| --- |
| base_model: |
| - jjzha/esco-xlm-roberta-large |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-classification |
| tags: |
| - text-classification |
| - skill-detection |
| - sentence-classification |
| - ESCO |
| library_name: transformers |
| metrics: |
| - f1 |
| - accuracy |
| --- |
| |
| We fine-tune jjzha/esco-xlm-roberta-large for sentence-level binary skill identification. The results show 94% accuracy and F1 score in English. Furthermore, the study demonstrates the model's effectiveness for cross-lingual transfer. Please refer to the original paper for more information, and if you use this work, please cite the following: |
|
|
| Musazade, N., Zhang, M., & Mezei, J. (2025, August). Cross-Lingual Sentence-Level Skill Identification in English and Danish Job Advertisements. In Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP-2025) (pp. 410-415). |
|
|
| https://aclanthology.org/2025.icnlsp-1.40.pdf |
|
|
| @inproceedings{musazade2025cross, |
| title={Cross-Lingual Sentence-Level Skill Identification in English and Danish Job Advertisements}, |
| author={Musazade, Nurlan and Zhang, Mike and Mezei, J{\'o}zsef}, |
| booktitle={Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP-2025)}, |
| pages={410--415}, |
| year={2025} |
| } |
