At the same time, I found NER models provided by spaCy to have a low accuracy while working with real data: we deal with news articles https://demo.newscatcherapi.com/
Also, while I see how much attention ML models get from the crowd, I think that many problems can be solved with rule-based approach: and spaCy is just amazing for these.
Mostly for non-production use cases, however, I can say that it is the most robust framework for NLP at the moment.
V3 added support for transformers: that's a killer feature as many models from https://huggingface.co/docs/transformers/index work great out of the box.
At the same time, I found NER models provided by spaCy to have a low accuracy while working with real data: we deal with news articles https://demo.newscatcherapi.com/
Also, while I see how much attention ML models get from the crowd, I think that many problems can be solved with rule-based approach: and spaCy is just amazing for these.
Btw, we recently wrote a blog post comparing spaCy to NLTK for text normalization task: https://newscatcherapi.com/blog/spacy-vs-nltk-text-normaliza...