Abstract: A computing server receives, through a message communication channel from a user, a documentation of a transaction. The computing server parses data in the documentation to create a data instance representing the transaction in a database. The computing server determines that the data instance needs an assignment of a category from a list of custom-defined categories. The computing server applies a machine-learned encoder model to features of the data instance to determine candidate categories from the list of custom-defined categories. The computing server transmits a response message to the user through the message communication channel. The response message includes at least one of the candidate categories determined by the machine learning model. Responsive to transmitting the response message to the user, the computing server receives feedback from the user. The computing server generates a category assignment for the data instance based on the user feedback.

Abstract: A computing server receives, through a message communication channel from a user, a documentation of a transaction. The computing server parses data in the documentation to create a data instance representing the transaction in a database. The computing server determines that the data instance needs an assignment of a category from a list of custom-defined categories. The computing server applies a machine-learned encoder model to features of the data instance to determine candidate categories from the list of custom-defined categories. The computing server transmits a response message to the user through the message communication channel. The response message includes at least one of the candidate categories determined by the machine learning model. Responsive to transmitting the response message to the user, the computing server receives feedback from the user. The computing server generates a category assignment for the data instance based on the user feedback.

Abstract: A computing server retrieves a list of custom-defined categories of a database and accesses a plurality of training samples for training a machine-learned encoder model. The computing server trains the machine-learned encoder model that generates embeddings of data instances. The machine-learned encoder model is trained to separate a plurality of embeddings of positive data instances belong to the target category from a plurality of embeddings of negative data instances. The computing server receives a target data instance that is to be imported to the third-party data platform and generates features of the target data instance to prepare the target data instance for further processing. The computing server applies the machine-learned encoder model to the target data instance to determine an assignment of a category from the list of custom-defined categories. The computing server exports the target data instance including the determined assignment to the third-party data platform.