multi-qa-mpnet-base-dot-v1

multi-qa-mpnet-base-dot-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 | 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, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We developed this model as part of the project: Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well