paraphrase-multilingual-MiniLM-L12-v2
by sentence-transformers
Lightweight multilingual sentence embeddings across 50+ languages
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2Overview
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.
Key Capabilities
- •384-dim multilingual sentence embeddings (50+ languages)
- •Cross-lingual semantic similarity and clustering
- •Fast, small, CPU-friendly
- •Drop-in Sentence-Transformers API
Use Cases on Mixpeek
- •Multilingual semantic search over metadata and transcripts
- •Clustering and dedup of multilingual text at low cost
- •Cheap first-stage recall before a reranker
- •Tagging and routing of international support content
Tags
Use paraphrase-multilingual-MiniLM-L12-v2 on Mixpeek
Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.
Open StudioHow It Runs on Mixpeek
On Mixpeek, paraphrase-multilingual-MiniLM-L12-v2 runs as a managed extractor inside a processing pipeline. Point a bucket of sentence similarity data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.
Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.
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Specification
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