# Glossary#

General model#

Any DNN model with no shape restriction on input and output data. For example, classification model, object detection model, next token prediction model, regression model.

Embedding model#

A DNN with any shape input (image/text/sequence) and an output ndarray in the shape [B x D], where B is the batch size same as the input, and D is the dimension of the embedding.

Class dataset#

A kind of dataset, where each Document has a class label stored in .tags['finetuner_label'], and does not have .matches.

Session dataset#

A kind of dataset, where each root Document contains .matches and no label; its matches contain label saved under .tags['finetuner_label']. That label can be either 1 (for a match similar to its reference Document) or -1 (for match dissimilar to its reference Document).

Instance dataset#

A kind of dataset, where each Document has no class labels, each instance is considered as it’s own class.

Tuner#

A component in Finetuner. Given an embedding model and a labeled dataset, it trains the model to fit the data.

Tailor#

A component in Finetuner. Converts any general model into an embedding model;