paraphrase-multilingual-MiniLM-L12-v2

Name: paraphrase-multilingual-MiniLM-L12-v2
Author: sentence-transformers

by sentence-transformers

Lightweight multilingual sentence embeddings across 50+ languages

118Mparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

Feature URI

Overview

This Sentence-Transformers model maps sentences and short passages from 50+ languages into a shared 384-dimensional space, so semantically similar text clusters together regardless of language. It is small, fast, and CPU-friendly, which makes it a popular default for multilingual semantic search and clustering when latency and cost matter more than top-of-leaderboard accuracy.

On Mixpeek, it is a text embedding extractor for metadata, transcripts, captions, and document chunks: useful when content spans many languages and you need cheap, fast recall.

Architecture

12-layer multilingual MiniLM encoder fine-tuned with a paraphrase/contrastive objective and mean-pooled to a 384-dim sentence embedding. Distilled for speed, so it runs well on CPU.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so paraphrase-multilingual-MiniLM-L12-v2 runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
  "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      collection_id: "col_your_collection",
      documents: [
        {
          document_id: "asset-00412",
          // The vector name has to match a vector index on the collection.
          vectors: { "text-embedding": yourVector },
          payload: { source_key: "archive/2026/asset-00412" },
        },
      ],
    }),
  },
);

// Managed alternative, if this exact model is not the requirement:
// text_extractor@v1 runs intfloat/multilingual-e5-large-instruct
// (1024-d) over a bucket, with no inference of your own.