These models were tuned to be used with dot-product:
| Model | Performance Semantic Search (6 Datasets) | Queries (GPU / CPU) per sec. | 
| --- | :---: | :---: |
| multi-qa-MiniLM-L6-dot-v1 | 49.19 | 18,000 / 750 |
| multi-qa-distilbert-dot-v1 | 52.51  | 7,000 / 350 |
| multi-qa-mpnet-base-dot-v1 | 57.60 | 4,000 / 170 |
These models produce normalized vectors of length 1, which can be used with dot-product, cosine-similarity and Euclidean distance:
| Model | Performance Semantic Search (6 Datasets) | Queries (GPU / CPU) per sec. | 
| --- | :---: | :---: |
| multi-qa-MiniLM-L6-cos-v1 | 51.83 | 18,000 / 750 |
| multi-qa-distilbert-cos-v1 |  52.83 | 7,000 / 350 |
| multi-qa-mpnet-base-cos-v1 | 57.46 | 4,000 / 170 |
MSMARCO Passage Models
The MSMARCO Passage Ranking Dataset contains 500k real queries from Bing search together with the relevant passages from various web sources. Given the diversity of the MSMARCO dataset, models also perform well on other domains. 
