Load an Arbitrary Hugging Face Embedding Model

Loads any public Hugging Face repository that ships an ONNX encoder export and a tokenizer.json, returning the same sbert_model object as load_model() so that encode(), topics(), and every downstream verb work unchanged.

Usage

load_custom(
  id,
  revision = NULL,
  onnx_path = NULL,
  tokenizer_path = "tokenizer.json",
  pooling = NULL,
  prefix = NULL,
  max_length = NULL,
  cache_dir = default_cache_dir(),
  backend = "cpu",
  threads = 1L,
  verify = TRUE,
  quiet = FALSE
)

Arguments

id: Hugging Face repository id, for example "thenlper/gte-small".
revision: Optional commit hash to pin. NULL resolves the current revision on first download (recorded in the manifest thereafter).
onnx_path: Repository path of the ONNX graph. NULL auto-detects onnx/model.onnx or model.onnx.
tokenizer_path: Repository path of the tokenizer. Default "tokenizer.json".
pooling: "mean" or "cls". NULL auto-detects from 1_Pooling/config.json and fails with a clear message when absent.
prefix: Text prepended to every input by encode(). NULL (default) keeps the recorded value ("" on first use); pass "" to clear a recorded prefix.
max_length: Maximum word pieces per input. NULL auto-detects from sentence_bert_config.json, falling back to 512.
cache_dir: Cache root returned by cache_dir().
backend: ONNX execution backend accepted by onnxr::onnx_model().
threads: Positive number of inference threads.
verify: Whether to verify recorded byte sizes and SHA-256 hashes when loading from an existing cache.
quiet: Whether to suppress download progress.

Value

An object of class sbert_model, with the same fields as load_model() returns; its short_name is "custom:<id>".

Details

This is the escape hatch below the curated registry. On first use the current revision is resolved (unless revision is given), the artifacts are downloaded, and their byte sizes and SHA-256 values are recorded in a local manifest ("trust on first use"); later loads verify the files against that manifest and never touch the network. Pooling, prefix, and maximum length are auto-detected from the repository's Sentence-Transformers configuration when present, and must be supplied explicitly otherwise. The ONNX input signature and embedding dimension are read from the graph itself. Unlike registry models, the package makes no numerical-parity claim: you vouch for the configuration.

Examples

# Cache root used for downloaded and custom models.
cache_dir()
#> [1] "/home/runner/.cache/R/sbert"

# \donttest{
if (interactive()) {
  model <- load_custom("thenlper/gte-small")
  encode(c("one sentence", "another sentence"), model)
}
# }