Jobbert Skill Extraction

🚀 技能提取模型演示项目

本项目是一个使用特定模型的演示，这些模型来自以下研究：

🚀 快速开始

此演示使用了来自论文的模型。该论文聚焦于从英文招聘信息中进行硬技能和软技能提取的任务。

📚 详细文档

本项目引用的论文为：

@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  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",
    doi = "10.18653/v1/2022.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.",
}

请注意，还有另一个端点 jjzha/jobbert_knowledge_extraction。知识可被视为硬技能，而技能则包含软技能和应用技能。