e5-small-korean
| Property | Value |
|---|---|
| Model Type | Sentence Transformer |
| Base Model | intfloat/multilingual-e5-small |
| Output Dimensions | 384 |
| Max Sequence Length | 512 tokens |
| Performance (STS) | 0.848 Pearson correlation |
What is e5-small-korean?
e5-small-korean is a specialized sentence transformer model fine-tuned on Korean STS (Semantic Textual Similarity) and NLI (Natural Language Inference) tasks. Built upon the multilingual E5-small architecture, it's specifically optimized for Korean language understanding, capable of converting text into 384-dimensional dense vector representations.
Implementation Details
The model utilizes a two-component architecture consisting of a transformer encoder followed by a pooling layer. It processes input text with a maximum sequence length of 512 tokens and employs mean pooling to generate fixed-size embeddings. The model achieves impressive performance on semantic similarity tasks, with a Pearson correlation of 0.848 on the Korean STS development set.
- Transformer-based architecture with mean pooling strategy
- 384-dimensional output embeddings
- Optimized for Korean language understanding
- Supports various similarity metrics (cosine, manhattan, euclidean)
Core Capabilities
- Semantic textual similarity analysis
- Semantic search implementation
- Text classification and clustering
- Paraphrase mining
- Cross-lingual text matching
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized optimization for Korean language processing, while maintaining the efficient architecture of E5-small. Its strong performance on Korean STS tasks (0.848 Pearson correlation) makes it particularly valuable for Korean NLP applications.
Q: What are the recommended use cases?
The model excels in applications requiring semantic understanding of Korean text, such as document similarity comparison, semantic search engines, content recommendation systems, and automated text classification. It's particularly suitable for projects requiring efficient computation due to its relatively compact 384-dimensional embeddings.
