Domain-specific GPT-3 engines

This section explores GPT-3 engines that can perform domain-specific tasks. We will run three models in the three subsections of this section:

• Embedding2ML to use GPT-3 to provide embeddings for ML algorithms
• Instruct series to ask GPT-3 to provide instructions for any task
• Content filter to filter bias or any form of unacceptable input and output

Open Domain_Specific_GPT_3_Functionality.ipynb.

We will begin with embedding2ML (embeddings as an input to ML).

Embedding2ML

OpenAI has trained several embedding models with different dimensions with different capabilities:

• Ada (1,024 dimensions)
• Babbage (2,048 dimensions)
• Curie (4,096 dimensions)
• Davinci (12,288 dimensions)

For more explanations on each engine, you will find more information on OpenAI’s website:

https://beta.openai.com/docs/guides/embeddings.

The Davinci model offers embedding with 12,288 dimensions...