multi qa MiniLM L6 dot v1 This is a sentence transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search . It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net Semantic Search Usage (Sentence Transformers) Using this model becomes easy when you have sentence transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling operation on top of the contextualized word embeddings. Technical Details In the following some technical details how this model must be used: Setting Value : : Dimensions 384 Produces normalized embeddings No Pooling Method CLS pooling Suitable score functions dot product (e.g. util.dot score ) Background The project aims to train sentence embedding models on very large sentence level datasets using a self supervised contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair…
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