Jobbert Base Cased

🚀 JobBERT

JobBERT是一個專門用於招聘信息處理的模型，它基於大量招聘信息數據進行預訓練，能夠有效從招聘信息中提取技能相關信息，為勞動力市場動態分析提供有力支持。

📚 詳細文檔

模型來源

JobBERT模型來自以下論文： Mike Zhang、Kristian Nørgaard Jensen、Sif Dam Sonniks和Barbara Plank所著的《SkillSpan: Hard and Soft Skill Extraction from Job Postings》，發表於2022年北美計算語言學協會人類語言技術會議論文集。

模型訓練

該模型是在bert-base-cased檢查點的基礎上，使用約320萬條招聘信息中的句子進行持續預訓練得到的。更多詳細信息可在上述論文中查看。

引用說明

如果您使用了該模型，請引用以下論文：

@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.",
}

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