feat: add TechWolf and SkillXL skill-extraction ranking tasks

README.md

@@ -86,6 +86,8 @@ pip install workrb
 | Skill Extraction House         | `HouseSkillExtractRanking` | multi_label | 262 queries x 13891 targets         | 28        |
 | Skill Extraction Tech          | `TechSkillExtractRanking` | multi_label | 338 queries x 13891 targets         | 28        |
 | Skill Extraction SkillSkape    | `SkillSkapeExtractRanking` | multi_label | 1191 queries x 13891 targets        | 28        |
+| Skill Extraction TechWolf      | `TechWolfSkillExtractRanking` | multi_label | 326 queries x 13891 targets         | 28        |
+| Skill Extraction SkillXL       | `SkillXLSkillExtractRanking` | multi_label | 944 queries x 13891 targets         | 28        |
 | Skill Similarity SkillMatch-1K | `SkillMatch1kSkillSimilarityRanking` | single_label | 900 queries x 2648 targets          | 1         |
 | Skill Normalization ESCO       | `ESCOSkillNormRanking` | multi_label | 72008 queries x 13939 targets       | 28        |
 | Skill Normalization MELS       | `MELSRanking` | multi_label | 1722 queries x 19466 targets        | 5         |

src/workrb/tasks/__init__.py

@@ -21,7 +21,9 @@
 from .ranking.skill_extraction import (
     HouseSkillExtractRanking,
     SkillSkapeExtractRanking,
+    SkillXLSkillExtractRanking,
     TechSkillExtractRanking,
+    TechWolfSkillExtractRanking,
 )
 from .ranking.skill_similarity import SkillMatch1kSkillSimilarityRanking
 from .ranking.skillnorm import ESCOSkillNormRanking
@@ -46,7 +48,9 @@
     "MELSRanking",
     "HouseSkillExtractRanking",
     "TechSkillExtractRanking",
+    "TechWolfSkillExtractRanking",
     "SkillSkapeExtractRanking",
+    "SkillXLSkillExtractRanking",
     "SkillMatch1kSkillSimilarityRanking",
     "ProjectCandidateRanking",
     "SearchQueryCandidateRanking",

src/workrb/tasks/ranking/__init__.py

@@ -20,7 +20,9 @@
 from workrb.tasks.ranking.skill_extraction import (
     HouseSkillExtractRanking,
     SkillSkapeExtractRanking,
+    SkillXLSkillExtractRanking,
     TechSkillExtractRanking,
+    TechWolfSkillExtractRanking,
 )
 from workrb.tasks.ranking.skill_similarity import SkillMatch1kSkillSimilarityRanking
 from workrb.tasks.ranking.skillnorm import ESCOSkillNormRanking
@@ -38,5 +40,7 @@
     "SearchQueryCandidateRanking",
     "SkillMatch1kSkillSimilarityRanking",
     "SkillSkapeExtractRanking",
+    "SkillXLSkillExtractRanking",
     "TechSkillExtractRanking",
+    "TechWolfSkillExtractRanking",
 ]

src/workrb/tasks/ranking/skill_extraction.py

@@ -102,9 +102,7 @@ def load_dataset(self, dataset_id: str, split: DatasetSplit) -> RankingDataset:
                 if uri in target_uris_to_skill:
                     original_skill_to_target_skill[orig_skill] = target_uris_to_skill[uri]
 
-            df["label"] = df["label"].apply(
-                lambda orig_skill: original_skill_to_target_skill.get(orig_skill)
-            )
+            df["label"] = df["label"].apply(original_skill_to_target_skill.get)
             # Drop rows where label is None
             df = df[df["label"].notna()].reset_index(drop=True).copy()
 
@@ -217,6 +215,142 @@ def citation(self) -> str:
 """
 
 
+@register_task()
+class TechWolfSkillExtractRanking(BaseESCOSkillExtractRanking):
+    """Skill Extraction from TechWolf Dataset Ranking Task."""
+
+    orig_esco_version = "1.1.0"
+
+    def __init__(self, esco_version: str = "1.1.0", **kwargs):
+        self.esco_version = esco_version
+        super().__init__(hf_name="TechWolf/skill-extraction-techwolf", **kwargs)
+
+    @property
+    def name(self) -> str:
+        """Skill extraction TechWolf task name."""
+        return "Skill Extraction TechWolf"
+
+    @property
+    def description(self) -> str:
+        """Skill extraction TechWolf task description."""
+        return (
+            "Extract skills from text descriptions in a generic distribution of job descriptions."
+        )
+
+    @property
+    def citation(self) -> str:
+        """Skill extraction TechWolf task citation."""
+        return """@article{decorte2023extreme,
+  title={Extreme multi-label skill extraction training using large language models},
+  author={Decorte, Jens-Joris and Verlinden, Severine and Van Hautte, Jeroen and Deleu, Johannes and Develder, Chris and Demeester, Thomas},
+  journal={arXiv preprint arXiv:2307.10778},
+  year={2023}
+}
+"""
+
+
+@register_task()
+class SkillXLSkillExtractRanking(BaseESCOSkillExtractRanking):
+    """Skill Extraction from SkillXL Dataset Ranking Task."""
+
+    orig_esco_version = "1.1.0"
+
+    def __init__(self, esco_version: str = "1.1.0", **kwargs):
+        self.esco_version = esco_version
+        super().__init__(hf_name="TechWolf/Skill-XL", **kwargs)
+
+    def load_dataset(self, dataset_id: str, split: DatasetSplit) -> RankingDataset:
+        """Load SkillXL data, filtering to relevant rows.
+
+        SkillXL differs from the base class in two ways: it includes
+        irrelevant sentences (filtered via the ``relevant`` boolean column),
+        and the skill column is named ``skill`` instead of ``label``.
+        """
+        language = Language(dataset_id)
+        # Load data
+        split_names = {DatasetSplit.TEST: "test", DatasetSplit.VAL: "validation"}
+        dataset = load_dataset(self.hf_name, split=split_names[split])
+        assert isinstance(dataset, Dataset)
+        df = dataset.to_pandas()
+        assert isinstance(df, pd.DataFrame)
+        assert "relevant" in df.columns, "Expected 'relevant' column in the dataset."
+
+        # Only keep rows where relevant is True
+        df = df[df["relevant"]].reset_index(drop=True).copy()
+        assert isinstance(df, pd.DataFrame)
+
+        # If ESCO version is not 1.1.0 and / or language is not en, we need to translate the skills
+        if self.esco_version != self.orig_esco_version or language != Language.EN:
+            original_esco = ESCO(version=self.orig_esco_version, language=Language.EN)
+            original_skill_uris = original_esco.get_skills_uris()
+            original_uris_to_skill = {v: k for k, v in original_skill_uris.items()}
+
+            target_esco = ESCO(version=self.esco_version, language=language)
+            target_skill_uris = target_esco.get_skills_uris()
+            target_uris_to_skill = {v: k for k, v in target_skill_uris.items()}
+
+            original_skill_to_target_skill = {}
+            for uri, orig_skill in original_uris_to_skill.items():
+                if uri in target_uris_to_skill:
+                    original_skill_to_target_skill[orig_skill] = target_uris_to_skill[uri]
+
+            df["skill"] = df["skill"].apply(original_skill_to_target_skill.get)
+            # Drop rows where skill is None
+            df = df[df["skill"].notna()].reset_index(drop=True).copy()
+
+        grouped_df = df.groupby("sentence")["skill"].apply(list).reset_index()
+
+        # Load ESCO skill vocabulary for target version/language
+        esco = ESCO(version=self.esco_version, language=language)
+        skill_vocab = esco.get_skills_vocabulary()
+        skill2label = {skill: i for i, skill in enumerate(skill_vocab)}
+
+        # Filter skills that exist in vocabulary (Excludes "LABEL NOT PRESENT" and "UNDERSPECIFIED")
+        filtered_queries = []
+        filtered_labels = []
+        for query, skill_list in zip(grouped_df["sentence"], grouped_df["skill"], strict=True):
+            filtered_skill_list = [skill for skill in skill_list if skill in skill2label]
+            if len(filtered_skill_list) == 0:
+                continue
+            filtered_queries.append(query)
+            filtered_labels.append([skill2label[skill] for skill in filtered_skill_list])
+
+        return RankingDataset(
+            query_texts=filtered_queries,
+            target_indices=filtered_labels,
+            target_space=skill_vocab,
+            dataset_id=dataset_id,
+        )
+
+    @property
+    def name(self) -> str:
+        """Skill extraction SkillXL task name."""
+        return "Skill Extraction SkillXL"
+
+    @property
+    def description(self) -> str:
+        """Skill extraction SkillXL task description."""
+        return (
+            "Extract skills from text descriptions in a generic distribution of job descriptions."
+        )
+
+    @property
+    def citation(self) -> str:
+        """Skill extraction SkillXL task citation."""
+        return """@ARTICLE{contextmatch_2025,
+  author={Decorte, Jens-Joris and van Hautte, Jeroen and Develder, Chris and Demeester, Thomas},
+  journal={IEEE Access},
+  title={Efficient Text Encoders for Labor Market Analysis},
+  year={2025},
+  volume={13},
+  number={},
+  pages={133596-133608},
+  keywords={Taxonomy;Contrastive learning;Training;Annotations;Benchmark testing;Training data;Large language models;Computational efficiency;Accuracy;Terminology;Labor market analysis;text encoders;skill extraction;job title normalization},
+  doi={10.1109/ACCESS.2025.3589147}
+}
+"""
+
+
 @register_task()
 class SkillSkapeExtractRanking(BaseESCOSkillExtractRanking):
     """Skill Extraction from SkillSkape Ranking Task."""