all mpnet base v2 This is a sentence transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or 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 right 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. Background The project aims to train sentence embedding models on very large sentence level datasets using a self supervised contrastive learning objective. We used the pretrained microsoft/mpnet base model and fine tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a s…
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