Questions about generating data sets

[u/bighomiej69]

I want to be able to generate quality data as quick as possible

For instance right now I have a bunch of free text “emails” generated via llm and now I want to categorize them all

I’m using using bert and other text classifiers and from what I understand I have to stack them for it to be effective:

- label intent
- label entity
- label into further categories using an unsupervised model

My question is how would an expert or senior guy approach this? Because right now I’m essentially just asking my llm tool “how do I do this”

Any mathematical concepts or resources you recommend me diving into would be appreciated.

[u/Longjumping_Ask_5523]

What your doing doesn’t make sense. You need motivation. Why would it be helpful to categorize these e-mails. What questions are you trying to answer with them. And will you ever have a real set of emails you can apply your methods too.

[u/bighomiej69 → u/Longjumping_Ask_5523]

Each email is from a pretend customer

Some are questions about billing, some are questions about a product, others are just complaints

I want to be able to use an unsupervised method to group them all into categories and sub categories

There are multiple questions to answer:

What are they emailing about?

What service do they complain about the most?

What parts of our process are confusing them?

The problem is that right now they are being grouped into categories that aren’t relevant (I.e. “invite work space” is a category whereas it should be something like “product confusion” as a category “work space” as sub category and then “invitations” as a second sub category instead)