open sourceembedding

Nomic Embed

Transform text data into meaningful embeddings for advanced semantic applications.

Developed by Nomic AI

Official Site
100MParams
YesAPI Available
stableStability
1.5Version
MIT LicenseLicense
PyTorchFramework
YesRuns Locally
Real-World Applications
  • Semantic searchOptimized Capability
  • Content recommendationOptimized Capability
  • Text classificationOptimized Capability
  • Information retrievalOptimized Capability
Implementation Example
Example Prompt
Generate embeddings for the text 'Artificial Intelligence and its impact on society'.
Model Output
"[0.123, -0.456, 0.789, ...]"
Advantages
  • ✓ High-quality embeddings that enhance semantic understanding.
  • ✓ Optimized for efficient semantic search and retrieval tasks.
  • ✓ Robust performance in various NLP applications due to state-of-the-art architecture.
Limitations
  • ✗ Requires significant computational resources for training.
  • ✗ Limited support for fine-tuning in some specific use cases.
  • ✗ Dependency on external libraries may complicate deployment.
Model Intelligence & Architecture

Technical Documentation

Nomic Embed excels in generating high-quality text embeddings, which are crucial for various natural language processing applications. Utilizing advanced techniques, this model enhances the accuracy and efficiency of semantic search, making it a robust solution for developers and researchers.

Technical Specification Sheet
Technical Details
Architecture
Transformer-based embedding model
Stability
stable
Framework
PyTorch
Signup Required
No
API Available
Yes
Runs Locally
Yes
Release Date
2024-04-03

Best For

Developers and researchers focused on NLP tasks requiring high-quality embeddings.

Alternatives

BERT, Sentence Transformers, FastText

Pricing Summary

Free to use under the open-source license with community support available.

Compare With

Nomic Embed vs BERTNomic Embed vs Sentence TransformersNomic Embed vs OpenAI EmbeddingsNomic Embed vs Universal Sentence Encoder

Explore Tags

#search#embeddings

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