Load a Pinned Sentence-BERT Model

Loads already-downloaded model artifacts. The function never downloads model files or native libraries. See models() for the available pinned models.

Usage

load_model(
  model = "all-MiniLM-L6-v2",
  cache_dir = default_cache_dir(),
  backend = "cpu",
  threads = 1L,
  verify = TRUE
)

Arguments

model

Name of a pinned model listed by models().

cache_dir

Cache root returned by cache_dir().

backend

ONNX execution backend accepted by onnxr::onnx_model().

threads

Positive number of inference threads, or "auto" to use the machine's performance-core count. Inference is the dominant cost of encode() — about 88% of a batch — and it is the only part of the package that threads. The default 1 is deliberate: it keeps runs reproducible across machines and keeps the package within the two-core limit that check environments impose.

Raising it is free of numerical consequence — output was identical() at 1, 2, 4, 6 and 8 threads across all 45 measured runs — but expect a modest gain, bounded by performance cores rather than logical ones. Over three repetitions at 200, 600 and 1,500 documents on an Apple M4 (4 performance + 6 efficiency), median speedups against a single thread were 1.22x at two threads, 1.29x at four, 1.22x at six and 1.18x at eight. Four threads was fastest at the two larger sizes (1.34x at 1,500 documents); beyond that the efficiency cores add contention rather than throughput. Threaded timings are also noisy — run-to-run spread reached 26% at the smallest size against under 3% single-threaded — so benchmark with repetition before tuning. Set threads = "auto" for the knee of that curve, or an integer to pin it.

verify

Whether to verify file sizes and SHA-256 hashes before loading.

Value

An object of class sbert_model (class c("sbert_static_model", "sbert_model") for potion-base-8M): a list holding the model's identity (id, short_name, revision), its encoding settings (dimension, max_length, pooling, prefix), the backend and threads it runs with, and the loaded tokenizer and model. Pass it to the model argument of encode() and the topic verbs.

Examples

# Pinned models that can be loaded.
models()
#>                                    model dimensions max_tokens      languages
#> 1                       all-MiniLM-L6-v2        384        256        English
#> 2                      all-MiniLM-L12-v2        384        128        English
#> 3                paraphrase-MiniLM-L3-v2        384        128        English
#> 4              multi-qa-MiniLM-L6-cos-v1        384        512        English
#> 5  paraphrase-multilingual-MiniLM-L12-v2        384        128  50+ languages
#> 6                      all-mpnet-base-v2        768        384        English
#> 7  paraphrase-multilingual-mpnet-base-v2        768        128  50+ languages
#> 8                      bge-small-en-v1.5        384        512        English
#> 9                       bge-base-en-v1.5        768        512        English
#> 10                 multilingual-e5-small        384        512 100+ languages
#> 11                 nomic-embed-text-v1.5        768       8192        English
#> 12           jina-embeddings-v2-small-en        512       8192        English
#> 13                  mxbai-embed-large-v1       1024        512        English
#> 14                        potion-base-8M        256    1000000        English
#>    size_mb
#> 1     90.9
#> 2    133.6
#> 3     69.5
#> 4     90.9
#> 5    479.4
#> 6    436.3
#> 7   1119.2
#> 8    133.8
#> 9    436.5
#> 10   487.4
#> 11   548.0
#> 12   130.5
#> 13  1337.6
#> 14    30.9

# \donttest{
if (interactive()) {
  model <- load_model()
}
# }