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MODEL Listed

paraphrase-multilingual-mpnet-base-v2

The paraphrase-multilingual-mpnet-base-v2 is a robust sentence-embedding model designed for cross-lingual semantic similarity tasks. Built on the MPNet architecture, it maps sentences from over 50 different languages into a shared vector space, ensuring that semantically identical phrases maintain proximity regardless of the input language. For developers, this is a practical tool for building multilingual search engines, clustering diverse datasets, or implementing efficient RAG (Retrieval-Augmented Generation) pipelines where queries and documents may be in different languages. It offers a strong balance between latency and accuracy, outperforming basic BERT-based embeddings in nuance and alignment, and integrates seamlessly into any pipeline supporting the sentence-transformers library.

sentence-transformerssentence similarity
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01 / MODEL CARD

Model card

The paraphrase-multilingual-mpnet-base-v2 is a robust sentence-embedding model designed for cross-lingual semantic similarity tasks. Built on the MPNet architecture, it maps sentences from over 50 different languages into a shared vector space, ensuring that semantically identical phrases maintain proximity regardless of the input language. For developers, this is a practical tool for building multilingual search engines, clustering diverse datasets, or implementing efficient RAG (Retrieval-Augmented Generation) pipelines where queries and documents may be in different languages. It offers a strong balance between latency and accuracy, outperforming basic BERT-based embeddings in nuance and alignment, and integrates seamlessly into any pipeline supporting the sentence-transformers library.

Model typesentence similarity
Providersentence-transformers
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model sentence-transformers/paraphrase-multilingual-mpnet-base-v2
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model sentence-transformers/paraphrase-multilingual-mpnet-base-v2 README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/sentence-transformers/paraphrase-multilingual-mpnet-base-v2.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/sentence-transformers/paraphrase-multilingual-mpnet-base-v2.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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