Advanced NLP with spaCy v3
We've been using spaCy a lot for the past few months.<p>Mostly for non-production use cases, however, I can say that it is the most robust framework for NLP at the moment.<p>V3 added support for transformers: that's a killer feature as many models from <a href="https://huggingface.co/docs/transformers/index" rel="nofollow">https://huggingface.co/docs/transformers/index</a> work great out of the box.<p>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 <a href="https://demo.newscatcherapi.com/" rel="nofollow">https://demo.newscatcherapi.com/</a><p>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.<p>Btw, we recently wrote a blog post comparing spaCy to NLTK for text normalization task: <a href="https://newscatcherapi.com/blog/spacy-vs-nltk-text-normalization-comparison-with-code-examples" rel="nofollow">https://newscatcherapi.com/blog/spacy-vs-nltk-text-normaliza...</a>