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.
About this tool
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
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Information
Websitehuggingface.co
PublishedMar 8, 2026
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