Mxbai Embed Xsmall V1

Crispy sentence embeddings

Have you ever wondered how AI models can be both efficient and powerful? Meet Mxbai Embed Xsmall V1, a game-changing English embedding model that achieves state-of-the-art performance while keeping things lean. By supporting both binary quantization and Matryoshka Representation Learning (MRL), this model reduces infrastructure costs and boosts efficiency. With a 93.9% retention of performance and a 32x increase in efficiency through binary quantization, and a 33% reduction in vector size with only a 3.8% loss in performance through MRL, Mxbai Embed Xsmall V1 is perfect for retrieval tasks and supports up to 4096 context. What does this mean for you? Faster, more accurate results without breaking the bank. Whether you're working on cloud computing or vector databases, this model is designed to make your life easier.

Mixedbread Ai apache-2.0 Updated 4 months ago
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Table of Contents

Model Overview

The Mixedbread Sentence Embedding Model is a powerful English embedding model that delivers top-notch results for retrieval tasks. It’s built upon a transformer architecture, similar to sentence-transformers/all-MiniLM-L6-v2, and trained with the AnglE loss and Espresso.

Capabilities

This model is designed to help you with various tasks, such as:

  • Text Retrieval: Find similar texts or documents based on their meaning.
  • Text Classification: Classify texts into categories or labels.
  • Text Clustering: Group similar texts together.

Strengths

Our model has several strengths that make it stand out:

  • State-of-the-art performance: It achieves high accuracy on various benchmarks.
  • Efficient: It supports binary quantization and Matryoshka Representation Learning (MRL), which reduce the model’s size and increase its speed.
  • Flexible: It can be used with different programming languages and frameworks, such as Python and FastAPI.

Unique Features

Our model has some unique features that make it special:

  • Binary Quantization: It can reduce the model’s size by a factor of 32 while retaining 93.9% of its performance.
  • Matryoshka Representation Learning (MRL): It can reduce the vector size by 33% while retaining 96.2% of the model’s performance.

Example Use Cases

Here are some examples of how you can use our model:

  • Text Similarity: Calculate the similarity between two texts to determine if they are related.
  • Text Search: Use our model to search for similar texts or documents in a database.
  • Text Classification: Classify texts into categories or labels to help with decision-making.
Examples
A man is eating a piece of bread. What is the similarity between this sentence and 'A man is eating food.'? 0.853
What is the cosine similarity between 'A man is riding a horse.' and 'A man is eating pasta.'? 0.001
Calculate the cosine similarity between 'The girl is carrying a baby.' and 'A man is eating a piece of bread.' 0.005

Getting Started

To get started with our model, you can use the following code examples:

  • Python: Use the sentence-transformers library to load and use our model.
  • FastAPI: Use our model with FastAPI to create a RESTful API for text embedding.

Performance

Our model is a powerhouse when it comes to performance. With binary quantization, it can increase efficiency by a factor of 32 while retaining 93.9% of its performance.

Comparison to Other Models

But how does our model compare to other models like BERT or RoBERTa? While our model has its strengths, it’s essential to consider the trade-offs and limitations when choosing a model for your specific task.

Limitations

Our model is a powerful tool, but it’s not perfect. Let’s explore some of its weaknesses and challenges:

  • Limited Context Understanding: While our model can process long texts, its understanding of context is limited to 4096 tokens.
  • Quantization Trade-Offs: Our model supports binary quantization and Matryoshka Representation Learning (MRL), which can significantly reduce infrastructure costs. However, these optimizations come at a cost:
    • Binary quantization: 6.1% performance loss
    • MRL: 3.8% performance loss (with a 33% reduction in vector size)
  • Retrieval Tasks Only: Our model is optimized for retrieval tasks, which means it’s not designed for other tasks like text generation or language translation.
  • Limited Multilingual Support: Our model is an English embedding model, which means it’s not suitable for tasks that require multilingual support.

Join the Community

Join our Discord community to share your feedback and thoughts on our model. We’re here to help and always happy to discuss the exciting field of machine learning!

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