Generate Text Embeddings — hf_embed • huggingfaceR

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

if (FALSE) { # \dontrun{
# Generate embeddings
embeddings <- hf_embed(c("Hello world", "Goodbye world"))

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