keras/mxbai_embed_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

Preset name	Parameters	Description	Base Architecture
mxbai_embed_large_en	335,141,888	24-layer MxbAI Embed large English model. Maps sentences to 1024-dimensional dense vectors with Matryoshka representation learning. Trained on high-quality English data. Top-ranked open-source embedding model on the MTEB leaderboard.	BERT-large
mxbai_embed_2d_large_en	335,141,888	24-layer MxbAI Embed 2D-Matryoshka large English model. Maps sentences to 1024-dimensional dense vectors. Supports both embedding dimension reduction and transformer layer reduction for flexible performance-efficiency tradeoffs.	BERT-large
mxbai_embed_xsmall_en	22,713,216	6-layer MxbAI Embed extra-small English model. Maps sentences to 384-dimensional dense vectors with Matryoshka representation learning. Ultra-fast and compact, based on MiniLM-L6-v2 architecture.	MiniLM-L6-v2

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