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--- |
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base_model: |
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- jjzha/esco-xlm-roberta-large |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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- text-classification |
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- skill-detection |
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- sentence-classification |
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- ESCO |
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library_name: transformers |
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metrics: |
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- f1 |
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- accuracy |
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--- |
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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: |
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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). |
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https://aclanthology.org/2025.icnlsp-1.40.pdf |
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@inproceedings{musazade2025cross, |
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title={Cross-Lingual Sentence-Level Skill Identification in English and Danish Job Advertisements}, |
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author={Musazade, Nurlan and Zhang, Mike and Mezei, J{\'o}zsef}, |
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booktitle={Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP-2025)}, |
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pages={410--415}, |
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year={2025} |
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} |
