paraphrase-multilingual-MiniLM-L12-v2-onnx-Q
| Property | Value |
|---|---|
| Author | Qdrant |
| Model Type | Quantized ONNX |
| Original Model | paraphrase-multilingual-MiniLM-L12-v2 |
| Primary Use | Text Embeddings & Similarity Search |
What is paraphrase-multilingual-MiniLM-L12-v2-onnx-Q?
This model is a quantized ONNX port of the popular paraphrase-multilingual-MiniLM-L12-v2, specifically optimized for efficient text classification and similarity searches. Created by Qdrant, it maintains the multilingual capabilities of the original model while offering improved performance through quantization.
Implementation Details
The model is implemented using the ONNX format, making it highly portable and efficient for production deployments. It can be easily integrated using FastEmbed, a Python library for text embeddings.
- Quantized architecture for reduced model size and faster inference
- ONNX format for cross-platform compatibility
- Compatible with FastEmbed library for simple implementation
- Supports multilingual text processing
Core Capabilities
- Text embedding generation for multiple languages
- Efficient similarity search operations
- Document classification and comparison
- Optimized for production environments
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its quantized ONNX format, which offers improved performance while maintaining the multilingual capabilities of the original MiniLM model. It's specifically optimized for production environments where efficiency is crucial.
Q: What are the recommended use cases?
The model is ideal for applications requiring multilingual text similarity searches, document classification, and semantic text matching. It's particularly well-suited for production environments where resource efficiency is important.
