Text-embedding-3-small

text-embedding-3-small API enhances text representation, offering better accuracy and cost-efficiency compared to its predecessor, text-embedding-ada-002.

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Text-embedding-3-small

Efficient embedding model with improved performance and reduced costs.

Model Overview Card: text-embedding-3-small

Basic Information

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

Description

Overview:text-embedding-3-small is an efficient and compact embedding model designed to enhance performance over its predecessor, text-embedding-ada-002. It transforms text into numerical representations that can be easily processed by machine learning models.
Key Features:
- Improved Performance: Achieves higher scores on benchmarks for multi-language retrieval (MIRACL) and English tasks (MTEB).
- Cost Efficiency: Offers a 5x reduction in cost compared to text-embedding-ada-002.
- Compact Size: Embedding size of 512 dimensions, suitable for memory and storage-constrained environments.
Intended Use:
- Search: Enhance search algorithms by ranking results based on relevance.
- Clustering: Group similar text strings for data analysis.
- Recommendations: Suggest related items based on text similarity.
- Anomaly Detection: Identify outliers in data.
- Diversity Measurement: Analyze the diversity of text data.
- Classification: Classify text strings by their most similar labels.
Language Support:Supports multiple languages, improving accessibility and usability across diverse linguistic datasets.

Technical Details

Architecture:Utilizes a transformer-based architecture optimized for efficiency and performance.
Training Data:Trained on a diverse set of text sources to capture a wide range of linguistic patterns and semantics.
Data Source and Size:Extensive dataset comprising millions of text documents, ensuring a broad understanding of language.
Diversity and Bias:Training data is selected to minimize bias and ensure robust performance across different demographics and use cases.

Performance Metrics

Comparison to Other Models:
- MIRACL Score: Improved from 31.4% (ada-002) to 44.0%.
- MTEB Score: Increased from 61.0% (ada-002) to 62.3%.
Accuracy:Demonstrates higher accuracy in both multi-language and English-specific benchmarks.
Speed:More efficient than previous models, reducing latency and computational requirements.
Robustness:Handles diverse inputs effectively, ensuring reliable performance across various applications.

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