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    1. Home
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    3. ModernBERT Embed

    ModernBERT Embed

    Open-source embedding model from Nomic AI based on ModernBERT-base with 149M parameters. Supports 8192 token sequences and Matryoshka Representation Learning for 3x memory reduction.

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    Information

    Websitehuggingface.co
    PublishedMar 8, 2026

    Categories

    1 Item
    Machine Learning Models

    Tags

    3 Items
    #Open Source#Embeddings#Nlp

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    Overview

    ModernBERT Embed is an embedding model trained from ModernBERT-base, bringing the new advances of ModernBERT to embeddings. Trained on the Nomic Embed weakly-supervised and supervised datasets.

    Key Features

    • Based on ModernBERT-base with 149M parameters
    • Outperforms both nomic-embed-text-v1 and nomic-embed-text-v1.5 on MTEB
    • Maximum sequence length of 8192 tokens
    • Supports Matryoshka Representation Learning dimensions of 256
    • 2 valid output dimensionalities: 768 and 256
    • 3x memory reduction with minimal performance loss using dimension 256

    Usage Requirements

    This model requires prefixes to be added to the input (similar to Nomic Embed):

    • Add search_query: prefix to queries
    • Add search_document: prefix to documents

    Applications

    • Semantic search
    • Classification
    • Clustering
    • Reranking tasks

    License

    Apache 2.0 licensed - fully commercially permissible

    Requirements

    Requires transformers>=4.48.0

    Availability

    Available on Hugging Face at nomic-ai/modernbert-embed-base