hf_cluster_texts: Cluster Texts by Semantic Similarity

In huggingfaceR: Access 'Hugging Face' Models and Datasets

Cluster Texts by Semantic Similarity

Description

Perform k-means clustering on text embeddings.

Usage

hf_cluster_texts(data, k = 3, ...)

Arguments

data	A data frame with an 'embedding' column (from hf_embed_text).
k	Integer. Number of clusters. Default: 3.
...	Additional arguments passed to stats::kmeans().

Value

The input data frame with an added 'cluster' column.

Examples

## Not run: 
library(ggplot2)

# Cluster documents
docs_clustered <- docs_embedded |>
  hf_cluster_texts(k = 3)

# Reduce dimensions and visualize
library(uwot)
emb_matrix <- do.call(rbind, docs_clustered$embedding)
coords <- umap(emb_matrix)

docs_clustered |>
  mutate(umap_1 = coords[, 1], umap_2 = coords[, 2]) |>
  ggplot(aes(umap_1, umap_2, color = factor(cluster))) +
  geom_point(size = 3)

## End(Not run)

huggingfaceR documentation built on Aug. 30, 2026, 1:06 a.m.