| multi-qa-MiniLM-L6-cos-v1 | | 65.55 | 64.66 | 18,000 / 750 | 2,800 / 180 
| multi-qa-distilbert-cos-v1 | | 67.59 | 66.46 | 7,000 / 350 | 1,100 / 70
| multi-qa-mpnet-base-cos-v1 | | 67.78 | 69.87 | 4,000 / 170 |  540 / 30
| Models tuned for dot-product | |
| msmarco-distilbert-base-tas-b | 34.43 | 71.04 | 69.78  | 7,000 / 350 | 1100 / 70
| msmarco-distilbert-dot-v5 | 37.25 | 70.14 | 71.08 | 7,000 / 350 | 1100 / 70
| msmarco-bert-base-dot-v5 | 38.08 | 70.51 | 73.45 | 4,000 / 170 |  540 / 30
| multi-qa-MiniLM-L6-dot-v1 | | 66.70 | 65.98 | 18,000 / 750 | 2,800 / 180 
| multi-qa-distilbert-dot-v1 | | 68.05 | 70.49 | 7,000 / 350 | 1,100 / 70
| multi-qa-mpnet-base-dot-v1 | | 70.66 | 71.18 | 4,000 / 170 |  540 / 30
Notes:
- We provide two type of models: One that produces normalized embedding and can be used with dot-product, cosine-similarity or euclidean distance (all three scoring function will produce the same results). The models tuned for dot-product will produce embeddings of different lengths and must be used with dot-product to find close items in a vector space.
