paraphrase-multilingual-MiniLM-L12-v2

Maintained By
sentence-transformers

paraphrase-multilingual-MiniLM-L12-v2

PropertyValue
Parameter Count118M parameters
LicenseApache 2.0
Research PaperSentence-BERT Paper
Supported Languages50+ languages
Output Dimensions384

What is paraphrase-multilingual-MiniLM-L12-v2?

This is a powerful multilingual sentence transformer model designed to create semantic embeddings for text in over 50 languages. It converts sentences and paragraphs into 384-dimensional dense vector representations, making it particularly effective for tasks like semantic search, clustering, and similarity comparison across multiple languages.

Implementation Details

The model utilizes a MiniLM architecture with 12 layers and implements mean pooling on top of contextualized word embeddings. It's built using the sentence-transformers framework and supports multiple deep learning frameworks including PyTorch, ONNX, and TensorFlow.

  • 384-dimensional dense vector output
  • Efficient architecture with 118M parameters
  • Support for 50+ languages including major European, Asian, and Middle Eastern languages
  • Compatible with multiple deep learning frameworks

Core Capabilities

  • Cross-lingual sentence embedding generation
  • Semantic similarity comparison
  • Document clustering
  • Information retrieval across languages
  • Parallel text mining

Frequently Asked Questions

Q: What makes this model unique?

The model's ability to handle 50+ languages while maintaining a relatively compact size (118M parameters) makes it particularly unique. It offers an excellent balance between performance and resource efficiency, making it suitable for production deployments.

Q: What are the recommended use cases?

This model excels in multilingual applications requiring semantic understanding, such as cross-lingual information retrieval, document similarity matching, clustering of multilingual content, and building semantic search engines that work across multiple languages.

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