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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, suitable for semantic tasks. The model provides both full (768-d) and truncated (256-d) embedding outputs and includes usage examples for SentenceTransformers, Transformers, and Transformers.js for multi-framework integration.

Key Features

  • Derived from ModernBERT-base for sentence embeddings
  • Full 768-dimensional embedding output
  • Truncated 256-dimensional embedding output
  • Supports sentence similarity and semantic search tasks
  • Usage examples for SentenceTransformers, Transformers, and Transformers.js

Ideal Use Cases

  • Semantic search over documents and datasets
  • Sentence similarity scoring and clustering
  • Generate embeddings for downstream classifiers or retrieval
  • Prototyping across Python and JavaScript frameworks

Getting Started

  • Open the model page on Hugging Face
  • Select preferred embedding dimensionality (768 or 256)
  • Install SentenceTransformers, Transformers, or Transformers.js
  • Load the model via your chosen library
  • Encode sentences to produce embeddings
  • Test embeddings on similarity or search tasks

Pricing

No pricing information is provided on the model page; hosting and inference costs depend on the provider you choose.

Key Information

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