hf_embed: Generate Text Embeddings

In huggingfaceR: Access 'Hugging Face' Models and Datasets

Generate Text Embeddings

Description

Generate dense vector representations (embeddings) for text using transformer models. Useful for semantic similarity, clustering, and as features for ML models. Vector inputs are sent in a single batched Inference API request when possible, which is substantially faster than one API request per text.

Usage

hf_embed(
  text,
  model = hf_default_model("embed"),
  token = NULL,
  endpoint_url = NULL,
  ...
)

Arguments

text	Character vector of text(s) to embed.
model	Character string. Model ID from Hugging Face Hub. Default: "BAAI/bge-small-en-v1.5" (384-dim embeddings).
token	Character string or NULL. API token for authentication.
endpoint_url	Character string or NULL. A custom Inference Endpoint URL. When provided, requests are sent to this URL instead of the public Inference API. Use for models deployed on dedicated Inference Endpoints.
...	Additional arguments (currently unused).

Value

A tibble with columns: text, embedding (list-column of numeric vectors), n_dims

Examples

## Not run: 
# Generate embeddings
embeddings <- hf_embed(c("Hello world", "Goodbye world"))

# Access embedding vectors
embeddings$embedding[[1]]  # First embedding vector

## End(Not run)

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