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Model Overview

Description

snowflake-arctic-embed is a suite of text embedding models that creates high-quality retrieval models optimized for performance. These models are ready for commercial use free-of-charge.

The snowflake-arctic-embedding models achieve state-of-the-art performance on the MTEB/BEIR leaderboard for each of their size variants. As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.

The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. Following pretraining, models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Name	MTEB Retrieval Score (NDCG @ 10)	Parameters (Millions)	Embedding Dimension
snowflake-arctic-embed-xs	50.15	22	384
snowflake-arctic-embed-s	51.98	33	384
snowflake-arctic-embed-m	54.90	110	768
snowflake-arctic-embed-m-long	54.83	137	768
snowflake-arctic-embed-l	55.98	335	1024

Based on the intfloat/e5-large-unsupervised model, the large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model Name	MTEB Retrieval Score (NDCG @ 10)
snowflake-arctic-embed-l	55.98
Google-gecko-text-embedding	55.7
text-embedding-3-large	55.44
Cohere-embed-english-v3.0	55.00
bge-large-en-v1.5	54.29
UAE-Large-V1	54.66
bge-large-en-v1.5	54.29
mxbai-embed-large-v1	54.39
e5-Large-v2	50.56

Terms of use

Arctic is licensed under the Apache-2.

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

References

HuggingFace

Github

Blog post

Model Architecture

Architecture Type: Transformer
\nNetwork Architecture: Fine-tuned E5-Large-Unsupervised Retriever

Input

Input Type: Text
\nInput Format: List of strings

Output

Output Type: Floating Points
\nOutput Format: list of float arrays
\nOther Properties Related to Output: Each array contains the embeddings for the corresponding input string.

Model Version

snowflake-arctic-embed-l\n

Supported Operating System(s):

Linux

Training Dataset:

Properties (Quantity, Dataset Descriptions, Sensor(s)): Pretrained on large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data.

Inference:

Engine: TensorRT-LLM with Triton
\nTest Hardware: L40

Get Help

NVIDIA Developer Community Forum

For support, Visit the NVIDIA Developer Community Forum

End of Support — \"This artifact is no longer supported. NVIDIA strongly recommends artifacts that are up to date and supported\"

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These models are ready for commercial use free-of-charge.\n\nThe snowflake-arctic-embedding models achieve state-of-the-art performance on the MTEB/BEIR leaderboard for each of their size variants. As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.\n\nThe models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. Following pretraining, models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy.\n\n| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |\n| :----------------------------------------------------------------------------------------------- | :------------------------------- | :-------------------- | :------------------ |\n| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 |\n| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 |\n| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 |\n| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 |\n| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 |\n\nBased on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, the large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| :------------------------------------------------------------------------------------- | :------------------------------- |\n| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |\n| Google-gecko-text-embedding | 55.7 |\n| text-embedding-3-large | 55.44 |\n| Cohere-embed-english-v3.0 | 55.00 |\n| bge-large-en-v1.5 | 54.29 |\n| UAE-Large-V1 | 54.66 |\n| bge-large-en-v1.5 | 54.29 |\n| mxbai-embed-large-v1 | 54.39 |\n| e5-Large-v2 | 50.56 |\n\n### Terms of use\n\nArctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0).\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### References\n\n[HuggingFace](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)\n\n[Github](https://github.com/Snowflake-Labs/arctic-embed)\n\n[Blog post](https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/)\n\n## Model Architecture\n\nArchitecture Type: Transformer\\\nNetwork Architecture: Fine-tuned E5-Large-Unsupervised Retriever\n\n## Input\n\nInput Type: Text\\\nInput Format: List of strings\n\n## Output\n\nOutput Type: Floating Points\\\nOutput Format: list of float arrays\\\nOther Properties Related to Output: Each array contains the embeddings for the corresponding input string.\n\n## Model Version\n\n snowflake-arctic-embed-l\n\n## Supported Operating System(s):\n\n- Linux\n\n## Training Dataset:\n\nProperties (Quantity, Dataset Descriptions, Sensor(s)): Pretrained on large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data.\n\n# Inference:\n\nEngine: [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) with [Triton](https://developer.nvidia.com/triton-inference-server)\\\nTest Hardware: L40\n\n## Get Help\n\n### NVIDIA Developer Community Forum\n\nFor support, Visit the [NVIDIA Developer Community Forum](https://forums.developer.nvidia.com/)\n\n**End of Support** — \"This artifact is no longer supported. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602],[549,550,551,552,553],{"_388":463,"_223":463,"_165":166,"_361":573},{"_388":437,"_223":437,"_165":166,"_361":568},{"_388":563,"_223":563,"_165":166,"_361":564},{"_388":558,"_223":558,"_165":166,"_361":559},{"_388":554,"_223":554,"_165":166,"_361":555},"nvidia.partners",[556],{"_77":557,"_394":554,"_395":30,"_165":431},"edge","app.hushly.com",[560],{"_77":561,"_394":558,"_395":30,"_165":562},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[565],{"_77":566,"_394":563,"_395":30,"_165":567},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. 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