Model reference · open weights

paraphrase-mpnet

paraphrase-mpnet is an open-weight embedding model from sentence-transformers, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.

Embeddings sentence-transformers 1 variants 1.9M downloads/mo
Request this model on EU hardware All served models Not on the shared API today — deployed on request.

About

What paraphrase-mpnet is

sentence-transformers/paraphrase-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. Full Model Architecture Citing & Authors This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

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

Specifications

What it is

Makersentence-transformers
TypeEmbedding models
Parameters (lead)109M
Context514 tokens
Variants1
Runs withsentence-transformers
Released2022-03-02
Popularity1.9M downloads / month
Likes50
LicenceOpen weights

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together — the basis of search and RAG.

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.

VariantParamsPrecisionVRAMFits 16 GBWeights
paraphrase-mpnet-base-v2109MBF16~0.3 GBWeights ↗

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys paraphrase-mpnet for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (paraphrase-mpnet 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":"paraphrase-mpnet","input":"text to embed"}'

Details

Languages, data & research

Tags

sentence-transformers pytorch tf onnx safetensors openvino mpnet feature-extraction sentence-similarity transformers text-embeddings-inference doi:10.57967/hf/2004 endpoints_compatible

Papers

Licence

Open weights

Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗

Sources

Weights & code

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