Model matrix
The full set of registered presets. Click any column header to sort — useful when you're picking by size, dimension, or pooling strategy. Refresh real-DB evidence with scripts/check_model_compatibility.py --all-presets.
| Preset | Task | HuggingFace repo | Dimensions | Size (FP32) | Pooling | Oracle name |
|---|---|---|---|---|---|---|
| all-MiniLM-L6-v2 | embedding | sentence-transformers/all-MiniLM-L6-v2 | 384 | ~90 MB | mean | ALL_MINILM_L6_V2 |
| all-MiniLM-L12-v2 | embedding | sentence-transformers/all-MiniLM-L12-v2 | 384 | ~130 MB | mean | ALL_MINILM_L12_V2 |
| all-mpnet-base-v2 | embedding | sentence-transformers/all-mpnet-base-v2 | 768 | ~420 MB | mean | ALL_MPNET_BASE_V2 |
| bge-small-en-v1.5 | embedding | BAAI/bge-small-en-v1.5 | 384 | ~130 MB | cls | BGE_SMALL_EN_V1_5 |
| nomic-embed-text-v1 | embedding | nomic-ai/nomic-embed-text-v1 | 768 | ~540 MB | mean | NOMIC_EMBED_TEXT_V1 |
| ms-marco-MiniLM-L-6-v2 | reranker | cross-encoder/ms-marco-MiniLM-L-6-v2 | — | ~90 MB | — | MS_MARCO_MINILM_L_6_V2 |
| ms-marco-MiniLM-L-12-v2 | reranker | cross-encoder/ms-marco-MiniLM-L-12-v2 | — | ~130 MB | — | MS_MARCO_MINILM_L_12_V2 |
Reranker presets
The two ms-marco-MiniLM-* entries above are cross-encoder rerankers, not embedding models. Oracle registers them with function:"regression" metadata; you query them via PREDICTION(model USING q AS DATA1, d AS DATA2) rather than VECTOR_EMBEDDING. Output is a scalar logit — higher means more relevant. Apply 1 / (1 + EXP(-score)) if you need a [0,1] probability.
SentencePiece-based rerankers like BAAI/bge-reranker-base are not supported (same constraint as the embedding path — Oracle's BertTokenizer op can't represent SentencePiece vocabularies). The cross-encoder/ms-marco-MiniLM-* family uses WordPiece and works first-class.
A note on sizes
The FP32 size is the on-disk footprint of the augmented ONNX — encoder plus tokenizer ops plus pooling plus L2 norm. The encoder alone is about 85% of that. These are the models as downloaded, before any Oracle-side compression or tablespace overhead.
Dimensions and storage
The output dimension drives disk usage for the vector column:
- 384-d · FP32 ≈ 1.5 KB per row.
- 768-d · FP32 ≈ 3.0 KB per row.
Oracle's HNSW index doubles that rough figure once built. Cosine distance and dot product rank identically on L2-normalized vectors — all listed presets normalize, so use whichever VECTOR_DISTANCE metric reads cleaner in your SQL.