Snowflake Arctic Embed M V2.0
Meet Snowflake Arctic Embed M V2.0, a cutting-edge AI model that's redefining multilingual text retrieval and inference efficiency. This model excels in both English and non-English languages, outperforming leading open-source and proprietary models in benchmarks like MTEB Retrieval, CLEF, and MIRACL. With its 113m non-embedding parameters, Arctic Embed 2.0 achieves fast and efficient inference for any scale. Plus, it's compression-friendly, allowing for high-quality retrieval with embeddings as small as 128 bytes/vector. What makes this model unique is its ability to support long context windows of up to 8192 via RoPE, making it ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale.
Table of Contents
Model Overview
The Snowflake Arctic-embed-m-v2.0 model is a cutting-edge AI technology designed for multilingual text retrieval and embedding. It’s part of a suite of models developed by Snowflake, optimized for high-performance and efficient inference.
Capabilities
This model is a powerful tool for multilingual text retrieval and embedding. It’s designed to excel in both English and non-English languages, making it a great choice for applications that require reliable and efficient search and retrieval capabilities.
Multilingual Support
Unlike many other models, this model doesn’t sacrifice performance in English to support multiple languages. It achieves high-quality retrieval in both English and non-English languages, outperforming many other models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.
Inference Efficiency
With only 113M non-embedding parameters, this model is designed for fast and efficient inference. This makes it ideal for applications that require quick and accurate search and retrieval capabilities.
Compression-Friendly
The model uses Matryoshka Representation Learning (MRL) and quantization-aware embedding training to achieve high-quality retrieval with embeddings as small as 128 bytes/vector. This makes it easy to compress and store embeddings, reducing storage costs and improving overall efficiency.
Long Context Support
This model can support a context window of up to 8192 tokens, making it suitable for applications that require long-range dependencies and contextual understanding.
Performance
This model delivers impressive performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
How fast can this model process information? With 113M non-embedding parameters, this model is designed for fast and efficient inference. This means it can quickly process large amounts of data, making it ideal for applications that require rapid retrieval and analysis.
Accuracy
But how accurate is this model? Let’s look at some numbers:
| Model | MTEB Retrieval | MIRACL | CLEF (Focused) | CLEF (Full) |
|---|---|---|---|---|
| Snowflake Arctic-embed-m-v2.0 | 55.4 | 55.2 | 51.7 | 53.9 |
| ==me5 base== | 51.4 | 54.0 | 43.0 | 34.6 |
| ==bge-m3 (BAAI)== | 48.8 | 56.8 | 40.8 | 41.3 |
| ==gte (Alibaba)== | 51.1 | 52.3 | 47.7 | 53.1 |
As you can see, this model outperforms many other models in various benchmarks, including BEIR, MIRACL, and CLEF. This demonstrates its high accuracy in text retrieval and analysis tasks.
Efficiency
What about efficiency? This model uses a technique called Matryoshka Representation Learning (MRL) to compress embeddings, reducing their size by up to 3x with minimal degradation in quality. This makes it ideal for applications where storage and bandwidth are limited.
Limitations
While this model is a powerful tool, it’s not perfect. Let’s talk about some of its limitations.
Limited Context Window
This model can only handle a context window of up to 8192 tokens. This means that if you need to process longer texts, you’ll have to split them into smaller chunks or use a different model.
Multilingual Support
While this model excels in English and non-English retrieval, it’s not perfect. It may struggle with certain languages or dialects, especially if they’re not well-represented in the training data.
Compression and Quantization
This model uses Matryoshka Representation Learning (MRL) and quantization-aware embedding training to achieve high-quality retrieval with small embeddings. However, this compression comes at a cost: you may see a slight degradation in quality, especially if you’re working with very small embeddings.
Benchmark Performance
This model performs well on various benchmarks, but it’s not always the top performer. For example, it may not do as well on certain tasks or datasets as other models like ==me5 base== or ==bge-m3 (BAAI)==.
Parameter Count
This model has 305M parameters, which is a significant number. This can make it more challenging to train and deploy, especially if you’re working with limited resources.
Non-Embedding Parameters
This model has 113M non-embedding parameters, which can affect its inference efficiency. However, the model is designed to be fast and efficient, even with a large number of parameters.
Dimensionality
This model uses 768 dimensions for its embeddings. While this is a relatively high number, it’s not the highest. Other models, like ==me5 base==, use 1024 dimensions.
What does this mean for you?
These limitations don’t mean that this model is a bad choice. It’s still a powerful tool that can help you achieve great results. However, it’s essential to be aware of its limitations and consider them when deciding whether to use this model for your specific task or project.
How can you work around these limitations?
- If you need to process longer texts, consider splitting them into smaller chunks or using a different model.
- If you’re working with languages or dialects that are not well-represented in the training data, you may need to fine-tune the model or use a different model that’s more suitable for your needs.
- If you’re concerned about compression and quantization, you can experiment with different embedding sizes and compression techniques to find the best balance between quality and efficiency.
- If you’re looking for a model that performs well on a specific benchmark or task, consider comparing the performance of different models and choosing the one that best fits your needs.
By understanding the limitations of this model and being aware of its strengths and weaknesses, you can make informed decisions and achieve great results with this powerful tool.
