snowflake-arctic-embed2:568m-l-fp16

snowflake-arctic-embed2

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

嵌入 568m

629 次拉取更新于昨天

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昨天

5de93a84837d · 1.2GB

Apache 许可证版本 2.0，200 年 1 月

11kB

自述文件

Snowflake 欣然宣布发布 Arctic Embed 2.0，这是我们前沿嵌入模型的下一代迭代，它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欢迎，导致数百万次下载，但我们始终收到一个请求：你能让这个模型支持多语言吗？Arctic Embed 2.0 在我们先前版本强大的基础上构建，增加了多语言支持，而不会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，这些用户群体的语言和应用程序范围广泛。

图 1. 参数少于 10 亿的开源多语言嵌入模型的单向量稠密检索性能。分数是 MTEB 检索和涵盖英语、法语、西班牙语、意大利语和德语的 CLEF（ELRA，2006）子集上的平均 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 模型是为大规模企业需求而构建的。即使是我们的“大型”模型，其参数也远低于 10 亿，并提供快速、高吞吐量的嵌入功能。根据内部测试，它在 NVIDIA A10 GPU 上轻松处理每秒超过 100 个文档（平均），并实现亚 10 毫秒的查询嵌入延迟，从而能够在经济实惠的硬件上进行实际部署。
对英语和非英语检索毫不妥协的质量：尽管 Arctic Embed 2.0 模型体积紧凑，但在各种英语和非英语基准数据集上都取得了令人印象深刻的 NDCG@10 分数，这证明了其即使对训练方案中未包含的语言也能很好地泛化的能力。这些令人印象深刻的基准分数使 Arctic Embed 2.0 成为前沿检索模型中的领导者。
通过 Matryoshka 表示学习 (MRL) 实现可扩展的检索：Arctic Embed 2.0 版本包含在 Arctic Embed 1.5 中引入的相同易于量化的 MRL 功能，允许用户在对大型数据集执行搜索时降低成本和优化规模。对于两种模型大小，用户都可以使用低至每向量 128 字节（比 OpenAI 流行的 text-embedding-3-large 模型1 的未压缩嵌入小 96 倍）的数据量实现高质量的检索。与 Arctic Embed 1.5 一样，Arctic Embed 2.0 模型在压缩状态下也优于许多支持 MRL 的同类产品，质量下降幅度明显更低，基准分数更高。
真正的开源：Arctic Embed 2.0 模型在宽松的 Apache 2.0 许可证下发布。

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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