---

base_model:

• Snowflake/snowflake-arctic-embed-l-v2.0

pipeline_tag: sentence-similarity tags:

• xlm-roberta
• mteb
• arctic
• snowflake-arctic-embed
• text-embeddings-inference

library_name: sentence-transformers language:

• af
• ar
• az
• be
• bg
• bn
• ca
• ceb
• cs
• cy
• da
• de
• el
• en
• es
• et
• eu
• fa
• fi
• fr
• gl
• gu
• he
• hi
• hr
• ht
• hu
• hy
• id
• is
• it
• ja
• jv
• ka
• kk
• km
• kn
• ko
• ky
• lo
• lt
• lv
• mk
• ml
• mn
• mr
• ms
• my
• ne
• nl
• pa
• pl
• pt
• qu
• ro
• ru
• si
• sk
• sl
• so
• sq
• sr
• sv
• sw
• ta
• te
• th
• tl
• tr
• uk
• ur
• vi
• yo
• zh

---

GGUF quants of Snowflake/snowflake-arctic-embed-l-v2.0 created using llama.cpp

Original model card:

---

Snowflake's Arctic-embed-l-v2.0

News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement

News

• 12/11/2024: Release of Technical Report
• 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.

Models

Snowflake arctic-embed-l-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency. Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English. Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale.

Key Features:

1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.
2. Inference efficiency: Its 303m non-embedding parameters inference is fast and efficient for any scale.
3. Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training.
4. Drop-In Replacement: arctic-embed-l-v2.0 builds on BAAI/bge-m3-retromae which allows direct drop-in inference replacement with any form of new libraries, kernels, inference engines etc.
5. Long Context Support: arctic-embed-l-v2.0 builds on BAAI/bge-m3-retromae which can support a context window of up to 8192 via the use of RoPE.

Quality Benchmarks

Unlike most other open-source models, Arctic-embed-l-v2.0 excels across English (via MTEB Retrieval) and multilingual (via MIRACL and CLEF). You no longer need to support models to empower high-quality English and multilingual retrieval. All numbers mentioned below are the average NDCG@10 across the dataset being discussed.

Model Name	# params	# non-emb params	# dimensions	BEIR (15)	MIRACL (4)	CLEF (Focused)	CLEF (Full)
snowflake-arctic-l-v2.0	568M	303M	1024	55.6	55.8	52.9	54.3
snowflake-arctic-m	109M	86M	768	54.9	24.9	34.4	29.1
snowflake-arctic-l	335M	303M	1024	56.0	34.8	38.2	33.7
me5 base	560M	303M	1024	51.4	54.0	43.0	34.6
bge-m3 (BAAI)	568M	303M	1024	48.8	56.8	40.8	41.3
gte (Alibaba)	305M	113M	768	51.1	52.3	47.7	53.1

Aside from high-quality retrieval arctic delivers embeddings that are easily compressible. Leverage vector truncation via MRL to decrease vector size by 4x with less than 3% degredation in quality. Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc.

Model		BEIR (15)	Relative Performance	MIRACL (4)	Relative Performance	CLEF (5)	Relative Performance	CLEF (Full)	Relative Performance
snowflake-arctic-l-v2.0	1024	55.6	N/A	55.8	N/A	52.9	N/A	54.3	N/A
snowflake-arctic-l-v2.0	256	54.3	-0.18%	54.3	-2.70%	51.9	-1.81%	53.4	-1.53%

Usage

Using Sentence Transformers

from sentence_transformers import SentenceTransformer

Load the model
model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
model = SentenceTransformer(model_name)

Define the queries and documents
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']

Compute embeddings: use prompt_name="query" to encode queries!
queryembeddings = model.encode(queries, promptname="query") 
document_embeddings = model.encode(documents)

Compute cosine similarity scores
scores = model.similarity(queryembeddings, documentembeddings)

Output the results
for query, query_scores in zip(queries, scores):
    docscorepairs = list(zip(documents, query_scores))
    docscorepairs = sorted(docscorepairs, key=lambda x: x[1], reverse=True)
    print("Query:", query)
    for document, score in docscorepairs:
        print(score, document)

Using Huggingface Transformers

You can use the transformers package to use Snowflake's arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).

import torch
from transformers import AutoModel, AutoTokenizer

model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
tokenizer = AutoTokenizer.frompretrained(modelname)
model = AutoModel.frompretrained(modelname, addpoolinglayer=False)
model.eval()

query_prefix = 'query: '
queries  = ['what is snowflake?', 'Where can I get the best tacos?']
querieswithprefix = ["{}{}".format(query_prefix, i) for i in queries]
querytokens = tokenizer(querieswithprefix, padding=True, truncation=True, returntensors='pt', max_length=8192)

documents = ['The Data Cloud!', 'Mexico City of Course!']
documenttokens =  tokenizer(documents, padding=True, truncation=True, returntensors='pt', max_length=8192)

Compute token embeddings
with torch.no_grad():
    queryembeddings = model(querytokens)[0][:, 0]
    documentembeddings = model(documenttokens)[0][:, 0]

normalize embeddings
queryembeddings = torch.nn.functional.normalize(queryembeddings, p=2, dim=1)
documentembeddings = torch.nn.functional.normalize(documentembeddings, p=2, dim=1)

scores = torch.mm(queryembeddings, documentembeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
    docscorepairs = list(zip(documents, query_scores))
    docscorepairs = sorted(docscorepairs, key=lambda x: x[1], reverse=True)
    #Output passages & scores
    print("Query:", query)
    for document, score in docscorepairs:
        print(score, document)

This should produce the following scores

Query: what is snowflake?
tensor(0.2715) The Data Cloud!
tensor(0.0661) Mexico City of Course!
Query: Where can I get the best tacos?
tensor(0.2797) Mexico City of Course!
tensor(0.1250) The Data Cloud!

Using Huggingface Transformers.js

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

You can then use the model for retrieval, as follows:

import { pipeline, dot } from '@huggingface/transformers';

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0', {
    dtype: 'q8',
});

// Generate sentence embeddings
const sentences = [
    'query: what is snowflake?',
    'The Data Cloud!',
    'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });

// Compute similarity scores
const [sourceembeddings, ...documentembeddings ] = output.tolist();
const similarities = documentembeddings.map(x => dot(sourceembeddings, x));
console.log(similarities); // [0.24783534471401417, 0.05313122704326892]

Contact

Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).

License

Arctic is licensed under the Apache-2. The released models can be used for commercial purposes free of charge.