An embeddings instance can optionally have an associated scoring instance. This scoring instance can serve two purposes, depending on the settings.
One use case is building sparse/keyword indexes. This occurs when the
terms parameter is set to
The other use case is with word vector term weighting. This feature has been available since the initial version but isn't quite as common anymore.
The following covers the available options
Sets the scoring method. Add custom scoring via setting this parameter to the fully resolvable class string.
Enables term frequency sparse arrays for a scoring instance. This is the backend for sparse keyword indexes.
Enables normalized scoring (ranging from 0 to 1). When enabled, statistics from the index will be used to calculate normalized scores.