Awesome project, I thank you for expanding the Go ecosystem in this area and as per our Reddit discussion I'm putting this down here.

It would be amazing to have real-world/practical training sets/models that ideally would be pretty much plug-and-play or at the very least great starting points like the yes-no you already have.

My immediate use case is identifying whether a user's query is an action request or a discussion, which would allow me to reduce the number of queries to the LLMs and save some money.

But it feels like this project is a good match for various customer support, task management, accounting, automation, development or psychology related text analysis.

I would love to see some real training data for something that you've implemented using this lib and are happy to share. It could be anything, but it's important that it's at least partially practical and a good example to work on.

Some examples:

• Github issue analyser to pick label issues (bug, feature, ignore, etc)
• Color shade analyzer to fit a color into a group (teal = green, pink = red or whatever)
• Tone of voice, urgency identification (maybe this one is easier for LLMs)
• Task priority rating (bug = 10, feature = 5, feature_with_guaranteed_revenue=5000)
• Mood (chill, angry, etc).

I'm fairly new to NLP/ML and the sort, so maybe some of these things are better left to LLMs (you be the judge of that), but the more I can offload and have baked into my binary the better :)