snowflake-arctic-embed2

Snowflake 的前沿嵌入模型。Arctic Embed 2.0 增加了多语言支持，同时不牺牲英语性能或可扩展性。

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5de93a84837d · 1.2GB

Apache License Version 2.0, January 200

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自述文件

Snowflake 很高兴地宣布推出 Arctic Embed 2.0，这是我们前沿嵌入模型的下一次迭代，现在支持多语言搜索。虽然我们之前的版本受到了客户、合作伙伴和开源社区的广泛好评，并获得了数百万次下载，但我们一直收到一个请求：你们能否让这个模型支持多语言？Arctic Embed 2.0 基于我们之前版本的强大基础，增加了多语言支持，同时不牺牲英语性能或可扩展性，以满足更广泛的用户群体的需求，这些用户群体涵盖多种语言和应用。

图 1. 参数少于 1B 的开源多语言嵌入模型的单向量密集检索性能。分数是 MTEB 检索和 CLEF（ELRA，2006 年）子集（涵盖英语、法语、西班牙语、意大利语和德语）的平均 nDCG@10。

Arctic Embed 2.0 多样化且强大的功能集

企业级吞吐量和效率： Arctic Embed 2.0 模型专为大规模企业需求而构建。即使是我们的“大型”模型，参数也远低于 1B，并提供快速、高吞吐量的嵌入功能。根据内部测试，它可以在 NVIDIA A10 GPU 上轻松处理每秒超过 100 个文档（平均），并实现低于 10 毫秒的查询嵌入延迟，从而可以在经济实惠的硬件上进行实际部署。
对英语和非英语检索的毫不妥协的质量： 尽管尺寸紧凑，但两个 Arctic Embed 2.0 模型都在各种英语和非英语基准数据集上实现了令人印象深刻的 NDCG@10 分数，证明了即使对于训练方案中未包含的语言也具有良好的泛化能力。这些令人印象深刻的基准分数使 Arctic Embed 2.0 成为前沿检索模型的领导者。
通过 Matryoshka 表示学习 (MRL) 实现可扩展检索： Arctic Embed 2.0 版本包括 Arctic Embed 1.5 中引入的对量化友好的 MRL 功能，允许用户在对大型数据集执行搜索时降低成本并优化规模。通过两种模型大小，用户可以使用每个向量少至 128 字节（比 OpenAI 流行的 text-embedding-3-large model1 的未压缩嵌入小 96 倍）实现高质量检索。就像 Arctic Embed 1.5 一样，Arctic Embed 2.0 模型在压缩机制中也优于几个支持 MRL 的同类模型，质量下降幅度大大降低，基准分数更高。
真正开源： Arctic Embed 2.0 模型在宽松的 Apache 2.0 许可下发布。

Snowflake is excited to announce the release of Arctic Embed 2.0, the next iteration of our frontier embedding models, which now empower multilingual search. While our previous releases have been well received by our customers, partners and the open source community, leading to millions of downloads, we have consistently received one request: Can you make this model multilingual? Arctic Embed 2.0 builds on the robust foundation of our previous releases, adding multilingual support without sacrificing English performance or scalability, to address the needs of an even broader user base that spans a wide range of languages and applications.

![Snowflake data](/assets/library/snowflake-arctic-embed2/0546501b-9897-4145-af38-1b352fafb89c)
Figure 1. Single-vector dense retrieval performance of open source multilingual embedding models with fewer than 1B parameters. Scores are average nDCG@10 on MTEB Retrieval and the subset of CLEF (ELRA, 2006) covering English, French, Spanish, Italian and German.

### The diverse and powerful feature set of Arctic Embed 2.0
1. **Enterprise-ready throughput and efficiency:** The Arctic Embed 2.0 models are built for large-scale enterprise demands. Even our “large” model weighs in well under 1B parameters and delivers fast, high-throughput embedding capabilities. Based on internal testing, it easily handles more than 100 documents per second (on average) on NVIDIA A10 GPUs and achieves sub-10ms query embedding latency, enabling practical deployment on budget-friendly hardware.
2. **Uncompromising quality for English and non-English retrieval:** Despite their compact sizes, both Arctic Embed 2.0 models achieve impressive NDCG@10 scores across a variety of English and non-English benchmark data sets, demonstrating a capability to generalize well even to languages not included in the training recipe. These impressive benchmark scores position Arctic Embed 2.0 as a leader among frontier retrieval models.
3. **Enabling scalable retrieval through Matryoshka Representation Learning (MRL):** The Arctic Embed 2.0 release includes the same quantization-friendly MRL functionality introduced in Arctic Embed 1.5, allowing users to reduce cost and optimize scale when performing searches over large data sets. With both model sizes, users can achieve high-quality retrieval with as few as 128 bytes per vector (96x smaller than uncompressed embeddings from OpenAI’s popular text-embedding-3-large model1). Just like Arctic Embed 1.5, the Arctic Embed 2.0 models also outshine several MRL-supporting peers with substantially lower quality degradation and higher benchmark scores in the compressed regime.
4. **Truly open source:** The Arctic Embed 2.0 models are released under the permissive Apache 2.0 license.

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