multilingual e5 small

Introduction

The multilingual-e5-small model by intfloat is designed for sentence similarity tasks and is built using the Sentence Transformers library. Utilizing PyTorch and ONNX frameworks, it supports 94 languages and is compatible with various inference endpoints.

Architecture

The model is based on BERT and integrates with the sentence-transformers library, optimized for multilingual text embedding tasks. It is designed to handle diverse language inputs efficiently, making it suitable for tasks involving multiple languages.

Training

The multilingual-e5-small model was trained on a variety of datasets to optimize for the multilingual embedding of sentences. It has been evaluated on the MTEB (Multilingual Textual Entailment Benchmark) and other text-embedding inference tasks, achieving competitive results.

Guide: Running Locally

1. Clone the Repository: Start by cloning the model's repository from Hugging Face.
2. Install Dependencies: Ensure you have Python and pip installed, then install the required libraries such as torch, sentence-transformers, and onnx.
3. Download the Model: You can download the model weights directly using the Hugging Face hub.
4. Load the Model: Use the Sentence Transformers library to load and initialize the model.
5. Run Inference: Prepare your text data and run it through the model to obtain embeddings.

For enhanced performance, especially with larger datasets or more complex tasks, consider using cloud GPUs such as those provided by AWS, GCP, or Azure.

License

The multilingual-e5-small model is released under the MIT License, allowing for flexible use in both academic and commercial applications.