Snowflake Arctic Embed L V2.0

Table of Contents

Model Overview

The Snowflake Arctic-embed-l-v2.0 model is a game-changer for multilingual text retrieval and embedding. This model is designed to excel in both English and non-English languages, making it a great choice for applications that require reliable and efficient multilingual search and retrieval.

Tasks and rank

Text Classification 3403rd

Sentence Embeddings 689th

Natural Language Understanding 9928th

Information Extraction 950th

Learn More About the Ranking System

Capabilities

So, what makes this model so special? Let’s take a closer look at its capabilities:

• Multilingual without compromise: It excels in both English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.
• Inference efficiency: With only 303M non-embedding parameters, its inference is fast and efficient for any scale.
• Compression-friendly: It achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training.
• Drop-In Replacement: Easily replace other models with this one, thanks to its compatibility with various libraries, kernels, and inference engines.

How does it work?

You can use this model with popular libraries like Sentence Transformers, Huggingface Transformers, and Huggingface Transformers.js. Simply load the model, define your queries and documents, compute embeddings, and calculate similarity scores.

Examples

What is the similarity between the query 'what is snowflake?' and the documents 'The Data Cloud!' and 'Mexico City of Course!? The similarity between the query and 'The Data Cloud!' is 0.2715 and 'Mexico City of Course!' is 0.0661.

Where can I get the best tacos? According to the similarity scores, the best match is 'Mexico City of Course!' with a score of 0.2797.

Provide the top 2 documents that match the query 'what is snowflake?' from the documents 'The Data Cloud!' and 'Mexico City of Course!? The top 2 documents that match the query are 'The Data Cloud!' with a score of 0.2715 and 'Mexico City of Course!' with a score of 0.0661.

Example Use Cases

• Multilingual search: Use this model to build a search engine that can handle queries in multiple languages.
• Text classification: Leverage the model’s embedding capabilities to classify text into different categories.
• Information retrieval: Employ the model to retrieve relevant documents based on a given query.

Performance

This model is a powerhouse when it comes to performance. Let’s dive into its speed, accuracy, and efficiency in various tasks.

Speed

How fast can this model process text? With 303M non-embedding parameters, its inference is fast and efficient for any scale. This means you can quickly process large amounts of text without sacrificing performance.

Accuracy

But how accurate is this model? The answer is impressive. It excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.

Efficiency

But what about efficiency? Can this model handle large-scale datasets without breaking a sweat? The answer is yes. With its compression-friendly design, it can achieve high-quality retrieval with embeddings as small as 128 bytes/vector.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

All MiniLM L6 V2 2389 Sentence Embeddings

Bert Base Uncased 1831 Uncased BERT model

All Mpnet Base V2 823 Sentence Embeddings

Limitations

While this model is a powerful tool, it’s not perfect. Let’s take a closer look at some of its limitations.

Limited Context Understanding

While this model can handle long context windows of up to 8192 tokens, it may still struggle to fully understand the nuances of very long documents or complex texts.

Dependence on Training Data

Like all machine learning models, this model is only as good as the data it was trained on. If the training data contains biases or inaccuracies, the model may learn to replicate these flaws.

Format

This model is a multilingual embedding model that uses a transformer architecture. It’s designed to optimize for retrieval performance and inference efficiency.

Architecture

The model has 568M parameters, with 303M non-embedding parameters. It supports long context windows of up to 8192 tokens via the use of RoPE.

Data Formats

This model accepts input in the form of tokenized text sequences. You can use the SentenceTransformer library to load the model and encode your queries and documents.

from sentence_transformers import SentenceTransformer

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