snowflake arctic embed m v2.0

Introduction

The SNOWFLAKE-ARCTIC-EMBED-M-V2.0 is a model hosted on Hugging Face, designed for sentence similarity tasks. The model is part of the Snowflake collection and utilizes the sentence-transformers library. It is compatible with multiple formats and frameworks, including ONNX, Safetensors, and Transformers.js, and supports 74 languages. This model is particularly tailored for feature extraction and integrates with the MTEB benchmark.

Architecture

The model leverages the sentence-transformers architecture, optimized for sentence embeddings and similarity tasks. It is structured to efficiently handle multi-language inputs, providing robust feature extraction capabilities. The model's architecture ensures compatibility with ONNX for efficient deployment and execution.

Training

Details on the specific training procedures for SNOWFLAKE-ARCTIC-EMBED-M-V2.0 are not explicitly provided in the document. However, given its classification under sentence-transformers, it likely underwent pre-training on extensive datasets followed by fine-tuning for specific tasks like sentence similarity. The model's performance can be evaluated through available metrics and benchmarks.

Guide: Running Locally

1. Prerequisites: Ensure you have Python installed along with necessary libraries such as transformers and sentence-transformers.
2. Clone the Repository: Download the model files from the Hugging Face repository.
3. Install Dependencies: Run pip install -r requirements.txt if a requirements file is available.
4. Load the Model: Use the sentence-transformers library to load the model in your Python script.
5. Inference: Pass your sentence pairs to the model for similarity scoring.

For enhanced performance, especially with large datasets, consider using cloud-based GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The SNOWFLAKE-ARCTIC-EMBED-M-V2.0 is released under the Apache 2.0 License, allowing for both commercial and non-commercial use with minimal restrictions.