The Need for a Better Model
Text embedding is vital in modern AI workloads, especially for enterprise search and retrieval systems. However, traditional text embedding models have limitations like subpar retrieval performance, high latency, and lack of scalability. These issues affect user experience and real – world enterprise deployment. Existing models struggle to provide consistent high – quality retrieval across various tasks such as classification, clustering, etc. Also, inefficient sampling and hard – negative mining lead to poor model quality. Relying on externally initialized models may not meet enterprise needs. Thus, there is a strong demand for improved text embedding models, and Snowflake’s Arctic embed family aims to address these challenges.
Beyond Benchmarks
The Snowflake Arctic embed models are tailored for real – world search and retrieval workloads. They leverage advanced research and proprietary search knowledge to outperform previous state – of – the – art models across all embedding variants. These models come in different context window sizes and model sizes, with the largest having 334 million parameters. Evaluated on the Massive Text Embedding Benchmark (MTEB), as of April 2024, each Snowflake model ranks first among similar – sized embedding models, demonstrating their excellence for real – world tasks.
Integration Made Easy
One of the key features of Snowflake Arctic embed models is their seamless integration with existing search stacks. Available on Hugging Face under an Apache 2 license, they can be integrated into enterprise search systems with just a few lines of Python code. This ease of integration allows organizations to enhance their search capabilities without much overhead or complexity, making it straightforward to incorporate these advanced models into their search infrastructure.
Under the Hood of Success
The technical superiority of Snowflake’s text – embedding models is due to a combination of web – searching techniques and state – of – the – art research. They use improved sampling strategies and competence – aware hard – negative mining, resulting in significant quality improvements. Building on initialized models like bert – base – uncased, these models, along with web search data and iterative improvements, have led to state – of – the – art embedding models that surpass previous benchmarks.
A Commitment to the Future
Snowflake is committed to continuous development and collaboration in text embedding models. The release of the Arctic embed family is just the start. Leveraging its search expertise from the Neeva acquisition and Snowflake’s Data Cloud processing power, the company plans to expand the types of models and targeted workloads. It is also developing new benchmarks and welcomes community collaboration and suggestions to further enhance its models.
In conclusion, Snowflake’s Arctic embed family represents a significant advancement in text embedding technology. These models achieve state – of – the – art retrieval performance, outperforming larger closed – source models. Their ability to power real – world retrieval, reduce latency, and lower costs, along with their availability in various sizes, showcases Snowflake’s dedication to providing top – notch models for enterprise use cases. The future development of these models is eagerly anticipated.
