Self-supervised learning for tabular data models

A method for self-supervised learning, comprising:

• identifying a plurality of training data sets, each training data set containing a plurality of data fields and data samples having values in the plurality of data fields;
• for each training data set of the plurality of data sets:
• selecting a target data field from the plurality of data fields;
• determining a training data input including a subset of data samples from the plurality of data samples and comprising one or more context data samples and one or more query data samples, each of the subset of data samples described as input features determined without the target data field and having a label based on the target data field;
• and
• training a tabular data model with the training batch to learn the label for the one or more queries of each of the plurality of training data inputs.

A non-transitory computer-readable medium for self-supervised learning for tabular data, the non-transitory computer-readable medium comprising instructions executable by a processor for:

• identifying a plurality of training data sets, each training data set containing a plurality of data fields and data samples having values in the plurality of data fields;
• for each training data set of the plurality of data sets:
• selecting a target data field from the plurality of data fields;
• determining a training data input including a subset of data samples from the plurality of data samples and comprising one or more context data samples and one or more query data samples, each of the subset of data samples described as input features determined without the target data field and having a label based on the target data field;
• and
• training a tabular data model with the training batch to learn the label for the one or more queries of each of the plurality of training data inputs.