NLTK is showing its age. In my information extraction pipelines, the heavy lifting for modelling is done by SpaCy, AllenNLP, and Huggingface (and Pytorch or TF ofc).

I only use NLTK since it has some base tools for low-resource languages for which noone has pretrained a transformer model or for specific NLP-related tasks. I still use their agreement metrics module, for instance. But that's about it. Dep parsing, NER, lemmatising and stemming is all better with the above mentioned packages.