Embed text using any HuggingFace Sentence Transformer model locally — no API key needed.
See the API reference for all parameters.
Sentence Transformer models run locally on your workers — there are no API calls and no
per-token costs. This makes them a good fit for large-scale embedding jobs where cost is a
concern.
Embeddings
Compare a lightweight and a high-quality model side by side:
GPU acceleration
Sentence Transformer models can run on CPU or GPU. Smaller models like all-MiniLM-L6-v2
work well on CPU, but larger models like bge-large-en-v1.5 benefit significantly from GPU
acceleration. Use the num_gpus parameter to request GPU resources for a worker:
Setting num_gpus to a fractional value (e.g., 0.5) tells the
Ray scheduler
to co-locate multiple workers on the same physical GPU. For example, two UDFs with
num_gpus=0.5 will be scheduled on a single GPU. Note that Ray does not enforce GPU memory
limits — it is your responsibility to ensure the combined models fit in GPU memory.
API Reference
- Embeddings —
sentence_transformer_udf() — all parameters including column, model, num_gpus, normalize, and batch_size
