msmarco-cotmae-MiniLM-L12_en-ko-ja
| Property | Value |
|---|---|
| Author | sangmini |
| Downloads | 39,856 |
| Output Dimension | 1536 |
| Framework | PyTorch |
What is msmarco-cotmae-MiniLM-L12_en-ko-ja?
This is a sophisticated sentence transformer model designed for multilingual text processing, specifically optimized for English, Korean, and Japanese languages. Built on the BERT architecture, it converts sentences and paragraphs into high-dimensional vector representations (1536 dimensions), enabling powerful semantic search and clustering capabilities.
Implementation Details
The model utilizes a three-component architecture: a Transformer layer based on BERT, a Pooling layer, and a Dense layer. It was trained using MSE Loss with AdamW optimizer over 10 epochs, featuring a learning rate of 1e-05 and warmup steps optimization.
- Maximum sequence length: 128 tokens
- Word embedding dimension: 384
- Final output dimension: 1536
- Pooling strategy: Mean tokens
Core Capabilities
- Multilingual sentence embedding generation
- Semantic similarity computation
- Cross-lingual text matching
- Document clustering
- Information retrieval across languages
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle three major Asian and Western languages (English, Korean, and Japanese) while producing high-dimensional embeddings makes it particularly valuable for cross-lingual applications and semantic search systems.
Q: What are the recommended use cases?
The model excels in multilingual document similarity matching, semantic search implementations, content clustering, and cross-lingual information retrieval systems. It's particularly useful for applications requiring understanding of semantic relationships across English, Korean, and Japanese content.
