For my specific example: "the tall dark handsome man wanders into a dark gloomy bar. he orders the biggest beer in the world and sits down on a bar stool surrounded by irish dancers" spacy splits out "the biggest beer" and "the world", whereas I want "the biggest beer in the world" to be the singular noun.
So, you send Jev a string of text and it sends back what? I thought it only output probabilities of a classifier.
When the user is typing the string, I seperate the last 10 words into reversed joined words, e.g. tall dark handsome man becomes "man", "handsome man", "dark handsome man" and "tall dark handsome man", and I send the full string to Jev and ask it to tell me with probabilities of each option which is the best one to fully capture the descriptive noun
I mean now with modern LLMs a lot of good packages and tools get forgotten about. When you have hammer everything looks like a nail. Even if said "old" tools are actually orders of magnitude faster, and sometimes better too for that specific task. (And I remember spacy being basically SOTA for generalist NLP tasks not that long ago, like 2020/2021).