🚀 JobBERT

JobBERT是一个专门用于招聘信息处理的模型，它基于大量招聘信息数据进行预训练，能够有效从招聘信息中提取技能相关信息，为劳动力市场动态分析提供有力支持。

📚 详细文档

模型来源

JobBERT模型来自以下论文： Mike Zhang、Kristian Nørgaard Jensen、Sif Dam Sonniks和Barbara Plank所著的《SkillSpan: Hard and Soft Skill Extraction from Job Postings》，发表于2022年北美计算语言学协会人类语言技术会议论文集。

模型训练

该模型是在bert-base-cased检查点的基础上，使用约320万条招聘信息中的句子进行持续预训练得到的。更多详细信息可在上述论文中查看。

引用说明

如果您使用了该模型，请引用以下论文：

@inproceedings{zhang-etal-2022-skillspan,
    title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings",
    author = "Zhang, Mike  and
      Jensen, Kristian N{\o}rgaard  and
      Sonniks, Sif  and
      Plank, Barbara",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.366",
    pages = "4962--4984",
    abstract = "Skill Extraction (SE) is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, we introduce SKILLSPAN, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans. We release its respective guidelines created over three different sources annotated for hard and soft skills by domain experts. We introduce a BERT baseline (Devlin et al., 2019). To improve upon this baseline, we experiment with language models that are optimized for long spans (Joshi et al., 2020; Beltagy et al., 2020), continuous pre-training on the job posting domain (Han and Eisenstein, 2019; Gururangan et al., 2020), and multi-task learning (Caruana, 1997). Our results show that the domain-adapted models significantly outperform their non-adapted counterparts, and single-task outperforms multi-task learning.",
}

标签信息