Update README.md

README.md

@@ -1,3 +1,40 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- Skill Extraction
+pretty_name: Skill Extraction - TechWolf
+size_categories:
+- n<1K
+---
+# Skill Extraction with ESCO skills - TechWolf subset
+
+## Dataset Description
+
+- **Paper:** https://arxiv.org/abs/2307.10778
+- **Point of Contact:** [email protected]
+
+## Dataset Summary
+
+The `TECHWOLF` subset, although smaller, represents a more generic distribution of job descriptions and skill spans. [ESCO](https://esco.ec.europa.eu/en/classification/skill_main) skills are directly annotated on the full sentence level, thus omitting the intermediate span identification step. ESCO v1.1.0 is used.
+
+This dataset is part of a three-part evaluation dataset for skill extraction:
+1. [skill-extraction-tech](https://huggingface.co/datasets/jensjorisdecorte/skill-extraction-tech)
+2. [skill-extraction-house](https://huggingface.co/datasets/jensjorisdecorte/skill-extraction-house)
+3. [skill-extraction-techwolf](https://huggingface.co/datasets/jensjorisdecorte/skill-extraction-techwolf)
+
+### Citation Information
+
+If you use this dataset, please include the following reference:
+
+```
+@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}
+}
+```