spaCy supports well, in a modular way, all low levels of text analysis, making easy to add support for new languages.

Spacy is basically used for Natural Language Processing(NLP) tasks in Machine Learning. We can optimise our tasks with this library in Python by using pre trained models of Part of Speech(PoS) tagging, Text Summarization and for Named Entity Recognition(NER) model. It also has the capabilities to do tokenization in which sentences can be divided into words and punctuation marks. All in all, It is a very useful library of python to use NLP tasks in multiple domains.

SpaCy continues to be open source and publicly accessible even with the most modern algorithms. Modern named entity recognition performs flawlessly and tags words into their correct parts of speech quickly and accurately. Over twenty languages can be used with its extensive library.