What should labels NOT be used to capture within a dataset?

Labels in a dataset are primarily meant to categorize or classify the data points based on relevant features or characteristics that contribute to the learning process of a model. Using labels to capture information present in the metadata is generally not advisable, as metadata usually refers to the underlying information about the dataset itself rather than the features intrinsic to the data points.

For instance, metadata can include details such as creation date, file size, or source of the data, which do not contribute directly to the behavior or properties that the model aims to learn. Labels, on the other hand, should focus more on functional attributes that are significant for the task at hand, such as sentiment expressed in text, relationships between entities, or any characteristics that are instrumental in guiding the machine learning process.

Elements like concepts expressing sentiment, hierarchical relationships, or training data for the model represent dimensions essential for the model to learn from, indicating that these should indeed be included within the labeling framework. Hence, the correct choice highlights the inappropriateness of capturing metadata as labels in a dataset.