入口:一个大型的金融数据集,用于理解表格
Elias Zavitsanos1, Dimitris Mavroeidis2, Eirini Spyropoulou2
1Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Aghia Paraskevi, 15341, Greece. izavits@iit.demokritos.gr.
Scientific data
|August 13, 2024
概括
进入是一个新的金融数据集,旨在训练大型模型理解表格数据. 这种结构化数据集有助于深度学习方法在表理解和下游任务,如细胞分类.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 表格数据对于在二维矩阵格式中组织和比较信息至关重要.
- 训练大型模型来理解结构表对于各种应用中的知识传输至关重要.
- 这些模型的有效预训练需要大,格式化好的数据集,以捕捉表和单元特征.
研究的目的:
- 介绍ENTRANT,一个新的财务数据集,专门为表理解的深度学习模型进行预培训.
- 提供机器可读的数据集,包含详细的表格和单元信息,包括元数据,属性和层次结构.
- 为了促进自动化数据处理和验证,以获得强大的数据集实用性.
主要方法:
- 输入数据集是通过转换数百万个财务表来创建的.
- 数据处理和策划是完全自动化的.
- 通过使用高代码覆盖率的单元测试进行了技术验证.
主要成果:
- 进入数据集包含数以百万计的转换表与细胞属性,位置和层次信息.
- 数据集以机器可读的格式提供,包括元数据.
- 自动化处理和验证确保数据质量和可用性.
结论:
- ENTRANT是促进基于深度学习的表理解的宝贵资源.
- 该数据集有效地支持用于解释结构化表格数据的模型的预培训任务.
- 在细胞分类的预训练任务中证明的使用突出了其实际适用性.
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