

Usage

python

from datasets import load_dataset

queries = load_dataset("TechWolf/Skill-normalisation-ESCO-graded", "queries", split="test")
corpus  = load_dataset("TechWolf/Skill-normalisation-ESCO-graded", "corpus",  split="corpus")
qrels   = load_dataset("TechWolf/Skill-normalisation-ESCO-graded", "qrels",   split="test")

Join on ids: qrels["query-id"] matches queries["_id"], and qrels["corpus-id"] matches corpus["_id"] (a full ESCO skill URI).

Attribution

This dataset uses the ESCO classification of the European Commission (ESCO v1.1.0, <https://esco.ec.europa.eu>), licensed under CC BY 4.0. The ESCO content has been extracted into a tabular subset (skill URI, English preferred label, English description); no semantic modifications were made. The European Commission is not responsible for any use of the data.

Source queries come from `ESCO v1.1.0 skill-normalisation pairs` (also CC BY 4.0). The judge labels themselves are released under CC BY 4.0.

Citation

To be announced