Automated data instance assignment

A non-transitory computer-readable medium configured to store code comprising instructions for automated data instance assignment, wherein the instructions, when executed by one or more processors, cause the one or more processors to:

• receive a documentation of a transaction from a user;
• parse data in the documentation to create a data instance representing the transaction in a database;
• determine that the data instance needs an assignment of a category from a list of custom-defined categories maintained by a third-party platform;
• train a machine-learned encoder model with a plurality of training samples, wherein the machine-learned encoder model is trained by applying a triplet loss function to minimize a distance between an embedding of an anchor data instance and an embedding of a positive data instance and to maximize a distance between the embedding of the anchor data instance and an embedding of a negative data instance;
• and
• apply the trained machine-learned encoder model to the data instance to generate an embedding for the data instance;
• compare the embedding for the data instance with embeddings associated with the list of custom-defined categories to determine one or more candidate categories for the data instance;
• and
• transmit a response message to the user, the response message including at least one of the candidate categories determined by the machine-learned encoder model.

A system comprising:

• one or more processors;
• and
• memory storing code comprising instructions for automated data instance assignment, wherein the instructions, when executed by one or more processors, cause the one or more processors to:
• receive a documentation of a transaction from a user;
• parse data in the documentation to create a data instance representing the transaction in a database;
• determine that the data instance needs an assignment of a category from a list of custom-defined categories maintained by a third-party platform;
• train a machine-learned encoder model with a plurality of training samples, wherein the machine-learned encoder model is trained by applying a triplet loss function to minimize a distance between an embedding of an anchor data instance and an embedding of a positive data instance and to maximize a distance between the embedding of the anchor data instance and an embedding of a negative data instance;
• and
• apply the trained machine-learned encoder model to the data instance to generate an embedding for the data instance;
• compare the embedding for the data instance with embeddings associated with the list of custom-defined categories to determine one or more candidate categories for the data instance;
• and
• transmit a response message to the user, the response message including at least one of the candidate categories determined by the machine-learned encoder model.