Snowflake Arctic Embed L
| Property | Value |
|---|---|
| Parameter Count | 334M |
| Embedding Dimension | 1024 |
| License | Apache 2.0 |
| Paper | Technical Report |
| Base Model | e5-large-unsupervised |
What is snowflake-arctic-embed-l?
Snowflake-arctic-embed-l is a state-of-the-art text embedding model that achieves superior performance in retrieval tasks, surpassing both open-source and closed-source alternatives with an MTEB Retrieval Score (NDCG@10) of 55.98. Built on the e5-large-unsupervised architecture, this model represents the largest variant in Snowflake's arctic-embed family.
Implementation Details
The model employs a multi-stage training pipeline, initially leveraging 400M samples of mixed public and proprietary web search data. It's further refined through focused training on 1M carefully curated triplets of query-positive-negative document samples. The architecture produces 1024-dimensional embeddings and supports a context window of 512 tokens.
- Multi-stage training with large-scale pretraining and focused fine-tuning
- Hard negative mining for optimal retrieval performance
- Optimized for both accuracy and practical deployment
- Compatible with popular frameworks including Sentence Transformers and Hugging Face
Core Capabilities
- State-of-the-art retrieval performance (55.98 NDCG@10)
- Robust text embedding generation for search and retrieval tasks
- Direct replacement for closed-source embedding APIs
- Efficient processing with 512 token context window
Frequently Asked Questions
Q: What makes this model unique?
The model achieves industry-leading retrieval performance through its innovative training approach and careful optimization, making it a viable alternative to closed-source solutions while maintaining full transparency and customization possibilities.
Q: What are the recommended use cases?
The model excels in enterprise search, document retrieval, semantic similarity tasks, and any application requiring high-quality text embeddings. It's particularly well-suited for production environments requiring state-of-the-art accuracy.
