Print a Semantic Topic Model

Shows the fit at a glance: how many documents and topics, the variance explained, and each topic with its size and its most distinctive terms.

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%