Update README.md

README.md

@@ -6,11 +6,13 @@ tags:
 - skill-extraction
 - esco
 - graded-relevance
-configs:  
+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
@@ -19,6 +21,8 @@ configs:
     data_files:
     - split: validation
       path: qrels/validation.parquet
+    - split: test
+      path: qrels/test.parquet
 ---
 
 # skill-extraction-tech-graded
@@ -27,8 +31,7 @@ Graded-relevance annotations for sentences from
 [`TechWolf/skill-extraction-tech`](https://huggingface.co/datasets/TechWolf/skill-extraction-tech)
 against the ESCO v1.1.0 skill taxonomy. Layout follows the
 [BEIR](https://github.com/beir-cellar/beir) convention. 
-At this moment, only the validation split is annotated. 
-The test split, together with the annotation mechanism, will be released after the ongoing challenge related to this (see: https://workrb.ai/).
+The validation split carries full graded (0-4) relevance. A test split is also available with real but binary relevance; see the Test split section below.
 
 ## Configs
 
@@ -48,6 +51,12 @@ The test split, together with the annotation mechanism, will be released after t
 | 3 | 309 | The skill is strongly relevant for this query, although it is more implied than explicitly demonstrated.
 | 4 | 169 | The skill is explicitly demonstrated or requested by the query, and is therefor a clearly correct recommendation.
 
+## Test split
+
+A `test` split is now available (338 queries, 583 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 https://workrb.ai/) and will be released afterwards. 
+
 ## Attribution
 
 This dataset uses the ESCO classification of the European Commission