Model reference · open weights
snowflake-arctic-embed-s is an open-weight embedding model from Snowflake, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
News 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions! 07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv. 07/18/2024: Release of snowflake-arctic-embed-m-v1.5, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the launch post on the Snowflake engineering blog. 05/10/2024: Release the technical report on Arctic Embed 04/16/2024: Release the snowflake-arctic-embed family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: Arctic-Text-Embed. Models snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance. The snowflake-arctic-embedding models achieve state-of-the-art performance on the MTEB/BEIR leaderboard for each of their size variants. Evaluation is performed using these scripts. 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. First, the models are trained with 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. 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. A detailed technical rep
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | Snowflake |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 33M |
| Context | 512 tokens |
| Variants | 1 |
| Runs with | sentence-transformers |
| Released | 2024-04-12 |
| Popularity | 577k downloads / month |
| Likes | 25 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| snowflake-arctic-embed-s | 33M | BF16 | ~0.1 GB | ✓ | Weights ↗ |
Benchmarks
As published on the model card — the maker's own numbers, not measured by AxForge.
| Task | Dataset | Metric | Score |
|---|---|---|---|
| Classification | MTEB AmazonCounterfactualClassification (en) | accuracy | 71.179 |
| Classification | MTEB AmazonCounterfactualClassification (en) | ap | 33.158 |
| Classification | MTEB AmazonCounterfactualClassification (en) | f1 | 64.862 |
| Classification | MTEB AmazonPolarityClassification | accuracy | 78.75 |
| Classification | MTEB AmazonPolarityClassification | ap | 72.832 |
| Classification | MTEB AmazonPolarityClassification | f1 | 78.64 |
| Classification | MTEB AmazonReviewsClassification (en) | accuracy | 38.264 |
| Classification | MTEB AmazonReviewsClassification (en) | f1 | 37.14 |
| Retrieval | MTEB ArguAna | map_at_1 | 32.646 |
| Retrieval | MTEB ArguAna | map_at_10 | 48.372 |
| Retrieval | MTEB ArguAna | map_at_100 | 49.207 |
| Retrieval | MTEB ArguAna | map_at_1000 | 49.214 |
| Retrieval | MTEB ArguAna | map_at_3 | 43.611 |
| Retrieval | MTEB ArguAna | map_at_5 | 46.601 |
| Retrieval | MTEB ArguAna | mrr_at_1 | 33.144 |
| Retrieval | MTEB ArguAna | mrr_at_10 | 48.557 |
| Retrieval | MTEB ArguAna | mrr_at_100 | 49.385 |
| Retrieval | MTEB ArguAna | mrr_at_1000 | 49.392 |
| Retrieval | MTEB ArguAna | mrr_at_3 | 43.777 |
| Retrieval | MTEB ArguAna | mrr_at_5 | 46.792 |
| Retrieval | MTEB ArguAna | ndcg_at_1 | 32.646 |
| Retrieval | MTEB ArguAna | ndcg_at_10 | 56.874 |
| Retrieval | MTEB ArguAna | ndcg_at_100 | 60.307 |
| Retrieval | MTEB ArguAna | ndcg_at_1000 | 60.466 |
Using it via the API
Once AxForge deploys snowflake-arctic-embed-s for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (snowflake-arctic-embed-s below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"snowflake-arctic-embed-s","input":"text to embed"}'
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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