jina embeddings v3

Introduction

The JINA-EMBEDDINGS-V3 model is designed for feature extraction and sentence similarity tasks. It utilizes multiple libraries including Transformers, PyTorch, and ONNX, and supports 94 languages. The model is suitable for applications in sentence-transformers and is evaluated under the MTEB benchmark.

Architecture

JINA-EMBEDDINGS-V3 is built on the Transformers library and can be deployed in environments supporting PyTorch and ONNX. The model is optimized for efficient sentence similarity comparisons and can be integrated into custom code applications.

Training

The model has been trained with a focus on multilingual capabilities, supporting a wide array of languages. It is designed for extracting semantic features from text, making it valuable for tasks requiring detailed linguistic analysis or comparison.

Guide: Running Locally

1. Clone the repository: Begin by cloning the JINA-EMBEDDINGS-V3 repository from Hugging Face.

2. Install dependencies: Ensure that all necessary libraries such as Transformers and PyTorch are installed.

3. Download the model: Use Hugging Face's model hub to download JINA-EMBEDDINGS-V3.

4. Run locally: Execute your scripts in an environment with GPU support for optimal performance.

   Cloud GPUs: Consider services like AWS, Google Cloud, or Azure for access to cloud-based GPUs to enhance processing speed and efficiency.

License

The JINA-EMBEDDINGS-V3 model is released under the Creative Commons Attribution-NonCommercial 4.0 International License (cc-by-nc-4.0), allowing for use and modification for non-commercial purposes with appropriate attribution.