Kanana-nano-2.1b-embedding
| Property | Value |
|---|---|
| Parameter Count | 2.1B |
| License | CC-BY-NC-4.0 |
| Author | Kakaocorp |
| Paper | arXiv:2502.18934 |
What is kanana-nano-2.1b-embedding?
Kanana-nano-2.1b-embedding is a specialized bilingual embedding model designed for effective text similarity and retrieval tasks in both Korean and English. As part of the larger Kanana model series developed by Kakao, it represents a compute-efficient approach to bilingual language modeling, achieving impressive performance particularly for Korean language tasks.
Implementation Details
The model utilizes advanced pre-training techniques including high-quality data filtering, staged pre-training, and depth up-scaling. It's specifically optimized for embedding generation, achieving 65% accuracy on Korean benchmarks and 51.56% on English tasks, outperforming several comparable models in its size range.
- Efficient compute architecture optimized for bilingual processing
- Specialized for text similarity and retrieval tasks
- Implements advanced embedding generation techniques
- Supports batch processing through DataLoader functionality
Core Capabilities
- Generates high-quality text embeddings for both Korean and English
- Supports variable length inputs up to 512 tokens
- Provides efficient batch processing capabilities
- Optimized for retrieval-based applications
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional performance in Korean language tasks while maintaining competitive performance in English, all within a compute-efficient 2.1B parameter architecture. It's specifically designed for embedding generation and retrieval tasks, making it ideal for bilingual applications.
Q: What are the recommended use cases?
The model is best suited for text similarity search, document retrieval, and question-answering systems that require strong bilingual capabilities, particularly in Korean-English contexts. It's optimized for generating embeddings that can be used for semantic search and retrieval tasks.
