| --- |
| license: cc-by-4.0 |
| language: en |
| tags: |
| - retrieval |
| - skill-extraction |
| - esco |
| - graded-relevance |
| configs: |
| - config_name: queries |
| data_files: |
| - split: validation |
| path: queries/validation.parquet |
| - split: test |
| path: queries/test.parquet |
| - config_name: corpus |
| data_files: |
| - split: corpus |
| path: corpus/corpus.parquet |
| - config_name: qrels |
| data_files: |
| - split: validation |
| path: qrels/validation.parquet |
| - split: test |
| path: qrels/test.parquet |
| --- |
| |
| # skill-normalisation-esco-graded |
|
|
| Graded-relevance annotations for surface skill terms (ESCO alt-labels) from |
| [`ESCO v1.1.0 skill-normalisation pairs`](https://esco.ec.europa.eu) |
| against the ESCO v1.1.0 skill taxonomy. Layout follows the |
| [BEIR](https://github.com/beir-cellar/beir) convention so it is drop-in for |
| MTEB-style retrieval evaluators. |
|
|
| ## Configs |
|
|
| | config | rows | columns | |
| |---|---:|---| |
| | `queries` | 50 | `_id` (query id), `text` (ESCO alt-label / surface term to normalise) | |
| | `corpus` | 13,891 | `_id` (ESCO skill URI), `title` (English preferred label), `text` (English description), `esco_version` | |
| | `qrels` | 694,550 | `query-id`, `corpus-id`, `score` (0-4) | |
|
|
| ### Score scale |
|
|
| | score | meaning | |
| |---:|---| |
| | | 0 | 665,668 | |
| | 1 | 28,438 | |
| | 2 | 312 | |
| | 3 | 58 | |
| | 4 | 74 | |
|
|
| Higher is more relevant. **Every query has one row per ESCO v1.1.0 |
| skill (13,891 rows per query).** |
|
|
| ## Test split |
|
|
| A `test` split is now available (450 queries, 450 qrels rows). |
|
|
| Unlike the `validation` split, the test split is **not yet fully graded** (0-4). Its relevance labels are **real but binary**: `score = 1` marks a genuinely relevant target (derived from the public non-graded ground truths), and every pair not listed is implicit grade 0. The fine-grained 0-4 graded annotations for the test split are withheld during the ongoing RecSys-HR challenge (see [`WorkRB website`](https://techwolf-ai.github.io/workrb-site/challenges/recsys-hr-2026.html)) and will be released afterwards. S |
|
|
| ## 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](https://creativecommons.org/licenses/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`](https://esco.ec.europa.eu) |
| (also CC BY 4.0). The judge labels themselves are released under CC BY 4.0. |
|
|
