Need embedding model for multilingual semantic search

For multilingual semantic search, the best choice is usually a sentence embedding model trained for cross-lingual retrieval, not a general text generator.

Good options

1. BAAI/bge-m3

   • Strong multilingual performance
   • Good for semantic search and retrieval
   • Works well across many languages
   • Popular default choice today

2. intfloat/multilingual-e5-large

   • Very solid multilingual embeddings
   • Great for search, retrieval, and RAG
   • Needs query/passage style formatting for best results:
     • Query: query: ...
     • Docs: passage: ...

3. sentence-transformers/paraphrase-multilingual-mpnet-base-v2

   • Older but reliable
   • Easier to use
   • Good baseline for many languages

My recommendation

• Best overall: BAAI/bge-m3
• Best if you want a simple, proven retrieval model: intfloat/multilingual-e5-large
• Best lightweight baseline: paraphrase-multilingual-mpnet-base-v2

Things to consider

• Language coverage: Make sure your target languages are supported well
• Embedding dimension / speed: Larger models are more accurate but slower
• Search type: For semantic search, use cosine similarity or dot product depending on the model guidance
• Indexing: Normalize embeddings if recommended by the model

Example usage pattern

• Embed all documents
• Embed the user query
• Retrieve nearest vectors via FAISS / Milvus / Pinecone / Elasticsearch / OpenSearch

If you want, I can also give you:

• a ranked list by accuracy vs speed
• a Python example
• a recommendation for your exact languages (e.g., English + Hindi + Arabic, etc.)