Print a Semantic Topic Model

Shows the fit at a glance: the number of documents and topics, the embedding source, the clustering algorithm, the size of every topic, and the share of the embedding variance that lies between topics. Use summary() for coherence per topic and topic_sizes() for the labeled size table.

Usage

# S3 method for class 'sbert_topic_model'
print(x, ...)

Arguments

x

An sbert_topic_model object.

...

Ignored.

Value

The topic model object, invisibly.

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)
)
print(topics(text, n_topics = 2, embeddings = embeddings, n_terms = 3))
#> <sbert_topic_model>
#>   documents: 6 
#>   topics: 2
#>   model: precomputed embeddings
#>   algorithm: deterministic k-means (Lloyd)
#>   topic sizes: 3, 3
#>   between/total SS: 99.5%