multi-qa-mpnet-cos

About

What multi-qa-mpnet-cos is

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: Note: When loaded with sentence-transformers, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used. 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 as intervention from Google's Flax, JAX, and Cloud team members about efficient deep learning frameworks. Intended use

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys multi-qa-mpnet-cos for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (multi-qa-mpnet-cos below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"multi-qa-mpnet-cos","input":"text to embed"}'

Details

Languages, data & research

Licence

Commercial licence needed

Sources

Weights & code

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