Text-embedding-3-large

Model Overview Card: text-embedding-3-large

Basic Information

• Model Name: text-embedding-3-large
• Developer/Creator: OpenAI
• Release Date: January 25th, 2024
• Version: text-embedding-3-large
• Model Type: Text Embedding

Description

• Overview:text-embedding-3-large is a next-generation embedding model that offers superior performance and flexibility. It converts text into high-dimensional numerical representations that are highly effective for various machine learning tasks.
• Key Features:
  • Top Performance: The highest performing embedding model with significant improvements over predecessors.
  • Flexible Embedding Size: Supports dimensions from 256 up to 3072, allowing for trade-offs between performance and resource usage.
  • Native Support for Shortening Embeddings: Developers can shorten embeddings without significant loss in conceptual representation.
• Intended Use:
  • High-Performance Search: Optimal for applications requiring precise and fast search results.
  • Advanced Clustering: Suitable for sophisticated data analysis and clustering tasks.
  • Enhanced Recommendations: Provides accurate recommendations by understanding text similarities.
  • Robust Anomaly Detection: Efficiently identifies outliers in large datasets.
  • Detailed Diversity Measurement: Analyzes the diversity of large text corpora.
  • Accurate Classification: Highly effective in classifying complex text data.
• Language Support:Offers improved support for multiple languages, making it suitable for global applications.

Technical Details

• Architecture:Advanced transformer-based architecture designed for high-dimensional embeddings and superior performance.
• Training Data:Trained on an extensive and diverse dataset to capture a wide array of linguistic nuances.
• Data Source and Size:Includes billions of text entries, ensuring a comprehensive understanding of language.
• Diversity and Bias:Ensures high diversity in training data to mitigate biases and enhance reliability.

Performance Metrics

• Comparison to Other Models:
  • MIRACL Score: Increased from 31.4% (ada-002) to 54.9%.
  • MTEB Score: Improved from 61.0% (ada-002) to 64.6%.
• Accuracy:Delivers top-tier accuracy across multiple benchmarks.
• Speed:Optimized for faster processing times despite larger dimensionality.
• Robustness:Maintains high performance across a variety of input types and contexts.