Datasets are essential for training and evaluating machine learning (ML) models. However, they are also at the root of many undesirable model behaviors, such as biased predictions. To address this issue, the machine learning community is proposing a data-centric cultural shift, where data issues are given the attention they deserve and more standard practices for gathering and describing datasets are discussed and established.

So far, these proposals are mostly high-level guidelines described in natural language and, as such, they are difficult to formalize and apply to particular datasets. In this sense, and inspired by these proposals, we define a new domain-specific language (DSL) to precisely describe machine learning datasets in terms of their structure, provenance, and social concerns. We believe this DSL will facilitate any ML initiative to leverage and benefit from this data-centric shift in ML (e.g., selecting the most appropriate dataset for a new project or better replicating other ML results). The DSL is implemented as a Visual Studio Code plugin, and it has been published under an open-source license.

数据集对于训练和评估机器学习 (ML) 模型至关重要。然而，它们也是许多不良模型行为的根源，例如有偏见的预测。为了解决这个问题，机器学习社区提出了一种以数据为中心的文化转变，即数据问题得到应有的重视，并讨论和建立更多收集和描述数据集的标准实践。

到目前为止，这些建议大多是用自然语言描述的高级指南，因此，它们很难形式化并应用于特定的数据集。从这个意义上说，并受到这些提议的启发，我们定义了一种新的特定领域语言 (DSL)，以根据其结构、出处和社会关注点来精确描述机器学习数据集。我们相信此 DSL 将促进任何 ML 计划利用 ML 中以数据为中心的转变并从中受益（例如，为新项目选择最合适的数据集或更好地复制其他 ML 结果）。DSL 作为 Visual Studio Code 插件实现，并已在开源许可下发布。