[screye]

The sad truth is - all of classical nlp is Dead, with a capital D.

The bottleneck for accuracy was always data quality and human effort, not model architecture.

Llms make the data and human problems so much easier, that the benefits of supporting different architectures just doesn't make sense. With quantization, I'm not even sure classical models win out on cost anymore, and they had already lost on (real world) accuracy.

LLMs are the O365 subscription that you just can't fight against with bespoke mini solution. An all in one solution is simply too appealing.

Also, if you have to learn pre-2020 NLP I would just learn to use spacy. It pretty much covers all of pre-2020 NLP out of the box in a well documented package with strong GPU and CPU support.

[lgessler]

> The sad truth is - all of classical nlp is Dead, with a capital D.

If your language is well served by LLMs. Which, generously, is true for maybe 20-50 of the world's 7000 or so languages. For all the rest, "classical" NLP is still how things get done at least at present.

[behnamoh]

So if anyone wants to learn language modeling stuff, do you recommend starting with transformer and just learn how to deploy and finetune LLMs (given that ordinary people can't train these LLMs)?