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 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 under 3 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.