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--- |
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language: |
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- da |
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tags: |
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- job postings |
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- DaJobBERT |
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--- |
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# JobBERT |
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This is the DaJobBERT model from: |
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Mike Zhang, Kristian Nørgaard Jensen, and Barbara Plank. __Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning__. Proceedings of the Language Resources and Evaluation Conference (LREC). 2022. |
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This model is continuously pre-trained from a `dabert-base-uncased`: https://huggingface.co/Maltehb/danish-bert-botxo checkpoint on ~24.5M Danish sentences from job postings. More information can be found in the paper. |
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If you use this model, please cite the following paper: |
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``` |
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@InProceedings{zhang-jensen-plank:2022:LREC, |
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author = {Zhang, Mike and Jensen, Kristian N{\o}rgaard and Plank, Barbara}, |
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title = {Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning}, |
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booktitle = {Proceedings of the Language Resources and Evaluation Conference}, |
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month = {June}, |
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year = {2022}, |
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address = {Marseille, France}, |
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publisher = {European Language Resources Association}, |
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pages = {436--447}, |
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abstract = {Skill Classification (SC) is the task of classifying job competences from job postings. This work is the first in SC applied to Danish job vacancy data. We release the first Danish job posting dataset: *Kompetencer* (\_en\_: competences), annotated for nested spans of competences. To improve upon coarse-grained annotations, we make use of The European Skills, Competences, Qualifications and Occupations (ESCO; le Vrang et al., (2014)) taxonomy API to obtain fine-grained labels via distant supervision. We study two setups: The zero-shot and few-shot classification setting. We fine-tune English-based models and RemBERT (Chung et al., 2020) and compare them to in-language Danish models. Our results show RemBERT significantly outperforms all other models in both the zero-shot and the few-shot setting.}, |
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url = {https://aclanthology.org/2022.lrec-1.46} |
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} |
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``` |
