基于DistilKoBERT的职业分类模型:使用韩国第五和第六次劳动条件调查
Tae-Yeon Kim1,2,3, Seong-Uk Baek1,2,4, Myeong-Hun Lim1,2,3
1Department of Occupational and Environmental Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Annals of occupational and environmental medicine
|August 27, 2024
概括
这项研究使用DistilKoBERT开发了一个自动化职业分类模型,达到84.44%的准确性. 该工具有助于开展职业安全和健康方面的流行病学研究.
科学领域:
- 计算语言学计算语言学
- 职业健康 职业健康 职业健康 职业健康
- 数据科学是数据科学.
背景情况:
- 准确的职业分类对于政策制定和流行病学研究至关重要.
- 现有的方法可能无法完全捕捉到职位责任的细微差别.
- 自动化系统可以提高这种分类过程中的效率和准确性.
研究的目的:
- 开发和评估使用DistilKoBERT的职业分类模型.
- 用关键评估指标评估模型的性能.
- 探索人工智能在职业健康和安全研究中的潜力.
主要方法:
- 利用了韩国第五和第六次劳动条件调查 (2017年,2020年) 的数据.
- 99,665名韩国工人的自然语言答案和韩国职业标准分类 (3位数码).
- 在7:3训练测试分割数据集上微调DistilKoBERT模型.
主要成果:
- 迪斯蒂尔科伯特模型在将28996名参与者分为142个职业代码时取得了84.44%的整体准确性.
- 权重精度,回忆和F1分数分别为0.83,0.84和0.83.
- 该模型对服务/销售人员和培训数据中普遍存在的职业显示出高精度,对管理人员的精度较低.
结论:
- 基于DistilKoBERT的自动化职业分类系统显示出有希望的性能.
- 该模型为职业安全和健康的流行病学研究提供了有价值的工具.
- 需要进一步改进,以提高所有职业群体的分类准确性.
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