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Embed text using any HuggingFace Sentence Transformer model locally — no API key needed. See the API reference for all parameters. 
pip install 'geneva[udf-text-sentence-transformers]'
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.

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Embeddings

Compare a lightweight and a high-quality model side by side: 
from geneva.udfs import sentence_transformer_udf

table.add_columns({
    # Lightweight default model — fast, CPU-friendly
    "embedding_mini": sentence_transformer_udf(
        column="body",
        model="sentence-transformers/all-MiniLM-L6-v2",
    ),
    # Larger model with GPU acceleration
    "embedding_bge": sentence_transformer_udf(
        column="body",
        model="BAAI/bge-large-en-v1.5",
        num_gpus=1.0,
    ),
})

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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: 
# CPU-only (default) — suitable for lightweight models
sentence_transformer_udf(column="body", model="sentence-transformers/all-MiniLM-L6-v2")

# GPU-accelerated — recommended for larger models
sentence_transformer_udf(column="body", model="BAAI/bge-large-en-v1.5", num_gpus=1.0)
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.