Generating Questions and Answers from Text for Fine-tuning AI Models

The video tutorial explains how to prepare questions and answers from a text file for fine-tuning a model. It introduces a tool by OpenAI that generates questions and answers from text, requiring an API key for access. The process involves inputting text, specifying paragraph separation, and generating multiple sets of questions. The tutorial also covers reviewing the generated content, estimating costs, and exporting the data for further use in model fine-tuning.

What is the first step in preparing questions and answers from a text file?

• Skipping the formatting process

• Sending raw text directly to the model

• Preparing data as prompts and completions

• Converting data to XML format

Which tool is recommended for generating questions and answers from a text?

• Adobe Acrobat

• Microsoft Word

• Google Docs

• Open AI's QA generator

What is required to use the online tool for generating questions?

• A subscription to a magazine

• An API key

• A social media account

• A credit card

How many sets of questions can be generated for each paragraph using the tool?

• Only one set

• Two sets

• Unlimited sets

• Three sets

What is the estimated cost for using the tool based on?

• The tokenization process

• The number of users

• The length of the text

• The number of questions generated

What should be done after generating questions and answers?

• Ignore the data

• Convert the data to a JSON format

• Print the data

• Delete the data

Why is it beneficial to have different structures for the same question?

• To confuse the model

• To make the model understand various question forms

• To increase the cost

• To reduce the number of questions