multi qa mpnet base cos v1 This is a sentence transformers model: It maps sentences & paragraphs to a 768 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. Usage (Text Embeddings Inference (TEI)) Text Embeddings Inference (TEI) is a blazing fast inference solution for text embedding models. CPU: NVIDIA GPU: Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API: Or check the Text Embeddings Inference API specification instead. Technical Details In the following some technical details how this model must be used: Setting Value : : Dimensions 768 Produces normalized embeddings Yes Pooling…
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