mxbai_embed_2d_large_v1_en

Model Overview

Model Overview

MxbAI Embed is a family of state-of-the-art sentence embedding models by mixedbread-ai. These models map text into high-quality dense vector representations suitable for semantic search, retrieval-augmented generation (RAG), clustering, and classification tasks.

All MxbAI Embed models support Matryoshka Representation Learning (MRL), which allows flexible dimensionality reduction — you can truncate the output embeddings to smaller sizes (e.g., 512, 256) while retaining strong performance. This enables a cost-performance tradeoff at inference time without retraining.

The family includes English, German, and efficiency-focused variants, ranging from ultra-compact (~24M parameters) to large-scale (~340M parameters).

Key features:

• High-quality dense sentence embeddings via BERT-based architectures
• Matryoshka Representation Learning for flexible embedding dimensions
• 2D-Matryoshka variant with both layer and dimension reduction
• Optimized for semantic search, retrieval, clustering, and classification

References:

• mxbai-embed-large-v1
• mxbai-embed-xsmall-v1
• mxbai-embed-2d-large-v1

More Details

• [Mxbai Embedding Quickstart Notebook](coming soon..!)
• Mxbai Embedding API Documentation
• Mxbai Embedding Model Card
• KerasHub Beginner Guide
• KerasHub Model Publishing Guide

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.

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Preset Table

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