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ModernBERT Embed - AI Embedding Models Tool

Overview

ModernBERT Embed is an embedding model derived from ModernBERT-base for generating sentence embeddings. It provides both full (768-d) and truncated (256-d) embedding outputs and includes usage examples for SentenceTransformers, Transformers, and Transformers.js.

Key Features

  • Derived from ModernBERT-base architecture
  • Generates sentence embeddings for semantic tasks
  • Full 768-dimensional and truncated 256-dimensional outputs
  • Usage examples for SentenceTransformers, Transformers, Transformers.js
  • Integrates into Python and browser frameworks

Ideal Use Cases

  • Sentence similarity scoring
  • Semantic search and retrieval
  • Indexing vectors for nearest-neighbor search
  • Embedding generation for downstream NLP tasks
  • Browser-based inference via Transformers.js examples

Getting Started

  • Open the model page on Hugging Face
  • Choose full (768-d) or truncated (256-d) embedding output
  • Follow SentenceTransformers example to generate embeddings in Python
  • Use Transformers or Transformers.js example for integration
  • Store embeddings in your vector database or search index

Pricing

Not disclosed on the model page. Hosting or inference costs depend on your infrastructure or Hugging Face usage.

Key Information

  • Category: Embedding Models
  • Type: AI Embedding Models Tool
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