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Ada 002

  • Context Window: 8,191
  • TPM: 1,000,000
  • RPM: 5,000
  • Embedding Size: 1,536
Attributes

Text search, Code search, Sentence similarity, Text classification

Ada 002

Ada 002 embedding is a powerful tool in natural language processing, offering remarkable efficiency in transforming text into numerical vectors. This model excels at capturing the nuanced relationships between words and phrases, enabling machines to understand language context with unprecedented accuracy.

The compact yet expressive nature of Ada 002 embeddings makes them ideal for a wide range of applications. From semantic search engines to content recommendation systems, this model enhances the performance of various AI-driven tasks. Its ability to represent complex linguistic information in a concise format is particularly valuable in scenarios where computational resources are at a premium.

Key features of Ada 002 embedding include:

  • High-dimensional vector representations for improved semantic capture.
  • Efficient processing for rapid text analysis and transformation.
  • Versatility in handling diverse language tasks and domains.
  • Compatibility with various machine learning frameworks and applications.

 

CogniTech AI Credits

Below you will find all supported platforms and the related CogniTech AI Credits costs.

Azure OpenAI Service Credits

Details Input Credits Output Credits Fine-Tuning
Details Input Credits Output Credits Fine-Tuning
Version: All
Region: eastus2
Context: 8,190
TPM: 350,000
RPM: 2,100
Chat: 0 / 1000 tokens
Chat: 0.013 / 1000 tokens
NA
Version: All
Region: uksouth
Context: 8,190
TPM: 350,000
RPM: 2,100
Chat: 0 / 1000 tokens
Chat: 0.013 / 1000 tokens
NA
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