Assign New Documents to Fitted Topics — predict.sbert_topic

Predicts the topic of unseen documents by embedding them and assigning each to the nearest stored topic centroid under cosine distance. The fitted model is not modified. Supply either a loaded model (to embed text) or a precomputed embedding matrix whose rows align with text.

Usage

# S3 method for class 'sbert_topic_model'
predict(object, text, model = NULL, embeddings = NULL, batch_size = 32L, ...)

Arguments

object: A fitted topics() model.
text: Character vector of new documents. A model fitted with segment = "sentence", "clause", or "phrase" splits them the same way first (with the fitted max_tokens, merge_below, and min_content) and assigns every segment.
model: A loaded sbert_model, a pinned model name, or NULL for the default model; ignored when embeddings are supplied. The embedding dimension must match the fitted model.
embeddings: Optional numeric matrix with one row per document, or one row per segment for a segmented model.
batch_size: Batch size passed to encode() when model is used.
...: Unused; included for S3 compatibility.

Value

A base data frame with one row per document and columns document_id, document_name, text, topic, label (the fitted topic label), and distance (cosine distance to the assigned centroid). For a segmented model, one row per segment, with document_id naming the source document and segment its position within it.

Examples

text <- c(
  "Cats chase mice", "Dogs chase balls",
  "Stocks and bonds trade", "Markets price shares"
)
embeddings <- rbind(c(1, 0), c(0.9, 0.1), c(0, 1), c(0.1, 0.9))
fitted <- topics(text, 2, embeddings = embeddings)
predict(fitted, "Bulls and bears move markets", embeddings = rbind(c(0.2, 0.8)))
#>   document_id document_name                         text topic
#> 1           1               Bulls and bears move markets     2
#>                    label   distance
#> 1 Stocks and bonds trade 0.01792973