Cuts the topic topic_hierarchy (see [topic_hierarchy()]) at the requested count and rebuilds the model: documents keep their cluster memberships (merged, never re-clustered), centroids are recomputed from the member documents, and terms, labels, sizes, and representatives are derived afresh for the merged topics. The result is a full `sbert_topic_model` — every downstream verb (`summary()`, `coherence()`, `predict()`, [representatives()], plots) works unchanged.
Arguments
- object
An `sbert_topic_model` returned by [topics()].
- n_topics
Target number of topics, at least 2 and below the current count.
- embeddings
The document embedding matrix used to fit `object`. Only needed when the model was fitted with `keep_embeddings = FALSE`; models fitted with `keep_embeddings = TRUE` carry their embeddings.
- method
Agglomeration method passed to [stats::hclust()]. Default `"average"`.
Examples
text <- c(
"Cats chase mice", "Dogs chase balls", "Kittens nap in sunshine",
"Stocks and bonds trade", "Markets price shares", "Banks report profit"
)
embeddings <- rbind(
c(1, 0), c(0.95, 0.05), c(0.9, 0.1),
c(0, 1), c(0.05, 0.95), c(0.1, 0.9)
)
topics <- topics(
text, 3,
embeddings = embeddings, n_terms = 3, keep_embeddings = TRUE
)
reduce_topics(topics, 2)
#> <sbert_topic_model>
#> documents: 6
#> topics: 2
#> model: precomputed embeddings
#> algorithm: hierarchical merge of deterministic k-means topics
#> topic sizes: 3, 3
#> between/total SS: 99.5%