Extract One Fitted Model from a Topic-Count Comparison — fitted.sbert_topic

Returns the model [compare_topics()] already fitted for a given topic count, so choosing a granularity from the comparison table costs no refitting.

Usage

# S3 method for class 'sbert_topic_sweep'
fitted(object, n_topics, segment = NULL, ...)

Arguments

object: A comparison returned by [compare_topics()] with `keep_models = TRUE` (the default).
n_topics: The topic count to extract. Must be one of the candidates in the comparison.
segment: The segment level to extract when the comparison spans several (`compare_topics(segment = c(...))`); one of its levels. Leave `NULL` for a single-level comparison.
...: Unused, present for generic consistency.

Value

The `sbert_topic_model` fitted for that candidate.

Examples

text <- c(
  "Cats chase mice", "Kittens chase mice too", "Cats nap daily",
  "Stocks and bonds trade", "Markets price shares", "Banks report profit"
)
embeddings <- rbind(
  c(1, 0, 0), c(0.98, 0.02, 0), c(0.96, 0, 0.04),
  c(0, 1, 0), c(0.02, 0.98, 0), c(0, 0.96, 0.04)
)
comparison <- compare_topics(
  text, n_topics = 2:3, embeddings = embeddings, n_terms = 3
)
fitted(comparison, n_topics = 2)
#> <sbert_topic_model>
#>   documents: 6 
#>   topics: 2
#>   model: precomputed embeddings
#>   algorithm: deterministic k-means (Lloyd)
#>   topic sizes: 3, 3
#>   between/total SS: 99.9%