— Embedding Model Reference
| Model Name | Size (Params) | Embedding Dim | MTEB Score | Pros | Cons |
|---|---|---|---|---|---|
| all-MiniLM-L6-v2 | ~22M | 384 | ~60-62 | Lightweight, fast, general-purpose | Less nuanced semantics |
| all-mpnet-base-v2 | ~110M | 768 | ~65 | Strong performance, balanced | Slower than smaller models |
| text-embedding-3-small | N/A (API-based) | 1536 | ~65-67 | Excellent semantics, easy to use | API cost, not local |
| text-embedding-3-large | N/A (API-based) | 3072 | ~67-70 | Top-tier accuracy | Higher cost/latency |
| intfloat/e5-small-v2 | ~33M | 384 | ~62 | Fast, retrieval-optimized | Slightly less general |
| intfloat/e5-large-v2 | ~335M | 1024 | ~66 | Strong semantics, multilingual | Larger, slower |
| BAAI/bge-small-en-v1.5 | ~33M | 384 | ~63 | Competitive, fast | English-focused |
| BAAI/bge-large-en-v1.5 | ~335M | 1024 | ~66-67 | Near SOTA, great for RAG | Resource-intensive |
| thenlper/gte-small | ~33M | 384 | ~62 | Versatile, solid performance | Less specialized |
| thenlper/gte-large | ~335M | 1024 | ~65-66 | High accuracy, good for RAG | Slower inference |
| facebook/dpr-ctx_encoder | ~110M | 768 | ~60-62 | Tailored for retrieval | Older, outperformed by newer |